Everybody needs to touch grass! And by touch grass I mean: get back out into the bars and hit on each other! OMG this algorithm stuff ::eyes bleeding emoji::
Bravo Lana 👏 You should consider rebranding your blog to "Dating Data Scientist" 😉 Wonder how long it will take for everyone on the apps to have AI Agents matching with other AI Agents...
I’ve relegated myself to asking out women when they’re working again, which feels creepy but has been a winning strategy for me in the past (I had sex with three women who worked at the same convenience store, they did not want a relationship ☹️)
I think it's really sweet and heartening to hear that you are looking for a relationship and not just sex. I feel like this is more common than what social media would make you believe about men.
interesting. I had wondered what it was like to live in Vegas. among tourists there is flirting unsurprisingly. Vegas also has a feel of a bit of gang culture among the locals, and the Nevadan vistas are like an alien landscape
Feels like the more attractive someone is, the more matches and dates they'll go on, but they won't actually meet their partner any faster than someone who isn't so attractive.
As a guy who gets... no where near that many likes on Hinge, that's at least some comfort.
If that is you in your Substack pic, I would not say that there is a hugely significant difference in attractiveness between the man being mentioned and yourself. If you are getting a lot less likes than infinite pussy glitch man, I would chalk it up to demographics and location.
Yeah the height would do it, unfortunately. Also, I would call it slight title inflation - it’s more that he was quite early at a startup, I think his LinkedIn says “founding team” or something.
Just the way it is. I've had a couple matches get angry at me because I look taller in pictures and they didn't see my height until after they matched (I always have it displayed it in the bio).
Lana, I loved this analysis, and your approach to this subject.
I found the data-based lens not only rigorous and refreshing, but also the visuals make this take more entertaining to wade through.
After reading it, I have some feedback for you, in the form of shout outs, questions/critiques, and reflections/suggestions:
Shout outs:
1. The diagrams and charts are soo cool. I forgot much of what I learned in my college stats class, so this was a great forcing function for me to refresh my memory.
2. The sankey diagram was new to me, but I liked it. Is it supposed to be seen as a birds eye view from a waterfall? Anyways, I appreciate you took the time to make it. While someone might read this article and see it as being written by a data nerd (probably accurate :p), the clever thing about these visuals is it allows artists and aestheticians to sneak their way in under the guise of data science, and it really keeps the content from being overly dry.
3. Props to being transparent about your data, and to whoever agreed to share theirs.
Questions / Critiques:
1. You say this in the article: "the more successful outbound likes you send, the better the visibility with attractive potential partners."
But how were you able to make a conclusion about one's visibility, and what units would that be in? Does the data tell you how visible you are? I can see from the Sankey diagram that he got an outbound match rate of 21%, but I don’t see any data about how "visible" he was. Also, in order to make a conclusion about that correlation, wouldn’t you need several input/outputs of outbound match/visibility score to hypothesize the nature of that function?
2. You also say this: “He messages 85% of inbound matches but only 60% of outbound matches. This suggests that he’s more intentional about matching with his inbound likes.. Outbound likes are more speculative, so if they do match, he’s less likely to message.”
I actually don’t think it suggests this, given both instances of matching represent the same expression of mutual attraction, with just different chronological sequencing. A more plausible explanation might be that he was actively in-app when he got an inbound match (since you’d have to be swiping to get these) and so he’s more likely to follow up than getting a push notification that he got an outbound match while he was busy doing something else.
3. You later say this: "“In the article examining my own data, I found a .93 correlation between outbound likes and inbound matches.”"
I’m not quite following on the statement, and was hoping you could clarify? Correct me if I'm wrong, but these appear to be two independent processes, so why would you attempt to correlate them? An inbound match is a result of your liking a profile that is served up to you, totally independent of who you’ve sent an outbound like to, no? Isn’t attempting a correlation here akin to trying to correlate how much email you’ve responded to in lana@aol.com relative to how many emails you’ve sent out from lana@gmail.com ?
Reflections/Suggestions:
1. I'd be curious to know if you've noticed homogeneity in the profiles as a result of collective awareness of what needs to be done to maximize match rate? And whether or not you find it to be dystopian concerning the dating app end game where every profile is uniformly maximized in such a way, and therefore, uniqueness is removed, or at a minimum, severely constrained.
