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Our secret recipe for customer churn prediction

The difficulty of defining churn

Every relationship has its own interaction frequency. Personal relationships can be intense where people meet daily. Other relationships can have a much lower interaction frequency, where people who meet every few months or even once a year have very significant relationships with each other.

Relationships with different interaction frequencies will also end in different ways. In intensive relationships, a break of a few weeks or months will be regarded as remarkable and might signal that something in the relationship has derailed. In the context of other relationships, a silence of a few months or even a year will not be so uncommon.

The same story goes for relationships between companies and their customers. Customers communicate in different ways and with different frequencies, making it difficult to determine when a customer is about to say goodbye to you.

The limited one-size-fits-all approach

From ML2Grow’s experience, we see that most companies are very aware of the importance of customer churn but lack the technology and know-how to accurately mitigate this risk. For example, a basic but commonly implemented approach is to use a basic, time-controlled metric. An example of this would be to consider a customer as churned after, say, three months. This one-size-fits-all approach ignores the interaction frequency of individual customers, the needs of the customer and usually leads to poorly timed and ineffective marketing strategies. In addition, it completely breaks down if a customer only partially churns, in other words, only stops buying some services or products.

When you automatically label customers as churned after a fixed time window, the behaviour of each customer in the context is not taken into account. Presume to apply this approach to personal relationships. Calling your partner after three weeks of silence will give you a firm scolding. On the other hand, texting your colleague every two days during his summer holidays makes no sense at all.

ML2Grow’s personalised approach

Dividing your sales team attended to all your customers does not scale with a growing customer base. Some of your loyal clients do not need a weekly promotion or reminder you exist, in some cases, such strategies might even have an opposite effect.

It’s therefore important to identify and model customers in the context of their own behavioural patterns to avoid harming customer relationships. This can easily be done using individualized churn definitions. A precise churn risk is calculated individually per customer based on the knowledge and insights extracted from all your customer interactions.

This approach gives companies a deeper and more accurate understanding of each customer’s behaviour. In addition, it gives you a much better chance to promote enduring corporations. Covered with this knowledge, companies can use personalized campaigns for customers at the right time to encourage loyalty and customer engagement.

ML2Grow has developed a method to accurately predict customer churn in which our unique approach for individual churn cutoff calculation is fundamental. This method has been tested and validated as an accurate and effective method.

Without revealing too much about the secret recipe of ML2Grow’s customer churn prediction technology, the approach combines individual churn cut-off calculation and a complex mathematical modeling system for prediction. By using more advanced models allows us to capture extra information in the system, such as details about the client, the orders and the products or services that are being sold.

Take advantage of ML2Grow’s advanced churn prediction technology

Contact us today to learn how we can help you with ML2Grow’s Customer Churn Prediction technology. Allow us to easily optimize your marketing campaigns to increase existing customer spending and reduce customer churn.

Julie Derumeaux


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