B2B SaaS Company · Technology & SaaS · Austin, TX · 10 weeks
Product analytics foundation built
A B2B SaaS company with 1,200 customers lacked any product analytics. Their product team made feature decisions without real usage data. We built a product analytics system to track feature adoption, user engagement, and expansion opportunities. This foundation enabled the company to create a data-driven product roadmap and uncovered $1.8 million in potential expansion revenue.
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
We had 1,200 customers but no clear idea which features they were actually using. Thinklytics got our product analytics up and running in 10 weeks. Their expansion model found $1.8 million in revenue in the first six months from customers we already had. It’s the best return we’ve seen on any data project.