2. You conclude with, “These numbers helped give some color to the sometimes depressing experience as a woman on dating apps in NYC”
I would be curious for an analysis from you about your historical dating life outside of the apps, and perhaps aggregating data from interviewing others with "non app" romantic experiences. That is, have you ever met anyone in an offline capacity? I say this because from my own experience, my past forays into dating apps have been nothing other than extremely depressing, where as my offline-origin based dating experiences have been, on the whole, incredibly fulfilling and the longest lasting. While you can't get raw data CSVs from someone's off-line-origin romantic life, I think this could be a very interesting pursuit given how well it served me, and might just be the salve you and many other romantic hopefuls have been looking for.
1) for visibility, you can’t see inbound likes that you didn’t accept in the data. Only people you actually matched with. Since people respond to a like within a few days, we then use the inbound match as a proxy for visibility, since we don’t have access to that data directly by time frame.
2) this hypothesis is based more on my personal experience - especially when I’m feeling a bit uncertain and there aren’t a lot of active matches, I find myself thinking, well I’ll just like this profile and let’s see if they even match, maybe they’ll be cool to chat with. I think that while he is actively online while matching with inbound likes, I’m presuming that he would eventually message them maybe just more slowly if that were the issue.
3) the correlation is that I get more inbound matches when I send more outbound likes - implying hinge shows my profile to men who like it, and I then match with them. Their algorithm rewards people in equal measure with how many successful outbound likes they send. I found with Adam’s data that it was actually outbound matches and not merely likes, but of course, more likes is correlated with more matches.
1) I have noticed homogeneity but it’s anti-correlated, it’s people who mention tropes like being fluent in sarcasm and liking pineapple on pizza and how the most spontaneous thing they’ve done is move to a new city, etc.
2) Sure, I could also analyze offline vs online, I definitely have both. In the past year, i can’t say that the offline sourced men were more successful. I think that there is a much higher likelihood of a conversation in person leading to a date, though. I’ve actually found Hinge the most successful at producing the longest lasting prospects so far, oddly!
Oh man this is so wild, I wish you could do this for San Francisco, but about a woman's profile. And then compare and contrast. Comparing NYC dating culture and SF dating culture is almost as fascinating as comparing gay dating culture and lesbian dating culture (almost).
Reason being is, opposite of NYC, in San Francisco women hold the power seat, due to insane ratio gaps (after all, SF is built on a single, homogenous, very male dominated industry-- tech).
Also, the average woman in San Francisco has a loooot more wealth than the average NYC woman (being product managers, investors, engineers, etc) , thereby forcing men to ante up far more than security and experiences.
So all of this creates a complete mirror to NYC dating culture!
Anyway. Thanks for the article! It was so cool to see something very data driven rather than a "sounds-good but ultimately baseless" opinion piece.
Oh! I see. So men are both pursued (female experience) and hold the pursestrings (male experience). And women in NYC act as pursuers (male experience) but don't hold the pursestrings.
Love a good dating breakdown! Interesting point about comments though. Some thoughts:
As someone whose dating app experience is closer to the median man, I found an imperceptible difference when sending comments. It was also becoming time consuming to think of comments. Most profiles don't give much to base a comment off, and there's also the pressure of coming up with the perfect comment otherwise you'd just be lost among the sea of men.
There's also the numbers game aspect. As an average guy, its kinda better to cast a wider net and then filter from there otherwise you'd end up with way fewer matches and opportunities. So if you're going to be swiping for 30 mins in a day, better to swipe on 30 people you find attractive rather than think of good comments for 5 people.
The most optimal and time efficient strategy then became to just send likes. The reasoning then being, if they like your profile enough, they'll still match back and wait for you to start a convo/message first. If they're so on the fence that you need a comment to squeak by, its probably not going to lead to anything.
But if I go back on the apps (god forbid), maybe I'll try this again 🤔
I actually have the perfect solution for you: use a canned comment. One I saw recently that should work would be “you look like the kind of girl that my mom would approve of” or something like that. On bumble, I used to send the same canned opener to every single guy: You’re so cute it makes me wanna knit you a sweater. I would also be happy to examine your data and tell you statistically if your intuition is true. It’s possible that if your outbound match rate is 3%, then a 60% increase wouldn’t feel very different.
