7 ways to benefit from a churn analysis

by | Oct 19, 2021 | Articles and Blog

Keeping customers longer is a profitable business

Retaining customers for longer can in many cases be a more profitable business than marketing efforts to gain new customers. Not only because acquisition costs for gaining new customers are often high, but also because retention efforts often have positive side-effects such as an increased level of satisfied ambassadors, who in turn attract new customers through recommendations. Retaining customers longer also means that the cost of acquisition can ultimately be increased, as you can expect higher income for each customer.

A company with more loyal customers may be more aggressive in acquiring new customers than its competitors. They know they are investing in longer customer relationships.

Find loyal patterns

What creates long-term customer relationships? A customer experience consists of so many steps that it is difficult to know what creates long-lasting customer relationships. And it is even more complex because different customers have different preferences and expectations.

And above all: how to maximise customer lifetime at the lowest possible effort?

Asking customers about their experiences is not enough, even though it can be a good addition. It is possible to say one thing and then act differently. We need to start from actual customer behaviour and that’s why you need a churn- or retention-analysis that is based on actual customer behaviour, and segments the customers based on all your currently available metrics.

To be more specific: If I now had a churn analysis, how would I use it to generate benefit? The benefits can be divided into two parts, based on how they are achieved: Descriptive analysis, the values that can be achieved through deeper insight and from that a better customer strategy and predictive analysis, the values that can be achieved by segmenting existing customers based on the churn analysis and then acting on these customers in different ways.

Descriptive analysis

Find good customers

It may turn out that different types of customers have different lifetimes. Perhaps certain age groups combined with certain geographic areas prove to be particularly loyal or they can be found depending on through which channel the customer used to initiate the customer relationship. These customers may initially not seem the best. They may not spend the most in new sales but make up for this by being customers for a long time, therefore there is a risk that the company missed these customer groups in the past. By giving extra focus to attracting these loyal customer groups through the right offers in the right channel, a higher overall lifetime is achieved and the company enjoys a higher overall level of recurring revenue.

Find good business practices

Not only customer’s demographics or behaviour are significant to loyalty. The way your own business handles different parts of the process can be very important. The obvious is how different problems in the relationship are handled. Without an understanding of the churn risks, choosing a problem management approach for a case type that looks good in the short term may be a common approach, because it could keep costs down. When the churn risk is taken into account, however, it may turn out that there are several better strategies to use. But there may also be other elements that create loyalty. Perhaps a specific campaign, channel or product that delivers longer customer lifetimes.

Improve product strategy

Some products can drive more complex customer relationships, which then also become stronger. If certain products prove to be associated with increased customer lifetimes, it may be an excellent idea to take this into consideration when pricing, for example, but also in product development in general.

Increase company value

A business that can demonstrate a high level of repeat customers can achieve a leverage effect in how the company is valued. This can be the extra motivator to risk investing in optimising customer lifetime value instead of short-term. Having insight into defection behaviour becomes the basis for demonstrating and explaining the rate of repeat customers.

Predictive analysis

Counteracting high-risk customers

By applying a predictive analytics model to existing customers, it is possible to identify customers who are at particularly high risk of defecting and act on this to try to reduce defections. This may be having special proactive outbound campaigns for high-risk customers or it could be employing special communication or offers for them when they contact you anyway through inbound channels like website, phone or in-store.

Take care of your loyal customers

By applying a predictive analytics model to existing customers, you can also find customers who have a particularly low risk of defecting and act based on this. It is possible that it’s actually best not to bother these customers any further, but that they simply really like the way you work today. However, it may be a good place to look for ambassadors and recommendations or just show appreciation to increase loyalty further? Otherwise, it is obvious that from a loyalty perspective there is no reason to change the status quo for these customers, but it might be useful to keep an eye on events that could make a satisfied, loyal customer dissatisfied? Maybe it’s a good idea to take some extra action if a problem occurs in the delivery? Or take extra action if competitors try to drive these loyal customers away from your business?

Improve customer lifetime value

Using churn risk as a parameter in the calculation of customer lifetime value to obtain a customer lifetime value based on each specific customer’s demographics and customer behaviour allows customer lifetime and product margins to be put in relation to each other in an optimal way for decision making and planning customer retention efforts.


Getting started

As customer lifetime value is one of the most important factors for a good business, there are a tremendous number of ways to benefit from churn analysis in both planning and purely operational terms. These seven are just a few examples that can be both broken down into detail and expanded by more. A good way to get started is to begin an exploratory analysis based on which to determine where there is the lowest input cost for getting started and use the model to improve operations.   Author: Gustav Rengby  
Random Forest, Gustav Rengby Säljansvarig, Random Forest
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