Christine Gu
Context
This was one of the first high-urgency projects I tackled after Wagr's launch. As a designer at a small startup, the stakes were clear — growth and retention weren't just metrics, they were survival
Wagr was a social sports betting app built around a simple but powerful idea: you should always know who you're betting against. Where traditional platforms kept opponents anonymous, Wagr put transparency front and center — letting users send bets directly to friends rather than betting against the house. Our team believed that the social layer was the product.
But weeks after launch, a troubling pattern emerged: bets were going unanswered. Through user testing and team discussions, the insight was straightforward — users didn't necessarily need to bet against someone they knew, they just needed to know someone was on the other side. That distinction changed everything.
Working closely with product and engineering, I designed the interfaces of an automatic bet-matching system that expanded each user's network beyond their immediate circle to the broader Wagr community. The result was stronger retention and a meaningful increase in average handle
Bigger network, more action, better retention
+30.00%
Bets successfully matched
+20.00%
Average handle
-20.00%
Churn rates
Role
Product Designer
Timeline
April 2022 - August 2022
Building a betting feature
What differs Wagr from other sports betting platforms?
Wagr lets you see exactly who’s on the other side of your bet by shifting the experience from betting the house to betting people in your own network
Traditional sports betting platforms
Wagr’s betting platform
Building a betting feature
Too many bets left behind
Despite its social appeal, Wagr experienced high churn after bettors invited friends to take the other side. Because bets relied on direct invites—and most went ignored—many wagers never completed, making bettors more likely to leave.
Building a betting feature
Why were bets going unanswered or being declined?
Well, it turns out, because Wagr depended on friends taking the opposite side, and most friend groups back the same teams, rival wagers rarely happened
Wrong side?
Were users expected to support teams they didn't love in real life?
Verdict: Yes
Users were turning down bets for teams they didn't already support
Was the price not right?
Were users rejecting bets because they were too expensive?
Verdict: No
Users were accepting bets of all price ranges
Stranger Danger?
Were users rejecting bets from people they were unfamiliar with?
Verdict: No
Bets were taken regardless of relationship closeness
Mapping out the key issue
Limiting bets to users’ immediate networks led to higher rejection rates, particularly when both sender and recipient were fans of the same team
There was an opportunity here to improve match rates by opening bets to all users
Data showed users mainly wanted their bets accepted, regardless of who accepted them. This revealed an opportunity: enable bets to be matched with anyone on Wagr, not just a user’s immediate network
Building a betting feature
Identifying the key areas for feature implementation
What user flows from the current experience will be most impacted by the introduction of bet matching?
Where on Wagr would introducing bet matching be most effective?
Home screen
Bet confirmation
Invite friends
Building a betting feature
Be bold about the strategy
I explored different ways to introduce the new feature, aiming for a bold, clear initial release and seamless, intuitive interactions in later iterations
Maximizing our odds of success
To support the success of bet matching, I refined key details across the experience. Since users gravitated toward trending matchups—not just upcoming ones—I added a ‘popular games’ section to surface high-interest games and increase engagement.
Previous game order
New game order
What difference was made?
80% of all bets matched
Users saw a massive uptick in their number of bets being accepted
20% increase in average handle
Betting more money was indicative of trust in bets being accepted
35% reduction in churn rates
User activity amongst public and private league cohort were relatively similar over time