Using Data: customer insights and finding product–market fit

Startups are supposed to be learning machines. That’s the whole game: test, learn, adapt, grow. And at the heart of that loop is data.

But “using data” isn’t only about spinning up dashboards or quoting retention numbers in pitch decks. Data is only useful if it sharpens decisions. It’s about spotting patterns in how users behave, not just what they say. And most critically, it’s about finding the signals that you’re building something people truly want.

Here’s how early-stage startups should think about data, how to collect the right kind of insight, and how all of it ties into finding Product–Market Fit.

1. You don’t need scale to start learning

Early founders often default to thinking of data as quantitative: graphs, funnels, dashboards. But at the beginning, your most valuable data is qualitative - conversations, pain points, friction, and workarounds.

Jono, Brighteye's Product Mentor, has been there:

“There’s no substitute for speaking with users directly. Make it part of your activities.”

If a customer is angry, that’s data. If a user hacks your product to get to their intended outcome, that’s data. Hearing repeated “I don’t get it’s” during a feedback call - that’s data!

You don’t need scale to start learning. What you need is discipline to capture signals systematically.

Practical ways to make qualitative data useful:

• Log and tag support tickets. Don’t let them languish in your CRM — turn them into patterns.

• Summarise customer calls. One line per call: “What problem did they want solved?”

• Bucket feedback. Is this about onboarding, pricing, usability, missing features?

• Review weekly. Don’t wait until the next strategy offsite — make themes visible early.

Jono recommends keeping your whole team abreast of these insights:

“Review data and customer feedback with the whole product team.*”

*In your early days, this could be your whole team...!

When everyone sees the same inputs, alignment improves. Over time, you’ll see repeat patterns emerge - and that’s when insight becomes actionable.

2. Quantitative data: don’t over-index too soon

You should be tracking metrics, but if you’re still pre- or just-post-launch, dashboards can be a dangerous distraction. It’s easy to obsess over vanity numbers like signups, daily actives, or click-throughs. They look exciting but often mean nothing.

What matters early on is behaviour that signals value:

• Repeat usage – Do people come back on their own?

• Depth of engagement – Are they using the core feature deeply, or just poking around?

• Time to value – How quickly does a new user experience the “aha” moment?

Pro tip: Create one or two north star metrics tied directly to your product’s promise. Disregard (but don’t totally ignore) the rest until you’ve hit early traction.

Founders who stay close to their numbers build better instincts - they can sense when something’s off long before the graph confirms it.

3. Finding product–market fit: it’s a feeling, then a fact

Product–Market Fit isn’t a single metric; it’s a shift in energy. It usually starts as a feeling:

• Users stop churning.

• Feedback shifts from “I wish it did X” to “Please don’t change this.”

• Growth starts to come from referrals, not just sales or hustle.

Only later does it show up in the numbers. Signals you’re getting close:

• Retention curves flatten instead of diving.

• Activation rates improve as people “get it” faster.

• Users express frustration when you break something - because they care.

A useful tool: ask new users, “How disappointed would you be if this product went away?” If 40%+ say “very disappointed,” you’re close.

But remember, metrics are lagging indicators. The leading ones are emotion, enthusiasm, urgency. If people care enough to complain loudly, you’re onto something.

4. Build feedback loops into the product (not just around it)

Customer interviews are gold, but they don’t scale. What scales is when your product becomes a listening tool itself.

Ways to embed feedback into the product:

• In-app surveys or prompts at key moments (after onboarding, after completing a task).

• Behaviour tagging to see where users succeed or drop off.

• Smart defaults that adapt based on usage.

• Heatmaps or session replays to watch real behaviour.

The goal is to turn your product into a conversation. Let users show you what matters. Combine what they say with what they do - neither alone gives the full picture.

5. Data that informs, not data that slows

Data should never become a bottleneck. If every decision requires a report, you’ll lose your speed advantage. Early-stage teams can’t afford to wait for statistical significance on every choice.

Use data to:

• Narrow the cone of uncertainty.

• Validate or disprove risky assumptions.

• Sharpen instincts - not replace them.

It's also a helpful unifier for your growing team, as Jono shares:

“A visible dashboard of key company metrics helps everyone stay close to impact and feel urgency.”

Dashboards shouldn’t just live in Notion or hidden analytics tools - they should be visible and alive, reminding everyone what matters most right now.

The goal is not precision; it’s insight. You’re aiming for “directionally right,” not “perfectly measured.” Startups win by learning faster than anyone else. Data, both qualitative and quantitative, is the fuel for that loop. But the power isn’t in dashboards or decks; it’s in how you use it to decide, adapt, and focus.

So, be data-driven - but more importantly, be user-led.