Amazon India: Sales change by 23.4% [270L-→333L]
19.4% Change in DRR [9.00L—>10.75L DRR]
3.3% Change Driven by Number of Days [30D —>31D]
Vendor Central: Sales change by +20% [5.00L —> 6.00L DRR]
Organic Sales: Overall change by +10.6% [2.27L —> 2.50L DRR] Category A Product Sales: change by +x% [5.00L —> 6.00L DRR] Traffic: change by +20% [50,000 —> 60,000 DRR] Hypothesis 1: Amazon Search Ranking & SEO Factors 1.1 Did most of the SKUs gain/lose organic rankings while others dropped?
Likely Cause: Improved/Decreased ranking positions for high-converting SKUs lead to greater/lesser share of organic traffic landing on them. Use tools like Helium 10 or Jungle Scout to track SKU-level keyword rank changes from November to December. See if top performers in December rank higher for key search terms. End: If confirmed, traffic increase or decrease is largely explained by ranking improvements for high-converting SKUs. 1.2 Did we optimize listings for x% of these SKUs (titles, bullet points, backend keywords) in December?
Likely Cause: Listing optimizations (e.g., better keywords, enhanced images, A+ content) can improve both visibility and click share from organic search. Compare listing versions from November vs. December (possibly use “Manage Your Experiments” in Seller Central or track version changes for vendor). End: If these optimizations correlate with increased traffic to those SKUs, you’ve found a key driver. No → If search ranking and listing optimizations don’t explain it, move to Hypothesis 2. Hypothesis 2: Amazon Browse Nodes & Category Placement 2.1 Were signifcant proportion of SKUs reassigned with high/low-traffic browse nodes (sub-categories)?
Likely Cause: Relevant/Irrelevant sub-category placement can drive more/less browsing traffic to those SKUs. Check the “Product Category” and “Sub-Categories” in Seller Central/Vendor Central for changes. End: If SKUs that gained/lost traffic were newly placed in a top-browsed category, that explains the traffic change 2.2 Did any top-level browse node promotions occur (e.g., Amazon’s “Best Sellers,” “New Releases,” “Movers & Shakers”)?
Likely Cause: SKUs featured on these high-visibility pages can see a surge in organic traffic. Check if our SKUs appeared in the “Top 100,” “Best Seller,” or “Amazon’s Choice” lists. End: If correlated with the timeframe of traffic shift, you have your explanation. No → Move to Hypothesis 3 if category changes do not account for the redistribution. Hypothesis 3: Buy Box & Availability 3.1 Did majority of SKUs lose or gain the Buy Box more frequently?
Likely Cause: A SKU consistently winning the Buy Box will retain the bulk of traffic from search; losing it can drop traffic share significantly. Review Buy Box percentage and history (Seller Central reports or third-party tools) for each SKU. End: If high-converting SKUs gained the Buy Box more often, that explains the traffic shift. 3.2 Were bunch of these SKUs out of stock (OOS) or had limited availability in November or December?
Likely Cause: If lower-converting SKUs were OOS in December, traffic for those SKUs would vanish—and effectively “shift” to available SKUs (which might convert better). Check inventory logs or “Restock Inventory” reports in Seller/Vendor Central. End: If restocking correlates with traffic returning to normal distribution, you’ve confirmed a primary driver. No → If not Buy Box or stock-related, move to Hypothesis 4. Hypothesis 4: Demand Shifts & Seasonality 4.1 Is there a seasonal or holiday factor increasing demand for certain SKUs?
Likely Cause: During holiday or event periods, shoppers tend to flock to gift or seasonal SKUs, many of which convert well if they’re best-sellers in that category. Compare historical December performance for these SKUs. Look at external trends (e.g., Google Trends) to see if certain products are seasonally popular. End: If data shows cyclical interest, that’s your explanation. 4.2 Did competitor factors (pricing, stockouts, or brand pullback) drive shoppers to our SKUs?
Likely Cause: Competitors going OOS or raising prices can shift organic demand to your still-available, well-priced SKUs. Monitor competitor listings, track competitor price changes. End: If competitor SKUs dropped out exactly when your high-converting SKUs saw a boost, that explains the traffic increase No → Move to Hypothesis 5 if no broad demand change is found. Hypothesis 5: Brand Store & External Traffic 5.1 Were we driving external traffic (via social media, influencers, brand store, etc.) mostly to certain SKUs?
Likely Cause: If you or affiliates promoted specific SKUs heavily, that organic traffic could show up as direct/organic on Amazon (depending on the referral parameters). Check Amazon Attribution or brand referral data to see if certain SKU links were pushed externally. End: If these SKUs are also high-converting, that explains half the overall conversion improvement from traffic mix. 5.2 Were changes made to the Brand Store (storefront) layout that favored certain SKUs?
Likely Cause: Featuring certain products more prominently in the Brand Store can direct more organic store visitors to those SKUs. Compare Brand Store layouts from November vs. December, and check Store Insights for traffic sources. End: If the featured SKUs match those that gained traffic share, you’ve located a key factor. No → Move on to Hypothesis 6 if none of the Brand Store or external referral paths explain the traffic increase/decrease. Hypothesis 6: Measurement & Tagging 6.1 Did any parent-child ASIN merges or changes in listing variations (color/size) happen?
Likely Cause: Merging variations or changing parent-child relationships can shift the “main” traffic from one SKU to another, altering our perceived distribution. Check “Manage Inventory” or vendor product catalog for variation merges or splits. End: If these merges correlate with the timing of traffic shifts, you’ve confirmed the driver. No → If none of these apply, consider deeper, SKU-by-SKU analysis or revisiting earlier nodes in more detail. Conversion: change by +0.5 percentage points [2.0% —> 2.5%] Contribution of each SKU’s CVR, independent of Click share, affect the overall CVR Hypothesis 1: Product Listing & User Experience (UX) 1.1 Were product detail pages updated with improved images, A+ content, bullet points, or backend keywords?
Likely Cause: More compelling visuals, clearer info, or better SEO keywords typically boost conversions. Compare listing versions from November to December (via “Manage Your Experiments” or tracked listing edits). Note which SKUs had changes; see if conversion uplift aligns with the update dates. End: If confirmed, listing optimizations explain conversion gains for those SKUs. 1.2 Did changes in the Brand Store or enhanced brand content alter how shoppers view or buy these SKUs?
Likely Cause: Upgraded store navigation or featured modules can highlight certain SKUs, increasing conversion. Review Amazon Brand Store Insights to see if certain SKUs got more direct hits or better conversion paths. Map store layout changes to each SKU’s daily/weekly conversion trend. End: If the correlation is strong, you’ve found a primary conversion driver. No → If no listing or UX updates explain it, move to Hypothesis 2. Hypothesis 2: Pricing & Promotions 2.1 Did we run discounts, coupons, or Lightning Deals for these SKUs?
Likely Cause: Promotional pricing often leads to higher conversion rates if shoppers perceive a deal. Check the promotion schedules in Seller Central/Vendor Central (Deals, Coupons). Verify if the 20 SKUs with conversion gains had active promos during December. End: If yes, promotions are likely driving the lift. 2.2 Did we remove or reduce discounts for certain SKUs, causing conversion to drop?
Likely Cause: Ending a successful promotion can hurt conversions if customers lose an appealing deal. Examine the timeline for when promotions ended and match that to conversion declines in the 20 SKUs (if they had declined). End: If alignment exists, you’ve identified why those SKUs saw conversion dips. No → If not pricing or promotion, move to Hypothesis 3. Hypothesis 3: Product Reviews & Ratings 3.1 Did star ratings improve (e.g., from 4.2 to 4.5) or did we get a surge of positive reviews?
Likely Cause: Hitting a higher star threshold or gaining strong new reviews significantly increases customer confidence. Use Seller Central/Vendor Central “Voice of the Customer” or third-party review-tracking tools. Match rating boosts with increased conversion for specific SKUs. End: If the timing matches, reviews/ratings explain the uplift. 3.2 Did negative feedback or a rating drop occur for certain SKUs?
Likely Cause: A lower star rating or complaints can deter customers from buying, lowering conversion. Review negative feedback timeline and see if it coincides with the 94 SKUs’ conversion decline. End: If confirmed, poor reviews are the culprit. No → If no review-related factors, move to Hypothesis 4. Hypothesis 4: Competitive Landscape 4.1 Did a main competitor go out of stock or raise prices, making our SKU more attractive?
Likely Cause: If competitor listings become less favorable (OOS, price hikes), our SKUs get a conversion bump. Monitor competitor stock/pricing via third-party tools or manual checks. Correlate competitor disruptions with your SKU’s conversion lift dates. End: If the overlap is clear, competitor changes explain your SKU’s conversion growth. 4.2 Did a strong competitor enter the market or intensify promotions, undercutting our listings?
Likely Cause: A new competitor or an existing one offering steep discounts or better ratings can siphon away conversions. Track changes in competitor ads, brand campaigns, or price points. Confirm if your 94 declining SKUs directly overlap with their product. End: If the correlation is evident, competitor action is causing conversion drops. No → If not competitive factors, move to Hypothesis 5. Hypothesis 5: Stock & Fulfillment 5.1 Did we switch fulfillment methods (e.g., from FBA to MFN/FBM) or experience slower shipping times?
Likely Cause: Losing Prime eligibility or extending delivery windows often reduces conversion. Check each SKU’s fulfillment type in Inventory reports. See if the 94 SKUs with lower conversion lost FBA during December. End: If conversion dips align with the shift to slower fulfillment, that’s the reason. 5.2 Did any SKUs have low or “Only X left in stock” messages that boosted urgency, or did they go fully OOS?
Low stock can spur purchases (fear of missing out). Fully OOS means no sales, so conversion percentage might appear skewed. Review restock inventory reports for stock fluctuations. End: If SKUs with “limited stock” soared in conversions or OOS SKUs saw big drops, you’ve found the cause. No → If stock/fulfillment changes don’t explain it, move to Hypothesis 6. Hypothesis 6: Marketing & External Influences 6.1 Were certain SKUs promoted via off-Amazon channels (influencers, social media, brand referral links) to a more purchase-ready audience?
Likely Cause: Higher-intent external traffic typically yields better conversion rates than casual browsing. Check “Amazon Attribution” data for referral performance on specific SKUs. See if conversion lifts coincide with external campaign schedules. End: If confirmed, external traffic explains the SKU-level improvement. 6.2 Did any PR or editorial features (e.g., “Amazon Editor’s Choice,” “Featured in Gift Guides”) highlight certain SKUs?
Likely Cause: Third-party endorsements can significantly boost conversion if shoppers trust these recommendations. Track editorial recommendations, media mentions, or gift guide inclusions. Match the timeframe to your SKU’s conversion spike. End: If timing aligns, that’s the root cause. No → Move to Hypothesis 7 if none of these marketing or external pushes explain it. Hypothesis 7: Child & Parent Variant Mapping 7.1 Were any ASIN merges or parent-child variation changes done in December, redistributing reviews or sales data?
Likely Cause: Merging child variations can shift reviews/sales to a single parent listing, altering measured conversion rates. Check catalog changes in “Manage Inventory” or the vendor product catalog for merges/splits. Match the timing with conversion pattern changes. End: If merges or splits explain the conversion shift, that’s your answer. No → If no measurement or variation changes, we may need deeper SKU-by-SKU or time-of-day analysis Contribution of SKU Click share affecting the CVR ASP: change by -4% [₹500 —> ₹480 DRR] Category B Product Sales: change by +y% [7.00L —> 7.50L DRR] Traffic: change by -10% [30,000 —> 27,000 DRR] Hypothesis 1: Amazon Search Ranking & SEO Factors 1.1 Did most of the SKUs gain/lose organic rankings while others dropped?
Likely Cause: Improved/Decreased ranking positions for high-converting SKUs lead to greater/lesser share of organic traffic landing on them. Use tools like Helium 10 or Jungle Scout to track SKU-level keyword rank changes from November to December. See if top performers in December rank higher for key search terms. End: If confirmed, traffic increase or decrease is largely explained by ranking improvements for high-converting SKUs. 1.2 Did we optimize listings for x% of these SKUs (titles, bullet points, backend keywords) in December?
Likely Cause: Listing optimizations (e.g., better keywords, enhanced images, A+ content) can improve both visibility and click share from organic search. Compare listing versions from November vs. December (possibly use “Manage Your Experiments” in Seller Central or track version changes for vendor). End: If these optimizations correlate with increased traffic to those SKUs, you’ve found a key driver. No → If search ranking and listing optimizations don’t explain it, move to Hypothesis 2. Hypothesis 2: Amazon Browse Nodes & Category Placement 2.1 Were signifcant proportion of SKUs reassigned with high/low-traffic browse nodes (sub-categories)?
Likely Cause: Relevant/Irrelevant sub-category placement can drive more/less browsing traffic to those SKUs. Check the “Product Category” and “Sub-Categories” in Seller Central/Vendor Central for changes. End: If SKUs that gained/lost traffic were newly placed in a top-browsed category, that explains the traffic change 2.2 Did any top-level browse node promotions occur (e.g., Amazon’s “Best Sellers,” “New Releases,” “Movers & Shakers”)?
Likely Cause: SKUs featured on these high-visibility pages can see a surge in organic traffic. Check if your SKUs appeared in the “Top 100,” “Best Seller,” or “Amazon’s Choice” lists. End: If correlated with the timeframe of traffic shift, you have your explanation. No → Move to Hypothesis 3 if category changes do not account for the redistribution. Hypothesis 3: Buy Box & Availability 3.1 Did majority of SKUs lose or gain the Buy Box more frequently?
Likely Cause: A SKU consistently winning the Buy Box will retain the bulk of traffic from search; losing it can drop traffic share significantly. Review Buy Box percentage and history (Seller Central reports or third-party tools) for each SKU. End: If high-converting SKUs gained the Buy Box more often, that explains the traffic shift. 3.2 Were bunch of these SKUs out of stock (OOS) or had limited availability in November or December?
Likely Cause: If lower-converting SKUs were OOS in December, traffic for those SKUs would vanish—and effectively “shift” to available SKUs (which might convert better). Check inventory logs or “Restock Inventory” reports in Seller/Vendor Central. End: If restocking correlates with traffic returning to normal distribution, you’ve confirmed a primary driver. No → If not Buy Box or stock-related, move to Hypothesis 4. Hypothesis 4: Demand Shifts & Seasonality 4.1 Is there a seasonal or holiday factor increasing demand for certain SKUs?
Likely Cause: During holiday or event periods, shoppers tend to flock to gift or seasonal SKUs, many of which convert well if they’re best-sellers in that category. Compare historical December performance for these SKUs. Look at external trends (e.g., Google Trends) to see if certain products are seasonally popular. End: If data shows cyclical interest, that’s your explanation. 4.2 Did competitor factors (pricing, stockouts, or brand pullback) drive shoppers to our SKUs?
Likely Cause: Competitors going OOS or raising prices can shift organic demand to your still-available, well-priced SKUs. Monitor competitor listings, track competitor price changes. End: If competitor SKUs dropped out exactly when your high-converting SKUs saw a boost, that explains the traffic increase No → Move to Hypothesis 5 if no broad demand change is found. Hypothesis 5: Brand Store & External Traffic 5.1 Were we driving external traffic (via social media, influencers, brand store, etc.) mostly to certain SKUs?
Likely Cause: If you or affiliates promoted specific SKUs heavily, that organic traffic could show up as direct/organic on Amazon (depending on the referral parameters). Check Amazon Attribution or brand referral data to see if certain SKU links were pushed externally. End: If these SKUs are also high-converting, that explains half the overall conversion improvement from traffic mix. 5.2 Were changes made to the Brand Store (storefront) layout that favored certain SKUs?
Likely Cause: Featuring certain products more prominently in the Brand Store can direct more organic store visitors to those SKUs. Compare Brand Store layouts from November vs. December, and check Store Insights for traffic sources. End: If the featured SKUs match those that gained traffic share, you’ve located a key factor. No → Move on to Hypothesis 6 if none of the Brand Store or external referral paths explain the traffic increase/decrease. Hypothesis 6: Measurement & Tagging 6.1 Did any parent-child ASIN merges or changes in listing variations (color/size) happen?
Likely Cause: Merging variations or changing parent-child relationships can shift the “main” traffic from one SKU to another, altering our perceived distribution. Check “Manage Inventory” or vendor product catalog for variation merges or splits. End: If these merges correlate with the timing of traffic shifts, you’ve confirmed the driver. No → If none of these apply, consider deeper, SKU-by-SKU analysis or revisiting earlier nodes in more detail. Conversion: change by +0.5 percentage points [3.0% —> 3.5% ] Contribution of each SKU’s CVR, independent of Click share, affect the overall CVR Hypothesis 1: Product Listing & User Experience (UX) 1.1 Were product detail pages updated with improved images, A+ content, bullet points, or backend keywords?
Likely Cause: More compelling visuals, clearer info, or better SEO keywords typically boost conversions. Compare listing versions from November to December (via “Manage Your Experiments” or tracked listing edits). Note which SKUs had changes; see if conversion uplift aligns with the update dates. End: If confirmed, listing optimizations explain conversion gains for those SKUs. 1.2 Did changes in the Brand Store or enhanced brand content alter how shoppers view or buy these SKUs?
Likely Cause: Upgraded store navigation or featured modules can highlight certain SKUs, increasing conversion. Review Amazon Brand Store Insights to see if certain SKUs got more direct hits or better conversion paths. Map store layout changes to each SKU’s daily/weekly conversion trend. End: If the correlation is strong, you’ve found a primary conversion driver. No → If no listing or UX updates explain it, move to Hypothesis 2. Hypothesis 2: Pricing & Promotions 2.1 Did we run discounts, coupons, or Lightning Deals for these SKUs?
Likely Cause: Promotional pricing often leads to higher conversion rates if shoppers perceive a deal. Check the promotion schedules in Seller Central/Vendor Central (Deals, Coupons). Verify if the 20 SKUs with conversion gains had active promos during December. End: If yes, promotions are likely driving the lift. 2.2 Did we remove or reduce discounts for certain SKUs, causing conversion to drop?
Likely Cause: Ending a successful promotion can hurt conversions if customers lose an appealing deal. Examine the timeline for when promotions ended and match that to conversion declines in the 20 SKUs (if they had declined). End: If alignment exists, you’ve identified why those SKUs saw conversion dips. No → If not pricing or promotion, move to Hypothesis 3. Hypothesis 3: Product Reviews & Ratings 3.1 Did star ratings improve (e.g., from 4.2 to 4.5) or did we get a surge of positive reviews?
