Can one apply the life hacking ethos of analyzing and optimizing to the romantic sphere? Some do! If you read Amy Webb, Chris McKinlay, and Val Aurora you will see a focus on two things in particular: matching and selection.
Webb and McKinlay hacked dating systems to figure out how to improve their profiles. Neither broke into dating services to purloin data, but they did use fake accounts. Webb realized that pasting her resume into JDate was not working. So, she created fake profiles of ten men she’d like to date and used these puppets to learn the successful strategies of her competitors. She found that successful women’s profiles were short, nonspecific, used optimistic language, and that the photos were well done and showed a bit of skin (e.g., shoulders). When she applied this to her own profile, she became the most popular person on the site.
On OKCupid, a Q&A based matching service, McKinley also used fake profiles, this time collect answers to common questions from thousands of women. He then grouped 20,000 women into seven clusters based on their responses to the most popular questions. Example clusters included women who enjoyed their pets, those who had tattoos, and those who were religious. He targeted two of the clusters of greatest interest with custom profiles. MicKinley selected the 500 most popular questions with both groups, answered honestly, and used an algorithm to optimize the ranking of candidates. He then wrote a script to visit the top-ranked women’s profiles; OkCupid automatically notifies users of such visits, and many women reciprocated and sent messages.
Of course, hacking a dating system to find hundreds of possibly well matched candidates introduces another problem: how to choose? Depending on how you look at it, this is where Poulsen’s selection strategy of “brute force” failed. He went on eighty-eight dates before finding someone he would begin a relationship with! Conversely, Webb and Aurora showed that selection, too, is amenable to analysis and optimization.
Webb devised a two-tier system of traits, weighted by importance, with a high threshold for whom she would date. In fact, she had this scoring system before her data mining and analysis, but she could find anyone who met her 700 point minimum. But with a successful profile in hand, and many potential candidates, she was eventually contacted by an 850, and he would eventually become her husband. Like Webb, Val Aurora recommended matching hacks, like getting professional pictures taken. But she seems to have exceeded Webb in the sophistication of her selection. Aurora also developed a spreadsheet, which she publicly shared for other people to use.
In my blog post about how to have more fun online dating, I mentioned the spreadsheet I made to help with dating. Yes, a spreadsheet. For dating. Because when you’re feeling romantic, you just want to fire up Excel and input some data! Nothing like an evening of writing formulas to get you in the mood for love!
Both McKinlay and Webb went on to write books about their experiences and strategies, both of which end happily. Aurora, too, is now in a relationship.
However, I am struck that these approaches of optimizing matching and selection presume a good fit. “The one” is out there, they just need to be found. An alternative theory is that people should grow towards one another in a relationship. This is seen in stories of arranged marriages, as exemplified by Aziz Ansari’s own parents in his book Modern Romance. Aurora alludes to this in her reflection of why she created the spreadsheet in the first place.
My original intention for making this tool was to make me more aware of and responsive to my “dealbreakers” – things that meant a relationship wasn’t possible. But while making and using this tool, I discovered that my own ideas about what was a “dealbreaker” were frequently wrong. I am now in a happy relationship with someone who had six of what I labeled “dealbreakers” when we met. And if he hadn’t been interested in working those issues out with me, we would not be dating today. But he was, and working together we managed to resolve all six of them to our mutual satisfaction. Talking to my friends, I found that this was a pretty common experience.
This is true for me. I met my partner of fifteen years during a rather liberal period. At the time, I decided to enjoy meeting people instead of trying to find a relationship. Hence, her (occasional) smoking wasn’t the deal breaker it had been in the past—a habit she, fortunately, gave up.
The importance of working on issues, the practice of building and maintaining a relationship, is this also amenable to hacking? The only example of this I’ve found is David Finch’s Journal of Best Practices: A Memoir of Marriage, Asperger’s Syndrome, and One Man’s Quest to be a Better Husband. This isn’t so much about the mutual optimization of a relationship, but hacking his approach to it. And while his best practices aren’t “big data” quantitative, they are analytical, including the “final best practice”: “Don’t make everything a best practice.” For many life hackers, the hacking mindset seems to give way at some point. Is this the line where relationships becomes more an art than a science? Is hacking love less appropriate with an N of 1?
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