Data Analyst, Retention — Application Portfolio
Three years building retention analytics, designing lifecycle experiments, and turning behavioral data into decisions that moved churn, conversion, and engagement metrics for consumer-facing products.
Get in touchWhy Ollie
Most analytics work is about finding users. Retention analytics is about understanding why they stay, why they leave, and what you can do about it before it is too late. That is the work I find most meaningful, and it is exactly what the Ollie Data Analyst role is built around.
Subscription health is not about today's churn rate. It is about tracking cohort behavior over time and catching signals early enough to act. That is how I built every retention dashboard I have shipped.
At Vue.ai I owned the full experimentation workflow from hypothesis through results. I know what a poorly designed test costs and how to structure one that actually teaches you something.
The most useful analyst is not the one with the best SQL. It is the one product managers, CX leads, and marketers can actually work with. I have spent three years being that person.
Dogs live better when their owners have access to real nutrition data and tailored care. Ollie's approach to using data to improve animal health is not a product pitch. It is a model I want to contribute to.
Relevant Work
Every piece of work below maps directly to what the Ollie Data Analyst role asks for. No padding, no irrelevant projects.
Enterprise retail clients were losing users without knowing why. I queried and analyzed 100K+ clickstream sessions in SQL to identify the behavioral patterns that preceded disengagement. The output was a segmentation model that separated high-risk users from healthy ones, and a set of lifecycle campaign triggers built around those segments.
This was not a one-time analysis. I built it as a repeatable framework so client teams could monitor risk signals on an ongoing basis without coming back to analytics every time.
I owned the full A/B testing workflow independently. That meant defining hypotheses, scoping test groups, choosing the right statistical approach for each experiment type, running analysis, and presenting findings to client stakeholders in a way they could act on.
The experiments covered pricing tiers, promotional timing, personalization logic, and messaging cadence for global retail clients including Adidas, Zara, and Crocs. This is directly relevant to what Ollie needs across lifecycle comms, mobile, and member services.
Mapped full consumer funnels from acquisition through activation and conversion by combining product and web data. The goal was not to produce a funnel chart. It was to find the specific points where users were dropping and build a testable hypothesis about why.
For subscription businesses like Ollie, this kind of funnel work is the foundation of everything else. You cannot improve what you cannot see.
Built dashboards that tracked cohort behavior and engagement trends across 20K+ records, giving program teams visibility into retention signals early enough to act before drop-off occurred. The dashboards were designed for non-technical users who needed to make decisions without analyst support every time.
This is exactly the kind of self-serve analytics infrastructure that Ollie's cross-functional teams would use to monitor member health and prioritize interventions.
Skills
Organized around what the Ollie role actually requires, not a full list of everything I have ever touched.
Query and Analysis
Retention and Lifecycle
Experimentation
Platforms and BI
I would love to walk through how my experience maps to the specific problems Ollie's data team is working on. Happy to dig into any of the work above in more detail.