Likely Cause: Hitting a higher star threshold or gaining strong new reviews significantly increases customer confidence. Use Seller Central/Vendor Central “Voice of the Customer” or third-party review-tracking tools. Match rating boosts with increased conversion for specific SKUs. End: If the timing matches, reviews/ratings explain the uplift. 3.2 Did negative feedback or a rating drop occur for certain SKUs?
Likely Cause: A lower star rating or complaints can deter customers from buying, lowering conversion. Review negative feedback timeline and see if it coincides with the 94 SKUs’ conversion decline. End: If confirmed, poor reviews are the culprit. No → If no review-related factors, move to Hypothesis 4. Hypothesis 4: Competitive Landscape 4.1 Did a main competitor go out of stock or raise prices, making our SKU more attractive?
Likely Cause: If competitor listings become less favorable (OOS, price hikes), our SKUs get a conversion bump. Monitor competitor stock/pricing via third-party tools or manual checks. Correlate competitor disruptions with your SKU’s conversion lift dates. End: If the overlap is clear, competitor changes explain your SKU’s conversion growth. 4.2 Did a strong competitor enter the market or intensify promotions, undercutting our listings?
Likely Cause: A new competitor or an existing one offering steep discounts or better ratings can siphon away conversions. Track changes in competitor ads, brand campaigns, or price points. Confirm if your 94 declining SKUs directly overlap with their product. End: If the correlation is evident, competitor action is causing conversion drops. No → If not competitive factors, move to Hypothesis 5. Hypothesis 5: Stock & Fulfillment 5.1 Did we switch fulfillment methods (e.g., from FBA to MFN/FBM) or experience slower shipping times?
Likely Cause: Losing Prime eligibility or extending delivery windows often reduces conversion. Check each SKU’s fulfillment type in Inventory reports. See if the 94 SKUs with lower conversion lost FBA during December. End: If conversion dips align with the shift to slower fulfillment, that’s the reason. 5.2 Did any SKUs have low or “Only X left in stock” messages that boosted urgency, or did they go fully OOS?
Low stock can spur purchases (fear of missing out). Fully OOS means no sales, so conversion percentage might appear skewed. Review restock inventory reports for stock fluctuations. End: If SKUs with “limited stock” soared in conversions or OOS SKUs saw big drops, you’ve found the cause. No → If stock/fulfillment changes don’t explain it, move to Hypothesis 6. Hypothesis 6: Marketing & External Influences 6.1 Were certain SKUs promoted via off-Amazon channels (influencers, social media, brand referral links) to a more purchase-ready audience?
Likely Cause: Higher-intent external traffic typically yields better conversion rates than casual browsing. Check “Amazon Attribution” data for referral performance on specific SKUs. See if conversion lifts coincide with external campaign schedules. End: If confirmed, external traffic explains the SKU-level improvement. 6.2 Did any PR or editorial features (e.g., “Amazon Editor’s Choice,” “Featured in Gift Guides”) highlight certain SKUs?
Likely Cause: Third-party endorsements can significantly boost conversion if shoppers trust these recommendations. Track editorial recommendations, media mentions, or gift guide inclusions. Match the timeframe to your SKU’s conversion spike. End: If timing aligns, that’s the root cause. No → Move to Hypothesis 7 if none of these marketing or external pushes explain it. Hypothesis 7: Child & Parent Variant Mapping 7.1 Were any ASIN merges or parent-child variation changes done in December, redistributing reviews or sales data?
Likely Cause: Merging child variations can shift reviews/sales to a single parent listing, altering measured conversion rates. Check catalog changes in “Manage Inventory” or the vendor product catalog for merges/splits. Match the timing with conversion pattern changes. End: If merges or splits explain the conversion shift, that’s your answer. No → If no measurement or variation changes, we may need deeper SKU-by-SKU or time-of-day analysis Contribution of SKU Click share affecting the CVR ASP: change by +6.7% [₹750 —> ₹800 DRR] Category C Product Sales: change by +z% [4.00L —> 4.20L DRR] Traffic: change by +10% [40,000 —> 44,000 DRR] Hypothesis 1: Amazon Search Ranking & SEO Factors 1.1 Did most of the SKUs gain/lose organic rankings while others dropped?
Likely Cause: Improved/Decreased ranking positions for high-converting SKUs lead to greater/lesser share of organic traffic landing on them. Use tools like Helium 10 or Jungle Scout to track SKU-level keyword rank changes from November to December. See if top performers in December rank higher for key search terms. End: If confirmed, traffic increase or decrease is largely explained by ranking improvements for high-converting SKUs. 1.2 Did we optimize listings for x% of these SKUs (titles, bullet points, backend keywords) in December?
Likely Cause: Listing optimizations (e.g., better keywords, enhanced images, A+ content) can improve both visibility and click share from organic search. Compare listing versions from November vs. December (possibly use “Manage Your Experiments” in Seller Central or track version changes for vendor). End: If these optimizations correlate with increased traffic to those SKUs, you’ve found a key driver. No → If search ranking and listing optimizations don’t explain it, move to Hypothesis 2. Hypothesis 2: Amazon Browse Nodes & Category Placement 2.1 Were signifcant proportion of SKUs reassigned with high/low-traffic browse nodes (sub-categories)?
Likely Cause: Relevant/Irrelevant sub-category placement can drive more/less browsing traffic to those SKUs. Check the “Product Category” and “Sub-Categories” in Seller Central/Vendor Central for changes. End: If SKUs that gained/lost traffic were newly placed in a top-browsed category, that explains the traffic change 2.2 Did any top-level browse node promotions occur (e.g., Amazon’s “Best Sellers,” “New Releases,” “Movers & Shakers”)?
Likely Cause: SKUs featured on these high-visibility pages can see a surge in organic traffic. Check if your SKUs appeared in the “Top 100,” “Best Seller,” or “Amazon’s Choice” lists. End: If correlated with the timeframe of traffic shift, you have your explanation. No → Move to Hypothesis 3 if category changes do not account for the redistribution. Hypothesis 3: Buy Box & Availability 3.1 Did majority of SKUs lose or gain the Buy Box more frequently?
Likely Cause: A SKU consistently winning the Buy Box will retain the bulk of traffic from search; losing it can drop traffic share significantly. Review Buy Box percentage and history (Seller Central reports or third-party tools) for each SKU. End: If high-converting SKUs gained the Buy Box more often, that explains the traffic shift. 3.2 Were bunch of these SKUs out of stock (OOS) or had limited availability in November or December?
Likely Cause: If lower-converting SKUs were OOS in December, traffic for those SKUs would vanish—and effectively “shift” to available SKUs (which might convert better). Check inventory logs or “Restock Inventory” reports in Seller/Vendor Central. End: If restocking correlates with traffic returning to normal distribution, you’ve confirmed a primary driver. No → If not Buy Box or stock-related, move to Hypothesis 4. Hypothesis 4: Demand Shifts & Seasonality 4.1 Is there a seasonal or holiday factor increasing demand for certain SKUs?
Likely Cause: During holiday or event periods, shoppers tend to flock to gift or seasonal SKUs, many of which convert well if they’re best-sellers in that category. Compare historical December performance for these SKUs. Look at external trends (e.g., Google Trends) to see if certain products are seasonally popular. End: If data shows cyclical interest, that’s your explanation. 4.2 Did competitor factors (pricing, stockouts, or brand pullback) drive shoppers to our SKUs?
Likely Cause: Competitors going OOS or raising prices can shift organic demand to your still-available, well-priced SKUs. Monitor competitor listings, track competitor price changes. End: If competitor SKUs dropped out exactly when your high-converting SKUs saw a boost, that explains the traffic increase No → Move to Hypothesis 5 if no broad demand change is found. Hypothesis 5: Brand Store & External Traffic 5.1 Were we driving external traffic (via social media, influencers, brand store, etc.) mostly to certain SKUs?
Likely Cause: If you or affiliates promoted specific SKUs heavily, that organic traffic could show up as direct/organic on Amazon (depending on the referral parameters). Check Amazon Attribution or brand referral data to see if certain SKU links were pushed externally. End: If these SKUs are also high-converting, that explains half the overall conversion improvement from traffic mix. 5.2 Were changes made to the Brand Store (storefront) layout that favored certain SKUs?
Likely Cause: Featuring certain products more prominently in the Brand Store can direct more organic store visitors to those SKUs. Compare Brand Store layouts from November vs. December, and check Store Insights for traffic sources. End: If the featured SKUs match those that gained traffic share, you’ve located a key factor. No → Move on to Hypothesis 6 if none of the Brand Store or external referral paths explain the traffic increase/decrease. Hypothesis 6: Measurement & Tagging 6.1 Did any parent-child ASIN merges or changes in listing variations (color/size) happen?
Likely Cause: Merging variations or changing parent-child relationships can shift the “main” traffic from one SKU to another, altering our perceived distribution. Check “Manage Inventory” or vendor product catalog for variation merges or splits. End: If these merges correlate with the timing of traffic shifts, you’ve confirmed the driver. No → If none of these apply, consider deeper, SKU-by-SKU analysis or revisiting earlier nodes in more detail. Conversion: change by 0% [2.5% —> 2.5% DRR] Contribution of each SKU’s CVR, independent of Click share, affect the overall CVR Hypothesis 1: Product Listing & User Experience (UX) 1.1 Were product detail pages updated with improved images, A+ content, bullet points, or backend keywords?
Likely Cause: More compelling visuals, clearer info, or better SEO keywords typically boost conversions. Compare listing versions from November to December (via “Manage Your Experiments” or tracked listing edits). Note which SKUs had changes; see if conversion uplift aligns with the update dates. End: If confirmed, listing optimizations explain conversion gains for those SKUs. 1.2 Did changes in the Brand Store or enhanced brand content alter how shoppers view or buy these SKUs?
Likely Cause: Upgraded store navigation or featured modules can highlight certain SKUs, increasing conversion. Review Amazon Brand Store Insights to see if certain SKUs got more direct hits or better conversion paths. Map store layout changes to each SKU’s daily/weekly conversion trend. End: If the correlation is strong, you’ve found a primary conversion driver. No → If no listing or UX updates explain it, move to Hypothesis 2. Hypothesis 2: Pricing & Promotions 2.1 Did we run discounts, coupons, or Lightning Deals for these SKUs?
Likely Cause: Promotional pricing often leads to higher conversion rates if shoppers perceive a deal. Check the promotion schedules in Seller Central/Vendor Central (Deals, Coupons). Verify if the 20 SKUs with conversion gains had active promos during December. End: If yes, promotions are likely driving the lift. 2.2 Did we remove or reduce discounts for certain SKUs, causing conversion to drop?
Likely Cause: Ending a successful promotion can hurt conversions if customers lose an appealing deal. Examine the timeline for when promotions ended and match that to conversion declines in the 20 SKUs (if they had declined). End: If alignment exists, you’ve identified why those SKUs saw conversion dips. No → If not pricing or promotion, move to Hypothesis 3. Hypothesis 3: Product Reviews & Ratings 3.1 Did star ratings improve (e.g., from 4.2 to 4.5) or did we get a surge of positive reviews?
Likely Cause: Hitting a higher star threshold or gaining strong new reviews significantly increases customer confidence. Use Seller Central/Vendor Central “Voice of the Customer” or third-party review-tracking tools. Match rating boosts with increased conversion for specific SKUs. End: If the timing matches, reviews/ratings explain the uplift. 3.2 Did negative feedback or a rating drop occur for certain SKUs?
Likely Cause: A lower star rating or complaints can deter customers from buying, lowering conversion. Review negative feedback timeline and see if it coincides with the 94 SKUs’ conversion decline. End: If confirmed, poor reviews are the culprit. No → If no review-related factors, move to Hypothesis 4. Hypothesis 4: Competitive Landscape 4.1 Did a main competitor go out of stock or raise prices, making our SKU more attractive?
Likely Cause: If competitor listings become less favorable (OOS, price hikes), our SKUs get a conversion bump. Monitor competitor stock/pricing via third-party tools or manual checks. Correlate competitor disruptions with your SKU’s conversion lift dates. End: If the overlap is clear, competitor changes explain your SKU’s conversion growth. 4.2 Did a strong competitor enter the market or intensify promotions, undercutting our listings?
Likely Cause: A new competitor or an existing one offering steep discounts or better ratings can siphon away conversions. Track changes in competitor ads, brand campaigns, or price points. Confirm if your 94 declining SKUs directly overlap with their product. End: If the correlation is evident, competitor action is causing conversion drops. No → If not competitive factors, move to Hypothesis 5. Hypothesis 5: Stock & Fulfillment 5.1 Did we switch fulfillment methods (e.g., from FBA to MFN/FBM) or experience slower shipping times?
Likely Cause: Losing Prime eligibility or extending delivery windows often reduces conversion. Check each SKU’s fulfillment type in Inventory reports. See if the 94 SKUs with lower conversion lost FBA during December. End: If conversion dips align with the shift to slower fulfillment, that’s the reason. 5.2 Did any SKUs have low or “Only X left in stock” messages that boosted urgency, or did they go fully OOS?
Low stock can spur purchases (fear of missing out). Fully OOS means no sales, so conversion percentage might appear skewed. Review restock inventory reports for stock fluctuations. End: If SKUs with “limited stock” soared in conversions or OOS SKUs saw big drops, you’ve found the cause. No → If stock/fulfillment changes don’t explain it, move to Hypothesis 6. Hypothesis 6: Marketing & External Influences 6.1 Were certain SKUs promoted via off-Amazon channels (influencers, social media, brand referral links) to a more purchase-ready audience?
Likely Cause: Higher-intent external traffic typically yields better conversion rates than casual browsing. Check “Amazon Attribution” data for referral performance on specific SKUs. See if conversion lifts coincide with external campaign schedules. End: If confirmed, external traffic explains the SKU-level improvement. 6.2 Did any PR or editorial features (e.g., “Amazon Editor’s Choice,” “Featured in Gift Guides”) highlight certain SKUs?
Likely Cause: Third-party endorsements can significantly boost conversion if shoppers trust these recommendations. Track editorial recommendations, media mentions, or gift guide inclusions. Match the timeframe to your SKU’s conversion spike. End: If timing aligns, that’s the root cause. No → Move to Hypothesis 7 if none of these marketing or external pushes explain it. Hypothesis 7: Child & Parent Variant Mapping 7.1 Were any ASIN merges or parent-child variation changes done in December, redistributing reviews or sales data?
Likely Cause: Merging child variations can shift reviews/sales to a single parent listing, altering measured conversion rates. Check catalog changes in “Manage Inventory” or the vendor product catalog for merges/splits. Match the timing with conversion pattern changes. End: If merges or splits explain the conversion shift, that’s your answer. No → If no measurement or variation changes, we may need deeper SKU-by-SKU or time-of-day analysis Contribution of SKU Click share affecting the CVR ASP: change by -3.3% [₹600 —> ₹580 DRR] Ad Sales: change by +25% [2.80L —> 3.50L DRR] Category A Product Sales: change by x% [5.00L —> 6.00L DRR] Impressions: change by x% Hypothesis 1: Auction & Bidding Factors 1.1 Were bids (or daily budgets) for our key SKUs/campaigns changed in December?
Yes → Possible cause: Increased bids can lead to higher ad rank and more impressions (or decreased bids the opposite).
Next Step: Compare November vs. December bidding and daily budget levels. If we increased them, we likely gained impressions; if we lowered them, we lost impressions. End: If confirmed, you have found your primary driver for impressions change. No → Proceed to 1.2.
1.2 Did competitors’ bidding behavior change significantly?
Yes → Cause: Competitor bid changes can raise or lower the overall auction dynamics—affecting how often your ads are shown.
Next Step: Review impression share reports, average position, and competitor metrics. Confirm if competition got stronger or weaker in December. End: If competitor shifts explain the impression changes, you have your answer. No → Proceed to 1.3.
1.3 Did budget constraints cause ads to stop running earlier or later than November?
Yes → Cause: If budgets were capped or exhausted sooner in the day, impressions could drop; if uncapped, impressions might have increased.
Next Step: Compare daily spend patterns and check if budgets were hitting limits. End: If confirmed, you’ve found your cause for impression fluctuation. No → If none of these Auction & Bidding factors explain the shift, move to Hypothesis 2.
Hypothesis 2: Keyword & Targeting Adjustments 2.1 Did we add or remove keywords or product targets (e.g., SKU-based targeting) since November?
Yes → Cause: Expanding keywords or product targets often increases impressions; removing them can reduce impressions.
Next Step: Look at performance by new vs. existing keywords. If the net effect is growth, that explains increased impressions; if removal was larger, it could explain a drop. End: If confirmed, you have your answer. No → Proceed to 2.2.
2.2 Were negative keywords added or targeting restricted?
Yes → Cause: This can reduce the eligible search queries or audience, thereby lowering impressions.
Next Step: Review change logs for negative keyword additions or tighter targeting criteria. End: If reversing or loosening those changes increases impressions again, it’s confirmed. No → Proceed to 2.3.
2.3 Were audience segments or geo-targeting expanded or reduced?
Yes → Cause: Broader audiences or geographies often yield more impressions, while restricting them does the opposite.
Next Step: Compare audience or location reports from November vs. December. End: If confirmed, you’ve found your cause. No → If all sub-hypotheses for Keyword & Targeting are “No,” move to Hypothesis 3.
Hypothesis 3: Ad Scheduling & Delivery 3.1 Were ad schedules changed, reducing or extending the total hours ads were shown?
Yes → Cause: Running ads fewer hours can drastically cut impressions; running them more hours can boost impressions.
Next Step: Check the dayparting or ad schedule settings for November vs. December. End: If scheduling changes align with the impression shift, we’ve found the reason. No → Proceed to 3.2.
3.2 Were different delivery methods (standard vs. accelerated) used?
Yes → Cause: Switching from accelerated to standard (or vice versa) can shift how quickly ads spend their budget and thus affect total impressions.
Next Step: Check the campaign delivery settings in November vs. December. End: If confirmed, no further exploration needed. No → Move to Hypothesis 4 if scheduling & delivery changes do not explain the difference.
Hypothesis 4: Ad Quality & Relevance 4.1 Did our Quality Score or relevance metrics change, impacting how often ads are shown?
Yes → Cause: A drop in Quality Score can reduce impression share; an improvement can boost it.
Next Step: Examine Quality Score components (expected CTR, ad relevance, landing page experience). Identify changes from November to December. End: If confirmed, focus on improving these factors for stable impression share. No → Proceed to 4.2.
4.2 Did changes in clickthrough rates (CTR) affect ad rank or impression frequency?
Yes → Cause: While CTR is partly a result of impressions, Amazon use CTR in ad rank calculations. A big CTR drop might reduce total impressions. Conversely, a big CTR rise might increase impression opportunities.
