How to Use Data to Drive Product Decisions
How early-stage founders can make confident product choices using the right data, even without a full analytics team.
When building a product in the early days, whether on your own or with a small team, one thing you do not have is the luxury of unlimited time or engineering bandwidth. Every decision you make comes at a cost and I cannot stress enough how important data is when making these decisions.
One problem I have found with most startup founders is that they rely too heavily on their gut or wait too long for “enough data” before acting.
I am a big believer of using data to lead your product to success and today’s post, I want to show you how to use small, scrappy, powerful data to make smarter product decisions, fast. Whether you’re debating a new feature, trying to understand drop-offs, or figuring out who your best customers are, this will give you a system you can return to over and over again.
What Counts as “Data” in Early-Stage Startups
Take it from me, you don’t need Mixpanel dashboards or an analytics hire to be “data-driven.” At this stage, data is anything that gives you signal. It can be:
Notes from customer interviews
Repeated questions in your DMs
Drop-off points in onboarding
Feature usage patterns (even if you track them manually)
Responses from surveys or feedback forms
Metrics from Stripe, Gumroad, or even email tools
Your job isn’t to collect all the data, it’s to collect the right data that helps you move forward.
Ask the Right Question Before You Collect Data
Before diving into tracking tools or spreadsheets, pause and ask:
What decision am I trying to make ?
What would change my mind?
What data would give me confidence to move forward, or stop?
For example if you’re asking a question of “Should we build this feature?”, then you need data like “Number of user requests, frequency of complaints”
If it’s a question of “Why are people dropping off?”, then we are talking Funnel data, user behavior, onboarding feedback.
If you’re trying to figure out if a problem is painful enough for the user to be attended to, you need data from Interview quotes, urgency levels, DIY workarounds
If it is a question of “Which segment should we target first?”, you need to look at Signup source, activation rate, churn across segments, etc.
The tip is to tie every data collection effort to a specific product question. Otherwise, it becomes noise.
My Go-to 5 Simple Data Sources you should be using as an early stage startup.
The hill I am willing to die on is that you don’t need a stack. You need clear observation habits.
1. User Interviews
Record quotes, frustrations, patterns. What are they solving right now without you?
Listen for “I’ve tried X but still stuck” or “I’d pay if something just did Y…”
2. Manual Usage Tracking
If your MVP is delivered via Notion, Loom, email, etc, track views, opens, and follow-ups.
Keep a spreadsheet of who tried it, what they did, and where they dropped off.
3. Surveys
Ask open-ended questions like:
“What problem were you hoping this would solve?”
“What almost stopped you from signing up?”
“What feature would you be disappointed to lose?”
4. Behavioral Metrics
Even basic tools like Stripe, ConvertKit, Gumroad, or Tally can show:
Who signs up
Who upgrades
Who repeats
What action they take first
5. Support and Sales Conversations
Log every repeated question.
If 6 people DM about pricing, or 4 users can’t find a feature, it’s not random. It’s a signal.
How To Turn Data Into Decisions
Once you have input from all these sources, turn it into action using this 4-step loop:
1. Cluster Signals
Don’t act on one comment. Group feedback into themes.
E.g. “7 users asked for CSV export” = high-signal
E.g. “3 users struggled to connect Stripe” = friction to fix
2. Identify Leading Indicators
Look for correlations like:
“Users who complete onboarding are 3x more likely to convert.”
“Those who watch our intro video have better retention.”
3. Define Thresholds
Set your own bar for action:
“If 50%+ of trial users request this, then we prioritize it.”
“If churn spikes after feature removal, revert.”
4. Score with Simple Frameworks
Use ICE or RICE scoring to prioritize:
ICE = Impact x Confidence x Ease
RICE = Reach x Impact x Confidence ÷ Effort
Don’t overthink it. The goal is to act, not analyze forever.
Tools You Can Use (No Code, No Team Needed)
You don’t need a fancy setup to be data-informed. Here are simple, founder-friendly tools based on what you want to do:
If you want to log interviews or track feedback:
Notion is great for creating a lightweight database of user insights.
Google Sheets is easy to use, especially for spotting patterns across rows.
Airtable is like a smarter spreadsheet if you want to filter or categorize feedback by theme or feature.
If you want to run surveys:
Tally – Clean, no-code, and free. You can embed it or share a link.
Typeform – Polished UI, good for user research with open-ended questions.
Google Forms – Simple, free, and fast to set up.
If you want to track how users behave:
Splitbee – Lightweight analytics focused on startups and indie builders.
PostHog – Great for product teams; offers session replays and funnel tracking.
Mixpanel – Powerful, though a bit complex for solo founders unless you’ve used it before.
If you want to understand what’s happening on your site:
Plausible – Privacy-friendly and simple; shows core metrics without fluff.
Fathom – Clean design, easy to read reports.
Hotjar – Offers heatmaps and screen recordings to see where people drop off or hesitate.
If you're manually tracking usage during MVP testing:
Google Sheets + Loom + Notes – Watch what users do (with permission), record short videos of walkthroughs, and log any insights.
Notion or Docs – Use a template to document each user interaction, what they did, and what confused them.
I see a lot of startup founders make some of these mistakes but you have me so I can save you form them.
Collecting data you never use. If you won’t use it for a decision, don’t collect it yet.
Waiting for perfect analytics. You’ll always want more data. Start with what’s in front of you.
Dismissing qualitative feedback. Not everything worth knowing is measurable.
Using vanity metrics. Pageviews and likes won’t help you ship smarter. Focus on behaviors.
Letting tools replace judgment. Data informs. You still decide.
As a startup founder, you need learn to listen to signals, even small ones. Learn to move faster, prioritize better, and waste less time building the wrong thing.
You don’t need to master analytics. You need to master this loop:
Build → Watch → Listen → Adjust → Repeat.
I am working on a User Interview & Feedback Tracker Template to founders like you track feedback patterns, score requests, and connect data to product decisions, stay tuned for when it is Live!