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Learnings so far

Deducing social score from CultureX data
Focus on Open Accounts(Level 2) with 900+ Followers
Tap Micro-Influencers (500 to 2,000 Followers)- They offer the best mix of authenticity and strong engagement
After '2254 following count' There was no profile with missing data( Out of 54 Populated Profiles). However this category is more inclined towards a Creator Persona than a user.
Venue Onboarding
Need a method to categorize venues into Budget, Mid-tier, Premium and Luxury- currently doing this manually
Venues in similar locations have similar discounts
Some venues are on both Dineout and District, some on one, some on neither
AI-based earning score
Not dependable data for most customers
Don’t have logic based on the earning score. Build logic based on social score and spend potential. Everyone starts at low spend potential. When they redeem a perk, whatever they are spending above the perk, puts them into the mid or high spend potential tiers. The assumption here is that customers in mid and high spend potential tiers are less likely to go some place just for free perks, without spending more.
Reach and spend potential based perk logic
The customer is offered a perk when they can offer reach
The customer is offered a perk when they qualify as a potential customer based on their spend
Reach-Perk
Spend-Perk
Earn-Perk

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