Next Step: Correlate CTR changes with impression share in the ad platforms’ reporting. End: If confirmed, improving CTR or mitigating its drop can restore impressions. No → If not ad quality or relevance, move to Hypothesis 5.
Hypothesis 5: Seasonality & Macro Factors 5.1 Are there seasonal patterns or macro trends (e.g., holidays, economic shifts) that affected total search/ad volume?
Yes → Cause: Overall search volume for our category might have increased or decreased.
Next Step: Check historical data to see if this period typically experiences a change in search interest. End: If it aligns with known seasonal peaks/slumps, we’ve identified the macro cause. No → Proceed to 5.2.
5.2 Did competitor promotions or events draw user attention elsewhere or boost overall interest?
Yes → Cause: Competitor heavy promotions can draw more (or fewer) queries in our category, indirectly affecting our impressions.
Next Step: Monitor market-level demand, competitor promotions, or press coverage. End: If confirmed, this is largely outside direct control, but influences impression volume. No → If neither seasonality nor macro factors seem to explain it, At this point, consider more granular analyses (SKU-level, time-of-day, device-level, etc.) or revisit earlier hypotheses in more depth.
Impressions Click Through Rate (CTR): change by y% Contribution of each SKU’s CTR, independent of impression share, affect the overall CTR Hypothesis 1: Auction & Bidding Factors 1.1 Were bids for the SKUs reduced in December?
Yes → Likely cause: Lower bids may have decreased ad rank/visibility, leading to lower CTR. Next Step: Test by incrementally increasing bids for a select group of the SKUs and see if CTR improves. End: If confirmed, we have found our cause; we may not need to explore further. 1.2 Did competitors increase their bids substantially, hurting our visibility?
Yes → Likely cause: Even if our bids remained the same, competitor bid increases can lower our relative ad position, reducing CTR. Next Step: Compare average ad positions and competitor impression share from November vs. December to confirm. End: If confirmed, explore ways to adjust bidding or enhance ad relevance. 1.3 Were budget caps (daily or total) reached earlier than in November for these SKUs?
Yes → Likely cause: Ads may have paused prematurely, running in less optimal hours or positions, leading to lower CTR. Next Step: Increase or reallocate budget to see if extending ad availability improves CTR. End: If confirmed, we have found our cause. No → If all sub-hypotheses in Auction & Bidding are “No,” move to Hypothesis 2. Hypothesis 2: Ad Creative & Messaging 2.1 Were ad creatives or messaging changed for the 83 SKUs?
Yes → Possibly the new creatives/messaging are less appealing. Next Step: A/B test original (November) creative vs. current (December) creative on a subset of SKUs. End: If CTR rebounds with old creative, we have found our cause. 2.2 Could ad fatigue be impacting CTR? (e.g., same audiences seeing the same ads too often)
Yes → Likely cause: Users become “blind” to repeated ads, diminishing CTR. Next Step: Reduce frequency caps, refresh creative, or adjust audience segments. End: If confirmed, you have found your cause. No → If ad creative or messaging is not the issue, move to Hypothesis 3. Hypothesis 3: Audience & Targeting 3.1 Was there a shift in audience segments or targeting parameters? (e.g., new negative keywords, changed demographics)
Yes → Possibly showing ads to a less interested audience. Next Step: Compare November vs. December audience composition and performance by segment. Reverse or refine targeting changes. End: If CTR improves after reverting audience changes, we have found our cause. 3.2 Are seasonal or macro trends causing lower engagement overall?
Yes → Holiday promotions ended? Post-holiday slump? Next Step: Check historical data for past years, see if CTR traditionally drops during this period. End: If seasonality explains the drop, plan promotional strategies accordingly. No → If no audience or seasonality changes explain the drop, move to Hypothesis 4. Hypothesis 4: Offers & Pricing 4.1 Have offers or pricing changed unfavorably for these SKUs?
Yes → Less appealing offers/pricing can deter clicks (users might see lower value in the ad). Next Step: Compare pre/post pricing and evaluate promotional strategies. End: If reverting or improving offers reinstates CTR, cause is confirmed. No → If funnel/landing page is not the culprit, we may need to circle back and do a more granular analysis or revisit earlier hypotheses more deeply. Contribution of SKUs impression share affecting the CTR Conversion (CVR): change by z% Contribution of each SKU’s CVR, independent of Click share, affect the overall CVR Hypothesis 1: Product Listing & User Experience (UX) 1.1 Were product detail pages updated with improved images, A+ content, bullet points, or backend keywords?
Likely Cause: More compelling visuals, clearer info, or better SEO keywords typically boost conversions. Compare listing versions from November to December (via “Manage Your Experiments” or tracked listing edits). Note which SKUs had changes; see if conversion uplift aligns with the update dates. End: If confirmed, listing optimizations explain conversion gains for those SKUs. 1.2 Did changes in the Brand Store or enhanced brand content alter how shoppers view or buy these SKUs?
Likely Cause: Upgraded store navigation or featured modules can highlight certain SKUs, increasing conversion. Review Amazon Brand Store Insights to see if certain SKUs got more direct hits or better conversion paths. Map store layout changes to each SKU’s daily/weekly conversion trend. End: If the correlation is strong, you’ve found a primary conversion driver. No → If no listing or UX updates explain it, move to Hypothesis 2. Hypothesis 2: Pricing & Promotions 2.1 Did we run discounts, coupons, or Lightning Deals for these SKUs?
Likely Cause: Promotional pricing often leads to higher conversion rates if shoppers perceive a deal. Check the promotion schedules in Seller Central/Vendor Central (Deals, Coupons). Verify if the SKUs with conversion gains had active promos during December. End: If yes, promotions are likely driving the lift. 2.2 Did we remove or reduce discounts for certain SKUs, causing conversion to drop?
Likely Cause: Ending a successful promotion can hurt conversions if customers lose an appealing deal. Examine the timeline for when promotions ended and match that to conversion declines in the SKUs (if they had declined). End: If alignment exists, we’ve identified why those SKUs saw conversion dips. No → If not pricing or promotion, move to Hypothesis 3. Hypothesis 3: Product Reviews & Ratings 3.1 Did star ratings improve (e.g., from 4.2 to 4.5) or did we get a surge of positive reviews?
Likely Cause: Hitting a higher star threshold or gaining strong new reviews significantly increases customer confidence. Use Seller Central/Vendor Central “Voice of the Customer” or third-party review-tracking tools. Match rating boosts with increased conversion for specific SKUs. End: If the timing matches, reviews/ratings explain the uplift. 3.2 Did negative feedback or a rating drop occur for certain SKUs?
Likely Cause: A lower star rating or complaints can deter customers from buying, lowering conversion. Review negative feedback timeline and see if it coincides with the SKUs’ conversion decline. End: If confirmed, poor reviews are the culprit. No → If no review-related factors, move to Hypothesis 4. Hypothesis 4: Competitive Landscape 4.1 Did a main competitor go out of stock or raise prices, making our SKU more attractive?
Likely Cause: If competitor listings become less favorable (OOS, price hikes), our SKUs get a conversion bump. Monitor competitor stock/pricing via third-party tools or manual checks. Correlate competitor disruptions with your SKU’s conversion lift dates. End: If the overlap is clear, competitor changes explain your SKU’s conversion growth. 4.2 Did a strong competitor enter the market or intensify promotions, undercutting our listings?
Likely Cause: A new competitor or an existing one offering steep discounts or better ratings can siphon away conversions. Track changes in competitor ads, brand campaigns, or price points. Confirm if your 94 declining SKUs directly overlap with their product. End: If the correlation is evident, competitor action is causing conversion drops. No → If not competitive factors, move to Hypothesis 5. Hypothesis 5: Stock & Fulfillment 5.1 Did we switch fulfillment methods (e.g., from FBA to MFN/FBM) or experience slower shipping times?
Likely Cause: Losing Prime eligibility or extending delivery windows often reduces conversion. Check each SKU’s fulfillment type in Inventory reports. See if the SKUs with lower conversion lost FBA during December. End: If conversion dips align with the shift to slower fulfillment, that’s the reason. 5.2 Did any SKUs have low or “Only X left in stock” messages that boosted urgency, or did they go fully OOS?
Low stock can spur purchases (fear of missing out). Fully OOS means no sales, so conversion percentage might appear skewed. Review restock inventory reports for stock fluctuations. End: If SKUs with “limited stock” soared in conversions or OOS SKUs saw big drops, you’ve found the cause. No → If stock/fulfillment changes don’t explain it, move to Hypothesis 6. Hypothesis 6: Marketing & External Influences 6.1 Were certain SKUs promoted via off-Amazon channels (influencers, social media, brand referral links) to a more purchase-ready audience?
Likely Cause: Higher-intent external traffic typically yields better conversion rates than casual browsing. Check “Amazon Attribution” data for referral performance on specific SKUs. See if conversion lifts coincide with external campaign schedules. End: If confirmed, external traffic explains the SKU-level improvement. 6.2 Did any PR or editorial features (e.g., “Amazon Editor’s Choice,” “Featured in Gift Guides”) highlight certain SKUs?
Likely Cause: Third-party endorsements can significantly boost conversion if shoppers trust these recommendations. Track editorial recommendations, media mentions, or gift guide inclusions. Match the timeframe to your SKU’s conversion spike. End: If timing aligns, that’s the root cause. No → Move to Hypothesis 7 if none of these marketing or external pushes explain it. Hypothesis 7: Child & Parent Variant Mapping 7.1 Were any ASIN merges or parent-child variation changes done in December, redistributing reviews or sales data?
Likely Cause: Merging child variations can shift reviews/sales to a single parent listing, altering measured conversion rates. Check catalog changes in “Manage Inventory” or the vendor product catalog for merges/splits. Match the timing with conversion pattern changes. End: If merges or splits explain the conversion shift, that’s your answer. No → If no measurement or variation changes, we may need deeper SKU-by-SKU or time-of-day analysis Contribution of SKU Click share affecting the CVR Category B Product Sales: change by y% [7.00L —> 7.50L DRR] Impressions: change by x% Hypothesis 1: Auction & Bidding Factors 1.1 Were bids (or daily budgets) for our key SKUs/campaigns changed in December?
Yes → Possible cause: Increased bids can lead to higher ad rank and more impressions (or decreased bids the opposite).
Next Step: Compare November vs. December bidding and daily budget levels. If you increased them, you likely gained impressions; if you lowered them, you lost impressions. End: If confirmed, you have found your primary driver for impressions change. No → Proceed to 1.2.
1.2 Did competitors’ bidding behavior change significantly?
Yes → Cause: Competitor bid changes can raise or lower the overall auction dynamics—affecting how often your ads are shown.
Next Step: Review impression share reports, average position, and competitor metrics. Confirm if competition got stronger or weaker in December. End: If competitor shifts explain the impression changes, you have your answer. No → Proceed to 1.3.
1.3 Did budget constraints cause ads to stop running earlier or later than November?
Yes → Cause: If budgets were capped or exhausted sooner in the day, impressions could drop; if uncapped, impressions might have increased.
Next Step: Compare daily spend patterns and check if budgets were hitting limits. End: If confirmed, you’ve found your cause for impression fluctuation. No → If none of these Auction & Bidding factors explain the shift, move to Hypothesis 2.
Hypothesis 2: Keyword & Targeting Adjustments 2.1 Did we add or remove keywords or product targets (e.g., SKU-based targeting) since November?
Yes → Cause: Expanding keywords or product targets often increases impressions; removing them can reduce impressions.
Next Step: Look at performance by new vs. existing keywords. If the net effect is growth, that explains increased impressions; if removal was larger, it could explain a drop. End: If confirmed, you have your answer. No → Proceed to 2.2.
2.2 Were negative keywords added or targeting restricted?
Yes → Cause: This can reduce the eligible search queries or audience, thereby lowering impressions.
Next Step: Review change logs for negative keyword additions or tighter targeting criteria. End: If reversing or loosening those changes increases impressions again, it’s confirmed. No → Proceed to 2.3.
2.3 Were audience segments or geo-targeting expanded or reduced?
Yes → Cause: Broader audiences or geographies often yield more impressions, while restricting them does the opposite.
Next Step: Compare audience or location reports from November vs. December. End: If confirmed, you’ve found your cause. No → If all sub-hypotheses for Keyword & Targeting are “No,” move to Hypothesis 3.
Hypothesis 3: Ad Scheduling & Delivery 3.1 Were ad schedules changed, reducing or extending the total hours ads were shown?
Yes → Cause: Running ads fewer hours can drastically cut impressions; running them more hours can boost impressions.
Next Step: Check the dayparting or ad schedule settings for November vs. December. End: If scheduling changes align with the impression shift, you’ve found the reason. No → Proceed to 3.2.
3.2 Were different delivery methods (standard vs. accelerated) used?
Yes → Cause: Switching from accelerated to standard (or vice versa) can shift how quickly ads spend their budget and thus affect total impressions.
Next Step: Check the campaign delivery settings in November vs. December. End: If confirmed, no further exploration needed. No → Move to Hypothesis 4 if scheduling & delivery changes do not explain the difference.
Hypothesis 4: Ad Quality & Relevance 4.1 Did our Quality Score or relevance metrics change, impacting how often ads are shown?
Yes → Cause: A drop in Quality Score can reduce impression share; an improvement can boost it.
Next Step: Examine Quality Score components (expected CTR, ad relevance, landing page experience). Identify changes from November to December. End: If confirmed, focus on improving these factors for stable impression share. No → Proceed to 4.2.
4.2 Did changes in clickthrough rates (CTR) affect ad rank or impression frequency?
Yes → Cause: While CTR is partly a result of impressions, certain ad platforms use CTR in ad rank calculations. A big CTR drop might reduce total impressions. Conversely, a big CTR rise might increase impression opportunities.
Next Step: Correlate CTR changes with impression share in the ad platforms’ reporting. End: If confirmed, improving CTR or mitigating its drop can restore impressions. No → If not ad quality or relevance, move to Hypothesis 5.
Hypothesis 5: Seasonality & Macro Factors 5.1 Are there seasonal patterns or macro trends (e.g., holidays, economic shifts) that affected total search/ad volume?
Yes → Cause: Overall search volume for your category might have increased or decreased.
Next Step: Check historical data to see if this period typically experiences a change in search interest. End: If it aligns with known seasonal peaks/slumps, you’ve identified the macro cause. No → Proceed to 5.2.
5.2 Did competitor promotions or events draw user attention elsewhere or boost overall interest?
Yes → Cause: Competitor heavy promotions can draw more (or fewer) queries in your category, indirectly affecting your impressions.
Next Step: Monitor market-level demand, competitor promotions, or press coverage. End: If confirmed, this is largely outside direct control, but influences impression volume. No → If neither seasonality nor macro factors seem to explain it, At this point, consider more granular analyses (SKU-level, time-of-day, device-level, etc.) or revisit earlier hypotheses in more depth.
Impressions Click Through Rate (CTR): change by y% Contribution of each SKU’s CTR, independent of impression share, affect the overall CTR Hypothesis 1: Auction & Bidding Factors 1.1 Were bids for the 83 SKUs reduced in December?
Yes → Likely cause: Lower bids may have decreased ad rank/visibility, leading to lower CTR. Next Step: Test by incrementally increasing bids for a select group of the 83 SKUs and see if CTR improves. End: If confirmed, you have found your cause; you may not need to explore further. 1.2 Did competitors increase their bids substantially, hurting our visibility?
Yes → Likely cause: Even if our bids remained the same, competitor bid increases can lower our relative ad position, reducing CTR. Next Step: Compare average ad positions and competitor impression share from November vs. December to confirm. End: If confirmed, explore ways to adjust bidding or enhance ad relevance. 1.3 Were budget caps (daily or total) reached earlier than in November for these SKUs?
Yes → Likely cause: Ads may have paused prematurely, running in less optimal hours or positions, leading to lower CTR. Next Step: Increase or reallocate budget to see if extending ad availability improves CTR. End: If confirmed, you have found your cause. No → If all sub-hypotheses in Auction & Bidding are “No,” move to Hypothesis 2. Hypothesis 2: Ad Creative & Messaging 2.1 Were ad creatives or messaging changed for the 83 SKUs?
Yes → Possibly the new creatives/messaging are less appealing. Next Step: A/B test original (November) creative vs. current (December) creative on a subset of SKUs. End: If CTR rebounds with old creative, you have found your cause. 2.2 Could ad fatigue be impacting CTR? (e.g., same audiences seeing the same ads too often)
Yes → Likely cause: Users become “blind” to repeated ads, diminishing CTR. Next Step: Reduce frequency caps, refresh creative, or adjust audience segments. End: If confirmed, you have found your cause. No → If ad creative or messaging is not the issue, move to Hypothesis 3. Hypothesis 3: Audience & Targeting 3.1 Was there a shift in audience segments or targeting parameters? (e.g., new negative keywords, changed demographics)
Yes → Possibly showing ads to a less interested audience. Next Step: Compare November vs. December audience composition and performance by segment. Reverse or refine targeting changes. End: If CTR improves after reverting audience changes, you have found your cause. 3.2 Are seasonal or macro trends causing lower engagement overall?
Yes → Holiday promotions ended? Post-holiday slump? Next Step: Check historical data for past years, see if CTR traditionally drops during this period. End: If seasonality explains the drop, plan promotional strategies accordingly. No → If no audience or seasonality changes explain the drop, move to Hypothesis 4. Hypothesis 4: Offers & Pricing 4.1 Have offers or pricing changed unfavorably for these SKUs?
Yes → Less appealing offers/pricing can deter clicks (users might see lower value in the ad). Next Step: Compare pre/post pricing and evaluate promotional strategies. End: If reverting or improving offers reinstates CTR, cause is confirmed. No → If funnel/landing page is not the culprit, you may need to circle back and do a more granular analysis or revisit earlier hypotheses more deeply. Contribution of SKUs impression share affecting the CTR Conversion (CVR): change by z% Contribution of each SKU’s CVR, independent of Click share, affect the overall CVR Hypothesis 1: Product Listing & User Experience (UX) 1.1 Were product detail pages updated with improved images, A+ content, bullet points, or backend keywords?
Likely Cause: More compelling visuals, clearer info, or better SEO keywords typically boost conversions. Compare listing versions from November to December (via “Manage Your Experiments” or tracked listing edits). Note which SKUs had changes; see if conversion uplift aligns with the update dates. End: If confirmed, listing optimizations explain conversion gains for those SKUs. 1.2 Did changes in the Brand Store or enhanced brand content alter how shoppers view or buy these SKUs?
