7 Things Data Analytics Can Learn from Online Dating

Online dating is big business. 10% of American adults spend more than an hour a day on a dating app, according to Nielsen data. Use of online dating sites or apps among 18- to 24-year-olds has tripled since 2013, and the industry is worth an esimated $2.5 billion in the United States alone.

But what’s behind their success?

THE COMPLEXITY OF MATCHING PEOPLE, NOT PRODUCTS

Unlike recommending a movie or a new gadget, matching two people adds a new level of complexity. Both sides must feel the same spark, and that means the data models must predict mutual interest.

To achieve that, data scientists at dating companies continuously refine algorithms to identify the right match. Their work offers valuable insights for any organization looking to use data effectively. Here are seven lessons from the world of online dating that apply to almost any business.

1. Use the right tool for the job

The compatibility matching system at eHarmony once took more than two weeks to run on a traditional relational database. By switching to modern data tools like MongoDB, they cut that time by 95%, down to less than 12 hours. Today, big data and machine learning systems analyze a billion potential matches a day.

This kind of efficiency is possible because of distributed processing and indexing, similar to how Google Search operates. Instead of searching through every web page, Google scans its index, making real-time results possible. The same approach helps dating apps deliver quick, relevant matches.

At Match.com, the Synapse algorithm learns user preferences much like Amazon or Netflix. Based on the Gale–Shapley “stable marriage” model, it predicts matches that are mutually compatible, the same mathematical principles that also power ad placement, financial trading, and recommendation engines.

2. Gather data from multiple sources

Dating platforms collect data in many ways. Comprehensive questionnaires, sometimes with hundreds of questions, capture personality traits, interests, and lifestyle preferences. The answers form the foundation for the matching algorithms.

But questionnaires are just one piece of the puzzle. Platforms also track user behavior: which profiles people view, how long they spend browsing, and which messages they respond to. Many even enrich this with external data, such as social media activity, shopping habits, or entertainment choices.

The result is a deep, multi-layered understanding of each user, something every data-driven organization strives to achieve.

3. Account for data accuracy

One of the biggest obstacles in predictive modeling is the accuracy of self-reported data. People don’t always tell the truth, or at least, not the full truth, about their age, income, or interests.

To handle this, dating platforms use statistical models that assign different weights to different types of data. For example, while someone might say they love classical music, their Spotify playlist might reveal a different story. Behavior-based data, such as online activity or purchase history, often provides more reliable insights than what users write in forms.

4. Combine analytics with design thinking

Numbers alone can’t capture everything about human relationships, and dating companies know this. That’s where design thinking comes in. It encourages understanding people’s emotions, motivations, and cultural context, not just their digital footprints.

As Jason Chunk, Vice President of eHarmony, explained, data can reveal patterns like who’s more introverted or more likely to initiate contact. Yet, cultural differences also matter. For instance, eHarmony discovered that international users outside the U.S. were more open to matches who smoked or drank, so they adapted their algorithms accordingly.

This blend of data and empathy can guide businesses in any sector to design more human-centered solutions.

5. Use data to optimize business decisions

Data isn’t just for improving products, it’s also a powerful tool for optimizing business operations. eHarmony, for example, built its own attribution system to understand how marketing investments translate into new users. By analyzing 125 terabytes of data, the company cut its annual marketing spend from $100 million to $80 million while improving efficiency.

This approach can apply to any organization: use analytics not just to track performance but to inform every major decision.

6. Know your customer

Even with massive data sets, successful companies don’t rely solely on algorithms. They also listen to their customers. Dating platforms regularly gather feedback about users’ experiences — who they meet, what they enjoy, and what frustrates them. This human feedback loop helps refine both the service and the data models behind it.

For other industries, it’s a reminder that data should complement, not replace, real conversations with customers.

7. Stay aware of your competition

Finally, successful platforms always study their competitors. As Sam Yagan, CEO of The Match Group, once said: “I use our competitors’ products as much as we use our own. I have all of our competitors' apps on my phone.”

CONCLUSION

Data is one of the most valuable resources today. When used thoughtfully, it can transform industries and spark innovation.

Online dating companies show how combining data with analytics and empathy can create personalized experiences and meaningful connections. The same approach works for any business looking to better understand its audience, make smarter decisions, and improve interactions.

At SoftServe, we help clients make sense of their data and put it to work. From analytics and predictive models to process improvement, we turn information into actionable insights that drive better outcomes.

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