July 16, 2023

Data Science Hits the Dating World

Anyone who has ever shopped on Amazon has seen and felt the impact of data science.

Once you shop and/or make a purchase, you are immediately provided with lots of other suggestions – data science at work.

You are categorized by Amazon’s algorithms and presented with other choices that those algorithms have decided will interest you.

The same data science is used by almost every sector of our economy – your online behaviors are tracked, and you are presented with all sorts of options – from mortgage and bank loans to cars and clothing items.

These will even appear on your Facebook feed.

As you know, this results from mathematical algorithms that gather and sort information and churn it out to interested parties who want to “sell” you something.

Enter Online Dating Services:

Data Science Hits the Dating World: eAskme
Data Science Hits the Dating World: eAskme

Online dating services are nothing new. They have been around for decades. Singles join them, complete profiles, and then their preferences are matched with similarly inclined others.

In the beginning, matches were made by people looking at profiles and presenting possibilities to their users – pretty inefficient. But then, the user base was pretty small too.

As the user bases increased, establishing databases based on user profiles/qualities allowed a more efficient matching method via a cross-matching system.

But ultimately, as data science and AI garnered attention, dating sites began to see the huge benefit of incorporating this technology into their matching process.

Now, they could take the user information from these databases and develop algorithms to pull far more accurate and precise matches from them.

How Data is Collected?

Most dating services begin the collection of data by questionnaires when users first register on their sites. Some of these are incredibly detailed – the more information collected, the more precise the matches can be. For example, users will not only be asked about their levels of education.

They will be asked about their degrees, favorite courses, what type of school(s) they attended, their living arrangements while in college, whether they were in fraternities or sororities, what clubs and organizations they belonged to, etc.

You are probably getting the idea here. The same detail will be asked about your childhood and adolescence, the size of your family, your favorite memories, and so forth.

Some of these questions can become quite personal, and users can skip questions they are uncomfortable answering.

Many dating services will also access users’ social media accounts and online searches and ask for their favorite shopping sites and purchases. This compiles data on user behaviors, likes and dislikes, etc., to make matching even more precise.

It’s pretty startling for non-techies to understand how all this Data science is entering into the dating services industry. How is Data Collected? There are two sides to the coin: upside and downside. Know everythingata can be compiled, sifted, and sorted, but that’s what data analytics and AI do with oceans of data.

And because AI continues to learn as it operates, it gets better at what it does.

Two Sides to This Coin:

The Downside:

Using data science and AI on dating apps aims to find the most accurate matches for those seeking love and romance.

The idea is that somewhere, in this large pool of possibilities, your perfect match can be found, and you will “ride out into the sunset” for a beautiful life together.

But it isn't easy to reduce humans to products.

First, users can misrepresent themselves to seem more attractive or to attract what they believe will be their perfect match. This is one of the reasons why these sites attempt to gather data on user behaviors from various other sources.

There is also the belief that the more precise matches become, the more users may become disenchanted with those matches.

They may not be looking for “clones” of themselves – they want differences to balance their personality.

The Upside:

Facts are facts, and they don’t lie. And the more facts can be gathered about an individual; the better matches can be presented.

It’s much like Netflix and Amazon make suggestions and recommendations based on previous selections and purchases.

And when customers turn down recommendations and suggestions, that, too, becomes a part of the customer profile.

A scientific approach to dating and romance makes looking for dating partners far more efficient.

Matches are presented, and the user can swipe left or right, choosing those for further consideration and those not desirable.

And as matches are declined, those, too, become a part of the user behavior that is tracked.

The Future of Data Analytics and AI in Dating Apps:

Just like us, data science and AI continue to evolve.

Especially as AI continues to learn from the successes and failures of matches, and the swipes of each user, the process will continue to be refined and improved.

While humans will never become products, the science of dating matches narrows the field of potential to those most compatible.

Then, the human factor may come into play. Users can then establish communication channels on the dating app, get to know one another humanly, and make good decisions about whom to move forward with.

And this is the best outcome of all – a scientific approach to finding the matches and a human approach to exploring the depth of each match.

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