Likely Cause: Upgraded store navigation or featured modules can highlight certain SKUs, increasing conversion. Review Amazon Brand Store Insights to see if certain SKUs got more direct hits or better conversion paths. Map store layout changes to each SKU’s daily/weekly conversion trend. End: If the correlation is strong, you’ve found a primary conversion driver. No → If no listing or UX updates explain it, move to Hypothesis 2. Hypothesis 2: Pricing & Promotions 2.1 Did we run discounts, coupons, or Lightning Deals for these SKUs?
Likely Cause: Promotional pricing often leads to higher conversion rates if shoppers perceive a deal. Check the promotion schedules in Seller Central/Vendor Central (Deals, Coupons). Verify if the 20 SKUs with conversion gains had active promos during December. End: If yes, promotions are likely driving the lift. 2.2 Did we remove or reduce discounts for certain SKUs, causing conversion to drop?
Likely Cause: Ending a successful promotion can hurt conversions if customers lose an appealing deal. Examine the timeline for when promotions ended and match that to conversion declines in the 20 SKUs (if they had declined). End: If alignment exists, you’ve identified why those SKUs saw conversion dips. No → If not pricing or promotion, move to Hypothesis 3. Hypothesis 3: Product Reviews & Ratings 3.1 Did star ratings improve (e.g., from 4.2 to 4.5) or did we get a surge of positive reviews?
Likely Cause: Hitting a higher star threshold or gaining strong new reviews significantly increases customer confidence. Use Seller Central/Vendor Central “Voice of the Customer” or third-party review-tracking tools. Match rating boosts with increased conversion for specific SKUs. End: If the timing matches, reviews/ratings explain the uplift. 3.2 Did negative feedback or a rating drop occur for certain SKUs?
Likely Cause: A lower star rating or complaints can deter customers from buying, lowering conversion. Review negative feedback timeline and see if it coincides with the 94 SKUs’ conversion decline. End: If confirmed, poor reviews are the culprit. No → If no review-related factors, move to Hypothesis 4. Hypothesis 4: Competitive Landscape 4.1 Did a main competitor go out of stock or raise prices, making our SKU more attractive?
Likely Cause: If competitor listings become less favorable (OOS, price hikes), our SKUs get a conversion bump. Monitor competitor stock/pricing via third-party tools or manual checks. Correlate competitor disruptions with your SKU’s conversion lift dates. End: If the overlap is clear, competitor changes explain your SKU’s conversion growth. 4.2 Did a strong competitor enter the market or intensify promotions, undercutting our listings?
Likely Cause: A new competitor or an existing one offering steep discounts or better ratings can siphon away conversions. Track changes in competitor ads, brand campaigns, or price points. Confirm if your 94 declining SKUs directly overlap with their product. End: If the correlation is evident, competitor action is causing conversion drops. No → If not competitive factors, move to Hypothesis 5. Hypothesis 5: Stock & Fulfillment 5.1 Did we switch fulfillment methods (e.g., from FBA to MFN/FBM) or experience slower shipping times?
Likely Cause: Losing Prime eligibility or extending delivery windows often reduces conversion. Check each SKU’s fulfillment type in Inventory reports. See if the 94 SKUs with lower conversion lost FBA during December. End: If conversion dips align with the shift to slower fulfillment, that’s the reason. 5.2 Did any SKUs have low or “Only X left in stock” messages that boosted urgency, or did they go fully OOS?
Low stock can spur purchases (fear of missing out). Fully OOS means no sales, so conversion percentage might appear skewed. Review restock inventory reports for stock fluctuations. End: If SKUs with “limited stock” soared in conversions or OOS SKUs saw big drops, you’ve found the cause. No → If stock/fulfillment changes don’t explain it, move to Hypothesis 6. Hypothesis 6: Marketing & External Influences 6.1 Were certain SKUs promoted via off-Amazon channels (influencers, social media, brand referral links) to a more purchase-ready audience?
Likely Cause: Higher-intent external traffic typically yields better conversion rates than casual browsing. Check “Amazon Attribution” data for referral performance on specific SKUs. See if conversion lifts coincide with external campaign schedules. End: If confirmed, external traffic explains the SKU-level improvement. 6.2 Did any PR or editorial features (e.g., “Amazon Editor’s Choice,” “Featured in Gift Guides”) highlight certain SKUs?
Likely Cause: Third-party endorsements can significantly boost conversion if shoppers trust these recommendations. Track editorial recommendations, media mentions, or gift guide inclusions. Match the timeframe to your SKU’s conversion spike. End: If timing aligns, that’s the root cause. No → Move to Hypothesis 7 if none of these marketing or external pushes explain it. Hypothesis 7: Child & Parent Variant Mapping 7.1 Were any ASIN merges or parent-child variation changes done in December, redistributing reviews or sales data?
Likely Cause: Merging child variations can shift reviews/sales to a single parent listing, altering measured conversion rates. Check catalog changes in “Manage Inventory” or the vendor product catalog for merges/splits. Match the timing with conversion pattern changes. End: If merges or splits explain the conversion shift, that’s your answer. No → If no measurement or variation changes, we may need deeper SKU-by-SKU or time-of-day analysis Contribution of SKU Click share affecting the CVR Category C Product Sales: change by y% [7.00L —> 7.50L DRR] Impressions: change by x% Hypothesis 1: Auction & Bidding Factors 1.1 Were bids (or daily budgets) for our key SKUs/campaigns changed in December?
Yes → Possible cause: Increased bids can lead to higher ad rank and more impressions (or decreased bids the opposite).
Next Step: Compare November vs. December bidding and daily budget levels. If you increased them, you likely gained impressions; if you lowered them, you lost impressions. End: If confirmed, you have found your primary driver for impressions change. No → Proceed to 1.2.
1.2 Did competitors’ bidding behavior change significantly?
Yes → Cause: Competitor bid changes can raise or lower the overall auction dynamics—affecting how often your ads are shown.
Next Step: Review impression share reports, average position, and competitor metrics. Confirm if competition got stronger or weaker in December. End: If competitor shifts explain the impression changes, you have your answer. No → Proceed to 1.3.
1.3 Did budget constraints cause ads to stop running earlier or later than November?
Yes → Cause: If budgets were capped or exhausted sooner in the day, impressions could drop; if uncapped, impressions might have increased.
Next Step: Compare daily spend patterns and check if budgets were hitting limits. End: If confirmed, you’ve found your cause for impression fluctuation. No → If none of these Auction & Bidding factors explain the shift, move to Hypothesis 2.
Hypothesis 2: Keyword & Targeting Adjustments 2.1 Did we add or remove keywords or product targets (e.g., SKU-based targeting) since November?
Yes → Cause: Expanding keywords or product targets often increases impressions; removing them can reduce impressions.
Next Step: Look at performance by new vs. existing keywords. If the net effect is growth, that explains increased impressions; if removal was larger, it could explain a drop. End: If confirmed, you have your answer. No → Proceed to 2.2.
2.2 Were negative keywords added or targeting restricted?
Yes → Cause: This can reduce the eligible search queries or audience, thereby lowering impressions.
Next Step: Review change logs for negative keyword additions or tighter targeting criteria. End: If reversing or loosening those changes increases impressions again, it’s confirmed. No → Proceed to 2.3.
2.3 Were audience segments or geo-targeting expanded or reduced?
Yes → Cause: Broader audiences or geographies often yield more impressions, while restricting them does the opposite.
Next Step: Compare audience or location reports from November vs. December. End: If confirmed, you’ve found your cause. No → If all sub-hypotheses for Keyword & Targeting are “No,” move to Hypothesis 3.
Hypothesis 3: Ad Scheduling & Delivery 3.1 Were ad schedules changed, reducing or extending the total hours ads were shown?
Yes → Cause: Running ads fewer hours can drastically cut impressions; running them more hours can boost impressions.
Next Step: Check the dayparting or ad schedule settings for November vs. December. End: If scheduling changes align with the impression shift, you’ve found the reason. No → Proceed to 3.2.
3.2 Were different delivery methods (standard vs. accelerated) used?
Yes → Cause: Switching from accelerated to standard (or vice versa) can shift how quickly ads spend their budget and thus affect total impressions.
Next Step: Check the campaign delivery settings in November vs. December. End: If confirmed, no further exploration needed. No → Move to Hypothesis 4 if scheduling & delivery changes do not explain the difference.
Hypothesis 4: Ad Quality & Relevance 4.1 Did our Quality Score or relevance metrics change, impacting how often ads are shown?
Yes → Cause: A drop in Quality Score can reduce impression share; an improvement can boost it.
Next Step: Examine Quality Score components (expected CTR, ad relevance, landing page experience). Identify changes from November to December. End: If confirmed, focus on improving these factors for stable impression share. No → Proceed to 4.2.
4.2 Did changes in clickthrough rates (CTR) affect ad rank or impression frequency?
Yes → Cause: While CTR is partly a result of impressions, certain ad platforms use CTR in ad rank calculations. A big CTR drop might reduce total impressions. Conversely, a big CTR rise might increase impression opportunities.
Next Step: Correlate CTR changes with impression share in the ad platforms’ reporting. End: If confirmed, improving CTR or mitigating its drop can restore impressions. No → If not ad quality or relevance, move to Hypothesis 5.
Hypothesis 5: Seasonality & Macro Factors 5.1 Are there seasonal patterns or macro trends (e.g., holidays, economic shifts) that affected total search/ad volume?
Yes → Cause: Overall search volume for your category might have increased or decreased.
Next Step: Check historical data to see if this period typically experiences a change in search interest. End: If it aligns with known seasonal peaks/slumps, you’ve identified the macro cause. No → Proceed to 5.2.
5.2 Did competitor promotions or events draw user attention elsewhere or boost overall interest?
Yes → Cause: Competitor heavy promotions can draw more (or fewer) queries in your category, indirectly affecting your impressions.
Next Step: Monitor market-level demand, competitor promotions, or press coverage. End: If confirmed, this is largely outside direct control, but influences impression volume. No → If neither seasonality nor macro factors seem to explain it, At this point, consider more granular analyses (SKU-level, time-of-day, device-level, etc.) or revisit earlier hypotheses in more depth.
Impressions Click Through Rate (CTR): change by y% Contribution of each SKU’s CTR, independent of impression share, affect the overall CTR Hypothesis 1: Auction & Bidding Factors 1.1 Were bids for the 83 SKUs reduced in December?
Yes → Likely cause: Lower bids may have decreased ad rank/visibility, leading to lower CTR. Next Step: Test by incrementally increasing bids for a select group of the 83 SKUs and see if CTR improves. End: If confirmed, you have found your cause; you may not need to explore further. 1.2 Did competitors increase their bids substantially, hurting our visibility?
Yes → Likely cause: Even if our bids remained the same, competitor bid increases can lower our relative ad position, reducing CTR. Next Step: Compare average ad positions and competitor impression share from November vs. December to confirm. End: If confirmed, explore ways to adjust bidding or enhance ad relevance. 1.3 Were budget caps (daily or total) reached earlier than in November for these SKUs?
Yes → Likely cause: Ads may have paused prematurely, running in less optimal hours or positions, leading to lower CTR. Next Step: Increase or reallocate budget to see if extending ad availability improves CTR. End: If confirmed, you have found your cause. No → If all sub-hypotheses in Auction & Bidding are “No,” move to Hypothesis 2. Hypothesis 2: Ad Creative & Messaging 2.1 Were ad creatives or messaging changed for the 83 SKUs?
Yes → Possibly the new creatives/messaging are less appealing. Next Step: A/B test original (November) creative vs. current (December) creative on a subset of SKUs. End: If CTR rebounds with old creative, you have found your cause. 2.2 Could ad fatigue be impacting CTR? (e.g., same audiences seeing the same ads too often)
Yes → Likely cause: Users become “blind” to repeated ads, diminishing CTR. Next Step: Reduce frequency caps, refresh creative, or adjust audience segments. End: If confirmed, you have found your cause. No → If ad creative or messaging is not the issue, move to Hypothesis 3. Hypothesis 3: Audience & Targeting 3.1 Was there a shift in audience segments or targeting parameters? (e.g., new negative keywords, changed demographics)
Yes → Possibly showing ads to a less interested audience. Next Step: Compare November vs. December audience composition and performance by segment. Reverse or refine targeting changes. End: If CTR improves after reverting audience changes, you have found your cause. 3.2 Are seasonal or macro trends causing lower engagement overall?
Yes → Holiday promotions ended? Post-holiday slump? Next Step: Check historical data for past years, see if CTR traditionally drops during this period. End: If seasonality explains the drop, plan promotional strategies accordingly. No → If no audience or seasonality changes explain the drop, move to Hypothesis 4. Hypothesis 4: Offers & Pricing 4.1 Have offers or pricing changed unfavorably for these SKUs?
Yes → Less appealing offers/pricing can deter clicks (users might see lower value in the ad). Next Step: Compare pre/post pricing and evaluate promotional strategies. End: If reverting or improving offers reinstates CTR, cause is confirmed. No → If funnel/landing page is not the culprit, you may need to circle back and do a more granular analysis or revisit earlier hypotheses more deeply. Contribution of SKUs impression share affecting the CTR Conversion (CVR): change by z% Contribution of each SKU’s CVR, independent of Click share, affect the overall CVR Hypothesis 1: Product Listing & User Experience (UX) 1.1 Were product detail pages updated with improved images, A+ content, bullet points, or backend keywords?
Likely Cause: More compelling visuals, clearer info, or better SEO keywords typically boost conversions. Compare listing versions from November to December (via “Manage Your Experiments” or tracked listing edits). Note which SKUs had changes; see if conversion uplift aligns with the update dates. End: If confirmed, listing optimizations explain conversion gains for those SKUs. 1.2 Did changes in the Brand Store or enhanced brand content alter how shoppers view or buy these SKUs?
Likely Cause: Upgraded store navigation or featured modules can highlight certain SKUs, increasing conversion. Review Amazon Brand Store Insights to see if certain SKUs got more direct hits or better conversion paths. Map store layout changes to each SKU’s daily/weekly conversion trend. End: If the correlation is strong, you’ve found a primary conversion driver. No → If no listing or UX updates explain it, move to Hypothesis 2. Hypothesis 2: Pricing & Promotions 2.1 Did we run discounts, coupons, or Lightning Deals for these SKUs?
Likely Cause: Promotional pricing often leads to higher conversion rates if shoppers perceive a deal. Check the promotion schedules in Seller Central/Vendor Central (Deals, Coupons). Verify if the 20 SKUs with conversion gains had active promos during December. End: If yes, promotions are likely driving the lift. 2.2 Did we remove or reduce discounts for certain SKUs, causing conversion to drop?
Likely Cause: Ending a successful promotion can hurt conversions if customers lose an appealing deal. Examine the timeline for when promotions ended and match that to conversion declines in the 20 SKUs (if they had declined). End: If alignment exists, you’ve identified why those SKUs saw conversion dips. No → If not pricing or promotion, move to Hypothesis 3. Hypothesis 3: Product Reviews & Ratings 3.1 Did star ratings improve (e.g., from 4.2 to 4.5) or did we get a surge of positive reviews?
Likely Cause: Hitting a higher star threshold or gaining strong new reviews significantly increases customer confidence. Use Seller Central/Vendor Central “Voice of the Customer” or third-party review-tracking tools. Match rating boosts with increased conversion for specific SKUs. End: If the timing matches, reviews/ratings explain the uplift. 3.2 Did negative feedback or a rating drop occur for certain SKUs?
Likely Cause: A lower star rating or complaints can deter customers from buying, lowering conversion. Review negative feedback timeline and see if it coincides with the 94 SKUs’ conversion decline. End: If confirmed, poor reviews are the culprit. No → If no review-related factors, move to Hypothesis 4. Hypothesis 4: Competitive Landscape 4.1 Did a main competitor go out of stock or raise prices, making our SKU more attractive?
Likely Cause: If competitor listings become less favorable (OOS, price hikes), our SKUs get a conversion bump. Monitor competitor stock/pricing via third-party tools or manual checks. Correlate competitor disruptions with your SKU’s conversion lift dates. End: If the overlap is clear, competitor changes explain your SKU’s conversion growth. 4.2 Did a strong competitor enter the market or intensify promotions, undercutting our listings?
Likely Cause: A new competitor or an existing one offering steep discounts or better ratings can siphon away conversions. Track changes in competitor ads, brand campaigns, or price points. Confirm if your 94 declining SKUs directly overlap with their product. End: If the correlation is evident, competitor action is causing conversion drops. No → If not competitive factors, move to Hypothesis 5. Hypothesis 5: Stock & Fulfillment 5.1 Did we switch fulfillment methods (e.g., from FBA to MFN/FBM) or experience slower shipping times?
Likely Cause: Losing Prime eligibility or extending delivery windows often reduces conversion. Check each SKU’s fulfillment type in Inventory reports. See if the 94 SKUs with lower conversion lost FBA during December. End: If conversion dips align with the shift to slower fulfillment, that’s the reason. 5.2 Did any SKUs have low or “Only X left in stock” messages that boosted urgency, or did they go fully OOS?
Low stock can spur purchases (fear of missing out). Fully OOS means no sales, so conversion percentage might appear skewed. Review restock inventory reports for stock fluctuations. End: If SKUs with “limited stock” soared in conversions or OOS SKUs saw big drops, you’ve found the cause. No → If stock/fulfillment changes don’t explain it, move to Hypothesis 6. Hypothesis 6: Marketing & External Influences 6.1 Were certain SKUs promoted via off-Amazon channels (influencers, social media, brand referral links) to a more purchase-ready audience?
Likely Cause: Higher-intent external traffic typically yields better conversion rates than casual browsing. Check “Amazon Attribution” data for referral performance on specific SKUs. See if conversion lifts coincide with external campaign schedules. End: If confirmed, external traffic explains the SKU-level improvement. 6.2 Did any PR or editorial features (e.g., “Amazon Editor’s Choice,” “Featured in Gift Guides”) highlight certain SKUs?
Likely Cause: Third-party endorsements can significantly boost conversion if shoppers trust these recommendations. Track editorial recommendations, media mentions, or gift guide inclusions. Match the timeframe to your SKU’s conversion spike. End: If timing aligns, that’s the root cause. No → Move to Hypothesis 7 if none of these marketing or external pushes explain it. Hypothesis 7: Child & Parent Variant Mapping 7.1 Were any ASIN merges or parent-child variation changes done in December, redistributing reviews or sales data?