Thanks for writing up. Just curious, what was your data analysis methodology. Did you just manually go through and categorize all his matches? Or did you have a more automated method?
I used ChatGPT for analysis and graphs. Inbound and outbound were designated by the presence of a “like” timestamp - inbound matches don’t have a like timestamp attached. Conversation from Adam’s side is visible but woman’s messages are not.
I see. How did you export the data for ChatGPT? Just wondering if you had to do manual work to structure the data in any way since Hinge and most of, if not all, the dating apps don't let you export your data.
I once reverse-engineered Hinge's non-public REST API by running the app on an Android emulator on my computer. Had to MITM and decompile the app and everything. It was cool but a lot of work. But in the end you can create a client library that anyone can use to essentially export their data out in a structured way.
Maybe ChatGPT can just write the whole client library now by itself. 😅
Anyone can request their data. It’s structured as a JSON file. Tinder also lets you export your data. Bumble - not as much, it’s more simplified. the structure and level of granularity just varies from those three.
I definitely avoid commenting to avoid drama. That said, there are other reasons for comment, don’t take every comment as interest, I like ideas and will talk about them
This sounds horrifying and dehumanizing. There has to be a better way.
Our society is quite dehumanizing. We are all commodities to be sold.
good read! i feel more inclined to check my own data for that app
Appreciate the data perspective, analysis, and humor! Totally agree that one should be authentic in the pursuit of companionship (of any kind imo).
Thanks for reading!
My pleasure keep writing!
“I think you’re pleasant looking enough, but I’m really with you because I think you’re an amazing human being”?
This is what i love
the kind of physical attraction you talking about have diminishing returns
the first bite is the best
we are wired for novelty
i think after year 1 or 2 this really wears off even if you’re a 10/10 even the attraction wears off
only the human spirit has potential to express itself in novel forms every few weeks even
Everybody needs to touch grass! And by touch grass I mean: get back out into the bars and hit on each other! OMG this algorithm stuff ::eyes bleeding emoji::
Solid post tho!
"I’m really with you because I think you’re an amazing human being”
Me in a nutshell
Bravo Lana 👏 You should consider rebranding your blog to "Dating Data Scientist" 😉 Wonder how long it will take for everyone on the apps to have AI Agents matching with other AI Agents...
I haven’t read the article yet but I got more phone numbers in three days in NYC than I have in like eight months in Vegas it’s insane.
People my age everywhere except NYC don’t go out. Fuck not a singly twenty something from work left their house on New Year’s Eve. Insane
What age is that?
In 25, I actually realize one went out with her boyfriend to the Anyma concert at the sphere. Everyone else between 21 and 28 stayed in
Oh dear, that sounds tough. I guess that only leaves online🙃
And online here is… so bad. It’s so so bad.
I’ve relegated myself to asking out women when they’re working again, which feels creepy but has been a winning strategy for me in the past (I had sex with three women who worked at the same convenience store, they did not want a relationship ☹️)
I think it's really sweet and heartening to hear that you are looking for a relationship and not just sex. I feel like this is more common than what social media would make you believe about men.
interesting. I had wondered what it was like to live in Vegas. among tourists there is flirting unsurprisingly. Vegas also has a feel of a bit of gang culture among the locals, and the Nevadan vistas are like an alien landscape
Feels like the more attractive someone is, the more matches and dates they'll go on, but they won't actually meet their partner any faster than someone who isn't so attractive.
As a guy who gets... no where near that many likes on Hinge, that's at least some comfort.
If that is you in your Substack pic, I would not say that there is a hugely significant difference in attractiveness between the man being mentioned and yourself. If you are getting a lot less likes than infinite pussy glitch man, I would chalk it up to demographics and location.
Thank you! I'm in a major city though so I suspect it's more that my job title isn't as impressive as a startup founder and I'm only 5'5".
Yeah the height would do it, unfortunately. Also, I would call it slight title inflation - it’s more that he was quite early at a startup, I think his LinkedIn says “founding team” or something.
Just the way it is. I've had a couple matches get angry at me because I look taller in pictures and they didn't see my height until after they matched (I always have it displayed it in the bio).
C'est la vie.
Mad because they couldn’t be bothered to read is wild 🤮
Yup lol
"Wait wtf are you really only 5'5?!?!?!"
