# Stop Chasing Feature Requests: Why the Best Apps Rely on Behavioral Data

> Discover why building apps with behavioral data outperforms relying on user feedback, and learn four strategies to uncover what your users actually need.

- Canonical URL: https://coreiten.com/en/article/stop-chasing-feature-requests-why-the-best-apps-rely-on-behavioral-data
- Language: en
- Section: Best Apps
- Author: Sami
- Published: 2026-09-12T08:03:00+03:00
- Modified: 2026-09-12T08:03:00+03:00
- Publisher: CoreITen (https://coreiten.com)
- Keywords: app development behavioral data, user feedback, product management, usage data, InList, app retention, usability testing

## Summary

App founders should stop building apps based on loud user requests and instead rely on behavioral data to solve real product problems.

- Gideon Kimbrell, cofounder of InList and Syragon, points out that vocal feedback introduces bias because it comes from a self-selected group of users.
- User feature requests are typically workarounds, such as asking for more filters when the real issue is irrelevant search results.
- Silent usability sessions observing users attempt tasks reveal genuine friction points much faster than a week of survey feedback.
- Initial retention data for InList looked poor until conversations revealed users loved the app but simply did not need frequent event bookings.

**Why it matters:** Shifting from vocal support tickets to behavioral metrics prevents bloated apps and helps product managers address the root causes of user friction.

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Many app founders fall into the trap of building features based solely on what their loudest users request, leading to bloated products that fail to solve real problems. Shifting the focus to app development behavioral data allows teams to see what users actually do, rather than what they claim to want. Gideon Kimbrell, cofounder of the exclusive event booking platform [InList](http://inlist.com/) and software company Syragon, notes that usage data eliminates the blind spots inherent in vocal feedback.

Feedback is inherently biased because it represents a self-selected sample of users who are motivated enough to complain. To build a product shaped by actual scale rather than vocal minorities, product teams must adopt a behavior-first approach. Here are the four core strategies to make that shift:

1. **Read past the feature request to the root cause:** When users ask for a feature, they are usually describing a workaround rather than the actual problem. For example, a request for more filtering options might indicate that the initial search results are not relevant enough.
2. **Expect the same signal to mean different things in different places:** Usage patterns vary by segment. At InList, a high drop-off rate meant different things depending on the city or season. Segmenting data prevents averaging away critical signals.
3. **Watch before you ask:** People are unreliable narrators of their own confusion. Conducting a silent usability session where you observe a user attempting a task will surface more genuine friction points in 20 minutes than a week of collected survey feedback.
4. **Let data point you somewhere, then go find out why:** Behavioral data highlights where a problem exists, but direct conversation explains the reasoning. For instance, InList’s retention data initially looked poor, but conversations revealed users loved the app but simply did not need to book events as frequently as assumed.

### The Hidden Cost of Pleasing Everyone

The reliance on vocal user feedback often creates a "Frankenstein" application, where disparate features are bolted on without a cohesive vision. While the insights from InList demonstrate the value of behavioral tracking, the broader implication for the tech industry is that product managers must act as translators rather than order-takers. A feature request is merely a symptom; the behavioral data is the diagnostic tool required to find the cure.

As AI-driven analytics become more accessible, the gap between stated user intent and actual behavior will only become more transparent. Companies that continue to prioritize support ticket volume over silent usage metrics risk alienating their quiet majority. Ultimately, users will simply abandon an app when it becomes too cluttered with niche feature requests that do not serve the core experience.

## Sources

- [forbes.com](https://www.forbes.com/councils/forbestechcouncil/2026/09/11/why-the-best-apps-are-built-on-behavioral-data-not-just-user-feedback/)