Likely Cause: Merging child variations can shift reviews/sales to a single parent listing, altering measured conversion rates. Check catalog changes in “Manage Inventory” or the vendor product catalog for merges/splits. Match the timing with conversion pattern changes. End: If merges or splits explain the conversion shift, that’s your answer. No → If no measurement or variation changes, we may need deeper SKU-by-SKU or time-of-day analysis Contribution of SKU Click share affecting the CVR Seller Central: Sales change by +18.75% [4.00L —> 4.75L DRR]
Organic Sales: Overall change by +13.6% [2.50L —> 2.95L DRR] Category A Product Sales: change by +20% [4.00L —> 4.80L DRR] Traffic: change by +20% [45,000 —> 54,000 DRR] Hypothesis 1: Amazon Search Ranking & SEO Factors 1.1 Did most of the SKUs gain/lose organic rankings while others dropped?
Likely Cause: Improved/Decreased ranking positions for high-converting SKUs lead to greater/lesser share of organic traffic landing on them. Use tools like Helium 10 or Jungle Scout to track SKU-level keyword rank changes from November to December. See if top performers in December rank higher for key search terms. End: If confirmed, traffic increase or decrease is largely explained by ranking improvements for high-converting SKUs. 1.2 Did we optimize listings for x% of these SKUs (titles, bullet points, backend keywords) in December?
Likely Cause: Listing optimizations (e.g., better keywords, enhanced images, A+ content) can improve both visibility and click share from organic search. Compare listing versions from November vs. December (possibly use “Manage Your Experiments” in Seller Central or track version changes for vendor). End: If these optimizations correlate with increased traffic to those SKUs, you’ve found a key driver. No → If search ranking and listing optimizations don’t explain it, move to Hypothesis 2. Hypothesis 2: Amazon Browse Nodes & Category Placement 2.1 Were signifcant proportion of SKUs reassigned with high/low-traffic browse nodes (sub-categories)?
Likely Cause: Relevant/Irrelevant sub-category placement can drive more/less browsing traffic to those SKUs. Check the “Product Category” and “Sub-Categories” in Seller Central/Vendor Central for changes. End: If SKUs that gained/lost traffic were newly placed in a top-browsed category, that explains the traffic change 2.2 Did any top-level browse node promotions occur (e.g., Amazon’s “Best Sellers,” “New Releases,” “Movers & Shakers”)?
Likely Cause: SKUs featured on these high-visibility pages can see a surge in organic traffic. Check if your SKUs appeared in the “Top 100,” “Best Seller,” or “Amazon’s Choice” lists. End: If correlated with the timeframe of traffic shift, you have your explanation. No → Move to Hypothesis 3 if category changes do not account for the redistribution. Hypothesis 3: Buy Box & Availability 3.1 Did majority of SKUs lose or gain the Buy Box more frequently?
Likely Cause: A SKU consistently winning the Buy Box will retain the bulk of traffic from search; losing it can drop traffic share significantly. Review Buy Box percentage and history (Seller Central reports or third-party tools) for each SKU. End: If high-converting SKUs gained the Buy Box more often, that explains the traffic shift. 3.2 Were bunch of these SKUs out of stock (OOS) or had limited availability in November or December?
Likely Cause: If lower-converting SKUs were OOS in December, traffic for those SKUs would vanish—and effectively “shift” to available SKUs (which might convert better). Check inventory logs or “Restock Inventory” reports in Seller/Vendor Central. End: If restocking correlates with traffic returning to normal distribution, you’ve confirmed a primary driver. No → If not Buy Box or stock-related, move to Hypothesis 4. Hypothesis 4: Demand Shifts & Seasonality 4.1 Is there a seasonal or holiday factor increasing demand for certain SKUs?
Likely Cause: During holiday or event periods, shoppers tend to flock to gift or seasonal SKUs, many of which convert well if they’re best-sellers in that category. Compare historical December performance for these SKUs. Look at external trends (e.g., Google Trends) to see if certain products are seasonally popular. End: If data shows cyclical interest, that’s your explanation. 4.2 Did competitor factors (pricing, stockouts, or brand pullback) drive shoppers to our SKUs?
Likely Cause: Competitors going OOS or raising prices can shift organic demand to your still-available, well-priced SKUs. Monitor competitor listings, track competitor price changes. End: If competitor SKUs dropped out exactly when your high-converting SKUs saw a boost, that explains the traffic increase No → Move to Hypothesis 5 if no broad demand change is found. Hypothesis 5: Brand Store & External Traffic 5.1 Were we driving external traffic (via social media, influencers, brand store, etc.) mostly to certain SKUs?
Likely Cause: If you or affiliates promoted specific SKUs heavily, that organic traffic could show up as direct/organic on Amazon (depending on the referral parameters). Check Amazon Attribution or brand referral data to see if certain SKU links were pushed externally. End: If these SKUs are also high-converting, that explains half the overall conversion improvement from traffic mix. 5.2 Were changes made to the Brand Store (storefront) layout that favored certain SKUs?
Likely Cause: Featuring certain products more prominently in the Brand Store can direct more organic store visitors to those SKUs. Compare Brand Store layouts from November vs. December, and check Store Insights for traffic sources. End: If the featured SKUs match those that gained traffic share, you’ve located a key factor. No → Move on to Hypothesis 6 if none of the Brand Store or external referral paths explain the traffic increase/decrease. Hypothesis 6: Measurement & Tagging 6.1 Did any parent-child ASIN merges or changes in listing variations (color/size) happen?
Likely Cause: Merging variations or changing parent-child relationships can shift the “main” traffic from one SKU to another, altering our perceived distribution. Check “Manage Inventory” or vendor product catalog for variation merges or splits. End: If these merges correlate with the timing of traffic shifts, you’ve confirmed the driver. No → If none of these apply, consider deeper, SKU-by-SKU analysis or revisiting earlier nodes in more detail. Conversion: change by +0.4 percentage points [2.2% —> 2.6%] Contribution of each SKU’s CVR, independent of Click share, affect the overall CVR Hypothesis 1: Product Listing & User Experience (UX) 1.1 Were product detail pages updated with improved images, A+ content, bullet points, or backend keywords?
Likely Cause: More compelling visuals, clearer info, or better SEO keywords typically boost conversions. Compare listing versions from November to December (via “Manage Your Experiments” or tracked listing edits). Note which SKUs had changes; see if conversion uplift aligns with the update dates. End: If confirmed, listing optimizations explain conversion gains for those SKUs. 1.2 Did changes in the Brand Store or enhanced brand content alter how shoppers view or buy these SKUs?
Likely Cause: Upgraded store navigation or featured modules can highlight certain SKUs, increasing conversion. Review Amazon Brand Store Insights to see if certain SKUs got more direct hits or better conversion paths. Map store layout changes to each SKU’s daily/weekly conversion trend. End: If the correlation is strong, you’ve found a primary conversion driver. No → If no listing or UX updates explain it, move to Hypothesis 2. Hypothesis 2: Pricing & Promotions 2.1 Did we run discounts, coupons, or Lightning Deals for these SKUs?
Likely Cause: Promotional pricing often leads to higher conversion rates if shoppers perceive a deal. Check the promotion schedules in Seller Central/Vendor Central (Deals, Coupons). Verify if the 20 SKUs with conversion gains had active promos during December. End: If yes, promotions are likely driving the lift. 2.2 Did we remove or reduce discounts for certain SKUs, causing conversion to drop?
Likely Cause: Ending a successful promotion can hurt conversions if customers lose an appealing deal. Examine the timeline for when promotions ended and match that to conversion declines in the 20 SKUs (if they had declined). End: If alignment exists, you’ve identified why those SKUs saw conversion dips. No → If not pricing or promotion, move to Hypothesis 3. Hypothesis 3: Product Reviews & Ratings 3.1 Did star ratings improve (e.g., from 4.2 to 4.5) or did we get a surge of positive reviews?
Likely Cause: Hitting a higher star threshold or gaining strong new reviews significantly increases customer confidence. Use Seller Central/Vendor Central “Voice of the Customer” or third-party review-tracking tools. Match rating boosts with increased conversion for specific SKUs. End: If the timing matches, reviews/ratings explain the uplift. 3.2 Did negative feedback or a rating drop occur for certain SKUs?
Likely Cause: A lower star rating or complaints can deter customers from buying, lowering conversion. Review negative feedback timeline and see if it coincides with the 94 SKUs’ conversion decline. End: If confirmed, poor reviews are the culprit. No → If no review-related factors, move to Hypothesis 4. Hypothesis 4: Competitive Landscape 4.1 Did a main competitor go out of stock or raise prices, making our SKU more attractive?
Likely Cause: If competitor listings become less favorable (OOS, price hikes), our SKUs get a conversion bump. Monitor competitor stock/pricing via third-party tools or manual checks. Correlate competitor disruptions with your SKU’s conversion lift dates. End: If the overlap is clear, competitor changes explain your SKU’s conversion growth. 4.2 Did a strong competitor enter the market or intensify promotions, undercutting our listings?
Likely Cause: A new competitor or an existing one offering steep discounts or better ratings can siphon away conversions. Track changes in competitor ads, brand campaigns, or price points. Confirm if your 94 declining SKUs directly overlap with their product. End: If the correlation is evident, competitor action is causing conversion drops. No → If not competitive factors, move to Hypothesis 5. Hypothesis 5: Stock & Fulfillment 5.1 Did we switch fulfillment methods (e.g., from FBA to MFN/FBM) or experience slower shipping times?
Likely Cause: Losing Prime eligibility or extending delivery windows often reduces conversion. Check each SKU’s fulfillment type in Inventory reports. See if the 94 SKUs with lower conversion lost FBA during December. End: If conversion dips align with the shift to slower fulfillment, that’s the reason. 5.2 Did any SKUs have low or “Only X left in stock” messages that boosted urgency, or did they go fully OOS?
Low stock can spur purchases (fear of missing out). Fully OOS means no sales, so conversion percentage might appear skewed. Review restock inventory reports for stock fluctuations. End: If SKUs with “limited stock” soared in conversions or OOS SKUs saw big drops, you’ve found the cause. No → If stock/fulfillment changes don’t explain it, move to Hypothesis 6. Hypothesis 6: Marketing & External Influences 6.1 Were certain SKUs promoted via off-Amazon channels (influencers, social media, brand referral links) to a more purchase-ready audience?
Likely Cause: Higher-intent external traffic typically yields better conversion rates than casual browsing. Check “Amazon Attribution” data for referral performance on specific SKUs. See if conversion lifts coincide with external campaign schedules. End: If confirmed, external traffic explains the SKU-level improvement. 6.2 Did any PR or editorial features (e.g., “Amazon Editor’s Choice,” “Featured in Gift Guides”) highlight certain SKUs?
Likely Cause: Third-party endorsements can significantly boost conversion if shoppers trust these recommendations. Track editorial recommendations, media mentions, or gift guide inclusions. Match the timeframe to your SKU’s conversion spike. End: If timing aligns, that’s the root cause. No → Move to Hypothesis 7 if none of these marketing or external pushes explain it. Hypothesis 7: Child & Parent Variant Mapping 7.1 Were any ASIN merges or parent-child variation changes done in December, redistributing reviews or sales data?
Likely Cause: Merging child variations can shift reviews/sales to a single parent listing, altering measured conversion rates. Check catalog changes in “Manage Inventory” or the vendor product catalog for merges/splits. Match the timing with conversion pattern changes. End: If merges or splits explain the conversion shift, that’s your answer. No → If no measurement or variation changes, we may need deeper SKU-by-SKU or time-of-day analysis Contribution of SKU Click share affecting the CVR ASP: change by -3.8% [₹520 —> ₹500 DRR] Category B Product Sales: change by +10.8% [6.50L —> 7.20L DRR] Traffic: change by -7.1% [35,000 —> 32,500 DRR] Hypothesis 1: Amazon Search Ranking & SEO Factors 1.1 Did most of the SKUs gain/lose organic rankings while others dropped?
Likely Cause: Improved/Decreased ranking positions for high-converting SKUs lead to greater/lesser share of organic traffic landing on them. Use tools like Helium 10 or Jungle Scout to track SKU-level keyword rank changes from November to December. See if top performers in December rank higher for key search terms. End: If confirmed, traffic increase or decrease is largely explained by ranking improvements for high-converting SKUs. 1.2 Did we optimize listings for x% of these SKUs (titles, bullet points, backend keywords) in December?
Likely Cause: Listing optimizations (e.g., better keywords, enhanced images, A+ content) can improve both visibility and click share from organic search. Compare listing versions from November vs. December (possibly use “Manage Your Experiments” in Seller Central or track version changes for vendor). End: If these optimizations correlate with increased traffic to those SKUs, you’ve found a key driver. No → If search ranking and listing optimizations don’t explain it, move to Hypothesis 2. Hypothesis 2: Amazon Browse Nodes & Category Placement 2.1 Were signifcant proportion of SKUs reassigned with high/low-traffic browse nodes (sub-categories)?
Likely Cause: Relevant/Irrelevant sub-category placement can drive more/less browsing traffic to those SKUs. Check the “Product Category” and “Sub-Categories” in Seller Central/Vendor Central for changes. End: If SKUs that gained/lost traffic were newly placed in a top-browsed category, that explains the traffic change 2.2 Did any top-level browse node promotions occur (e.g., Amazon’s “Best Sellers,” “New Releases,” “Movers & Shakers”)?
Likely Cause: SKUs featured on these high-visibility pages can see a surge in organic traffic. Check if your SKUs appeared in the “Top 100,” “Best Seller,” or “Amazon’s Choice” lists. End: If correlated with the timeframe of traffic shift, you have your explanation. No → Move to Hypothesis 3 if category changes do not account for the redistribution. Hypothesis 3: Buy Box & Availability 3.1 Did majority of SKUs lose or gain the Buy Box more frequently?
Likely Cause: A SKU consistently winning the Buy Box will retain the bulk of traffic from search; losing it can drop traffic share significantly. Review Buy Box percentage and history (Seller Central reports or third-party tools) for each SKU. End: If high-converting SKUs gained the Buy Box more often, that explains the traffic shift. 3.2 Were bunch of these SKUs out of stock (OOS) or had limited availability in November or December?
Likely Cause: If lower-converting SKUs were OOS in December, traffic for those SKUs would vanish—and effectively “shift” to available SKUs (which might convert better). Check inventory logs or “Restock Inventory” reports in Seller/Vendor Central. End: If restocking correlates with traffic returning to normal distribution, you’ve confirmed a primary driver. No → If not Buy Box or stock-related, move to Hypothesis 4. Hypothesis 4: Demand Shifts & Seasonality 4.1 Is there a seasonal or holiday factor increasing demand for certain SKUs?
Likely Cause: During holiday or event periods, shoppers tend to flock to gift or seasonal SKUs, many of which convert well if they’re best-sellers in that category. Compare historical December performance for these SKUs. Look at external trends (e.g., Google Trends) to see if certain products are seasonally popular. End: If data shows cyclical interest, that’s your explanation. 4.2 Did competitor factors (pricing, stockouts, or brand pullback) drive shoppers to our SKUs?
Likely Cause: Competitors going OOS or raising prices can shift organic demand to your still-available, well-priced SKUs. Monitor competitor listings, track competitor price changes. End: If competitor SKUs dropped out exactly when your high-converting SKUs saw a boost, that explains the traffic increase No → Move to Hypothesis 5 if no broad demand change is found. Hypothesis 5: Brand Store & External Traffic 5.1 Were we driving external traffic (via social media, influencers, brand store, etc.) mostly to certain SKUs?
Likely Cause: If you or affiliates promoted specific SKUs heavily, that organic traffic could show up as direct/organic on Amazon (depending on the referral parameters). Check Amazon Attribution or brand referral data to see if certain SKU links were pushed externally. End: If these SKUs are also high-converting, that explains half the overall conversion improvement from traffic mix. 5.2 Were changes made to the Brand Store (storefront) layout that favored certain SKUs?
Likely Cause: Featuring certain products more prominently in the Brand Store can direct more organic store visitors to those SKUs. Compare Brand Store layouts from November vs. December, and check Store Insights for traffic sources. End: If the featured SKUs match those that gained traffic share, you’ve located a key factor. No → Move on to Hypothesis 6 if none of the Brand Store or external referral paths explain the traffic increase/decrease. Hypothesis 6: Measurement & Tagging 6.1 Did any parent-child ASIN merges or changes in listing variations (color/size) happen?
Likely Cause: Merging variations or changing parent-child relationships can shift the “main” traffic from one SKU to another, altering our perceived distribution. Check “Manage Inventory” or vendor product catalog for variation merges or splits. End: If these merges correlate with the timing of traffic shifts, you’ve confirmed the driver. No → If none of these apply, consider deeper, SKU-by-SKU analysis or revisiting earlier nodes in more detail. Conversion: change by +0.5 percentage points [3.1% —> 3.6% DRR] Contribution of each SKU’s CVR, independent of Click share, affect the overall CVR Hypothesis 1: Product Listing & User Experience (UX) 1.1 Were product detail pages updated with improved images, A+ content, bullet points, or backend keywords?
Likely Cause: More compelling visuals, clearer info, or better SEO keywords typically boost conversions. Compare listing versions from November to December (via “Manage Your Experiments” or tracked listing edits). Note which SKUs had changes; see if conversion uplift aligns with the update dates. End: If confirmed, listing optimizations explain conversion gains for those SKUs. 1.2 Did changes in the Brand Store or enhanced brand content alter how shoppers view or buy these SKUs?
Likely Cause: Upgraded store navigation or featured modules can highlight certain SKUs, increasing conversion. Review Amazon Brand Store Insights to see if certain SKUs got more direct hits or better conversion paths. Map store layout changes to each SKU’s daily/weekly conversion trend. End: If the correlation is strong, you’ve found a primary conversion driver. No → If no listing or UX updates explain it, move to Hypothesis 2. Hypothesis 2: Pricing & Promotions 2.1 Did we run discounts, coupons, or Lightning Deals for these SKUs?
Likely Cause: Promotional pricing often leads to higher conversion rates if shoppers perceive a deal. Check the promotion schedules in Seller Central/Vendor Central (Deals, Coupons). Verify if the 20 SKUs with conversion gains had active promos during December. End: If yes, promotions are likely driving the lift. 2.2 Did we remove or reduce discounts for certain SKUs, causing conversion to drop?
Likely Cause: Ending a successful promotion can hurt conversions if customers lose an appealing deal. Examine the timeline for when promotions ended and match that to conversion declines in the 20 SKUs (if they had declined). End: If alignment exists, you’ve identified why those SKUs saw conversion dips. No → If not pricing or promotion, move to Hypothesis 3. Hypothesis 3: Product Reviews & Ratings 3.1 Did star ratings improve (e.g., from 4.2 to 4.5) or did we get a surge of positive reviews?
Likely Cause: Hitting a higher star threshold or gaining strong new reviews significantly increases customer confidence. Use Seller Central/Vendor Central “Voice of the Customer” or third-party review-tracking tools. Match rating boosts with increased conversion for specific SKUs. End: If the timing matches, reviews/ratings explain the uplift. 3.2 Did negative feedback or a rating drop occur for certain SKUs?