🙄
bro take the heightpill and stop wasting your time talking to PUA foid.
Lana, I loved this analysis, and your approach to this subject.
I found the data-based lens not only rigorous and refreshing, but also the visuals make this take more entertaining to wade through.
After reading it, I have some feedback for you, in the form of shout outs, questions/critiques, and reflections/suggestions:
Shout outs:
1. The diagrams and charts are soo cool. I forgot much of what I learned in my college stats class, so this was a great forcing function for me to refresh my memory.
2. The sankey diagram was new to me, but I liked it. Is it supposed to be seen as a birds eye view from a waterfall? Anyways, I appreciate you took the time to make it. While someone might read this article and see it as being written by a data nerd (probably accurate :p), the clever thing about these visuals is it allows artists and aestheticians to sneak their way in under the guise of data science, and it really keeps the content from being overly dry.
3. Props to being transparent about your data, and to whoever agreed to share theirs.
Questions / Critiques:
1. You say this in the article: "the more successful outbound likes you send, the better the visibility with attractive potential partners."
But how were you able to make a conclusion about one's visibility, and what units would that be in? Does the data tell you how visible you are? I can see from the Sankey diagram that he got an outbound match rate of 21%, but I don’t see any data about how "visible" he was. Also, in order to make a conclusion about that correlation, wouldn’t you need several input/outputs of outbound match/visibility score to hypothesize the nature of that function?
2. You also say this: “He messages 85% of inbound matches but only 60% of outbound matches. This suggests that he’s more intentional about matching with his inbound likes.. Outbound likes are more speculative, so if they do match, he’s less likely to message.”
I actually don’t think it suggests this, given both instances of matching represent the same expression of mutual attraction, with just different chronological sequencing. A more plausible explanation might be that he was actively in-app when he got an inbound match (since you’d have to be swiping to get these) and so he’s more likely to follow up than getting a push notification that he got an outbound match while he was busy doing something else.
3. You later say this: "“In the article examining my own data, I found a .93 correlation between outbound likes and inbound matches.”"
I’m not quite following on the statement, and was hoping you could clarify? Correct me if I'm wrong, but these appear to be two independent processes, so why would you attempt to correlate them? An inbound match is a result of your liking a profile that is served up to you, totally independent of who you’ve sent an outbound like to, no? Isn’t attempting a correlation here akin to trying to correlate how much email you’ve responded to in lana@aol.com relative to how many emails you’ve sent out from lana@gmail.com ?
Reflections/Suggestions:
1. I'd be curious to know if you've noticed homogeneity in the profiles as a result of collective awareness of what needs to be done to maximize match rate? And whether or not you find it to be dystopian concerning the dating app end game where every profile is uniformly maximized in such a way, and therefore, uniqueness is removed, or at a minimum, severely constrained.
2. You conclude with, “These numbers helped give some color to the sometimes depressing experience as a woman on dating apps in NYC”
I would be curious for an analysis from you about your historical dating life outside of the apps, and perhaps aggregating data from interviewing others with "non app" romantic experiences. That is, have you ever met anyone in an offline capacity? I say this because from my own experience, my past forays into dating apps have been nothing other than extremely depressing, where as my offline-origin based dating experiences have been, on the whole, incredibly fulfilling and the longest lasting. While you can't get raw data CSVs from someone's off-line-origin romantic life, I think this could be a very interesting pursuit given how well it served me, and might just be the salve you and many other romantic hopefuls have been looking for.
Also, hello from Brooklyn!
- Yanik
1) for visibility, you can’t see inbound likes that you didn’t accept in the data. Only people you actually matched with. Since people respond to a like within a few days, we then use the inbound match as a proxy for visibility, since we don’t have access to that data directly by time frame.
2) this hypothesis is based more on my personal experience - especially when I’m feeling a bit uncertain and there aren’t a lot of active matches, I find myself thinking, well I’ll just like this profile and let’s see if they even match, maybe they’ll be cool to chat with. I think that while he is actively online while matching with inbound likes, I’m presuming that he would eventually message them maybe just more slowly if that were the issue.
