Microsoft • Case Study
Customer intelligence system
Built a unified customer intelligence system that identified revenue-driving cohorts and enabled smarter acquisition, churn reduction, and upsell strategy.
Why this case matters
Most teams had data. They did not have understanding.
Azure had rich customer data across product usage, support interactions, documentation engagement, deployment behavior, and subscription signals, but the data was fragmented and teams were optimizing in silos.
I built a system that connected those signals, surfaced meaningful patterns, and moved teams from reporting to decision-making.
Case Study
Context
Signals existed, but they were fragmented.
Different teams saw different parts of the customer journey. Product teams saw usage, support teams saw issues, documentation teams saw engagement, and business teams saw revenue.
There was no unified way to answer what behaviors actually drive customer success, what predicts churn, or which cohorts create the most value.
What I did
Unified signals into a decision system.
I built a shared customer view across subscriptions, usage, support, documentation, and deployment signals, then introduced behavioral clustering and cohort-based timeline modeling.
That shifted analysis from “what happened?” toward “what behavior actually drives outcomes?” and gave teams a stronger foundation for acquisition, churn reduction, upsell, and experimentation.
Outcome(s)
Better insight led to better action.
Identified that a small percentage of customers drove a disproportionate share of revenue, enabled stronger targeting for acquisition and upsell, improved understanding of engaged versus disengaged users, and created a foundation for experimentation and predictive modeling.
Why it matters
Metrics are not enough.
This work proves that I can unify fragmented signals, translate behavior into insight, and connect insight to action. That matters because competitive advantage increasingly comes from how well teams understand and act on customer behavior.