Optimizing settings for visibility and quality matches.
- Too narrow: Missing good people just outside radius
- Too wide: Matches from impractical distances, dilutes pool
| Situation | Suggested Radius |
|---|---|
| Major city (London, NYC) | 15-25 miles / 25-40 km |
| Suburban area | 25-50 miles / 40-80 km |
| Rural area | 50+ miles / 80+ km |
| Open to relocating | Maximum |
- "How far would you actually travel for a first date?"
- "Would you date someone who lives [X] away long-term?"
- "Are you getting enough people in your current radius?"
- Setting too narrow based on arbitrary numbers
- Not considering who actually fits their criteria
- Mismatch between stated preference and who they're attracted to
- "What age range are you genuinely open to?"
- "Have your best past relationships/dates fit this range?"
- "Is this based on real preference or just what sounds right?"
- Men typically set wider ranges (often younger)
- Women in 40s competing with women in 30s for men in their age range
- Very narrow ranges dramatically shrink the pool
- Consider: does the person they're looking for actually date people their age?
The research context: Bruch & Newman (2018, Science Advances) analyzed ~200,000 users and found that most people pursue partners ~25% more desirable than themselves, desirability follows a power-law distribution, and women's desirability peaked earlier than men's in their dataset. This isn't about anyone's worth — it's about understanding the market you're operating in so settings don't accidentally filter out good matches.
- Height
- Children (has/wants)
- Religion
- Smoking
- Drinking
- Drugs
- Politics
- Ethnicity
- Education
Be careful with:
- Dealbreakers that filter on things that don't actually matter to them
- Multiple dealbreakers that compound to tiny pool
Questions to ask:
- "Is this actually a dealbreaker, or just a preference?"
- "Have you dated people outside this criterion happily?"
- "Is this filtering signal or actual incompatibility?"
Example: Setting "no smokers" when they'd actually date a social smoker filters people they might like.
Hinge shows certain info prominently. Some can be hidden.
Consider hiding if not relevant:
- Star sign (adds nothing for most, clutters profile)
- Height (if not a differentiator)
- Politics (if not crucial to filtering)
- Religion (if not practicing/important)
Keep visible if:
- It's a filtering mechanism they want active
- It's a positive differentiator
- It invites conversation
Every piece of info at top of profile competes for attention. If it's not adding value, it's taking space from things that do.
If they have premium:
- See who liked you: Can be strategic - prioritize people who already liked
- Roses: Use sparingly on standout profiles (limited resource = costly signal)
- Advanced preferences: More filtering options
Roses strategy:
- Don't spam roses
- Use on profiles where you'd actually want to stand out
- The scarcity is the point - it signals genuine interest
- Daily activity (10-15 mins) keeps profile visible
- Extended inactivity = algorithm deprioritizes
- Evidence tier: conventional wisdom. Hinge hasn't published specifics, but this is consistent with how recommendation systems work and is widely reported across dating coaching.
- Comments on likes significantly outperform bare likes — Hinge's own data says they're more likely to lead to conversations (Hinge press data, circa 2018-2019)
- Respond to matches promptly
- Extend conversations (not one-word answers)
- Evidence tier: platform data for the comments claim. The rest is conventional wisdom.
- Why comments work (research): signaling theory (Donath, 2007) — a comment is a costly signal requiring effort, indicating genuine interest. A bare like is a cheap signal that communicates nothing specific.
- Being too selective may limit who the algorithm shows you to
- Being not selective enough means matches don't convert
- Evidence tier: conventional wisdom. The "10-20% like rate sweet spot" is widely cited in dating coaching but has no published source. The principle (some selectivity signals quality) is plausible but unverified.
- Check daily (refreshes every 24 hours)
- Hinge claims these are significantly more likely to result in dates (they've cited 8x in press materials around the 2018 feature launch). Uses the Gale-Shapley matching algorithm
- Algorithm learns from your choices here
- Evidence tier: platform data — Hinge's own claim. The methodology behind the 8x figure has never been published.
Walk through before implementation:
- Current setting: ___
- Appropriate for their situation?
- Discussed tradeoffs?
- Current setting: ___
- Realistic for market?
- Genuine preference or arbitrary?
- Current dealbreakers: ___
- Each one actually a dealbreaker?
- Not over-filtering?
- What's showing at top: ___
- Anything to hide? (clutter reduction)
- Anything to add/feature?
- Has premium: Y/N
- Discussed roses strategy?
- Using "who liked you" feature?
When adjusting settings:
- Make changes incrementally - Don't change everything at once
- Give time to see effect - Algorithm needs time to recalibrate
- Track what changes - Note what settings were vs what they become
- Revisit after 2 weeks - Assess if changes helped or hurt