Episode 17 - Leveraging Customer Data to Identify & Act on Drivers of Churn

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📰 TOP STORY

Leveraging Customer Data to Identify & Act on Drivers of Churn 

Incremental retention is the realisation of value in customer data. 

Customer data enables us to:  

1. Gain a better understanding of an individual customer's immediate and long-term needs and assist them in recognising and communicating those needs.

2. Understand groups of customers, allowing us to improve existing products or create new products and to develop policies and programs to support customer retention at moments of truth. 

The key to realising value in customer data is developing experiences that support customers at moments of truth and as a result improve customer retention. 

Ok, so this makes sense, I hear you say, but how do you do that practically? Let's dive in.

Step 1 - Analyse all recent voluntary churn events e.g. 12- 24 months of data   

• Ensure you have a complete view of customer interactions immediately before the churn event; this will be easy if you have CDP with this data consolidated. Otherwise, you must build a temporary data store to conduct the analysis. 

• These are the types of variables that you will need (lots of these are widely applicable, but some are more relevant to one kind of subscription business vs another): 

  • Tenure

  • Usage patterns 

  • Service contact reason 

  • Service contact resolution customer effort 

  • Technical/service issues 

  • Delivery problems/delays

  • Actions in early life vs tenured customers 

  • Content consumed 

  • Acquisition source/channel 

  • Most recent NPS response 

Step 2 - Identify the most significant cohorts. 

• Identify the most significant cohorts— in terms of the volume of customers that experienced one of the above categories immediately before churn, e.g., the volume of customers churning who experienced the same service issue prior to churn or the volume of customer cancelling in the first month.

• Your customers are all different. There are likely to be different 'events' that trigger churn for different cohorts. 

• The data is likely to contain some quick wins. For example, the way a specific service issue was handled may lead to churn for a particular cohort. If this is the case, you can jump to step 5 for this cohort; if not, proceed to step 3.

Step 3 - Research your most significant cohorts to understand why

• 1-1 interviews, focus groups and even ethnography can help you understand the why behind the churn decision.

• Then, using qualitative insights, extrapolate them to the base through surveys to ensure you do not make assumptions based on small samples.

• Additional customer data and analytics may help here, too. 

• Sometimes, a combination of things cumulates in the churn event, not just the most recent moment, research / further analysis can help uncover false positives.

Step 4 - Leverage learnings from others 

• Case studies from how other brands handle similar moments can help massively.  

• I shared four examples from Netflix, Starbucks, The FT and Apple in episode 15 of this newsletter here 

• Another from Amazon in episode 1 is here.

• Learning what others do can help you identify what to do and sometimes what not to do.  

Step 5 - Adjust the experience 

• Armed with knowledge of what happens immediately before a churn event, why it happens and what others do, plan your tests to optimise the experience.

• Tests could be conducted on Products, CRM Marketing, Customer Service, Operations (like delivery), Instructional information, and more.  

• Remember to ensure you run an A/B test, keeping the existing experience as is for a proportion of the customer base and adjusting the experience for the others.  

• Running with the idea without setting up a proper test is dangerous because you could positively impact churn but not be aware of it because of other external factors like market dynamics, seasonality, and other significant events impacting overall churn.

 Step 6 - Monitor & Iterate 

• Monitor the incremental impact on retention of your actions vs your control experience.  

• Iterate the experience to improve it further.

• Track CLV improvements and map them to your actions.   

• (There is more on CLV in episode 1).

Note, we have looked at churn events here, but this approach can also work for retention by examining the patterns and behaviours of long-term customers and the interactions and experiences that drive tenure.  

Final Thoughts: 

To effectively realise the retention opportunity in customer data, brands must focus on transforming customer data into actionable insights. By analysing churn events, identifying key customer cohorts, and understanding the deeper reasons behind churn, you can craft targeted experiences that address critical moments of truth and turn a churn event into a retention event. 

Until next week,

Tom

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