Leveraging AI for Customer Churn – Part 1
Managing a business isn’t a cakewalk, especially when you fear that some of your customers are leaving you. They are moving on to better ventures.
In marketing, that is what we call, “customer churn.”
Customer Churn or Customer Attrition is the phenomenon that occurs when customers cease their relationship with your brand – they stop doing business with you. A business can treat a customer as ‘churned’ if a significant amount of time has passed between their last interaction (purchase).
Once a customer turns their back on you, it can become very expensive to win them back. Needless to say, the cost of acquiring new customers is even higher. According to stats, the cost of acquiring new customers is five times higher than the costs incurred in retaining old customers.
Thus, the idea is to identify the customer churn rate of your organization. This helps you to plan in advance and take appropriate steps to reduce the high churn rate. So, how do you predict customer attrition rate?
Through Customer Churn Forecasting.
Customer churn forecasting refers to the process of leveraging past attrition data and predicting the churn rate which indicates the total no. of customers, who might discontinue their relationship with a business/brand in the future.
The importance of Customer Churn Forecasting
Customer churn forecasting is essential to build and maintain a healthy functioning business. It allows marketers to:
Project the income revenue
When marketers can accurately predict the churn rates of their existing customers, they can get a better understanding and knowledge of the future income revenue of a business.
Reformulate retention strategies
AI-powered models not only allow you to forecast the number of customers that are going to churn but also give you reasons behind the churn rate. There can be several aspects behind the high churn rate that can’t be thought of. Getting an idea of these features helps you in reshaping your retention strategies.
Improve customer experience (CX)
By predicting churn rates of customers, marketers can identify and improve upon the weak links in the customer service. As customers begin to enjoy improved customer experience with the brand, they retain their loyalty.
AI and Machine Learning specialists can best predict customer churn using advanced AI, Machine Learning, and Data Science techniques.
Why is it difficult to forecast churn?
When it comes to forecasting customer churn, the biggest challenge is that one has to rely largely on historical data to make future estimates. And since many enterprises may not have a significant amount of past customer data required to make accurate churn forecasts. Forecasting churn with limited or no data gives rise to numerous challenges. While on the quantitative side, the shortage of data generally results in inaccurate and misleading mathematical models, on the qualitative side, the results (forecasts) generated might be overly-optimistic, influenced either by personal agendas or a herd mentality within an organization.
Inaccurate churn forecasts can cause everything to go haywire – marketers may end up focusing their customized offers/discounts/campaigns on happy and loyal customers instead of those who are actually on the verge of churning.
This is why there’s an urgent need to incorporate AI, Machine Learning, and Data Science to generate accurate and precise churn prediction results. But more on that in Part 2!