The Loyalty Equation
The Context
SeatGeek's growth engine depends far more on repeat buyers than on any single acquisition channel. My stakeholders were the growth and lifecycle marketing teams, alongside a data scientist who owned the underlying behavioral data.
The gap was obvious once you looked at the numbers. Our best customers order 4.82 times a year; a one-time buyer who never returns orders just once and is worth a fraction of that in lifetime value. But product, marketing, and growth were still talking about "fans" as one group, running the same messages and the same features at a loyal season-ticket type and a fan who bought once two years ago and never came back. Without a shared, specific language for who these fans actually were, every team kept re-deriving its own definition of a "good" customer, and nobody could agree on where to invest.
Moderated interviews across different customer types pointed at the same two ingredients, over and over, for why a fan sticks around. First, there has to be trust, without it, there's no foundation for anything else. Second, a fan has to feel like they're getting a deal. Those two things together are what create habit, and habit, given enough time, is what turns into loyalty.
What We Found
Habit starts with a great value moment. Users told us they started with SeatGeek because they realized we had good prices, especially for sports, or got a promo code that got them in the door. Persistent good prices, a strong UX, and CRM promo campaigns are what kept them coming back. Trust follows from that pattern of good experiences, it isn't what hooks a fan in the first place. But there were some core problems that interviews revealed about what was keeping our non loyal users from coming back.
- Users can't pick up where they left off: browse, leave, come back, and we've forgotten everything, on the Browse tab and the event page alike.
- App + CRM is underused for habit building: 43% of app users have never purchased, and 29% have purchased only once before drifting away.
- Small UX fixes are outsized levers: most app sessions start on the Browse tab, and most drop off there too.
The follow-up behavioral analysis found that loyalty is behavioral, not aspirational: a loyal fan has stopped opening a second tab, they default to SeatGeek and buy. Native app usage is the single biggest predictor of that, loyal users are nearly twice as active on the app as anyone else, and partnerships bring us an outsized share of our most valuable fans, about 67% first came to us through a full-stack or integrated partnership. My recommendation: treat Trust + Price Confidence = Habit as the operating model, and build the roadmap segment by segment from there.
Sizing the Opportunity
The segments weren't just descriptive, Strat Fin used them to size exactly what this priority is worth. A +15% relative lift in our owned repeat rate (20.7% to 23.5%) would produce an estimated $30 to 50M in incremental annual contribution profit company-wide, with $20 to 30M treated as the realistic near-term threshold. In H2 2026 alone, focusing on the segments this research identified, First-Time Buyers, Light Buyers, Frequent Evaluators, and Loyal fans, was sized at $2.2 to 7M in incremental revenue.
The segmentation also surfaced where the concentrated bet lives: VIPs. Frequent Evaluators, our second most valuable segment, are worth about $93 in contribution profit per user; a retained VIP is worth roughly four of them. With VIP churn running around 45% year over year, retaining even a quarter of the users we'd otherwise lose, alongside shifting more of their spend to owned channels, was sized at close to $20M in incremental contribution profit from VIPs alone. That case, sitting right next to the broader segment-by-segment roadmap, is what turned this research into a funded company priority rather than a one-off readout.
From Moderated Interviews to Quant Segmentation
The segments started as qual, not quant. Moderated interviews surfaced the broad behavioral characteristics of our fans, the value-moment story, the trust pattern, the drop-in-drop-out browsing, before I knew what to call any of it or where the lines between groups should sit.
From there I used AI to help draft candidate segment definitions, bouncing that thinking back and forth with our data analyst, who checked each definition against the real data until we agreed it was accurate. The quant analysis, orders per year, gross ticket value, tenure, recency, app share, and how a fan was acquired, is what turned those broad qualitative characteristics into precise, data-backed segments, and let us see exactly what each group was doing inside the SeatGeek ecosystem rather than just how they described themselves.
The Artifact
Each segment was built from real order history and behavior data, then paired with a recommended play so it wasn't just a label, it was a next step:
- Rookie, first-time buyer — highest browse rate of any segment; capture the second purchase fast, before that curiosity fades.
- Everyday Player, light buyer — a steady contributor who still comparison shops; nudge a subset toward the habit before they plateau.
- Free Agent, frequent evaluator — buys almost as often as our loyalists but hasn't committed; win with differentiation and trust, not another discount.
- All Star, the loyalist — recent, frequent, and high-spending; protect and reward them, don't take them for granted.
- Injured List, at risk — no purchase in 12 months but still browsing constantly; a timely, personalized nudge before they fully churn.
- Released, dormant — gone for years with little browsing left; deprioritize spend here in favor of cheaper new-user acquisition.
- Stale, the one-timer — bought exactly once and never came back; research first, find out why before spending to win them back.
To stay on brand (SeatGeek's roots are in sports), I turned each segment into a baseball card, stat line and scouting report on the back, name and face on the front. I had them printed for an exec onsite and we played trivia with them: given a scouting report, guess the segment. It turned out to be a fun, interactive way to get a room full of execs to actually sit with the insights instead of skimming a deck, and it's what built the shared language the team still uses today for who our fans are and who we need to move into the Loyal bucket.
From Segments to a Roadmap
This wasn't just a shared language and a set of plays, it became the backbone of a 3-year roadmap for building lifetime value, with a specific feature set now shipping against each segment. First-Time Buyers get post-purchase journeys that reassure them and cross-sell at the right moment. Light Buyers and Frequent Evaluators are getting SeatGeek Credits and real-time price and on-sale alerts to win the comparison moment. Frequent Evaluators and Loyalists are getting synced recently-viewed events across web, app, and CRM, plus return-to-shopping prompts, so the next purchase is effortless. VIPs who've gone stale are getting their unused credits converted and a targeted win-back series, alongside deeper research into what would keep them from churning. And across every segment, new CRM behavioral triggers turn a single action, attending an event, connecting Spotify, into a personalized follow-up journey instead of one message.
Together, Strat Fin sized these bets at $2.2 to 7M in incremental 2026 revenue and $30 to 50M in incremental annual contribution profit longer term, the same segments this research defined. Rollout is in progress.