Will Booth
Senior Product Manager
Using Gacha mechanics and an AI-powered personalisation loop to build user loyalty and increase daily logins.
Overview
Reveals is a daily free-to-play pack-opening game built to increase login frequency. It borrows habit-forming mechanics from mobile gaming and uses AI to identify and reward users at the moment they are most likely to churn.
Impact
30%+
Daily logins
Increase in daily login rate
AI-Powered
Personalisation layer
AI layer recalculates and adapts week-on-week
3 Months
Idea to delivered product
Conceived, designed, and shipped end-to-end
Role
Product Manager
Services
Vision
Game Design
AI Architecture
Commercial Model
Year
Q1 2026
Opportunity
In iGaming, acquisition is expensive and loyalty is fragile. Most operators rely on financial incentives — free bets, free spins — to retain users. This attracts bonus hunters rather than genuine fans, inflating Customer Acquisition Costs while doing nothing for long-term Lifetime Value.
“Up to 40% of users disappear after their first session, long before any real value is captured.”
InData Labs, Sports Betting Churn Study
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Mobile gaming had spent years perfecting daily engagement mechanics. Clash of Clans, TCG Pocket, FIFA Ultimate Team — these products had cracked habit formation through variable rewards, scarcity, and personalisation. Almost none of this thinking had crossed over into sports betting.
The product
A new Reveal every day
Each weekday a fresh player card becomes available to open. The 24-hour window is deliberate — miss a day and it's gone.
You don't know who you're getting
Players are hidden until the moment of reveal. A Haaland is rare. A Tarkowski is common. The mystery is the mechanic.
Your player plays
If your revealed player hits their target stat — goals, assists, points — over the weekend, you win a reward.
Collect and repeat
Rewards land at the start of the week. A new set of Reveals opens. The cycle starts again.
The Reveal experience is the hook. Beneath it, an AI layer makes the product commercially intelligent.
User profiling
A predictive model profiles users early in their lifecycle, identifying signals of high-value or bonus-abusive behaviour.
Dynamic player selection
An AI agent adjusts the potential players a user can return -- balancing individual engagement likelihood against operator-defined KPI targets and financial caps.
Continuous optimisation
The system adjusts week-on-week, continuously closing the gap between actual and target outcomes.
Why it works
Reveals works because it is built on psychological triggers that are deeply embedded in how people form habits. These are the mechanisms behind every habit-forming product that has ever achieved genuine daily engagement. Nir Eyal documents exactly this in Hooked, and his framework serves as evidence for why these triggers work, not the source of them.
“A habit is when not doing an action causes a bit of pain.”
Nir Eyal, Hooked
Trigger
A new pick drops at the same time every weekday. A push notification is the initial pull. Over time the internal trigger takes over. The habit becomes checking Reveals at 17:00, not responding to a reminder.
Action
Free to play. No deposit. No odds to understand. The action is as frictionless as possible: a single tap to reveal. Exactly as Hooked prescribes.
Variable reward
The player reveal is inherently unpredictable. A rare player feels like winning the lottery. This variability is the core driver of compulsive re-engagement across every habit-forming product.
Scarcity
Each Reveal is only available for 24 hours. The window is deliberate. It creates urgency that passive products simply cannot manufacture. Users return not out of habit alone but because not returning has a cost.
Investment
Users build a squad of revealed players over five days. The more picks they have, the more they care about the weekend, and the more certain they are to return on Monday.
User experience
From the reveal animation and card visual language to the reward feedback moments that follow a weekend result the goal was to make something immediately familiar to anyone who had ever opened a pack in a mobile game, while remaining native to a sports betting context.
The reveal animation is designed to feel like unpacking a collectible. For anyone who grew up opening FIFA Ultimate Team packs, the mental model is instant — that same mix of anticipation and not knowing what you are about to get.
The launch
With the first operator now live, early results are promising. The clearest signal is user activity spiking in the window just before a new Reveal drops each weekday — exactly the anticipation behaviour the habit loop was designed to produce. Daily login rate is trending upward, and the pattern is consistent enough to suggest the mechanic is forming the routine we intended.
Two further operators go live in time for the FIFA World Cup — the highest-traffic window in the sports calendar. By continuously monitoring user activity and game interaction, we will keep optimising the habit loop and tuning the AI model to stay aligned with each operator's KPI targets as volume scales.
I've learned that
Prototype earlier
Securing internal buy-in was harder than building the product. Describing pack-opening mechanics for football predictions in a meeting room is harder than showing it. A faster path to something visual would have compressed the approval timeline significantly.
Cross-industry research compounds
The idea came entirely from outside iGaming — from mobile gaming, TCG mechanics, and Nir Eyal's Hooked. The biggest product insights here were borrowed, not invented. Looking outside the category first is almost always worth it.
Commercial intelligence from day one
Building the AI feedback loop as a core layer — not a bolt-on — meant the commercial model was part of the design from the start. Operator KPI guardrails built in early cost far less than retrofitting them later.
Speed to something real
Went from idea to delivered product in under three months. The first operator integration taught more than months of internal review ever would. Getting something live and learning from real usage is always the faster path.