While analyzing subscription performance for a consumer brand, I noticed that monthly and quarterly subscribers were being evaluated as a single population despite exhibiting very different retention behaviors.

When I segmented retention by billing cadence, the differences became clear. Monthly subscribers retained at significantly higher rates at each renewal point, while quarterly subscribers faced renewal decisions at entirely different stages of their product journey due to receiving a larger supply upfront. This analysis revealed that a one-size-fits-all retention strategy was overlooking important differences in customer behavior.

Using these insights, I developed cadence-specific retention approaches that aligned messaging, timing, and interventions with where customers actually were in their lifecycle rather than simply when a renewal occurred. The result was a more nuanced retention framework that helped the business better understand subscriber behavior, prioritize retention efforts, and view billing cadence as a strategic lever for improving long-term customer value.

While leading retention strategy for a subscription-based consumer brand, I wanted to better understand where customers were dropping off and what was driving churn.

By analyzing retention by order number, I uncovered a significant drop-off between the first and second order, with retention declining sharply again by the third. The challenge was particularly important because customers typically needed approximately 90 days of consistent product usage before experiencing meaningful results. Many customers were leaving before they had enough time to see the product's value.

To understand the business impact, I paired retention analysis with lifetime value modeling and found that customers who made it through the early stages of their journey were worth substantially more over time.

Using these insights, I developed a retention strategy focused on the critical first 90 days of the customer lifecycle. This included improving customer education, setting expectations around results, and identifying opportunities to keep customers engaged through the period where they were most likely to experience success with the product.

As a result, second-order retention improved from the mid-60% range to the high-70% range, creating meaningful gains in customer lifetime value and long-term subscription revenue.

While leading subscription strategy for a consumer brand, I owned the subscription cancellation experience, one of the most important retention touchpoints in the customer lifecycle.

Using data from hundreds of cancellation sessions each month, I analyzed subscriber behavior at the point of churn to better understand which interventions were most effective at retaining customers. Rather than relying on a single discount offer, I tested a range of retention strategies including pauses, skips, and targeted save offers based on customer intent and behavior.

Over time, these optimizations increased save rate from the low 20% range to the high 30% range, retaining hundreds of subscriptions each month that would have otherwise churned.

When save rate later declined due to an influx of promotion-driven subscribers, I dug deeper into the underlying customer mix and found that the issue wasn't the cancellation flow itself, but that the existing offers no longer matched the reasons customers were leaving. By adjusting the retention strategy to better align with subscriber behavior, we were able to restore save rates to previous levels.

This work reinforced the importance of treating cancellation as a source of customer insight rather than simply the end of the customer journey, helping the business improve retention while gaining a deeper understanding of subscriber behavior.