Strengthening the Cord With Cause-and-Effect Analysis

Amidst the mind-boggling number of churn reduction strategies communications service providers (CSPs) have at their disposal, how are some leaders generating tens of millions of dollars successfully navigating the retention challenge? They are applying a test vs. control approach to answer questions such as: How should renewal pricing vary based on promotional price, tenure, PSUs, etc.? Which customers should we proactively call, and when? Can we profitably reduce the extent of save offers for some customers? Should we offer free streaming, a price discount or encourage a tier downgrade?

While provider executives know the importance of answering these questions — and most are invested in using data to curb churn — many still fall short in answering them accurately. Isolating how business actions affect churn over its natural rate is challenging. A few issues at play are:

Home ownership: Apartment dwellers are naturally more likely to churn than homeowners as they have expiring leases that cause relocation.

Tenure: Subscribers with less tenure are more likely to have promotions up for expiration.

Seasonality: Customers who moved into a new apartment in the summer versus winter may behave differently.

Without accurately identifying each customer’s baseline churn, CSPs often incorrectly predict the impact of their retention efforts (accidently measuring differences in baseline churn for various customer segments instead of incremental churn caused by their actions). As a result, concessions are made to subscribers who would have renewed anyway; meanwhile, investments aren’t made in subscribers who could have been saved with the proper offer or outreach.

A growing number of CSPs are applying test versus control analytics to understand which retention initiatives are incrementally effective. The “test group” consists of subscribers who experience a given action, such as a price increase at the end of a promotional period. The control group is then constructed from subscribers who are similar to test customers across all other dimensions (e.g., age, tenure, homeownership), but did not experience that action (e.g., still on promotion). When highly similar subscribers are compared, any resulting performance differences can be directly attributed to the business action taken.

Knowing the overall impact of a retention program is important. Even more critical is understanding which resulting actions should be deployed to profitably retain each customer.

In an industry faced with an onslaught of new competitive threats, accurately understanding the impact of your business actions is critical. Before you incorrectly target more subscribers, consider using advanced test vs. control analytics.

Marek Polonski is a senior vice president at APT, an Arlington, Va., based provider of cloud-based cause-and-effect analytics software.