Loyalty as a revenue system. Here’s what that looks like in practice.

The model surfaced a completely different set of churn predictors than the sales team expected. Some of the variables the team was most confident about had weak predictive power. Others that nobody was tracking turned out to be among the strongest signals.

This is the “intuition to evidence” pattern at its most concrete: experienced operators aren’t wrong in spirit, but when you test their assumptions against event-level data, the specifics shift — and in loyalty, the specifics are where the margin lives.

The output was a productized churn scoring feed that flagged at-risk accounts with enough lead time for targeted save campaigns. Instead of blanket retention offers, the team could focus resources on the accounts most worth saving, with the interventions most likely to work.

GameStop’s PowerUp Rewards program had over 65 million members generating transaction-level data across every store and online purchase. Game Informer — the largest gaming publication in the world — kept the audience engaged between purchases. The owned channels (email, in-store, web, direct mail) reached the most engaged gaming audience on the planet.

The opportunity: treat these assets as a unified ecosystem, not three separate programs. Publishers and game developers paid to reach specific segments of the loyalty database — new console owners, lapsed members, high-frequency buyers, genre enthusiasts — with targeting built on actual purchase behavior, not modeled data.

Closed-loop measurement was built in: because we owned the loyalty data and the point of sale, we could answer the question that matters — did the customer who saw a campaign actually buy? Transactional proof, not click proxies.

This was modern loyalty design: instead of a traditional points-for-discount model, the program created a new value exchange between a retailer and a brand partner. Dick’s members engaged with TaylorMade’s experience using their loyalty points. TaylorMade got direct customer engagement and a pathway to custom driver orders. Dick’s got incremental points usage and enriched member profiles.

The back-end data sharing enabled continued engagement for both brands — follow-up email campaigns, personalized offers, and a feedback loop that informed future programming. Both brands got something they couldn’t build alone.

The architecture was deliberately platform-native on Salesforce, making it repeatable for other brand partnerships without custom builds each time.