Computer with red hearts and flowers on table close up

At the start of the new year, my colleague Nick Quinn, our principal engineer here at Objectivity, examined signaling in applications that provide recommendations as a use case for graph databases in his blog, “Peacocking.” He used the peacock’s plumage as an analogy to how we use online dating sites to express mutual romantic interest.

I felt that since it’s the week before Valentine’s Day, it would be timely to revisit the topic of online dating as a treasure trove for big data, and specifically, relationship and graph analytics. This time, however, I’m approaching it from a woman’s perspective—because let’s face it, no matter how flashy male peacocks look, it’s their female counterparts who possess the most decision-making power in selecting a mate.

It’s no surprise that the online dating ecosystem is generating massive volumes of data. According to an analysis from datascience@berkeley, The Master of Information and Data Science (MIDS) at UC Berkeley, five of the largest sites (eHarmony, Match, Zoosk, Chemistry, and OkCupid) had between 15-30 million members each. Online dating apps are even more impressive, with Tinder leading the pack at an estimated 50 million users making 1 billion swipes and 12 million matches per day!

So if you’re scrambling to find someone to spend Feb. 14th with, you might be asking yourself: how can I use all this data to stand out?

The experts at have crunched the numbers and come across some interesting findings, which they revealed in their sixth-annual “Singles in America” survey:

  • The ideal dinner: Going for sushi increases your chance at a 2nd date by 170%
  • Make them laugh: Messaging “LOL” or “Haha” improves 2nd date odds by 255%
  • Don’t leave them hanging: Men wait an average of 11.25 days to hear back from someone before moving on. Women? Only 7 days.

As amusing as those results might be, it’s crucial to point out that online dating is not just about big data, but also—and more importantly—about the relationships between data points. It seems intuitive enough that to find a connection with someone, you need to determine precisely how you are connected to him or her.

OkCupid is especially unique, because it’s the only online dating platform that reveals the secret sauce behind its matching algorithm. By answering questions about your own preferences as well as your potential match’s, then weighting those answers in terms of importance to you, the site determines your compatibility with others and recommends users based on that overall percentage. Relationship and graph analytics are all about using these algorithms to achieve a particular result—in this case, to recommend a user’s strongest matches.

Whether you’re clustering by a set of attributes or navigating the shortest path between two people based on their social networks (birds of a feather flock together, after all, if you’ll excuse my peacock humor), you’re essentially traversing a gigantic social graph or network of relationships. To use another dating cliché, if there are plenty of fish in the sea, relationship analytics helps you cast the right net to catch that perfect fish for you.

What’s my real-world opinion on the efficacy of online dating recommendation engines? After researching several platforms, I have to hand it to OkCupid’s matching algorithm. It’s not a miracle worker, but it’s pretty accurate in determining whom I’m most compatible with. Case in point? I’m spending this Valentine’s Day with my 97% match who loves sushi, makes me laugh, and enjoys talking about technology even more than I do. If that’s not a relationship analytics success story, I don’t know what is.

But enough about me! If your organization would like to be another relationship analytics success story, contact us to learn more about how Objectivity’s latest platform ThingSpan can enable you to discover hidden connections in your data.

Note: While I support all forms of coupling and view them as equally important, I can only speak to the online dating phenomenon as a cisgender heterosexual woman. I’m curious to learn how members of the LGBT community navigate online dating and encourage people to share their experiences!



Alyssa Jarrett

Senior Marketing Manager

Alyssa Jarrett - Marketing Manager