Case Study 03
MPL Rummy — D7 Retention System
Engineering a Minimum Viable Habit Loop
Role
Principal Product Designer
Scope
New user onboarding, behavioral design
Collaborators
PM, Engineering, Marketing
Platform
Real Money Gaming — Rummy
01 — Problem Statement
Problem Statement
A new user in a real-money gaming context is high-anxiety, low-trust, and motivation is fragile. The design problem is not how to reward users — it's how to build return intent before the user has a reason to trust the product.
MPL Rummy's Day 7 retention for new users was the key metric the business needed to improve. The problem was not engagement depth — it was early dropout before any habit had formed.
The challenge: design a system that creates return intent within the first 7 days, without relying on the trust and motivation that only comes after day 7.
02 — My Role vs. Team Contribution
My Role vs. Team Contribution
03 — The Hardest Decision
The Hardest Decision
Decision
Variable rewards over fixed reward ladder; Goal-gradient over streak mechanics
Fixed rewards plateau motivation. Streaks penalize lapses and increase drop-off anxiety early in lifecycle. For Day 1–7, reducing cognitive and emotional load was more important than enforcing consistency.
| Factor | Option A | Option B — Chosen |
|---|---|---|
| Reward structure | Fixed ladder — predictable, low anxiety | Variable rewards — maintains anticipation, reduces habituation |
| Progress mechanic | Streak — enforces consistency, punishes lapses | Goal-gradient bar — safer for new users, reduces lapse anxiety |
| Reward crediting | Auto-credit — frictionless | Intentional claim moment — dopamine reinforcement |
| Launch scope | Full campaign integration | MVP behavioral loop — ship behavioral completeness, not feature completeness |
04 — Rollout Strategy & Learning
Rollout Strategy & Learning
We instrumented leading indicators from day one: D1 return rate, task completion rate, reward claim rate, D1→D2 drop-off, and progress bar completion %. We did not wait for D7.
What Went Wrong
Marketing wanted full campaign integration in one month. Engineering pushed back on claim flow complexity due to backend dependencies.
How It Was Corrected
I reframed using a visual behavioral model — Trigger → Action → Reward → Progress feedback — showing urgency + progress feedback were sufficient to create return intent. Enhanced mechanics pushed to V2.
Within the first few days post-launch, improvements in D1 return and task completion gave us directional confidence the loop was working before D7 data matured.
05 — Org-Level Impact
Org-Level Impact
- Established a behavioral design framework referenceable for other retention problems across MPL products.
- Demonstrated that lifecycle-aware mechanic selection — not just gamification — drives early retention.
- The MVP framing approach became a model for scoping behaviorally complex features under tight timelines.
We designed the measurement system alongside the experience. That's the difference between shipping and shipping with signal.
06 — What I'd Do Differently
What I'd Do Differently
- Push for richer leading indicators earlier — progress bar completion by cohort would have given cleaner signal.
- Document the behavioral framework formally so it could be reused without re-explaining from first principles.
- Build V2 mechanics in parallel during V1 development to reduce the gap between launch and enhancement.
07 — Design Principles Demonstrated