What Is Customer Churn Prediction, And How Can A Prediction Tool Help Curb Churn?

The rate of attrition or customer churn measures “customers terminating their business with you.” Churn prediction Identifies the causes and helps mitigate a significant portion of their customers from leaving.

A study was conducted to identify the most significant challenge a small business owner faced, and the top response was attracting new customers. For every business, it is much cheaper to keep their old customers than to get new ones. Hence, it is essential to know how this prediction works. The following details will help you get a better understanding.

How Do Churn Prediction Work

Customer data is combined with machine learning algorithms to rank the likelihood of churn. Once all of the customers likely to leave are identified, aggressive communication and targeted marketing are required to stop them from leaving your business. Churn prediction is based on data modeling, and you can either make your own model or refer to experts with knowledge in this department. This process may feel complicated, and with the help of experts, your data changes into insights that can lead to informed decisions.

The prediction will be based on the customer data, which is of two types.

Personal data

Their demography, how long they have been using your product, if they have active use of the product, if they’re using your product by themselves or because of an institution they’re associated with, are there any special features for them, help analyze the consumer churn.

Revenue data

How much a customer impacts your revenue, how often, what are the usual periods of contract, what category of product they use, what plan/price tier they purchase from, which are the ones closer to expiration, etc

Then you need to identify your high-risk customers and target them immediately with upgrades, coupons, etc. Identifying which customers respond to what kind of rewards is also an important step. Your customers should be ranked by risk, and they should be treated with the appropriate level of emergency. 

Once you have this data, you can either build a model or refer to experts for a custom solution. Custom solutions will often come with neat insights that showcase the practical side of data modeling, the cause of churn can be easily identified when you study the churning pattern. When you need to study complex relationships between data and churn, it is advisable to go for outside experts with industry knowledge and experience. While in-house is also achievable in the long run, churning is often limited to a time frame, and predicting a churn is a time-bound process because unless you predict it on time, you cannot fix it. And this isn’t only true for small businesses; many global companies opt for outsourcing to use data on time. Moreover, customer churn prediction isn’t limited to predicting the next spike but identifying the causes and minimizing it as much as possible.

Prediction models, once run successfully, should uncover correlations between decisions and customer churn. Once the causes are identified, they can be modified to minimize the churn, if not avoid it altogether.

Importance Of Customer Churn Prediction Tool

Not every single customer will stay with your business forever. However, a prediction tool will help analyze when there’s a spike and how to mitigate the loss. The tool can help with customer retention, especially old customers, to ensure higher profits. It is essential to target old customers, as they are more patient given the established relations and usually more willing to try your new products. Loyal customers will also place your business in a higher position in the market.

Hence companies of every scale must move toward customer retention with a professional prediction tool.

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