I’ve downloaded and deleted Hinge every few months since December 2019, and decided to run the data on my most recent download to see how things were going. I did not go on any dates with anyone new due to the pandemic, but I did chat with a few. Show What is Online Dating?Online dating is algorithmic matchmaking. Most apps ask you a series of questions or require you to list preferences, the answers of which are assessed by an algorithm and used to pair you to potential partners. It’s really a gamification of connection with others. There are a host of issues that can accompany use (such as safety, objectification, superficiality, etc.) but there are also benefits. The apps also assume that ‘love’ is quantifiable, to an extent. Love has patterns, and these algorithms take advantage of those patterns to recommend compatible partners across the network. And it’s a BUSINESS. Revenue was almost $1B in the U.S. in 2019, and is expected to be $1.1B in 2024. The number of users is expected to grow by 5M, up to 35.4M, over the same timeframe. Match Group, the online dating conglomerate, owns Hinge, Tinder, Match.com, OkCupid, PlentyofFish, and many more. They recently separated from IAC, the details of which are outside the scope of this article and doesn’t impact the apps noticeably. The apps seem to be doing well. Most of them rely on a freemium model, in which the core features of the app are free, but premium features are offered on either a subscription or a one-time purchase basis. Tinder is definitely the biggest focus of Match Group, with a 123% 5Y Revenue CAGR, but the company has also invested substantially in Hinge. The pandemic has driven a lot of users to the apps, as the more traditional way of meeting someone (the bars, the gyms, etc.) are closed down. People are also paying for more match opportunities, as shown by the growth in Average Revenue per User to $0.60. Hinge has grown its user base 10x over the past three years, with a +60% increase in ARPU year-over-year, showing that users are more willing to pay for matches. What is Hinge?Specifically, the company describes Hinge as
From a user perspective, Hinge is kind of like Tinder, but less aggressive. It’s the “app that is designed to be deleted” and you have to like someone back before they can message you. You answer 3 questions of your choice that others see, and upload 6 pictures of yourself, like above. You can get matches in two ways:
2. Or you can initiate the conversation by ‘liking’ them in the same fashion: You can also set ‘dealbreakers’. For example, if you are looking for a person who might follow a certain religion or doesn’t drink, you can set it as such. The Gale-Shapley AlgorithmHinge uses the Gale-Shapley algorithm that pairs people “who are likely to mutually like one another”. It measures this based off your engagement and who engages with you, as well as matches you to people with similar preferences. For a brief overview (skip this part if you don’t want the juicy algo details): The dating market is two-sided: one person seeks out another, with the platform serving to enable interaction. It broadly relies on network effects: the larger the pool the app the pulls from, the higher probability of finding a person that meets preferences. This gets into the ‘Stable Marriage Problem’, which searches for a stable matching between two entities, given the preferences of said entities. More specifically:
(Source: Wiki) The Gale-Shapley algorithm solves this through a series of iterations in which Element A proposes to their highest ranked Element B. Element B responds yes or no — if no, Element A then goes to propose to their next most-preferred Element B until everyone is engaged. The matching is considered stable when there is no match (A, B) that prefer each other over their current partners. For a more in-depth example, let’s say that there are 4 bugs and 4 trees. We need to match the bug to a tree, through stable pairings. Stable doesn’t mean perfect — not everyone is going to be completely satisfied with their pair, but they wouldn’t prefer anyone else who is available over the partner that they currently have (Pareto-optimal). The Trees and the Bugs Optimality:So we have four bugs: a bumblebee, a ladybug, a caterpillar, and a butterfly. We also have four trees: a pinetree, a cactus, a tulip, and an oak tree. The Preferences of the Trees We can build out preferences in matrices to begin the matchmaking. All the trees prefer the bumblebee the most (of course, this makes perfect sense, as bumblebees support 85% of all plants and pollinate 30% of our nutrition). The Preference of the Bugs The bumblebee prefers the pinetree, the ladybug prefers the oak tree, caterpillar likes the cactus the most, and the butterfly likes the tulip. So the preference web look like something like this: The matching process is as follows: all the trees go to the bug that they prefer — and the bug accepts whoever they prefer most out of who comes over.
