There is one thing that’s just as important as finding new clients: keeping your current clients. This blog will give you some practical tips to increase your loyal customer group!
First things first, we need to get insights into our customers. What are the characteristics of our loyal customers, and maybe more important, what are these characteristics of the customers that’ll probably leave our organization?
Lots of companies are focusing on getting new customers instead of keeping their existing customer base. For example, why are gyms and telecom providers only giving a discount to their new customers, and not to their current customers? Due to these strategies, a lot of customers are hopping from company to company, which won’t lead to a loyal customer base.
In this blog, we’ll go into the practical way of creating insights in the customers that most probably will leave your organization within a period. These customers are going to churn, so from now on, we’ll call them churners.
Getting a list of your future churners is incredibly valuable to your business. You could give them a little bit extra TLC (tender, love & care) or a discount, depending on which marketing strategy is the best fit for each churner, to make sure that these churners won’t leave.
We need to do some study so we can say which factors are most important for customers to become a churner. To do this research, we’ll work with Alteryx, which is an easy low-coding application to investigate your business’ data. Important to note is that you don’t need any code/programming skills to understand this blog. We’ll keep it clean and simple, to give you the information to start your analytical churners journey!
To investigate which of these factors are essential, we’ll do some research on the data of formal churners and not-churners. For now, we’ll use our business’ data from the last three years. In this data, we’ll try to discover some (hidden) patterns in the customer’s behavior that separate the churners from the not-churners. For example, you might find that almost all the churners filed a complaint in the last six months of being a customer of your business. This might indicate the follow up of the presented claims is terrible, which leads to a lot of churners.
Within Alteryx, we’ll use the preparation tools to clean and enrich the data. When these steps are finalized, we have well-structured data, which can be analyzed by the predictive models. These models are training themselves by looking for patterns. Once these patterns are discovered, the model will deploy these patterns on our new data. This new data exists in our current customer base. We want to check if some existing customers behaving like one of the churners in the past so that we could alert the account managers.
By knowing which customers are most likely to churn, we could prevent all these customers from churning. By doing this over and over, you know when it’s time to give them some extra TLC.
Preventing churn isn’t that hard; you need to be consistent. If you give some extra TLC from time to time, your consistency will win in the end. Knowing which customers are likely to churn not only brings in extra money, it can change your business model. If you reward your loyal customers on a regular base, you don’t need to spend a fortune on finding new customers.
In our next blog, we will go deeper into the details of churn prediction.
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