About
Schmooze is a dating app that matches you on personality, not just looks. You can swipe on memes, search with AI, or talk to a AI matchmaker. It picks up on your vibe and finds people who get you.
Role:
⋅ Everything design
⋅ Strategy
Team:
⋅ Dewanshi (Sr PD)
⋅ Asif (Lead PD)
Timeline:
2023-26
Desc:
How we got users to build better profiles, without being intrusive
The challenge
Your matching algorithm is only as smart as the profiles you feed it. A half-empty profile with a blurry photo and a bio that says "ask me anything" is useless to the algorithm. Garbage in, ghosting out.
We'd kept onboarding lean on purpose, get people in fast, ask for the rest later. Good for signup numbers, rough for match quality. Women users in particular left more blank, and incomplete profiles on both sides meant weaker matches for everyone.
The challenge wasn't "make people fill out forms." It was: raise profile quality over the course of normal use, without ever making users feel nagged.
Meeting people where they already are
Rather than building one big "complete your profile!" screen that everyone learned to ignore at that time. we planted small, contextual nudges throughout the app - each one timed to a moment where the request felt natural instead of needy.
Under the match profile. When you're looking at someone you might match with, we slipped a quiet prompt beneath their nicely filled-out sections: add your photo / bio / prompt to improve your chances. Gender-specific copy, no modal, no guilt trip - just a nudge delivered at the exact moment you're feeling the gap between their effort and yours. 14% of users who saw it went and filled something in.
Add from a meme swipe. The ambitious one that didn't work. Swipe right on an Office meme, we'd nudge user to add "The Office" to profile. Problem is liking a meme doesn't mean you watched the show. Keeping it relevant also meant curating trending memes often to avoid same collection in every profile. Weak signal, high upkeep.
Verification nudge. When an unverified user reacted to a verified profile, we nudged them to verify. That single prompt drove 10-15% of all verifications on the platform.
What happened?
Lesson: The best moment to ask for a piece of profile data is the instant the user just revealed it themselves. Catch the interest while it's warm - don't make them recreate it from memory later.
Ask for matches - letting users do the nudging
Schmooze wanted to push profile completion higher. On the flip side, people in chatrooms often wanted to know more about their match. We saw an opportunity in the overlap: what if we let one user ask the other about their movies, playlist, or a question from the prompt list? The asker gets their answer, the profile gets more complete. Win-win.
An "Ask for matches" button in the chat lets you request a specific detail from your match. If they answer, it arrives as a DM and gets added to their profile at the same time.
Discovery was low, not many users found it. But those who did converted hard: around 70% of requests led to the detail being shared and added to the profile.
Lesson: Sometimes the most effective nudge isn't from the product at all. It's from the person on the other side of the screen, and your job is just to hand them the tool.
AI as a profile building co-pilot
AI Prompt Writer. On Schmooze, women were completing prompts at 30-35% compared to around 65% for men. And some weren't great- short, low-effort, or in some cases users were dropping UPI IDs, phone numbers, or Telegram handles instead of an actual bio. We added an AI layer that offered to improve whatever you typed, flagged low-quality or unsafe inputs, and nudged users to write something real instead.
Impact: We tested it free in onboarding for women specifically, since that's where the gap was widest. Women's bio completion jumped ~12% in onboarding, and overall prompt completion rose ~20%.
AI Photo Selection. Users were spending 5-10 minutes on average on the photo selection step during onboarding, and the worst cases ran much longer. That's not engagement. That's someone standing in front of their own camera roll, second-guessing every shot, slowly talking themselves out of the whole thing.
We built AI Photo Picker. Instead of scrolling their entire gallery, the user takes a quick selfie. The system scans their photos and surfaces the ones where their face matches, doing the shortlisting so they don't have to agonize over it. Adoption beat expectations. Photos uploaded per user went up, and so did verification rates (2 in 1)
Once adoption was strong, we put it behind a paywall. Adoption dropped, expected. But a real chunk of users kept paying anyway. People don't pay to avoid work. They pay to avoid discomfort.
Together, AI Prompt Writer and AI Photo Picker contributed 5-10% of daily revenue.
Lesson: People weren't lazy about their profiles, they were stuck. One was stuck at a blank field, the other at a full camera roll. AI worked here because it removed the first step, not the whole effort.
Meme swiping dropping off
The more memes a user swipes, the better the algorithm understands them. Better understanding means better matches, and better matches mean retention. So meme swiping wasn't just a feature, it was the engine behind everything.
First match reaction rate improved by ~20-25%. A small progress widget on the meme screen told users how close they were to their first profile suggestion. It made the wait feel purposeful instead of random, and users who understood that swiping memes leads to matches were far more likely to stick around and react when one showed up.
Schmooze personality: Back when Schmooze operated in the US, we'd shipped Schmooze Wrap, a personality summary built from meme action behaviour. It got real traction and shares outside the app. That validated the idea that people genuinely want to see a reflection of themselves through the memes they found funny. We took that insight and built something more continuous for the India product.
We gamified the swipe itself. Every 80 memes, you unlock a fresh Schmooze Insight - a short, sharp, AI-written personality card based purely on what made you laugh. (The algorithm leans on your considered swipes over your rage swipes, so the read is honest rather than chaotic, with a feedback loop to keep it accurate.)
Swipe rate went up, which was the goal. But 60% of users who got an insight added it to their profile, and the share funnel into Instagram and other socials was strong. Schmooze branding on every shared card meant free marketing, paid for in self-expression. We started calling these joy metrics, things that don't convert directly but make people feel seen. Stickiest work we ever shipped.
When gamification backfired
The hypothesis. We built a Behaviour Score: a single visible number summarizing how a user behaved across three areas - meme swiping, profile quality, and trust & safety. The theory was elegant in that way that doomed theories often are: make behaviour visible, and people will self-correct.
What actually happened. It moved exactly one of the three needles - meme swiping hygiene. Profile quality and trust & safety didn't budge. Users either didn't get what the number meant or didn't care enough to chase it. Turns out a vague score is a weak motivator when you can't see what it does for you.
The one piece that survived. When someone was rage swipe, we'd interrupt with a modal and surface Shortcut, the feature built for people who just want matches now. It worked, so it stayed. The visible score and everything else got retired. Three problems needed three real solutions, not one elegant dashboard.
Lesson: Not every problem wants to be gamified. A score that doesn't clearly pay off just adds noise to the screen and weight to the user's head. Three problems needed three real solutions, not one elegant dashboard.
2023 user research before india launch
