Product analytics foundation built

Challenge

The product team picked features to build based on customer requests and gut feeling. They had no tracking to show which features customers actually used, who the heavy users were, or what behavior led to account growth. Meanwhile, the customer success team lacked any signals to spot customers at risk of leaving.

Approach

We set up event tracking with Segment, transformed the data using dbt, and analyzed product usage in Amplitude. We mapped out 140 key user actions, developed feature adoption scorecards for each customer, and created a model that flagged customers ready to expand based on their product activity.

Outcome

We tracked adoption across 140 product events to understand how customers use new features. Using those signals, we built a product-qualified lead model that flagged 84 customers ready to expand, resulting in $1.8M in revenue within six months. We also cut the churn prediction window from 60 days to 14 days by analyzing product usage, giving customer success teams more time to act.

How We Made It Easy to Dig Into Product Usage Data

So, we grabbed the product team and got down to business, just trying to figure out which user actions actually moved the needle. We tracked 140 events across 12 features. Along the way, we cleaned up the event names and made sure everyone was on the same page with the data. That clear, tidy setup made the analytics way more trustworthy and set us up perfectly for the next steps.

We put together a lead scoring model by digging into how customers really use the product. Then, we ran tests to see how it held up.

So here’s the deal: we put together a lead scoring model that tracked how customers were using features, like what they adopted, how often, and the size of their teams. The point? To figure out who was primed to expand with us. The model picked out 84 accounts that were heavy hitters but hadn’t upgraded yet. Those accounts turned into $1.8 million in new business. Not too shabby, right?

Results

• $1.8M Expansion revenue identified in first 6 months
• 140 Product events instrumented and tracked
• 60 to 14 days Churn early warning improvement
• 84 High-expansion-readiness customers identified

In the client's terms

The question

What decisions is this reporting supposed to support?

The situation

The product team chose what to build from customer requests and instinct. There was no instrumentation, so nobody could say which features were used, who the heavy users were, or what behaviour preceded an account growing. Customer success had no early signal on accounts at risk.

What we found

Both teams were working without the same missing input. Product could not tell adoption from noise and customer success could not tell a quiet account from a leaving one, and one instrumentation layer answered both.

What it produced

One hundred and forty events instrumented. The product-qualified lead model identified 84 customers ready to expand, which converted to $1.8M in six months. The churn early warning window moved from 60 days to 14.

The roadmap

Event tracking through Segment, transformation in dbt, analysis in Amplitude, with 140 meaningful user actions mapped rather than everything the application emits. Feature adoption scorecards per customer, and a model that identifies expansion readiness from product behaviour instead of from account manager sentiment.

The work behind this result

• We react instead of predicting, the problem this engagement resolved.
• Forecasting and Optimization Consulting
• Pipeline and Revenue Analytics Consulting
• Healthcare Analytics Consulting Firm
• Account Growth Pilot Success Criteria, for whoever has to approve the spend.
• The 30 day Corporate Drag and Risk Diagnostic, findings yours either way.

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