🧭 Context

As a Gen Z Schmooze user and product-minded analyst, I spotted a common pain point in the dating app experience: match overload.
While Schmooze already does an incredible job matching users through meme-based humour and compatibility algorithms, once matches pile up - genuine connections often get lost in the noise.

So I built this case study to reimagine how Schmooze could use AI, behaviour signals, and gamified friction to make matches feel more meaningful, not just more frequent.

💡 Approach

Problem Statement:
Schmooze solves for who we match with - but not which matches actually matter.
Female users, especially, face “inbox fatigue” and choice overload, leading to lost liquidity and missed meaningful conversations.

My Solution: Three layered product ideas to declutter, prioritise, and revive intent-driven matches 👇

🧠 Intent Score – Ranking Matches by Behaviour, Not Just Recency

AI model combining text depth, profile completeness, and mutual engagement to rank conversations by “intent.”
Reorders inbox from High Intent → Low Intent with an optional toggle.
Impact: Reduces noise, improves female experience, increases quality interactions.

💬 Learning Loops – Nudges + Feedback That Make AI Smarter

Contextual notifications like “You and Tanmay are on a DM streak - don’t lose the vibe 😎”
Mini prompts (“Was this convo worth your time?”) to train the model over time.
Impact: Turns behavioural feedback into a self-learning, emotionally aware system.

🎮 ShootYourShot + Roast AI – Gamified Chat Resurrection

Users can revive a cold match for ₹99 or “roast” the AI to earn discounts based on creativity.
Impact: Adds friction that signals genuine intent, while keeping the experience playful and brand-consistent.

Together, these ideas help Schmooze evolve from a meme-based dating app to a behaviour-intelligent, emotionally aware matchmaking system.

🚀 Learnings (Product + Growth Perspective)

Intent > Volume: True user satisfaction comes from fewer, higher-quality connections.

AI can be emotional: Behavioural data can teach algorithms empathy and prioritisation.

Playful friction drives engagement: When users “earn” actions, it signals intent and creates delight.

Retention insight: Nudges that feel human outperform pushy reminders.