The cactus knows that it can never get the bumblebee — the bumblebee is completely enamored with the pinetree, so the cactus is happy with the butterfly. The butterfly would be happier with the tulip, but because the tulip is with the caterpillar (which it prefers over the butterfly) the matching is stable. Thus, we have stable matches. Basically, everyone prefers each other. There is a chance that a bug prefers some other tree more — but they can only go to that tree if they are available (which they aren’t). It’s not perfect — but it’s optimal. S = {(Pinetree, Bumblebee), (Cactus, Butterfly), (Tulip, Caterpillar), (Oak Tree, Ladybug)} T = {(Pinetree, Bumblebee), (Cactus, Caterpillar), (Tulip, Butterfly), (Oak Tree, Ladybug)}. This is the pseudocode for our tree-bug scenario: Hitsch et al wrote a comprehensive article applying the above algorithm to the online dating world (in 2010) and their ultimate finding was:
Source: Matching and Sorting in Online Dating This stable matching pairing is actually quite effective. Hinge applies this through the ‘stable roommate problem’ grouping people into a common pool, without the gender division. The same effect applies — organizing people based off a set of preferences (with knowledge that your person will probably never be ‘perfect’) does actually work well. My Date Data So, I wanted to see what my data looked like. I knew I wasn’t going to be able to backtest any algorithm due to information asymmetry, but I wanted to see what the iterations of interaction did look like. The DataThe data came in a JSON file which I imported into R using JSONlite. The data was super messy (lots of nested lists), so I ended up converting into into a CSV and doing output through Excel. It contained:
About MeMy profile is pretty dorky. Most of my pictures are me either wearing a pi shirt or doing yoga, but I do have a serious selfie in there. My ‘thought prompts’ are
Most people tend to comment about the breakfast food question, but most of the time, interactions are led by someone liking a picture. Notes
This chart was compiled using SankeyMATIC. It details who ‘liked’ me, who I ‘liked’, who messaged first, and if a conversation was had. As you can see, there was a large pool initially, which was whittled down through interaction (or lack thereof). Key Terms
Key Takeaways
ConclusionThere is a lot of analysis on the nuances of dating apps, including the weird science of attractiveness, in which
Search costs are still relatively high on most apps, due to information asymmetry and the potential gaps in the matching process. It does increase sample size of available partners, but can also work to depersonalize the entire exchange (primarily through the gamification). However, online dating has become the most popular way that people meet their partners, as shown above. People who have had a positive experience with the apps have cited the increased opportunity to meet people as the top upside, but dishonesty and misrepresentation as the biggest downside. Pictures and openness about intent seems to be the most important to users. Overall, it seems that if someone was actively pursuing a relationship (which I am not) it seems the best thing to do is to optimize the algorithm: make a fun profile, be responsive, and engage actively. But please don’t be creepy. Long term happiness is something that you yourself create- not an app. Also, this question from HBS is worth considering:
If the app matches everyone perfectly, does that mean its working? Or does that mean its losing users? Is Hinge algorithm based on looks?Hinge uses the Gale-Shapley algorithm that pairs people “who are likely to mutually like one another”. It measures this based off your engagement and who engages with you, as well as matches you to people with similar preferences.
How does Hinge choose who to show you?It's not just based on who you are likely to like, it's also based on who is likely to like you back. It's all about pairing people who are likely to mutually like one another. Over time, we see who do you like, who do you send comments to, who are you having conversations with.
How do I know if I'm a standout on Hinge?How Do I Know If I'm A Standout On Hinge? If you have to ask, you are likely not. With that said, if you notice getting an unusually high number of roses and fewer likes on a day, it's possible you are on standouts that day but not guaranteed. Ask one of your matches.
What demographic is Hinge for?Hinge's core user base is millennials, with around 49% of users between 18 and 29, with most of the remaining users between 30 and 49 years old. Only around 2% of Hinge users are over 50.
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