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Bootcamps - Initiatives H2

New initiatives:

Blog-led funnel: using blog articles on TOFU for filtering prospects

New campaign types including demand-gen campaigns

Deeper funnel optimisations on Facebook for warm+

Adesha lead scoring model testing on all platforms

Creating virtual MDRs (up to 5): without changing % discount adding additional MDRs to create urgency

Budget and bidding strategy testing

Initiatives built on earlier successes

YOUTUBE

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We’ve been testing YouTube for some programs since last quarter and are seeing different, program-specific results: BH-PCMLAI - Early metrics give a +ve sign with L2A trending at 7.35%, and 1 PA generated at 64% CAC (marketing cycle currently active) MO-PCDS - While we’re yet to see a PA conversion here, the L2A has been trending strong at ~9% for two consecutive runs now MO-PCCO has seen high CPLs while in the case of MO-PCTPM the L2A has been weak. - Given the mixed performance across courses, the channel warrants being experimented at a program-specific level. - Video ads for different programs are WIP and will be taken live as and when ready over Q3 and Q4.

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SEARCH REMARKETING

MO-PCTPM -

Tried search remarketing for PCTPM in the November run which resulted in 1 PA @ a CAC of 44%. Trying this campaign yet again in this run to see its consistency for this course.

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MO-PCGD

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Search Remarketing campaigns have consistently worked well for PCGD. Every run we managed to get conversions at a very healthy CAC%.

We can look into scaling the campaigns and use for other programs.

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PERFORMANCE MAX WITH WARM+

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Trying performance max campaign using the Warm+ goal objective for the first time. So far, we have gotten 1 PA @ a CAC of 90%.

Campaign performance looks good and warm+ CPL is @ 236$ which is way better than the Warm+ CPL of Non_Brand campaign.

Basis performance at run end, we can look to scale spends on this and also expand to other courses.

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TWITTER

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Twitter has been consistently working for IMP-PCMLAI with decent number of PA’s & decent CAC.

We can try and open up this channel for other courses in future runs.

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LinkedIn had yielded positive results for BH-PCMLAI in Q2 and is being continued to be tested in Q3. Q2 was targeted only to the California region, which has now been extended to RoUS in Q3. Basis the final results from the 24-01 run, we can continue the campaigns in all of US or move back to region specific campaigns. The same initiative can be extended across programs wherein we test region-specific campaigns, particularly for programs where LinkedIn hasn’t worked in the past. Plus, remarketing (and prospecting using predictive AI) on LinkedIn is something that we plan to explore in the coming runs.

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FACEBOOK ADVANTAGE+

IMP-PCDM -

Been trying advantage+ audience on FB for IMP-PCDM in the November run & current run as well. The experiment has yielded positive result with 4 PA’s @ a CAC of 65%.

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BH-PCMLAI - Adv+ is being tested on BH-PCMLAI this run and is showing very good early performance metrics.

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PCDE:

Early signs of Advantage+ targeting looks promising.

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- We will continue to test this at a larger scale moving into next run for these programs, and extend this further to other programs in the portfolio.

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WARM+ Optimization on Search

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We are currently testing warm+ campaign optimization for IMP-PCBA-24-02 run. The initial top funnel metrics viz. C2L and CPL are trending pretty similar to our BAU campaign optimized on leads. The mid-funnel is showing better traction with L2A trending at 11% vs 5.6% (although the lead base currently is small). We can plan to extend this initiative across the portfolio in a phased manner depending on the outcome of this experiment.

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Countdown Ads:

Experiment with Countdown ads to see if it helps in improving the CTRs (*For Launch date)

Geo-Targeting:

Experiment to see if creating a separate campaign for top GEOs with a slightly increased TCPA, help in increasing the lead volume.

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PCCO -lifetime data

Weekday v/s weekend optimisation

MarTech things

> warm+ and adesha-based optimisation on Facebook

> Advantage + audience

> App-based optimisation

> Attribution model

> mFilterit

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