11.6%
Email click-through
From 3.2%
Case Study
Recommendation Engine
Show each person what they would actually want next.
Case Study
Show each person what they would actually want next.
A per-customer recommendation layer for products, events, services, and content. The system builds a profile from behavior and preference, then surfaces the right next thing — on web, in the app, in email, and at the point of sale. Members discover more of what your operation offers; revenue concentration spreads beyond the headline programming.
Recommendations are useful when they support the relationship rather than feel like generic promotion. Every suggestion comes with a plain-language reason, so staff can use the same insight in personal outreach as the digital channels use in automated prompts. The system increases engagement without leaning on discounting — growth comes from relevance, not from training members to wait for promotions.
Capabilities
Behavioral and transactional profile built per customer.
Real-time recommendations across web, app, email, and point of sale.
Cross-sell and upsell signals tuned for each context (browsing, checkout, post-purchase).
Cold-start handling for new customers and new inventory.
Explainability built in: why this customer, why this item.
From Our Work
Across five venues and 240+ events per season.
The organization was struggling to fill mid-season programming and second-tier events. Subscribers heard about everything; non-subscribers heard about almost nothing relevant to them. We delivered a recommendation engine spanning ticket history, browsing behavior, donation patterns, and program metadata. Each patron now sees a tailored “you might like” stream on the website, in email, and in the venue app. Mid-season sell-through climbed from 41% to 64%, click-through on email more than tripled, and the share of conversions tied to discount-driven offers fell from 38% to 24% — growth came from relevance, not promotion.
11.6%
Email click-through
From 3.2%
64%
Mid-season ticket sell-through
From 41%
4.7
Average tickets per patron, per season
From 3.1
48%
New-patron return rate within 90 days
From 22%
24%
Discount-driven share of conversions
Down from 38%
Key Insight
Discovery is a revenue line item once each member sees the version of the catalog that fits them. The headline programming sells itself; recommendations are how the rest of the calendar fills.
