Early-life churn often accounts for ten to fifteen percent of all customer losses – we look at the reasons why customers leave and how service providers can address the underlying drivers. 

Monitoring & Mitigating Early-Life Churn

By Carly Christian and Sam Kornstein

Service providers must prioritize customer retention to compete in markets where customers have multiple options. We previously stressed the importance of customer retention initiatives, specifically focusing on churn due to moves, churn mitigation for OTT offers, as well as the benefits of analytics-driven customer retention strategies. Another important topic for service providers to proactively manage is early-life churn, often defined as customers who churn in their first three months of service.

In our work, we find that for some service providers early-life churn can account for 10-15% of all customer losses – most of these customers had an initial experience that was so bad that they decided to switch to a competitor.

In addition to competitive losses, high early-life churn rates have undesirable effects on a service provider’s business. For example, brand reputations can suffer as disgruntled subscribers relay bad initial experiences to their networks.  Further, the short tenure of these subscribers results in low or negative customer lifetime value (CLV), as a provider has incurred the cost to acquire that customer and received little to no revenue.

Clearly, tackling early-life churn and understanding the underlying drivers is an area that requires attention.

Where Early Customer Experience Go Wrong

Churn due to a customer’s initial experience is almost entirely within the service provider’s control. Examples of poor initial experiences that can be addressed include:

  • Sales processes that are confusing, frustrating, or misleading and result in the products ordered not aligning with needs or budget
  • Scheduling problems including a lack of availability at a convenient time, too many touch points, technician cancellations, missed appointments, and late arrivals
  • Installations that are completed incorrectly, such that the services do not work properly or the customer requires a follow-up appointment
  • Welcome communications with incorrect or incomplete information
  • Quality of service issues that result in a poor initial product experience
  • Fees or charges that are hidden from or unexpected by the customer on the initial bill
  • Customer support experiences that are inefficient, frustrating, or do not solve the underlying problem
  • Product resources that do not sufficiently inform the customer about the full set of features and functionality of the ordered services

Creating a Process for Identifying and Acting on Early-Life Churn

Early customer experience issues and the corresponding customer losses are best addressed through a structured process of measurement, analysis, and action. Service providers must first quantify and trend early-life churn to evaluate its scale relative to other customer retention problems, and then conduct data-driven analysis of the initial customer journey to identify which specific factors are driving customers to leave shortly after they are onboarded. Identifying the exact causes of early-life churn can be difficult, as the process generally requires the integration of many disparate data sources and a robust statistical analysis of the trends.

Once the trends are identified, both proactive and reactive mitigation strategies can be pursued:

  • Proactive strategies involve targeted programs that address the root causes of early-life churn. These often focus on operational process improvements that streamline to sales to onboarding to initial use processes, reducing the rate at which things go wrong. In many cases, these programs lead directly to secondary benefits, such as operational cost savings and NPS score improvements.
  • Reactive strategies involve identifying which customers have had a bad experience and then quickly acting to retain them such as offering unprompted discounts or credits. This can be achieved by developing an early-life churn propensity model that scores customers based on churn risk. Once built, validated to have meaningful predictive power, and tested, these models provide daily updates of customer churn scores based on their probability of a near-term cancellation. Customers with the greatest risk can then be singled out and targeted with tailored, ROI-positive retention programs.

Early-life churn is an ongoing problem for many providers, but there are ways to preempt these customers from leaving. Leveraging subscriber data to investigate early-life churn and develop mitigation initiatives will help retain new customers, improve customer experience across the base, and achieve sustained growth over time. <>