What If You Could Use Big Data to Improve Your Love Life?
Well…technically, many of us already are attempting to do so. Although dating sites have been around for years and years, their popularity is growing rapidly. We are beginning to utilize data in all areas of our personalized lives, from our health, to our dating practices. However, much of this data has been “unstructured” thus far. Now, there are some “players” in the world that are beginning to implement structured data into their “dataing” process (ha ha, very punny). One individual, Amy Webb shows us that if online dating isn’t working as well as you thought, all you have to do is structure your data, and create a personalized algorithm.
Unstructured Data/ Dating Sites:
On an individual and personal level we have mostly been utilizing unstructured data to help our love-lives. We use online dating sites to connect with possible partners. We sign up for online dating profiles (sure they use structured data, but we do not, directly, as individuals), which often times access our social media profiles to gather information regarding our interests, patterns of behavior etc. We use geolocation data through a GPS to match with others on Tinder. We even “Facebook stalk” people we just went on a first date with, or just “met” online.
OkCupid. eHarmony. Zoosk. OurTime. Chemistry. The number of dating sites are increasing, and with it, the number of users are increasing. According to the Pew Research Center, one-in-five adults between 25 and 34 years old has used online dating. Furthermore (and perhaps more importantly), the percentage of people that met their partner online is growing.
There is a lot of “trash talk” about these sites, yet they seem to be somewhat effective, or people wouldn’t use them. The infamous app, Tinder (the King and Queen of trash talk and dating app jokes), is notorious for being a “hook up” app. But is this perhaps just talk?
Browsing the internet on the topic, I found countless blogs, such as “My Tinder Story,” about how many people are finding partners using the app. Sure it may be a bit of a meat market, but isn’t the local bar as well?
Regardless of the hype and talk around dating sites, it is undeniable that their presence in our love lives is expanding. We simply come across more people, and more information about those people, in short amounts of time by using the tools available to us on the internet. It may have taken 4 dates to learn that: Billy has 2 sisters he doesn’t talk to, 4 dogs, and spends an average of 4.87 hours a day playing video games. Now, with the help of Big Data, I can find out all three of these facts in under 10 minutes.
Amy Webb Implements Structured Data:
Yet, it still isn’t helping all of us to find the perfect partner. This was the case for TEDTalks presenter, Amy Webb. Amy, the CEO of Webbmedia Group, likes the idea of utilizing dating services, and other forms of Big Data to help her find the one.
Like most of us, she doesn’t have much time to waste. And sprawling the bars for potential dates, and “man-hunting” in every coffee shop she walks into is certainly a waste of time. So, naturally online dating would work well for her.
In five minutes, she found out that Steve is an IT guy too, he is 6 feet tall (dreamy), muscular, and is a foodie that likes to cook.
Unfortunately, in reality, Steve was a very short 6 foot, and by foodie, he meant he liked to order ridiculously expensive food. And of course, he “forgot his wallet.”
Because Grandma told her to “play the field,” she had a lot of dates that more-or-less were similar to this. She went on a lot of crappy dates. So, Grandma and the rest of the family simply declared that Amy was being too picky.
Amy wanted to prove them wrong.
So she began using structured data.
In order to prove that her dates were crappy, she had to turn dating into a standardized, mathematical process that could be analyzed. She began recording information about her dates.
She found a few funny correlations:
Men that gave high fives were more likely to abuse the English Language.
The more shots a man ordered, the more likely he was to lie about his job.
Lawyers were 62% more likely to check their phones on a date than teachers.
The Algorithm Problem:
After officially determining that her dates were crappy, she lost some faith in online dating. She decided that the problem was their algorithm. So, naturally, she got started on creating her own.
She created a list of 72 attributes (it was at this point in the video, I started agreeing with Grandma that Amy was being too picky). She then categorized them into “top-tier” traits, “second-tier” traits etc. She weighted the attributes based on what tier they fell into.
She then told herself that under no circumstances would she email a man unless he had 700 points. She required 900 points for a date, and 1500 to consider him as a long-term partner.
Essentially, she created her own, personalized algorithm.
Trouble in Paradise:
Any man that earned a 1500 on Amy’s list was hard to get. Even if she did email him, with all the other women out there competing for the good ones, there was no way he would email back. Especially considering that at this point, Amy had basically copy and pasted her resume into her profile, hoping that this would get her some catches. She was about to find out that this certainly would not fly.
It was time to put together an action plan. She had to analyze her competition, and determine what worked, and what didn’t. What were these women doing to get attention? And what could she do to get a 700-pointer to email back?
Her Methods and Findings:
She created profiles of 10 men to assist her in this analyzation process. She associated large amounts of data with these profiles, and made them very, very real. She knew everything about them; their whole story.
Using these profiles, she was able to analyze her competition (other women on the dating sites). She analyzed specifically the women that got a lot of attention.
Qualitatively: she found that these women used aspirational language in their profiles (such as “love” and “fun”).
Quantitatively: These women claimed their average height was 5’1” however, they often lied about this, and rounded down (in opposition to Steve, and really, most other men).
The Super Profile:
She used her findings to create her own “Super Profile.” It wasn’t long until she met “Mister 800 Points.” She emailed him promptly. After her first date (which I might add, lasted 14 hours), she rescored him, and he had 1,050 points! Today, they are married. Evidently, by structuring her data, she was able to create a personalized algorithm that was effective in finding her a partner. She still used Big Data (unstructured) to help her, but combined it with structured data to achieve the optimal outcome: marriage.
You can watch the video below, or watch it throughTEDTalks.
Also check out this video. It is an exaggerated representation of how we are already using data to discover information about our dates. Using this information, we can date more efficiently. We have the information readily available that will better inform us if we can quickly end the coffee date, or simply, swipe left. It may seem unrealistic to us now…but who knows what lies ahead in the future. You can find other BIG DATA VIDEOS HERE.