Likely Cause: A lower star rating or complaints can deter customers from buying, lowering conversion. Review negative feedback timeline and see if it coincides with the 94 SKUs’ conversion decline. End: If confirmed, poor reviews are the culprit. No → If no review-related factors, move to Hypothesis 4. Hypothesis 4: Competitive Landscape 4.1 Did a main competitor go out of stock or raise prices, making our SKU more attractive?
Likely Cause: If competitor listings become less favorable (OOS, price hikes), our SKUs get a conversion bump. Monitor competitor stock/pricing via third-party tools or manual checks. Correlate competitor disruptions with your SKU’s conversion lift dates. End: If the overlap is clear, competitor changes explain your SKU’s conversion growth. 4.2 Did a strong competitor enter the market or intensify promotions, undercutting our listings?
Likely Cause: A new competitor or an existing one offering steep discounts or better ratings can siphon away conversions. Track changes in competitor ads, brand campaigns, or price points. Confirm if your 94 declining SKUs directly overlap with their product. End: If the correlation is evident, competitor action is causing conversion drops. No → If not competitive factors, move to Hypothesis 5. Hypothesis 5: Stock & Fulfillment 5.1 Did we switch fulfillment methods (e.g., from FBA to MFN/FBM) or experience slower shipping times?
Likely Cause: Losing Prime eligibility or extending delivery windows often reduces conversion. Check each SKU’s fulfillment type in Inventory reports. See if the 94 SKUs with lower conversion lost FBA during December. End: If conversion dips align with the shift to slower fulfillment, that’s the reason. 5.2 Did any SKUs have low or “Only X left in stock” messages that boosted urgency, or did they go fully OOS?
Low stock can spur purchases (fear of missing out). Fully OOS means no sales, so conversion percentage might appear skewed. Review restock inventory reports for stock fluctuations. End: If SKUs with “limited stock” soared in conversions or OOS SKUs saw big drops, you’ve found the cause. No → If stock/fulfillment changes don’t explain it, move to Hypothesis 6. Hypothesis 6: Marketing & External Influences 6.1 Were certain SKUs promoted via off-Amazon channels (influencers, social media, brand referral links) to a more purchase-ready audience?
Likely Cause: Higher-intent external traffic typically yields better conversion rates than casual browsing. Check “Amazon Attribution” data for referral performance on specific SKUs. See if conversion lifts coincide with external campaign schedules. End: If confirmed, external traffic explains the SKU-level improvement. 6.2 Did any PR or editorial features (e.g., “Amazon Editor’s Choice,” “Featured in Gift Guides”) highlight certain SKUs?
Likely Cause: Third-party endorsements can significantly boost conversion if shoppers trust these recommendations. Track editorial recommendations, media mentions, or gift guide inclusions. Match the timeframe to your SKU’s conversion spike. End: If timing aligns, that’s the root cause. No → Move to Hypothesis 7 if none of these marketing or external pushes explain it. Hypothesis 7: Child & Parent Variant Mapping 7.1 Were any ASIN merges or parent-child variation changes done in December, redistributing reviews or sales data?
Likely Cause: Merging child variations can shift reviews/sales to a single parent listing, altering measured conversion rates. Check catalog changes in “Manage Inventory” or the vendor product catalog for merges/splits. Match the timing with conversion pattern changes. End: If merges or splits explain the conversion shift, that’s your answer. No → If no measurement or variation changes, we may need deeper SKU-by-SKU or time-of-day analysis Contribution of SKU Click share affecting the CVR ASP: change by +5.1% [₹780 —> ₹820 DRR] Category C Product Sales: change by +8.6% [3.50L —> 3.80L DRR] Traffic: change by +10% [42,000 —> 46,200 DRR] Hypothesis 1: Amazon Search Ranking & SEO Factors 1.1 Did most of the SKUs gain/lose organic rankings while others dropped?
Likely Cause: Improved/Decreased ranking positions for high-converting SKUs lead to greater/lesser share of organic traffic landing on them. Use tools like Helium 10 or Jungle Scout to track SKU-level keyword rank changes from November to December. See if top performers in December rank higher for key search terms. End: If confirmed, traffic increase or decrease is largely explained by ranking improvements for high-converting SKUs. 1.2 Did we optimize listings for x% of these SKUs (titles, bullet points, backend keywords) in December?
Likely Cause: Listing optimizations (e.g., better keywords, enhanced images, A+ content) can improve both visibility and click share from organic search. Compare listing versions from November vs. December (possibly use “Manage Your Experiments” in Seller Central or track version changes for vendor). End: If these optimizations correlate with increased traffic to those SKUs, you’ve found a key driver. No → If search ranking and listing optimizations don’t explain it, move to Hypothesis 2. Hypothesis 2: Amazon Browse Nodes & Category Placement 2.1 Were signifcant proportion of SKUs reassigned with high/low-traffic browse nodes (sub-categories)?
Likely Cause: Relevant/Irrelevant sub-category placement can drive more/less browsing traffic to those SKUs. Check the “Product Category” and “Sub-Categories” in Seller Central/Vendor Central for changes. End: If SKUs that gained/lost traffic were newly placed in a top-browsed category, that explains the traffic change 2.2 Did any top-level browse node promotions occur (e.g., Amazon’s “Best Sellers,” “New Releases,” “Movers & Shakers”)?
Likely Cause: SKUs featured on these high-visibility pages can see a surge in organic traffic. Check if your SKUs appeared in the “Top 100,” “Best Seller,” or “Amazon’s Choice” lists. End: If correlated with the timeframe of traffic shift, you have your explanation. No → Move to Hypothesis 3 if category changes do not account for the redistribution. Hypothesis 3: Buy Box & Availability 3.1 Did majority of SKUs lose or gain the Buy Box more frequently?
Likely Cause: A SKU consistently winning the Buy Box will retain the bulk of traffic from search; losing it can drop traffic share significantly. Review Buy Box percentage and history (Seller Central reports or third-party tools) for each SKU. End: If high-converting SKUs gained the Buy Box more often, that explains the traffic shift. 3.2 Were bunch of these SKUs out of stock (OOS) or had limited availability in November or December?
Likely Cause: If lower-converting SKUs were OOS in December, traffic for those SKUs would vanish—and effectively “shift” to available SKUs (which might convert better). Check inventory logs or “Restock Inventory” reports in Seller/Vendor Central. End: If restocking correlates with traffic returning to normal distribution, you’ve confirmed a primary driver. No → If not Buy Box or stock-related, move to Hypothesis 4. Hypothesis 4: Demand Shifts & Seasonality 4.1 Is there a seasonal or holiday factor increasing demand for certain SKUs?
Likely Cause: During holiday or event periods, shoppers tend to flock to gift or seasonal SKUs, many of which convert well if they’re best-sellers in that category. Compare historical December performance for these SKUs. Look at external trends (e.g., Google Trends) to see if certain products are seasonally popular. End: If data shows cyclical interest, that’s your explanation. 4.2 Did competitor factors (pricing, stockouts, or brand pullback) drive shoppers to our SKUs?
Likely Cause: Competitors going OOS or raising prices can shift organic demand to your still-available, well-priced SKUs. Monitor competitor listings, track competitor price changes. End: If competitor SKUs dropped out exactly when your high-converting SKUs saw a boost, that explains the traffic increase No → Move to Hypothesis 5 if no broad demand change is found. Hypothesis 5: Brand Store & External Traffic 5.1 Were we driving external traffic (via social media, influencers, brand store, etc.) mostly to certain SKUs?
Likely Cause: If you or affiliates promoted specific SKUs heavily, that organic traffic could show up as direct/organic on Amazon (depending on the referral parameters). Check Amazon Attribution or brand referral data to see if certain SKU links were pushed externally. End: If these SKUs are also high-converting, that explains half the overall conversion improvement from traffic mix. 5.2 Were changes made to the Brand Store (storefront) layout that favored certain SKUs?
Likely Cause: Featuring certain products more prominently in the Brand Store can direct more organic store visitors to those SKUs. Compare Brand Store layouts from November vs. December, and check Store Insights for traffic sources. End: If the featured SKUs match those that gained traffic share, you’ve located a key factor. No → Move on to Hypothesis 6 if none of the Brand Store or external referral paths explain the traffic increase/decrease. Hypothesis 6: Measurement & Tagging 6.1 Did any parent-child ASIN merges or changes in listing variations (color/size) happen?
Likely Cause: Merging variations or changing parent-child relationships can shift the “main” traffic from one SKU to another, altering our perceived distribution. Check “Manage Inventory” or vendor product catalog for variation merges or splits. End: If these merges correlate with the timing of traffic shifts, you’ve confirmed the driver. No → If none of these apply, consider deeper, SKU-by-SKU analysis or revisiting earlier nodes in more detail. Conversion: change by -0.2 percentage points [2.5% —> 2.3% DRR] Contribution of each SKU’s CVR, independent of Click share, affect the overall CVR Hypothesis 1: Product Listing & User Experience (UX) 1.1 Were product detail pages updated with improved images, A+ content, bullet points, or backend keywords?
Likely Cause: More compelling visuals, clearer info, or better SEO keywords typically boost conversions. Compare listing versions from November to December (via “Manage Your Experiments” or tracked listing edits). Note which SKUs had changes; see if conversion uplift aligns with the update dates. End: If confirmed, listing optimizations explain conversion gains for those SKUs. 1.2 Did changes in the Brand Store or enhanced brand content alter how shoppers view or buy these SKUs?
Likely Cause: Upgraded store navigation or featured modules can highlight certain SKUs, increasing conversion. Review Amazon Brand Store Insights to see if certain SKUs got more direct hits or better conversion paths. Map store layout changes to each SKU’s daily/weekly conversion trend. End: If the correlation is strong, you’ve found a primary conversion driver. No → If no listing or UX updates explain it, move to Hypothesis 2. Hypothesis 2: Pricing & Promotions 2.1 Did we run discounts, coupons, or Lightning Deals for these SKUs?
Likely Cause: Promotional pricing often leads to higher conversion rates if shoppers perceive a deal. Check the promotion schedules in Seller Central/Vendor Central (Deals, Coupons). Verify if the 20 SKUs with conversion gains had active promos during December. End: If yes, promotions are likely driving the lift. 2.2 Did we remove or reduce discounts for certain SKUs, causing conversion to drop?
Likely Cause: Ending a successful promotion can hurt conversions if customers lose an appealing deal. Examine the timeline for when promotions ended and match that to conversion declines in the 20 SKUs (if they had declined). End: If alignment exists, you’ve identified why those SKUs saw conversion dips. No → If not pricing or promotion, move to Hypothesis 3. Hypothesis 3: Product Reviews & Ratings 3.1 Did star ratings improve (e.g., from 4.2 to 4.5) or did we get a surge of positive reviews?
Likely Cause: Hitting a higher star threshold or gaining strong new reviews significantly increases customer confidence. Use Seller Central/Vendor Central “Voice of the Customer” or third-party review-tracking tools. Match rating boosts with increased conversion for specific SKUs. End: If the timing matches, reviews/ratings explain the uplift. 3.2 Did negative feedback or a rating drop occur for certain SKUs?
Likely Cause: A lower star rating or complaints can deter customers from buying, lowering conversion. Review negative feedback timeline and see if it coincides with the 94 SKUs’ conversion decline. End: If confirmed, poor reviews are the culprit. No → If no review-related factors, move to Hypothesis 4. Hypothesis 4: Competitive Landscape 4.1 Did a main competitor go out of stock or raise prices, making our SKU more attractive?
Likely Cause: If competitor listings become less favorable (OOS, price hikes), our SKUs get a conversion bump. Monitor competitor stock/pricing via third-party tools or manual checks. Correlate competitor disruptions with your SKU’s conversion lift dates. End: If the overlap is clear, competitor changes explain your SKU’s conversion growth. 4.2 Did a strong competitor enter the market or intensify promotions, undercutting our listings?
Likely Cause: A new competitor or an existing one offering steep discounts or better ratings can siphon away conversions. Track changes in competitor ads, brand campaigns, or price points. Confirm if your 94 declining SKUs directly overlap with their product. End: If the correlation is evident, competitor action is causing conversion drops. No → If not competitive factors, move to Hypothesis 5. Hypothesis 5: Stock & Fulfillment 5.1 Did we switch fulfillment methods (e.g., from FBA to MFN/FBM) or experience slower shipping times?
Likely Cause: Losing Prime eligibility or extending delivery windows often reduces conversion. Check each SKU’s fulfillment type in Inventory reports. See if the 94 SKUs with lower conversion lost FBA during December. End: If conversion dips align with the shift to slower fulfillment, that’s the reason. 5.2 Did any SKUs have low or “Only X left in stock” messages that boosted urgency, or did they go fully OOS?
Low stock can spur purchases (fear of missing out). Fully OOS means no sales, so conversion percentage might appear skewed. Review restock inventory reports for stock fluctuations. End: If SKUs with “limited stock” soared in conversions or OOS SKUs saw big drops, you’ve found the cause. No → If stock/fulfillment changes don’t explain it, move to Hypothesis 6. Hypothesis 6: Marketing & External Influences 6.1 Were certain SKUs promoted via off-Amazon channels (influencers, social media, brand referral links) to a more purchase-ready audience?
Likely Cause: Higher-intent external traffic typically yields better conversion rates than casual browsing. Check “Amazon Attribution” data for referral performance on specific SKUs. See if conversion lifts coincide with external campaign schedules. End: If confirmed, external traffic explains the SKU-level improvement. 6.2 Did any PR or editorial features (e.g., “Amazon Editor’s Choice,” “Featured in Gift Guides”) highlight certain SKUs?
Likely Cause: Third-party endorsements can significantly boost conversion if shoppers trust these recommendations. Track editorial recommendations, media mentions, or gift guide inclusions. Match the timeframe to your SKU’s conversion spike. End: If timing aligns, that’s the root cause. No → Move to Hypothesis 7 if none of these marketing or external pushes explain it. Hypothesis 7: Child & Parent Variant Mapping 7.1 Were any ASIN merges or parent-child variation changes done in December, redistributing reviews or sales data?
Likely Cause: Merging child variations can shift reviews/sales to a single parent listing, altering measured conversion rates. Check catalog changes in “Manage Inventory” or the vendor product catalog for merges/splits. Match the timing with conversion pattern changes. End: If merges or splits explain the conversion shift, that’s your answer. No → If no measurement or variation changes, we may need deeper SKU-by-SKU or time-of-day analysis Contribution of SKU Click share affecting the CVR ASP: change by -3.3% [₹610 —> ₹590 DRR] Ad Sales: change by +20% [1.50L —> 1.80L DRR] Category A Product Sales: change by x% [5.00L —> 6.00L DRR] Impressions: change by x% Hypothesis 1: Auction & Bidding Factors 1.1 Were bids (or daily budgets) for our key SKUs/campaigns changed in December?
Yes → Possible cause: Increased bids can lead to higher ad rank and more impressions (or decreased bids the opposite).
Next Step: Compare November vs. December bidding and daily budget levels. If you increased them, you likely gained impressions; if you lowered them, you lost impressions. End: If confirmed, you have found your primary driver for impressions change. No → Proceed to 1.2.
1.2 Did competitors’ bidding behavior change significantly?
Yes → Cause: Competitor bid changes can raise or lower the overall auction dynamics—affecting how often your ads are shown.
Next Step: Review impression share reports, average position, and competitor metrics. Confirm if competition got stronger or weaker in December. End: If competitor shifts explain the impression changes, you have your answer. No → Proceed to 1.3.
1.3 Did budget constraints cause ads to stop running earlier or later than November?
Yes → Cause: If budgets were capped or exhausted sooner in the day, impressions could drop; if uncapped, impressions might have increased.
Next Step: Compare daily spend patterns and check if budgets were hitting limits. End: If confirmed, you’ve found your cause for impression fluctuation. No → If none of these Auction & Bidding factors explain the shift, move to Hypothesis 2.
Hypothesis 2: Keyword & Targeting Adjustments 2.1 Did we add or remove keywords or product targets (e.g., SKU-based targeting) since November?
Yes → Cause: Expanding keywords or product targets often increases impressions; removing them can reduce impressions.
Next Step: Look at performance by new vs. existing keywords. If the net effect is growth, that explains increased impressions; if removal was larger, it could explain a drop. End: If confirmed, you have your answer. No → Proceed to 2.2.
2.2 Were negative keywords added or targeting restricted?
Yes → Cause: This can reduce the eligible search queries or audience, thereby lowering impressions.
Next Step: Review change logs for negative keyword additions or tighter targeting criteria. End: If reversing or loosening those changes increases impressions again, it’s confirmed. No → Proceed to 2.3.
2.3 Were audience segments or geo-targeting expanded or reduced?
Yes → Cause: Broader audiences or geographies often yield more impressions, while restricting them does the opposite.
Next Step: Compare audience or location reports from November vs. December. End: If confirmed, you’ve found your cause. No → If all sub-hypotheses for Keyword & Targeting are “No,” move to Hypothesis 3.
Hypothesis 3: Ad Scheduling & Delivery 3.1 Were ad schedules changed, reducing or extending the total hours ads were shown?
Yes → Cause: Running ads fewer hours can drastically cut impressions; running them more hours can boost impressions.
Next Step: Check the dayparting or ad schedule settings for November vs. December. End: If scheduling changes align with the impression shift, you’ve found the reason. No → Proceed to 3.2.
3.2 Were different delivery methods (standard vs. accelerated) used?
Yes → Cause: Switching from accelerated to standard (or vice versa) can shift how quickly ads spend their budget and thus affect total impressions.
Next Step: Check the campaign delivery settings in November vs. December. End: If confirmed, no further exploration needed. No → Move to Hypothesis 4 if scheduling & delivery changes do not explain the difference.
Hypothesis 4: Ad Quality & Relevance 4.1 Did our Quality Score or relevance metrics change, impacting how often ads are shown?
Yes → Cause: A drop in Quality Score can reduce impression share; an improvement can boost it.
Next Step: Examine Quality Score components (expected CTR, ad relevance, landing page experience). Identify changes from November to December. End: If confirmed, focus on improving these factors for stable impression share. No → Proceed to 4.2.
4.2 Did changes in clickthrough rates (CTR) affect ad rank or impression frequency?
Yes → Cause: While CTR is partly a result of impressions, certain ad platforms use CTR in ad rank calculations. A big CTR drop might reduce total impressions. Conversely, a big CTR rise might increase impression opportunities.
