An investor once told me that she would rather to see 10,000 active customers than $10,000 in monthly income. I was struck by that comment. It provided more insight into current tech valuations than a comprehensive financial statement could. Data is becoming the product rather than a byproduct, particularly first-party behavioral data.
You leave a digital breadcrumb each time you launch an application. What you tap, how long you scroll, and where you pause all combine to paint an incredibly precise picture of your needs and thought processes. Although the portrait doesn’t bring in money at the register, businesses influencing the digital experiences of the future find it to be quite important.
I’ve observed in recent years that app founders are beginning to think more like information architects than shopkeepers. They are asking, “What are we learning from them?” rather than, “How do we charge the user?” Media businesses’ transition from selling subscriptions to selling targeted impressions is remarkably analogous to that change.
This trend is especially advantageous because it lets products expand without immediately putting a strain on consumers. Apps can continue to be free or inexpensive while discreetly creating datasets that support future revenue-generating tactics. Some founders even acknowledge that they utilize early adopters as “algorithm training partners.”
| Key Concept | Explanation |
|---|---|
| Core Asset | User data (behavioral, demographic, location, in-app activity) |
| Key Monetization Methods | Targeted advertising, AI model training, product personalization, data licensing |
| Valuation Leverage | Investors use metrics like MAU, engagement depth, and data granularity to assess value |
| Competitive Edge | Enables development of proprietary AI systems and unique user experiences |
| Revenue vs. Data Value | Revenue reflects current performance; data signals future monetization and strategic potential |
| Benchmark Examples | Meta, Google, Amazon, and TikTok prioritize data-rich engagement over immediate revenue |
| Risk Consideration | Privacy laws (GDPR, CCPA), data ethics, and user trust can influence data utilization |
| External Source | The Conversation – Hidden Cost of Convenience |
Businesses may teach AI algorithms to predict behaviors with uncanny accuracy by recording not only actions but also sequences, such as whether you touch before or after reading reviews. Over time, that prediction power significantly increases, particularly when multiplied among millions of users and a variety of contexts.
I’ve seen pitch decks that barely make a profit, but you’ll sit up straighter when you see cross-device activity logs, segment breakdowns of emotional mood, and heatmaps of user clicks. It’s similar to looking at a product’s blueprint five steps ahead of the app’s current state.
Platforms for advertising were the first to realize this. Targeting accuracy was what made Meta a juggernaut, not just the volume of ads. Smaller apps, including those not intended for advertising, are now subject to the same rule. Data from a sleep-tracking app with only 30,000 users may nevertheless be useful for consumer electronics, healthcare research, or insurance modeling.
The pressure to introduce paid features early has been greatly lessened by this type of indirect monetization. Rather, it allows developers to improve their experience while the data discreetly builds up in the background. The organization gains influence in product planning, collaborations, and potentially fundraising rounds once that dataset reaches critical mass.
One of the founders of a mental health app said during a panel I moderated last year that they had not yet made a profit but had been approached by three acquisition companies. Why? due to the fact that their dataset contained finely structured, anonymized behavioral insights related to sleep patterns, emotional journaling, and even background noise. That’s gold for a wellness platform or pharmaceutical company.
Naturally, not all data is created equal. Data that is organized, granular, and ethically gathered is particularly noteworthy. These days, especially creative apps are developing data strategies from the beginning, planning each contact with transparency, purpose, and machine-readability in mind. When it comes to internal automation and product revisions, this proactive approach makes data not only informative but extremely efficient.
Some applications guarantee the security and decentralization of the data they collect by incorporating blockchain or edge computing technology, which has proven very comforting in regulated markets. Additionally, having consent-based data is now more than just a matter of compliance—it’s a competitive advantage with the implementation of privacy-first regulations like GDPR.
Many of these apps now co-create with bigger entities through strategic alliances, providing access to anonymised analytics in return for funding, reach, or technological assistance. Compared to advertisements or subscriptions, this strategy frequently keeps the lights on for early-stage firms.
The question of whether data matters has been replaced by the question of how it is used. Many applications use the data they gather to create dashboards that help with product-market fit, churn reduction, and logistics. Internally, this results in a leaner, sharper operation that uses less resources to operate more quickly.
However, these apps can appear to be merely repeating themselves from the outside. No showy launches, no income increases. Under the hood, however, they are creating maps of human behavior that, when combined with AI, enable businesses to predict demand, shape habits, and address issues before users even report them.
The finest aspect? Users gain as well. Features become seamless, algorithms get smarter, and recommendations get more individualized. Tech goods are subtly changing from static tools to responsive systems thanks to this feedback loop.
Even non-tech investors have started to see the benefits of real-time, context-rich data since the emergence of AI personalization. They are supporting applications that collect sentiment, motion, or even biometrics in order to improve user experiences and enable more intelligent automation, not to resell.
I don’t seek for flashy product releases when I consider the next big tech values. I search for cluttered data logs, heatmap-covered dashboards, and founders who can explain to me why a user tapped twice rather than once. The value is concealed there, gradually and quietly developing beneath the surface.
In the end, an app can be profitable without charging you. It only needs to understand you better than anybody else and know how much the perfect spouse values that understanding.