3) the correlation is that I get more inbound matches when I send more outbound likes - implying hinge shows my profile to men who like it, and I then match with them. Their algorithm rewards people in equal measure with how many successful outbound likes they send. I found with Adam’s data that it was actually outbound matches and not merely likes, but of course, more likes is correlated with more matches.
1) I have noticed homogeneity but it’s anti-correlated, it’s people who mention tropes like being fluent in sarcasm and liking pineapple on pizza and how the most spontaneous thing they’ve done is move to a new city, etc.
2) Sure, I could also analyze offline vs online, I definitely have both. In the past year, i can’t say that the offline sourced men were more successful. I think that there is a much higher likelihood of a conversation in person leading to a date, though. I’ve actually found Hinge the most successful at producing the longest lasting prospects so far, oddly!
this is insane lol
Oh man this is so wild, I wish you could do this for San Francisco, but about a woman's profile. And then compare and contrast. Comparing NYC dating culture and SF dating culture is almost as fascinating as comparing gay dating culture and lesbian dating culture (almost).
Reason being is, opposite of NYC, in San Francisco women hold the power seat, due to insane ratio gaps (after all, SF is built on a single, homogenous, very male dominated industry-- tech).
Also, the average woman in San Francisco has a loooot more wealth than the average NYC woman (being product managers, investors, engineers, etc) , thereby forcing men to ante up far more than security and experiences.
So all of this creates a complete mirror to NYC dating culture!
Anyway. Thanks for the article! It was so cool to see something very data driven rather than a "sounds-good but ultimately baseless" opinion piece.
Also I suspect that it’s more that men in NYC have a female experience, not that women in NYC have a male experience, if that makes any sense.
Oh! I see. So men are both pursued (female experience) and hold the pursestrings (male experience). And women in NYC act as pursuers (male experience) but don't hold the pursestrings.
Thanks for the idea!!
So much data and thought wasted, don’t use apps to find someone.
i feel sick
… with envy?
no
Love a good dating breakdown! Interesting point about comments though. Some thoughts:
As someone whose dating app experience is closer to the median man, I found an imperceptible difference when sending comments. It was also becoming time consuming to think of comments. Most profiles don't give much to base a comment off, and there's also the pressure of coming up with the perfect comment otherwise you'd just be lost among the sea of men.
There's also the numbers game aspect. As an average guy, its kinda better to cast a wider net and then filter from there otherwise you'd end up with way fewer matches and opportunities. So if you're going to be swiping for 30 mins in a day, better to swipe on 30 people you find attractive rather than think of good comments for 5 people.
The most optimal and time efficient strategy then became to just send likes. The reasoning then being, if they like your profile enough, they'll still match back and wait for you to start a convo/message first. If they're so on the fence that you need a comment to squeak by, its probably not going to lead to anything.
But if I go back on the apps (god forbid), maybe I'll try this again 🤔
I actually have the perfect solution for you: use a canned comment. One I saw recently that should work would be “you look like the kind of girl that my mom would approve of” or something like that. On bumble, I used to send the same canned opener to every single guy: You’re so cute it makes me wanna knit you a sweater. I would also be happy to examine your data and tell you statistically if your intuition is true. It’s possible that if your outbound match rate is 3%, then a 60% increase wouldn’t feel very different.
Thanks for writing up. Just curious, what was your data analysis methodology. Did you just manually go through and categorize all his matches? Or did you have a more automated method?
I used ChatGPT for analysis and graphs. Inbound and outbound were designated by the presence of a “like” timestamp - inbound matches don’t have a like timestamp attached. Conversation from Adam’s side is visible but woman’s messages are not.
I see. How did you export the data for ChatGPT? Just wondering if you had to do manual work to structure the data in any way since Hinge and most of, if not all, the dating apps don't let you export your data.
I once reverse-engineered Hinge's non-public REST API by running the app on an Android emulator on my computer. Had to MITM and decompile the app and everything. It was cool but a lot of work. But in the end you can create a client library that anyone can use to essentially export their data out in a structured way.
Maybe ChatGPT can just write the whole client library now by itself. 😅
Anyone can request their data. It’s structured as a JSON file. Tinder also lets you export your data. Bumble - not as much, it’s more simplified. the structure and level of granularity just varies from those three.
I definitely avoid commenting to avoid drama. That said, there are other reasons for comment, don’t take every comment as interest, I like ideas and will talk about them