Matthew
@matthew
last chance to sign up for a @coffeebot chat this week! Drop your telegram at the cast below to join 😃☕️ farcaster://casts/0x9661add1165bd8a6978f01f48262db62ab0de82529939325cf306c8562c7b6bf/0x9661add1165bd8a6978f01f48262db62ab0de82529939325cf306c8562c7b6bf
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Daniel Svonava
@svonava
Hey @matthew, want to take @coffeebot up a notch and match people smartly based on what they do on the app? Ain’t nobody got time for random intros! (most churn after 2-3 random matches, this is what killed lunchclub.com - their matches were bad + the avg user quality went down).
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Matthew
@matthew
what would you want to see beyond random matches? @ishika and i have talked about making it more personalized! re lunchclub, i think what killed them is that people don’t want to pay for things they think they can get for free— e.g. intros to new friends. Also, the pandemic. And raising as an “AI” company.
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Gregory F.
@gf
My friends are making thebreakfast.app and spent a lot of time thinking about matching. Initially they did it like so: placed people in buckets semi-manually based on profile review, then made a matrix of probabilities of matching between buckets (e.g. A-A: 40%, A-B: 20%, A-C: 10%, ...). Had pretty good results
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Graham Siener
@gsiener
$20/month. Great they are trying to establish a high price. Not sure I’d sign on for that though
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Daniel Svonava
@svonava
What data do they use? I think that these 3 are a must: 1. Let people express preference using some broad but community specific categories (which cat. do you fall in, which cat. you want to connect with) 2. Let people plug in their socials & use that info to tailor the matches 3. Behavioral data from using the app
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Matthew
@matthew
that is good context, thank you! maybe we can try something like it
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