Next Step: Correlate CTR changes with impression share in the ad platforms’ reporting. End: If confirmed, improving CTR or mitigating its drop can restore impressions. No → If not ad quality or relevance, move to Hypothesis 5.
Hypothesis 5: Seasonality & Macro Factors 5.1 Are there seasonal patterns or macro trends (e.g., holidays, economic shifts) that affected total search/ad volume?
Yes → Cause: Overall search volume for your category might have increased or decreased.
Next Step: Check historical data to see if this period typically experiences a change in search interest. End: If it aligns with known seasonal peaks/slumps, you’ve identified the macro cause. No → Proceed to 5.2.
5.2 Did competitor promotions or events draw user attention elsewhere or boost overall interest?
Yes → Cause: Competitor heavy promotions can draw more (or fewer) queries in your category, indirectly affecting your impressions.
Next Step: Monitor market-level demand, competitor promotions, or press coverage. End: If confirmed, this is largely outside direct control, but influences impression volume. No → If neither seasonality nor macro factors seem to explain it, At this point, consider more granular analyses (SKU-level, time-of-day, device-level, etc.) or revisit earlier hypotheses in more depth.
Impressions Click Through Rate (CTR): change by y% Contribution of each SKU’s CTR, independent of impression share, affect the overall CTR Hypothesis 1: Auction & Bidding Factors 1.1 Were bids for the 83 SKUs reduced in December?
Yes → Likely cause: Lower bids may have decreased ad rank/visibility, leading to lower CTR. Next Step: Test by incrementally increasing bids for a select group of the 83 SKUs and see if CTR improves. End: If confirmed, you have found your cause; you may not need to explore further. 1.2 Did competitors increase their bids substantially, hurting our visibility?
Yes → Likely cause: Even if our bids remained the same, competitor bid increases can lower our relative ad position, reducing CTR. Next Step: Compare average ad positions and competitor impression share from November vs. December to confirm. End: If confirmed, explore ways to adjust bidding or enhance ad relevance. 1.3 Were budget caps (daily or total) reached earlier than in November for these SKUs?
Yes → Likely cause: Ads may have paused prematurely, running in less optimal hours or positions, leading to lower CTR. Next Step: Increase or reallocate budget to see if extending ad availability improves CTR. End: If confirmed, you have found your cause. No → If all sub-hypotheses in Auction & Bidding are “No,” move to Hypothesis 2. Hypothesis 2: Ad Creative & Messaging 2.1 Were ad creatives or messaging changed for the 83 SKUs?
Yes → Possibly the new creatives/messaging are less appealing. Next Step: A/B test original (November) creative vs. current (December) creative on a subset of SKUs. End: If CTR rebounds with old creative, you have found your cause. 2.2 Could ad fatigue be impacting CTR? (e.g., same audiences seeing the same ads too often)
Yes → Likely cause: Users become “blind” to repeated ads, diminishing CTR. Next Step: Reduce frequency caps, refresh creative, or adjust audience segments. End: If confirmed, you have found your cause. No → If ad creative or messaging is not the issue, move to Hypothesis 3. Hypothesis 3: Audience & Targeting 3.1 Was there a shift in audience segments or targeting parameters? (e.g., new negative keywords, changed demographics)
Yes → Possibly showing ads to a less interested audience. Next Step: Compare November vs. December audience composition and performance by segment. Reverse or refine targeting changes. End: If CTR improves after reverting audience changes, you have found your cause. 3.2 Are seasonal or macro trends causing lower engagement overall?
Yes → Holiday promotions ended? Post-holiday slump? Next Step: Check historical data for past years, see if CTR traditionally drops during this period. End: If seasonality explains the drop, plan promotional strategies accordingly. No → If no audience or seasonality changes explain the drop, move to Hypothesis 4. Hypothesis 4: Offers & Pricing 4.1 Have offers or pricing changed unfavorably for these SKUs?
Yes → Less appealing offers/pricing can deter clicks (users might see lower value in the ad). Next Step: Compare pre/post pricing and evaluate promotional strategies. End: If reverting or improving offers reinstates CTR, cause is confirmed. No → If funnel/landing page is not the culprit, you may need to circle back and do a more granular analysis or revisit earlier hypotheses more deeply. Contribution of SKUs impression share affecting the CTR Conversion (CVR): change by z% Contribution of each SKU’s CVR, independent of Click share, affect the overall CVR Hypothesis 1: Product Listing & User Experience (UX) 1.1 Were product detail pages updated with improved images, A+ content, bullet points, or backend keywords?
Likely Cause: More compelling visuals, clearer info, or better SEO keywords typically boost conversions. Compare listing versions from November to December (via “Manage Your Experiments” or tracked listing edits). Note which SKUs had changes; see if conversion uplift aligns with the update dates. End: If confirmed, listing optimizations explain conversion gains for those SKUs. 1.2 Did changes in the Brand Store or enhanced brand content alter how shoppers view or buy these SKUs?
Likely Cause: Upgraded store navigation or featured modules can highlight certain SKUs, increasing conversion. Review Amazon Brand Store Insights to see if certain SKUs got more direct hits or better conversion paths. Map store layout changes to each SKU’s daily/weekly conversion trend. End: If the correlation is strong, you’ve found a primary conversion driver. No → If no listing or UX updates explain it, move to Hypothesis 2. Hypothesis 2: Pricing & Promotions 2.1 Did we run discounts, coupons, or Lightning Deals for these SKUs?
Likely Cause: Promotional pricing often leads to higher conversion rates if shoppers perceive a deal. Check the promotion schedules in Seller Central/Vendor Central (Deals, Coupons). Verify if the 20 SKUs with conversion gains had active promos during December. End: If yes, promotions are likely driving the lift. 2.2 Did we remove or reduce discounts for certain SKUs, causing conversion to drop?
Likely Cause: Ending a successful promotion can hurt conversions if customers lose an appealing deal. Examine the timeline for when promotions ended and match that to conversion declines in the 20 SKUs (if they had declined). End: If alignment exists, you’ve identified why those SKUs saw conversion dips. No → If not pricing or promotion, move to Hypothesis 3. Hypothesis 3: Product Reviews & Ratings 3.1 Did star ratings improve (e.g., from 4.2 to 4.5) or did we get a surge of positive reviews?
Likely Cause: Hitting a higher star threshold or gaining strong new reviews significantly increases customer confidence. Use Seller Central/Vendor Central “Voice of the Customer” or third-party review-tracking tools. Match rating boosts with increased conversion for specific SKUs. End: If the timing matches, reviews/ratings explain the uplift. 3.2 Did negative feedback or a rating drop occur for certain SKUs?
Likely Cause: A lower star rating or complaints can deter customers from buying, lowering conversion. Review negative feedback timeline and see if it coincides with the 94 SKUs’ conversion decline. End: If confirmed, poor reviews are the culprit. No → If no review-related factors, move to Hypothesis 4. Hypothesis 4: Competitive Landscape 4.1 Did a main competitor go out of stock or raise prices, making our SKU more attractive?
Likely Cause: If competitor listings become less favorable (OOS, price hikes), our SKUs get a conversion bump. Monitor competitor stock/pricing via third-party tools or manual checks. Correlate competitor disruptions with your SKU’s conversion lift dates. End: If the overlap is clear, competitor changes explain your SKU’s conversion growth. 4.2 Did a strong competitor enter the market or intensify promotions, undercutting our listings?
Likely Cause: A new competitor or an existing one offering steep discounts or better ratings can siphon away conversions. Track changes in competitor ads, brand campaigns, or price points. Confirm if your 94 declining SKUs directly overlap with their product. End: If the correlation is evident, competitor action is causing conversion drops. No → If not competitive factors, move to Hypothesis 5. Hypothesis 5: Stock & Fulfillment 5.1 Did we switch fulfillment methods (e.g., from FBA to MFN/FBM) or experience slower shipping times?
Likely Cause: Losing Prime eligibility or extending delivery windows often reduces conversion. Check each SKU’s fulfillment type in Inventory reports. See if the 94 SKUs with lower conversion lost FBA during December. End: If conversion dips align with the shift to slower fulfillment, that’s the reason. 5.2 Did any SKUs have low or “Only X left in stock” messages that boosted urgency, or did they go fully OOS?
Low stock can spur purchases (fear of missing out). Fully OOS means no sales, so conversion percentage might appear skewed. Review restock inventory reports for stock fluctuations. End: If SKUs with “limited stock” soared in conversions or OOS SKUs saw big drops, you’ve found the cause. No → If stock/fulfillment changes don’t explain it, move to Hypothesis 6. Hypothesis 6: Marketing & External Influences 6.1 Were certain SKUs promoted via off-Amazon channels (influencers, social media, brand referral links) to a more purchase-ready audience?
Likely Cause: Higher-intent external traffic typically yields better conversion rates than casual browsing. Check “Amazon Attribution” data for referral performance on specific SKUs. See if conversion lifts coincide with external campaign schedules. End: If confirmed, external traffic explains the SKU-level improvement. 6.2 Did any PR or editorial features (e.g., “Amazon Editor’s Choice,” “Featured in Gift Guides”) highlight certain SKUs?
Likely Cause: Third-party endorsements can significantly boost conversion if shoppers trust these recommendations. Track editorial recommendations, media mentions, or gift guide inclusions. Match the timeframe to your SKU’s conversion spike. End: If timing aligns, that’s the root cause. No → Move to Hypothesis 7 if none of these marketing or external pushes explain it. Hypothesis 7: Child & Parent Variant Mapping 7.1 Were any ASIN merges or parent-child variation changes done in December, redistributing reviews or sales data?
Likely Cause: Merging child variations can shift reviews/sales to a single parent listing, altering measured conversion rates. Check catalog changes in “Manage Inventory” or the vendor product catalog for merges/splits. Match the timing with conversion pattern changes. End: If merges or splits explain the conversion shift, that’s your answer. No → If no measurement or variation changes, we may need deeper SKU-by-SKU or time-of-day analysis Contribution of SKU Click share affecting the CVR Category B Product Sales: change by y% [7.00L —> 7.50L DRR] Impressions: change by x% Hypothesis 1: Auction & Bidding Factors 1.1 Were bids (or daily budgets) for our key SKUs/campaigns changed in December?
Yes → Possible cause: Increased bids can lead to higher ad rank and more impressions (or decreased bids the opposite).
Next Step: Compare November vs. December bidding and daily budget levels. If you increased them, you likely gained impressions; if you lowered them, you lost impressions. End: If confirmed, you have found your primary driver for impressions change. No → Proceed to 1.2.
1.2 Did competitors’ bidding behavior change significantly?
Yes → Cause: Competitor bid changes can raise or lower the overall auction dynamics—affecting how often your ads are shown.
Next Step: Review impression share reports, average position, and competitor metrics. Confirm if competition got stronger or weaker in December. End: If competitor shifts explain the impression changes, you have your answer. No → Proceed to 1.3.
1.3 Did budget constraints cause ads to stop running earlier or later than November?
Yes → Cause: If budgets were capped or exhausted sooner in the day, impressions could drop; if uncapped, impressions might have increased.
Next Step: Compare daily spend patterns and check if budgets were hitting limits. End: If confirmed, you’ve found your cause for impression fluctuation. No → If none of these Auction & Bidding factors explain the shift, move to Hypothesis 2.
Hypothesis 2: Keyword & Targeting Adjustments 2.1 Did we add or remove keywords or product targets (e.g., SKU-based targeting) since November?
Yes → Cause: Expanding keywords or product targets often increases impressions; removing them can reduce impressions.
Next Step: Look at performance by new vs. existing keywords. If the net effect is growth, that explains increased impressions; if removal was larger, it could explain a drop. End: If confirmed, you have your answer. No → Proceed to 2.2.
2.2 Were negative keywords added or targeting restricted?
Yes → Cause: This can reduce the eligible search queries or audience, thereby lowering impressions.
Next Step: Review change logs for negative keyword additions or tighter targeting criteria. End: If reversing or loosening those changes increases impressions again, it’s confirmed. No → Proceed to 2.3.
2.3 Were audience segments or geo-targeting expanded or reduced?
Yes → Cause: Broader audiences or geographies often yield more impressions, while restricting them does the opposite.
Next Step: Compare audience or location reports from November vs. December. End: If confirmed, you’ve found your cause. No → If all sub-hypotheses for Keyword & Targeting are “No,” move to Hypothesis 3.
Hypothesis 3: Ad Scheduling & Delivery 3.1 Were ad schedules changed, reducing or extending the total hours ads were shown?
Yes → Cause: Running ads fewer hours can drastically cut impressions; running them more hours can boost impressions.
Next Step: Check the dayparting or ad schedule settings for November vs. December. End: If scheduling changes align with the impression shift, you’ve found the reason. No → Proceed to 3.2.
3.2 Were different delivery methods (standard vs. accelerated) used?
Yes → Cause: Switching from accelerated to standard (or vice versa) can shift how quickly ads spend their budget and thus affect total impressions.
Next Step: Check the campaign delivery settings in November vs. December. End: If confirmed, no further exploration needed. No → Move to Hypothesis 4 if scheduling & delivery changes do not explain the difference.
Hypothesis 4: Ad Quality & Relevance 4.1 Did our Quality Score or relevance metrics change, impacting how often ads are shown?
Yes → Cause: A drop in Quality Score can reduce impression share; an improvement can boost it.
Next Step: Examine Quality Score components (expected CTR, ad relevance, landing page experience). Identify changes from November to December. End: If confirmed, focus on improving these factors for stable impression share. No → Proceed to 4.2.
4.2 Did changes in clickthrough rates (CTR) affect ad rank or impression frequency?
Yes → Cause: While CTR is partly a result of impressions, certain ad platforms use CTR in ad rank calculations. A big CTR drop might reduce total impressions. Conversely, a big CTR rise might increase impression opportunities.
Next Step: Correlate CTR changes with impression share in the ad platforms’ reporting. End: If confirmed, improving CTR or mitigating its drop can restore impressions. No → If not ad quality or relevance, move to Hypothesis 5.
Hypothesis 5: Seasonality & Macro Factors 5.1 Are there seasonal patterns or macro trends (e.g., holidays, economic shifts) that affected total search/ad volume?
Yes → Cause: Overall search volume for your category might have increased or decreased.
Next Step: Check historical data to see if this period typically experiences a change in search interest. End: If it aligns with known seasonal peaks/slumps, you’ve identified the macro cause. No → Proceed to 5.2.
5.2 Did competitor promotions or events draw user attention elsewhere or boost overall interest?
Yes → Cause: Competitor heavy promotions can draw more (or fewer) queries in your category, indirectly affecting your impressions.
Next Step: Monitor market-level demand, competitor promotions, or press coverage. End: If confirmed, this is largely outside direct control, but influences impression volume. No → If neither seasonality nor macro factors seem to explain it, At this point, consider more granular analyses (SKU-level, time-of-day, device-level, etc.) or revisit earlier hypotheses in more depth.
Impressions Click Through Rate (CTR): change by y% Contribution of each SKU’s CTR, independent of impression share, affect the overall CTR Hypothesis 1: Auction & Bidding Factors 1.1 Were bids for the 83 SKUs reduced in December?
Yes → Likely cause: Lower bids may have decreased ad rank/visibility, leading to lower CTR. Next Step: Test by incrementally increasing bids for a select group of the 83 SKUs and see if CTR improves. End: If confirmed, you have found your cause; you may not need to explore further. 1.2 Did competitors increase their bids substantially, hurting our visibility?
Yes → Likely cause: Even if our bids remained the same, competitor bid increases can lower our relative ad position, reducing CTR. Next Step: Compare average ad positions and competitor impression share from November vs. December to confirm. End: If confirmed, explore ways to adjust bidding or enhance ad relevance. 1.3 Were budget caps (daily or total) reached earlier than in November for these SKUs?
Yes → Likely cause: Ads may have paused prematurely, running in less optimal hours or positions, leading to lower CTR. Next Step: Increase or reallocate budget to see if extending ad availability improves CTR. End: If confirmed, you have found your cause. No → If all sub-hypotheses in Auction & Bidding are “No,” move to Hypothesis 2. Hypothesis 2: Ad Creative & Messaging 2.1 Were ad creatives or messaging changed for the 83 SKUs?
Yes → Possibly the new creatives/messaging are less appealing. Next Step: A/B test original (November) creative vs. current (December) creative on a subset of SKUs. End: If CTR rebounds with old creative, you have found your cause. 2.2 Could ad fatigue be impacting CTR? (e.g., same audiences seeing the same ads too often)
Yes → Likely cause: Users become “blind” to repeated ads, diminishing CTR. Next Step: Reduce frequency caps, refresh creative, or adjust audience segments. End: If confirmed, you have found your cause. No → If ad creative or messaging is not the issue, move to Hypothesis 3. Hypothesis 3: Audience & Targeting 3.1 Was there a shift in audience segments or targeting parameters? (e.g., new negative keywords, changed demographics)
Yes → Possibly showing ads to a less interested audience. Next Step: Compare November vs. December audience composition and performance by segment. Reverse or refine targeting changes. End: If CTR improves after reverting audience changes, you have found your cause. 3.2 Are seasonal or macro trends causing lower engagement overall?
Yes → Holiday promotions ended? Post-holiday slump? Next Step: Check historical data for past years, see if CTR traditionally drops during this period. End: If seasonality explains the drop, plan promotional strategies accordingly. No → If no audience or seasonality changes explain the drop, move to Hypothesis 4. Hypothesis 4: Offers & Pricing 4.1 Have offers or pricing changed unfavorably for these SKUs?
Yes → Less appealing offers/pricing can deter clicks (users might see lower value in the ad). Next Step: Compare pre/post pricing and evaluate promotional strategies. End: If reverting or improving offers reinstates CTR, cause is confirmed. No → If funnel/landing page is not the culprit, you may need to circle back and do a more granular analysis or revisit earlier hypotheses more deeply. Contribution of SKUs impression share affecting the CTR Conversion (CVR): change by z% Contribution of each SKU’s CVR, independent of Click share, affect the overall CVR Hypothesis 1: Product Listing & User Experience (UX) 1.1 Were product detail pages updated with improved images, A+ content, bullet points, or backend keywords?
Likely Cause: More compelling visuals, clearer info, or better SEO keywords typically boost conversions. Compare listing versions from November to December (via “Manage Your Experiments” or tracked listing edits). Note which SKUs had changes; see if conversion uplift aligns with the update dates. End: If confirmed, listing optimizations explain conversion gains for those SKUs. 1.2 Did changes in the Brand Store or enhanced brand content alter how shoppers view or buy these SKUs?
