Cohorts & Recommendations To Drive Engagement
Real-time cohorts and personalized bet recommendations that drive conversion.
Unified data powers better journeys.
Problem
- No single view of the fan
- Manual segmentation drains time
- Proper ML and Personalization stacks are expense to build and maintain
- Generic recommendations are low-converting
Why it matters
-
Personalization drives LTV and engagement
-
Unified data and a single view of the customer powers better journeys
What it does
The essentials
How it works
Three simple steps
Understand
Ingest fan signals and interpret behavior patterns.
- Affinity
- Recency/frequency
Cohort
Blend behavior + transactions to group fans in real time.
- Behavioral modeling
- AI routing
Recommend
Deliver personalized bets, promos, and insights instantly.
- Recommendations
- VIP promos
- Next-best action
Key capabilities
Built for sports
Real-Time Signals
Fandom, bet patterns, and risk profile in one stream
Cohort Engine
Blend behavioral and transactional data for grouping
Fan Routing
Match every user to the right personalization model
Recommendation Engine
Serve the next-best bet, promo, or insight
Intent Scoring
Predict engagement likelihood across the lifecycle
Content Personalization
Customize every touchpoint automatically
Results
Outcomes
Higher conversion on key markets
Serve the right bet, promo, or insight at the exact moment of intent.
Deeper fan retention
Behavior-based cohorts keep users active across the lifecycle.
Real-time personalization at scale
One engine powering recs, promos, and content for every user segment.
Lift in LTV across segments
Smarter routing → better experiences → more loyal high-value users.
Use cases
Where it lands
Cohort Identification
Segment bettors in real time by behavior, value, and betting patterns.
Personalized Bet & Promo Recommendations
Serve 1:1 bets and promotions that lift conversion and retention.
Why sportsstack
Don't Reinvent the Wheel
| Feature | SportsStack | DIY / Legacy |
|---|---|---|
| Cohort Updates |
|
Manual SQL, batch processing |
| Recommendation Models |
|
Generic, context-blind |
| Data Foundation |
|
Fragmented, stale, inconsistent |
| Activation |
|
Manual exports, delayed sync, maintain brittle integrations between systems |
| Conversion Impact |
|
Generic promos, low conversion |
Unify your data. Protect your ops. Engage your fans.