Hinge and Machine Learning: The makings of a perfect match
Hinge, an innovative dating app, is using AI and machine learning techniques to improve its matchmaking algorithm
鈥淭here are plenty of fish in the sea鈥︹ To a modern dater, this old adage about finding love seems almost eerie in its prescience of the emergence of online dating. With the rapid rise of Match.com, Tinder, Bumble, and more, it is unsurprising that recent estimates suggest that the proportion of the U.S. adult population using dating apps or websites has grown from 3% in 2008 to over 15% today [1].
One such app, Hinge, launched in 2012. Its basic premise is to show a user some number of profiles for other suitable singles. If a Hinge user spots someone of interest while browsing, he or she can reply to a particular element of that person鈥檚 profile to start a conversation [2] 鈥 much in the same way a user on Facebook can 鈥渓ike鈥 and comment on another user鈥檚 newsfeed posts.
This model is not a massive departure from the formulas used by older competitors like OkCupid and Tinder. However, Hinge differentiates itself with the pitch that it is the best of all the platforms in creating online matches that translate to quality relationships offline. 鈥3 out of 4 first dates from Hinge lead to seconds dates,鈥 touts their website [3].
One way that Hinge purports to offer better matches is by deploying AI and machine learning techniques to continuously optimize its algorithms that show users the highest-potential profiles.
Hinge first tests the AI waters
Hinge鈥檚 first public foray into machine learning was its 鈥淢ost Compatible鈥 feature, launched 2017.
The Hinge CEO shared that this feature was inspired by the classic Gale-Shapley matching algorithm, also known as the stable marriage algorithm [4]. Gale-Shapley is most famously used for matching medical residents to hospitals by assessing which set of pairings would lead to 鈥榮tability鈥 鈥 i.e., which configuration would lead to no resident/hospital pair willingly switching from the optimal partners they are each assigned [5].
At Hinge, the 鈥楳ost Compatible鈥 model looks at a user鈥檚 past behavior on the platform to guess with which profiles he or she would be most likely to interact. Using this revealed preference data, the algorithm then determines in an iterative fashion which pairings of users would lead to the highest-quality 鈥榮table鈥 matches. In this way, machine learning is helping Hinge solve the complex problem of which profile to display most prominently when a user opens the app.
Hinge creates valuable teaching data using 鈥榃e Met鈥
In 2018, Hinge launched another feature called 鈥榃e Met,鈥 in which matched users are prompted to answer a brief private survey on whether the pair actually met up offline, and what the quality of the offline connection was.
This was a simple, but powerfully important, step for Hinge. In addition to allowing Hinge to better track its matchmaking success, it can also use this data as feedback to teach its matching algorithms what truly predicts successful matches offline over time. 鈥溾We Met鈥 is actually focused on quantifying real world dating successes in Hinge, not in-app engagement,鈥 writes an analyst from TechCrunch [6]. 鈥淟onger term, [this feature] could help to establish Hinge as place that鈥檚 for people who want relationships, not just serial dates or hookups.鈥
Recommendations and actions
In the context of increasing competitive intensity in the market, Hinge must continue to do three things to continue its successful momentum with AI:
- Increase 鈥榙epth鈥 of its dataset: Invest in advertising to continue to add users to the platform. More users means more options for singles, but also better data for the machine to learn from over time.
- Increase 鈥榳idth鈥 of its dataset: Capture more information about each user鈥檚 preferences and behaviors on a micro level, to improve specificity and reliability of matching.
- Increase its iteration cycles and feedback loops (e.g., through 鈥榃e Met鈥): Ensure algorithms are truly delivering the objective: quality offline relationships for users.
Outstanding questions as Hinge looks ahead
In the near term, is machine learning truly a sustainable competitive advantage for Hinge? It is not yet clear whether Hinge is the best-positioned dating app to win with AI-enhanced algorithms. In fact, other dating apps like Tinder boast much larger user bases, and therefore much more data for an algorithm to absorb.
In the long term, should Hinge be worried that it may stunt its own growth by improving its matching protocols and tools? In other words, if the implementation of machine learning increases the number of stable matches created and leads to happy couples leaving the platform, will Hinge lose the user growth that makes it so compelling to its investors?
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Sources
[1] Aaron Smith and Monica Anderson, 鈥5 facts about online dating,鈥 Pew Research Center [website], February 2016, http://www.pewresearch.org/fact-tank/2016/02/29/5-facts-about-online-dating/, accessed November 2018.
[2] Hinge鈥檚 FAQ section, , accessed November 2018.
[3] Dale Markowitz, 鈥淗inge鈥檚 CEO says a good dating app relies on vulnerability, not algorithms,鈥 The Washington Post, September 2017, , accessed November 2018.
[4] Diamond Siu, 鈥淗inge dating app will begin using machine learning to make better matches,鈥 Mashable [blog], July 13, 2018, , accessed November 2018.
[5] Muriel Niederle. 鈥淢atching鈥, February 11, 2007, The New Palgrave Dictionary of Economics, 2nd edition, Palgrave Macmillan, , accessed November 2018.
[6] Sarah Perez, 鈥淗inge is first dating app to actually measure real world success,鈥 TechCrunch [blog], October 2018, , accessed November 2018.
To address your first question, I think that Hinge should be worried about competitors adapting its technology. Dating companies benefit significantly from the network effect, so Tinder has a definitive competitive edge. There seem to be low barriers from Tinder using its platform and user base to replicate Hinge’s competitive edge.
In response to your second question, I do not think Hinge should be worried. They may experience a slight “seasonality” in their user base, but overall the trend should be consistent. I believe this to be true for 2 reasons. 1. As users find success, this success should be leveraged and publicized by Hinge, thus attracting more users to the platform. 2. As users find matches and leave the platform, younger generations will age and begin to use the platform. Hinge should also be able to capitalize on the increasing population size.
To address your second question, Hinge should not be worried. In a dating landscape littered with apps promising you a match, successful matches is the key metric that will differentiate Hinge from its competitors. While increasing successful matches may decrease its user base in the short-term, it will boost customer acquisition in the long-term. Also with greater liquidity of users in the long-term, it will further improve Hinge’s match rate and thus drive a powerful, self-fulfilling cycle.
I’m going to disagree with the two comments above me, with respect to your second question. This reminded me of “big pharma” and the catch 22 of curing an illness so effectively that users never have to use their drug again. It’s sad, but it makes solid business sense to have repeat customers – and Hinge might be driving themselves into a space where they are better than competitors at making matches, and losing out on repeat customers. I suppose you’d have to look at the adoption rate of the app vs the rate at which people leave upon finding a perfect match to effectively evaluate this question.
To your first question regarding whether or not this is a sustainable competitive advantage, I do think it is a valuable one. However, if it proves extremely successful, the competitive advantage may be at risk as competitors seek to imitate it. One idea for how Hinge could seek to make this advantage more sustainable is to leverage the AI technology to help match in other capacities beyond the romantic. Bumble has begun matching not only romantic partners but also business partners in a networking-style matching concept, as well as friends. Hinge may want to consider diversifying the services offered in a similar way to sustain their edge.