Likely Cause: Upgraded store navigation or featured modules can highlight certain SKUs, increasing conversion. Review Amazon Brand Store Insights to see if certain SKUs got more direct hits or better conversion paths. Map store layout changes to each SKU’s daily/weekly conversion trend. End: If the correlation is strong, you’ve found a primary conversion driver. No → If no listing or UX updates explain it, move to Hypothesis 2. Hypothesis 2: Pricing & Promotions 2.1 Did we run discounts, coupons, or Lightning Deals for these SKUs?
Likely Cause: Promotional pricing often leads to higher conversion rates if shoppers perceive a deal. Check the promotion schedules in Seller Central/Vendor Central (Deals, Coupons). Verify if the 20 SKUs with conversion gains had active promos during December. End: If yes, promotions are likely driving the lift. 2.2 Did we remove or reduce discounts for certain SKUs, causing conversion to drop?
Likely Cause: Ending a successful promotion can hurt conversions if customers lose an appealing deal. Examine the timeline for when promotions ended and match that to conversion declines in the 20 SKUs (if they had declined). End: If alignment exists, you’ve identified why those SKUs saw conversion dips. No → If not pricing or promotion, move to Hypothesis 3. Hypothesis 3: Product Reviews & Ratings 3.1 Did star ratings improve (e.g., from 4.2 to 4.5) or did we get a surge of positive reviews?
Likely Cause: Hitting a higher star threshold or gaining strong new reviews significantly increases customer confidence. Use Seller Central/Vendor Central “Voice of the Customer” or third-party review-tracking tools. Match rating boosts with increased conversion for specific SKUs. End: If the timing matches, reviews/ratings explain the uplift. 3.2 Did negative feedback or a rating drop occur for certain SKUs?
Likely Cause: A lower star rating or complaints can deter customers from buying, lowering conversion. Review negative feedback timeline and see if it coincides with the 94 SKUs’ conversion decline. End: If confirmed, poor reviews are the culprit. No → If no review-related factors, move to Hypothesis 4. Hypothesis 4: Competitive Landscape 4.1 Did a main competitor go out of stock or raise prices, making our SKU more attractive?
Likely Cause: If competitor listings become less favorable (OOS, price hikes), our SKUs get a conversion bump. Monitor competitor stock/pricing via third-party tools or manual checks. Correlate competitor disruptions with your SKU’s conversion lift dates. End: If the overlap is clear, competitor changes explain your SKU’s conversion growth. 4.2 Did a strong competitor enter the market or intensify promotions, undercutting our listings?
Likely Cause: A new competitor or an existing one offering steep discounts or better ratings can siphon away conversions. Track changes in competitor ads, brand campaigns, or price points. Confirm if your 94 declining SKUs directly overlap with their product. End: If the correlation is evident, competitor action is causing conversion drops. No → If not competitive factors, move to Hypothesis 5. Hypothesis 5: Stock & Fulfillment 5.1 Did we switch fulfillment methods (e.g., from FBA to MFN/FBM) or experience slower shipping times?
Likely Cause: Losing Prime eligibility or extending delivery windows often reduces conversion. Check each SKU’s fulfillment type in Inventory reports. See if the 94 SKUs with lower conversion lost FBA during December. End: If conversion dips align with the shift to slower fulfillment, that’s the reason. 5.2 Did any SKUs have low or “Only X left in stock” messages that boosted urgency, or did they go fully OOS?
Low stock can spur purchases (fear of missing out). Fully OOS means no sales, so conversion percentage might appear skewed. Review restock inventory reports for stock fluctuations. End: If SKUs with “limited stock” soared in conversions or OOS SKUs saw big drops, you’ve found the cause. No → If stock/fulfillment changes don’t explain it, move to Hypothesis 6. Hypothesis 6: Marketing & External Influences 6.1 Were certain SKUs promoted via off-Amazon channels (influencers, social media, brand referral links) to a more purchase-ready audience?
Likely Cause: Higher-intent external traffic typically yields better conversion rates than casual browsing. Check “Amazon Attribution” data for referral performance on specific SKUs. See if conversion lifts coincide with external campaign schedules. End: If confirmed, external traffic explains the SKU-level improvement. 6.2 Did any PR or editorial features (e.g., “Amazon Editor’s Choice,” “Featured in Gift Guides”) highlight certain SKUs?
Likely Cause: Third-party endorsements can significantly boost conversion if shoppers trust these recommendations. Track editorial recommendations, media mentions, or gift guide inclusions. Match the timeframe to your SKU’s conversion spike. End: If timing aligns, that’s the root cause. No → Move to Hypothesis 7 if none of these marketing or external pushes explain it. Hypothesis 7: Child & Parent Variant Mapping 7.1 Were any ASIN merges or parent-child variation changes done in December, redistributing reviews or sales data?
Likely Cause: Merging child variations can shift reviews/sales to a single parent listing, altering measured conversion rates. Check catalog changes in “Manage Inventory” or the vendor product catalog for merges/splits. Match the timing with conversion pattern changes. End: If merges or splits explain the conversion shift, that’s your answer. No → If no measurement or variation changes, we may need deeper SKU-by-SKU or time-of-day analysis Contribution of SKU Click share affecting the CVR Category C Product Sales: change by y% [7.00L —> 7.50L DRR] Impressions: change by x% Hypothesis 1: Auction & Bidding Factors 1.1 Were bids (or daily budgets) for our key SKUs/campaigns changed in December?
Yes → Possible cause: Increased bids can lead to higher ad rank and more impressions (or decreased bids the opposite).
Next Step: Compare November vs. December bidding and daily budget levels. If you increased them, you likely gained impressions; if you lowered them, you lost impressions. End: If confirmed, you have found your primary driver for impressions change. No → Proceed to 1.2.
1.2 Did competitors’ bidding behavior change significantly?
Yes → Cause: Competitor bid changes can raise or lower the overall auction dynamics—affecting how often your ads are shown.
Next Step: Review impression share reports, average position, and competitor metrics. Confirm if competition got stronger or weaker in December. End: If competitor shifts explain the impression changes, you have your answer. No → Proceed to 1.3.
1.3 Did budget constraints cause ads to stop running earlier or later than November?
Yes → Cause: If budgets were capped or exhausted sooner in the day, impressions could drop; if uncapped, impressions might have increased.
Next Step: Compare daily spend patterns and check if budgets were hitting limits. End: If confirmed, you’ve found your cause for impression fluctuation. No → If none of these Auction & Bidding factors explain the shift, move to Hypothesis 2.
Hypothesis 2: Keyword & Targeting Adjustments 2.1 Did we add or remove keywords or product targets (e.g., SKU-based targeting) since November?
Yes → Cause: Expanding keywords or product targets often increases impressions; removing them can reduce impressions.
Next Step: Look at performance by new vs. existing keywords. If the net effect is growth, that explains increased impressions; if removal was larger, it could explain a drop. End: If confirmed, you have your answer. No → Proceed to 2.2.
2.2 Were negative keywords added or targeting restricted?
Yes → Cause: This can reduce the eligible search queries or audience, thereby lowering impressions.
Next Step: Review change logs for negative keyword additions or tighter targeting criteria. End: If reversing or loosening those changes increases impressions again, it’s confirmed. No → Proceed to 2.3.
2.3 Were audience segments or geo-targeting expanded or reduced?
Yes → Cause: Broader audiences or geographies often yield more impressions, while restricting them does the opposite.
Next Step: Compare audience or location reports from November vs. December. End: If confirmed, you’ve found your cause. No → If all sub-hypotheses for Keyword & Targeting are “No,” move to Hypothesis 3.
Hypothesis 3: Ad Scheduling & Delivery 3.1 Were ad schedules changed, reducing or extending the total hours ads were shown?
Yes → Cause: Running ads fewer hours can drastically cut impressions; running them more hours can boost impressions.
Next Step: Check the dayparting or ad schedule settings for November vs. December. End: If scheduling changes align with the impression shift, you’ve found the reason. No → Proceed to 3.2.
3.2 Were different delivery methods (standard vs. accelerated) used?
Yes → Cause: Switching from accelerated to standard (or vice versa) can shift how quickly ads spend their budget and thus affect total impressions.
Next Step: Check the campaign delivery settings in November vs. December. End: If confirmed, no further exploration needed. No → Move to Hypothesis 4 if scheduling & delivery changes do not explain the difference.
Hypothesis 4: Ad Quality & Relevance 4.1 Did our Quality Score or relevance metrics change, impacting how often ads are shown?
Yes → Cause: A drop in Quality Score can reduce impression share; an improvement can boost it.
Next Step: Examine Quality Score components (expected CTR, ad relevance, landing page experience). Identify changes from November to December. End: If confirmed, focus on improving these factors for stable impression share. No → Proceed to 4.2.
4.2 Did changes in clickthrough rates (CTR) affect ad rank or impression frequency?
Yes → Cause: While CTR is partly a result of impressions, certain ad platforms use CTR in ad rank calculations. A big CTR drop might reduce total impressions. Conversely, a big CTR rise might increase impression opportunities.
Next Step: Correlate CTR changes with impression share in the ad platforms’ reporting. End: If confirmed, improving CTR or mitigating its drop can restore impressions. No → If not ad quality or relevance, move to Hypothesis 5.
Hypothesis 5: Seasonality & Macro Factors 5.1 Are there seasonal patterns or macro trends (e.g., holidays, economic shifts) that affected total search/ad volume?
Yes → Cause: Overall search volume for your category might have increased or decreased.
Next Step: Check historical data to see if this period typically experiences a change in search interest. End: If it aligns with known seasonal peaks/slumps, you’ve identified the macro cause. No → Proceed to 5.2.
5.2 Did competitor promotions or events draw user attention elsewhere or boost overall interest?
Yes → Cause: Competitor heavy promotions can draw more (or fewer) queries in your category, indirectly affecting your impressions.
Next Step: Monitor market-level demand, competitor promotions, or press coverage. End: If confirmed, this is largely outside direct control, but influences impression volume. No → If neither seasonality nor macro factors seem to explain it, At this point, consider more granular analyses (SKU-level, time-of-day, device-level, etc.) or revisit earlier hypotheses in more depth.
Impressions Click Through Rate (CTR): change by y% Contribution of each SKU’s CTR, independent of impression share, affect the overall CTR Hypothesis 1: Auction & Bidding Factors 1.1 Were bids for the 83 SKUs reduced in December?
Yes → Likely cause: Lower bids may have decreased ad rank/visibility, leading to lower CTR. Next Step: Test by incrementally increasing bids for a select group of the 83 SKUs and see if CTR improves. End: If confirmed, you have found your cause; you may not need to explore further. 1.2 Did competitors increase their bids substantially, hurting our visibility?
Yes → Likely cause: Even if our bids remained the same, competitor bid increases can lower our relative ad position, reducing CTR. Next Step: Compare average ad positions and competitor impression share from November vs. December to confirm. End: If confirmed, explore ways to adjust bidding or enhance ad relevance. 1.3 Were budget caps (daily or total) reached earlier than in November for these SKUs?
Yes → Likely cause: Ads may have paused prematurely, running in less optimal hours or positions, leading to lower CTR. Next Step: Increase or reallocate budget to see if extending ad availability improves CTR. End: If confirmed, you have found your cause. No → If all sub-hypotheses in Auction & Bidding are “No,” move to Hypothesis 2. Hypothesis 2: Ad Creative & Messaging 2.1 Were ad creatives or messaging changed for the 83 SKUs?
Yes → Possibly the new creatives/messaging are less appealing. Next Step: A/B test original (November) creative vs. current (December) creative on a subset of SKUs. End: If CTR rebounds with old creative, you have found your cause. 2.2 Could ad fatigue be impacting CTR? (e.g., same audiences seeing the same ads too often)
Yes → Likely cause: Users become “blind” to repeated ads, diminishing CTR. Next Step: Reduce frequency caps, refresh creative, or adjust audience segments. End: If confirmed, you have found your cause. No → If ad creative or messaging is not the issue, move to Hypothesis 3. Hypothesis 3: Audience & Targeting 3.1 Was there a shift in audience segments or targeting parameters? (e.g., new negative keywords, changed demographics)
Yes → Possibly showing ads to a less interested audience. Next Step: Compare November vs. December audience composition and performance by segment. Reverse or refine targeting changes. End: If CTR improves after reverting audience changes, you have found your cause. 3.2 Are seasonal or macro trends causing lower engagement overall?
Yes → Holiday promotions ended? Post-holiday slump? Next Step: Check historical data for past years, see if CTR traditionally drops during this period. End: If seasonality explains the drop, plan promotional strategies accordingly. No → If no audience or seasonality changes explain the drop, move to Hypothesis 4. Hypothesis 4: Offers & Pricing 4.1 Have offers or pricing changed unfavorably for these SKUs?
Yes → Less appealing offers/pricing can deter clicks (users might see lower value in the ad). Next Step: Compare pre/post pricing and evaluate promotional strategies. End: If reverting or improving offers reinstates CTR, cause is confirmed. No → If funnel/landing page is not the culprit, you may need to circle back and do a more granular analysis or revisit earlier hypotheses more deeply. Contribution of SKUs impression share affecting the CTR Conversion (CVR): change by z% Contribution of each SKU’s CVR, independent of Click share, affect the overall CVR Hypothesis 1: Product Listing & User Experience (UX) 1.1 Were product detail pages updated with improved images, A+ content, bullet points, or backend keywords?
Likely Cause: More compelling visuals, clearer info, or better SEO keywords typically boost conversions. Compare listing versions from November to December (via “Manage Your Experiments” or tracked listing edits). Note which SKUs had changes; see if conversion uplift aligns with the update dates. End: If confirmed, listing optimizations explain conversion gains for those SKUs. 1.2 Did changes in the Brand Store or enhanced brand content alter how shoppers view or buy these SKUs?
Likely Cause: Upgraded store navigation or featured modules can highlight certain SKUs, increasing conversion. Review Amazon Brand Store Insights to see if certain SKUs got more direct hits or better conversion paths. Map store layout changes to each SKU’s daily/weekly conversion trend. End: If the correlation is strong, you’ve found a primary conversion driver. No → If no listing or UX updates explain it, move to Hypothesis 2. Hypothesis 2: Pricing & Promotions 2.1 Did we run discounts, coupons, or Lightning Deals for these SKUs?
Likely Cause: Promotional pricing often leads to higher conversion rates if shoppers perceive a deal. Check the promotion schedules in Seller Central/Vendor Central (Deals, Coupons). Verify if the 20 SKUs with conversion gains had active promos during December. End: If yes, promotions are likely driving the lift. 2.2 Did we remove or reduce discounts for certain SKUs, causing conversion to drop?
Likely Cause: Ending a successful promotion can hurt conversions if customers lose an appealing deal. Examine the timeline for when promotions ended and match that to conversion declines in the 20 SKUs (if they had declined). End: If alignment exists, you’ve identified why those SKUs saw conversion dips. No → If not pricing or promotion, move to Hypothesis 3. Hypothesis 3: Product Reviews & Ratings 3.1 Did star ratings improve (e.g., from 4.2 to 4.5) or did we get a surge of positive reviews?
Likely Cause: Hitting a higher star threshold or gaining strong new reviews significantly increases customer confidence. Use Seller Central/Vendor Central “Voice of the Customer” or third-party review-tracking tools. Match rating boosts with increased conversion for specific SKUs. End: If the timing matches, reviews/ratings explain the uplift. 3.2 Did negative feedback or a rating drop occur for certain SKUs?
Likely Cause: A lower star rating or complaints can deter customers from buying, lowering conversion. Review negative feedback timeline and see if it coincides with the 94 SKUs’ conversion decline. End: If confirmed, poor reviews are the culprit. No → If no review-related factors, move to Hypothesis 4. Hypothesis 4: Competitive Landscape 4.1 Did a main competitor go out of stock or raise prices, making our SKU more attractive?
Likely Cause: If competitor listings become less favorable (OOS, price hikes), our SKUs get a conversion bump. Monitor competitor stock/pricing via third-party tools or manual checks. Correlate competitor disruptions with your SKU’s conversion lift dates. End: If the overlap is clear, competitor changes explain your SKU’s conversion growth. 4.2 Did a strong competitor enter the market or intensify promotions, undercutting our listings?
Likely Cause: A new competitor or an existing one offering steep discounts or better ratings can siphon away conversions. Track changes in competitor ads, brand campaigns, or price points. Confirm if your 94 declining SKUs directly overlap with their product. End: If the correlation is evident, competitor action is causing conversion drops. No → If not competitive factors, move to Hypothesis 5. Hypothesis 5: Stock & Fulfillment 5.1 Did we switch fulfillment methods (e.g., from FBA to MFN/FBM) or experience slower shipping times?
Likely Cause: Losing Prime eligibility or extending delivery windows often reduces conversion. Check each SKU’s fulfillment type in Inventory reports. See if the 94 SKUs with lower conversion lost FBA during December. End: If conversion dips align with the shift to slower fulfillment, that’s the reason. 5.2 Did any SKUs have low or “Only X left in stock” messages that boosted urgency, or did they go fully OOS?
Low stock can spur purchases (fear of missing out). Fully OOS means no sales, so conversion percentage might appear skewed. Review restock inventory reports for stock fluctuations. End: If SKUs with “limited stock” soared in conversions or OOS SKUs saw big drops, you’ve found the cause. No → If stock/fulfillment changes don’t explain it, move to Hypothesis 6. Hypothesis 6: Marketing & External Influences 6.1 Were certain SKUs promoted via off-Amazon channels (influencers, social media, brand referral links) to a more purchase-ready audience?
Likely Cause: Higher-intent external traffic typically yields better conversion rates than casual browsing. Check “Amazon Attribution” data for referral performance on specific SKUs. See if conversion lifts coincide with external campaign schedules. End: If confirmed, external traffic explains the SKU-level improvement. 6.2 Did any PR or editorial features (e.g., “Amazon Editor’s Choice,” “Featured in Gift Guides”) highlight certain SKUs?
Likely Cause: Third-party endorsements can significantly boost conversion if shoppers trust these recommendations. Track editorial recommendations, media mentions, or gift guide inclusions. Match the timeframe to your SKU’s conversion spike. End: If timing aligns, that’s the root cause. No → Move to Hypothesis 7 if none of these marketing or external pushes explain it. Hypothesis 7: Child & Parent Variant Mapping 7.1 Were any ASIN merges or parent-child variation changes done in December, redistributing reviews or sales data?
Likely Cause: Merging child variations can shift reviews/sales to a single parent listing, altering measured conversion rates. Check catalog changes in “Manage Inventory” or the vendor product catalog for merges/splits. Match the timing with conversion pattern changes. End: If merges or splits explain the conversion shift, that’s your answer. No → If no measurement or variation changes, we may need deeper SKU-by-SKU or time-of-day analysis Contribution of SKU Click share affecting the CVR
Amazon Mbr
