DIY Dating Algorithms: A Signal of Platform Shortcomings?

Singles are building their own matching algorithms. Not the sophisticated machine learning kind that dating platforms spend millions developing—the Excel kind. According to the Wall Street Journal, thousands of young adults are now logging post-date data into homemade decision-making tools, quantifying everything from conversation quality to red flags, then analyzing the results before deciding whether to see someone again.

The trend speaks to something more significant than quirky dating behaviour. When 10,000 people pay $9.99 for a dating spreadsheet template, as brand strategist Peyton Knight has managed to sell, you're looking at a market signal. Users are literally purchasing the structure and decision-making frameworks they aren't getting from the platforms themselves.

The Trust Vacuum in Commercial Form

This is what a trust vacuum looks like in commercial form. Dating apps have spent a decade selling algorithmic matching as their core value proposition, yet users are now building parallel systems to capture what those algorithms miss: actual interaction data, subjective compatibility, the granular detail that only emerges when two people sit across a table from each other.

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The commercialisation of dating spreadsheets—a micro-industry built on platform inadequacy—should concern every operator whose engagement metrics look healthy but whose relationship outcomes remain opaque.

Users aren't abandoning apps, but they're clearly supplementing them with tools that do what the apps claim to do but demonstrably don't: help people make better partner decisions. The gap these DIY tools fill is instructive. Dating platforms optimise for the swipe, the match, the message exchange.

What the Apps Can't Capture

Their algorithms parse profile data, interaction patterns, and behavioural signals to predict compatibility. What they can't measure is what Carolyn Travis, a 27-year-old using a spreadsheet-style app called Spread, described to the Journal: the gap between stated values and actual compatibility that only becomes visible after several dates.

Travis used the tool to assess an engineer she'd met in an entrepreneurship course, inputting her values alongside details about him. Friends contributed their observations. The resulting analysis highlighted incompatibilities—specifically a decade-plus age gap and misaligned priorities—that prompted her to end things.

The practice echoes the quantified self movement that's tracked everything from sleep patterns to caloric intake for years. Applied to dating, it serves a similar function: imposing structure on chaos, pattern recognition where emotional investment creates bias. Skyler Wang, an assistant sociology professor at McGill University who studies self-datafication in dating, noted to the Journal that these tools help people manage an unpredictable process whilst capturing real-world interaction data that matching algorithms simply cannot access.

That last point deserves emphasis. Match Group, Bumble, and every platform operator have poured resources into algorithmic sophistication. Yet their fundamental limitation remains unchanged: they can only work with pre-date data and in-app behaviour. The spreadsheet users are logging post-date reality—how the conversation actually flowed, whether stated values matched observed behaviour, how physical chemistry translated offline.

The Vulnerability Problem

Not everyone accepts that data-driven dating constitutes progress. Lily Womble, a former matchmaker quoted in the Journal piece, cautioned that heavy reliance on metrics can lead people to minimize emotional responses in the moment. 'Rigidity can actually be a shield against vulnerability,' she observed.

You can quantify shared interests and values alignment, but not whether someone makes you laugh in a way that surprises you, or whether their particular brand of chaos happens to complement yours.

Whether spreadsheet dating actually produces better relationship outcomes remains an open question. The evidence is entirely anecdotal. What's documented is that it feels more controlled—which may be the entire point for a generation navigating dating apps that promise structure but deliver an experience many describe as chaotic and exhausting.

The commercial traction tells you something about unmet demand. Knight's $9.99 template includes a 'friends feedback' tab, turning partner assessment into a collaborative process. She told the Journal the tracker helped her reconsider reaching out to a past date after reviewing notes about an uncomfortable interaction. More than 10,000 sales suggest she's not alone in wanting that kind of structured reflection.

What Operators Should Watch

The spreadsheet trend won't replace dating apps. Most users are running these systems alongside existing platforms, not instead of them. But the fact that they're running them at all indicates where product development needs to evolve. If users are this hungry for decision-making frameworks and post-date reflection tools, platforms should be building those features natively.

Some obvious possibilities: structured post-date feedback prompts, compatibility tracking that learns from actual dates rather than just profile data, friend input mechanisms that acknowledge how partner selection often benefits from outside perspective. The technology isn't complex. The question is whether platforms want to optimise for relationship outcomes or engagement metrics—and whether those two things can coexist within the same business model.

The cynic's read is that they can't. Dating apps make money from continued usage, not successful partnerships that remove users from the platform. That's the structural tension the industry hasn't solved, and user-built spreadsheets are one visible symptom of the problem.

The optimist's read is that platforms which genuinely help people find suitable partners will command pricing power, retention, and word-of-mouth that engagement-optimised competitors can't match. That's the bet the niche platforms have made. Whether mainstream operators can credibly make the same pivot whilst satisfying public market growth expectations is another matter entirely.

The Compliance Question

For compliance and trust teams, the trend raises different questions. When users are systematically logging and sharing data about dates—potentially including identifying details, behavioural observations, and subjective assessments—where does platform responsibility begin and end? The data isn't on your servers, but it's generated by your matching. That's a grey area worth thinking through before a spreadsheet full of unflattering date assessments becomes a reputational or legal problem for someone involved.

The dating spreadsheet trend may prove a passing fad, or it may represent the early stage of something more fundamental: users taking algorithmic matching into their own hands because the platforms optimised for growth aren't optimised for the thing users actually want. The fact that young adults are experiencing a broader dating recession, with many feeling less confident about finding suitable partners, suggests deeper structural issues that spreadsheets alone won't solve.

Meanwhile, research shows that Gen Z is seeking to blend traditional dating approaches with technology, wanting genuine in-person connection without the rejection risk of purely offline methods. Which outcome seems more likely probably depends on whether operators treat this as a curiosity or a market signal worth acting on.