What's something you measured that completely changed how you build product?

For months, we were building features based on what users said they wanted. Feature requests.

Sales calls. "It would be great if you added X."

We built X. Nobody used it.

So we stopped trusting what people said and started tracking what they actually did.

The dataset

We pulled 12 months of usage data from Rankfender. 1,247 active users. 27 distinct features across 8 modules. We looked at three metrics for each feature:

• Adoption rate — % of users who tried it at least once

• Retention rate — % who used it again after 30 days

• Request volume — how many users asked for it

The gap between what people asked for and what they actually used was massive.

What the data said

The features people asked for most (RASE, Ranklink, Social Engine) were used by less than 15% of users. The features they barely mentioned (Workflow Engine, RAIVE, RCGE) had adoption rates above 68%.

What we learned

People are terrible at predicting what they'll actually use. They ask for things that sound good. They use things that solve a real pain.

The Workflow Engine had 3 requests. Not 30. Not 300. Three. But 78% of users touched it. And 64% came back to it. That's not a feature request. That's a product.

RASE had 22 requests. We built an MVP. 11% of users tried it. 3% came back. We spent 4 months building something people asked for but didn't actually stick with.

The signals we track now

Before building anything, we now look at three things:

1. Usage patterns — What do power users do every day? We tracked the top 10% of accounts by engagement. Their most-used feature was the Workflow Engine. Not because they requested it. Because it saved them time. They built automation for content publishing, citation alerts, and competitor tracking. That told us more than any sales call.

2. Support ticket clusters — What problems keep coming up? We used RAISA to cluster 1,200 support tickets. The largest cluster (34%) was "automation rules are confusing." Not "add app store tracking." Not "add backlink discovery." People wanted us to fix what was already there, not add new stuff. So we paused new features for 6 weeks and just fixed automation.

3. Churn signals — What do people stop using before they leave? We tracked feature usage in the 30 days before cancellation. Users who stopped using RAIVE were 4.2x more likely to churn. Users who never touched the Workflow Engine were 3.7x more likely to churn. Users who used RASE had no correlation with retention. That told us where to invest.

What we decided

Building now:

• Workflow Engine v2 — Conditional logic, scheduled runs, webhook triggers. 78% adoption, 64% retention. This is what keeps people around.

• RAIVE enhancements — Real-time citation alerts, competitor share of voice, platform-specific tracking (ChatGPT vs Perplexity vs Gemini). 71% adoption, 58% retention.

• RCGE improvements — Proofreader v2, information gain scoring, bulk content generation. 68% adoption, 52% retention.

Delayed (not killed, just not now):

• RASE (app store tracking) — 11% adoption, 3% retention. The signal isn't there yet. We'll revisit when app store AI visibility becomes a bigger pain point.

• Ranklink (backlink discovery) — 9% adoption, 2% retention. People asked for it, but they didn't stick with it. We need to understand why before investing more.

• Social/Community Engine — 14% adoption, 4% retention. The idea is good. The execution isn't there yet. We're collecting more data on what people actually need from social listening.

What we fixed instead of building new:

• Automation UI — Simplified the workflow builder. Support tickets on automation dropped 47%.

• RAIVE data freshness — Increased citation update frequency from daily to twice daily. User satisfaction scores improved 22%.

• RCGE proofreader — Added "information gain" scoring. Content quality scores improved 34%.
  

The results

After 6 months of building based on usage, not requests:

We didn't add more features. We fixed the ones people actually used. That was the lesson.

What this means for you

If you're building something, stop asking what people want. Look at what they do.

• Pull usage data. What features do your power users touch every day? That's your roadmap.

• Cluster support tickets. What problems keep coming up? Fix those before adding new things.

• Track churn signals. What do people stop doing before they leave? That's where you're losing them.

You don't need more data. You need to look at the data you already have.

What I'm curious about

What's something you measured that completely changed how you build? Not what people said. What the data showed.

Imed Radhouani
Founder & CTO – Rankfender
Evidence-based product development