Customer life-stage

Most customer lifecycles should present as a curved graph when we factor in Churn. When customers are newer, there is an increased chance they will leave you and this diminishes over time. Often there is a period after say 5/10 years when they naturally look for a new provider and you’ll see a tail off at this point. Understanding the customer journey allows you to unpick key points for intervention.

Segmentation

Market segmentation is the process of grouping or dividing an audience into subgroups based on commonalities and shared characteristics. This allows you to understand at scale how different types of customers interact with your brand, what their expectations are, and when they are more likely to leave.

Customer lifetime value

Segmentation will also help you to identify the groups of customers with the highest lifetime value, allowing you to allocate your resources accordingly. While every customer might receive an automated message to try and win them back, only the most profitable should get a personal call for example. Lifetime Value can help to reduce churn in channels/products with the highest likelihood of becoming long-term and profitable customers.

Motivation and needs data

When you understand a customer’s initial reasons for entering your category, then it can become easier to understand the motivation for them leaving. It can often be difficult for businesses to separate out what is desirable for customers with the reality of the service/product they can provide but more times than not, customers leave when their needs are unfulfilled or their motivation has changed.

Customer service

Data from customer service is another useful tool that can highlight when a customer’s experience is not going smoothly. Where you see an individual contacting support regularly it may well mean you need to review the account and identify the problem. Where you regular see requests for the same information, it may mean a gap in your onboarding process or product info.

Transactional data

Any data on how a customer interacts with your touchpoints can help inform you of their potential to churn and the right medium in which to prevent this. If you have identified key intervention points on your customer journey, then be sure to personalise any communication to that individual.

For example: If you were a streaming service, it’s somewhat likely that customers would subscribe to binge-watch a certain show and then cancel their subscription afterwards. Sending them a personalised email suggesting similar shows would be significantly more effective than a generic email.

Transactional data will often give you the earliest indication that a customer has churned due to either inactivity or active cancellation.

All this at scale

You can see how by combining and adding different layers of data it becomes easier not only to identify when someone is likely to churn, but also the best strategy to try and retain them and the best medium in which to communicate that offer. While all this information creates a deep profile of a single customer, achieving this at scale is incredibly difficult. That’s why we use the latest in and Natural Language Processing in which to accurately pinpoint at-risk customers to apply them to your CRM.

Brands who arm themselves with the who, what and why of customer churn are at a distinct advantage in trying to prevent it both short and long term.