The Hinge Labs Method
Study your successful and unsuccessful users, extract the patterns, then coach everyone up
- Difficulty
- Advanced
- Time to result
- ~months to results
- Steps
- 4
- Confidence
- 88%
A dating app isn't just software — the user's experience is made of other people's behaviour, which no amount of UX can fully control. Hinge Labs exists to close that gap: it runs deep-dive research studies on daters who succeed and daters who don't, identifies the behavioural patterns that separate them, and feeds those findings back into product changes and user guides that level everyone up. It reframes the company's job from building a marketplace to coaching its participants, and it is the research engine behind Hinge's current internal effort — 'flatten the power curve' — to lift the users who aren't getting matches rather than serving the ones who already win. Note the structural symmetry with Hinge's hiring method: same succeed/fail attribute analysis, applied to users instead of employees.
Origin
McLeod's premise is that a dating app is relatively unique — the product is the people and their behaviour as much as the technology — so Hinge built a dedicated research function to study what makes daters successful and use it as fuel for product and guidance.
Core principles
- 01In a marketplace product, other users' behaviour is part of your product surface.
- 02UX can only guide behaviour so far; the rest requires coaching and teaching.
- 03Study both cohorts — successful and unsuccessful — not just the winners.
- 04Research is fuel for two outputs: product changes and user guides.
- 05Aim to lift the bottom of the distribution, not to better serve the top.
How to run it
- 1
Stand up a dedicated research function
Create a team whose sole job is studying user effectiveness rather than shipping features.
Watch out If research reports into a growth team, it will drift toward engagement questions.
- 2
Study both the successful and the unsuccessful cohort
Run deep-dive studies on daters who succeed and daters who don't, and identify the behavioural patterns that distinguish them.
Pro tip Resist packaging users into neat discrete categories — McLeod found it never works because people are complex and every story is unique.
Watch out Persona-style bucketing produces tidy slides and unusable findings.
- 3
Convert findings into product and coaching
Feed the patterns into both product design and direct user guidance — better photo selection, thoughtful prompt answers, more deliberate likes.
- 4
Target the bottom of the curve
Direct the interventions at the users who aren't getting to success, and constrain the behaviours that concentrate attention on a small group.
Pro tip Hinge limits how many likes and matches users can accumulate so the rest of the user base isn't over-engaged.
Watch out Left alone, marketplaces concentrate attention: Hinge's 2021 data showed the top 1% of men receiving over 16% of all likes.
In the wild
Hinge's internal effort to help struggling users zero in on people who will like them back, put their best foot forward, and avoid self-sabotage like bad photos and one-word prompt answers.
→ McLeod's stated belief that the concentration of attention can be corrected on dating apps and, through them, more broadly in society.
Hinge Labs findings drive guidance to be deliberate with likes, because indiscriminate liking gives the algorithm no signal about a user's actual taste.
→ Better-calibrated matching for users who follow the guidance.
Common mistakes
Bucketing users into fixed categories
Every attempt to sort daters into discrete types failed at Hinge because people are complex and each story is unique; general principles travel, categories don't.
Treating the product as pure technology
However good the product design gets, the user's experience depends on other people's behaviour — ignoring that leaves the biggest lever untouched.
Is it for you?
Best for
Marketplace and network products where outcomes depend on participant behaviour as much as on features.
Not ideal for
Single-player tools where the user's outcome doesn't depend on how other users behave.
From the transcript
“study daters who are successful, study daters who are not successful, figure out what are the patterns”
“We also have to like kind of coach people and guide people and teach people how to become better daters.”
“we're calling it flatten the power curve”
“the top 1% of men on the app receive more than 16% of all of the likes”
From the episode
Hinge Dating App CEO: Everyone Said Hinge Was A TERRIBLE idea! The 7 Love & Dating Secrets From Hinge’s Founder! AI Will Change Love & Dating!
Justin McLeod