Knowing that you cannot get the churn prediction right is crucial as many factors are out of your control. More importantly, no one can avoid customer churn as many factors are not in your control. In today’s time, every customer is looking to switch from one brand to another most of the time. Does every significant business have this question in its mind as to why customers are leaving? Are we not providing the best of services to them?
Every business owner would have asked this question at some point in life, right? Wrong! Business owners look forward to growth rather than letting their customers slip away. In addition, businesses look forward to increasing sales. Many businesses out there cannot predict if they will lose a customer or not based on their churn prediction method. Poor forecasting models and estimations can destroy your business, and you cannot understand why customers leave the company.
The main thing here is to target the right customers who will come again to make a higher repurchase and are loyal enough. The most important thing is that you cannot control the attrition rate, but you can have control for balancing the amount of revenue you lose and the profits you can gain from the retained customers for good growth.
What exactly is customer churn
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Customer Churn is the number of customers leaving your services or products in a given period. It will involve those customers who have shifted to another brand and those who have left your service or are not using your product anymore.
Customer churn is one of the most critical metrics that can help you understand how well you retain customers. It can also help you determine whether your business is growing or shrinking.
The customer churn rate is calculated by dividing the number of lost customers by the total number of customers at any time. The formula for calculating customer churn rate is as follows:
Churn Rate = (Lost Customers / Total Customers) * 100%
The effects of not looking after the customer churn prediction and working on preventing it are very acute in the industries like telecommunication as there are many different competitors.
How to get the prediction right for customer churn in business
Except for one or two valuable customers, a significant percentage of your customers will not give up on you instantly. They will change their purchasing power frequency and usage of the account or reduce the purchase amount once they are unhappy with your products and services.
There are some ways to take care of this problem. They are as follows:
Taking the help of customer experience score
When we talk about customer experience, it is about keeping track of the metrics like the CES, NPS and CSAT. You will receive the data from the customer directly to help you understand their perception of your brand. It is one of the best ways to help in customer churn prediction.
What is the Customer Experience score?
A customer experience score (CES) is a numerical rating that helps companies understand how they perform with their customers. The score can be between 1 and 10 or an A-F grade.
Here are some examples of how companies use CES:
To improve customer service and enhance operational efficiency by identifying areas where there’s room for improvement and suggesting solutions. That helps companies improve their service delivery capabilities.
To track progress toward goals and measure improvements over time.
To align employees around a standard set of objectives.
Help from customer data
Another essential thing for tracking is taking the help of internal customer data. With the help of a CRM, you can get a significant number of activity trends in time and spot any changes that will showcase if there is a possibility of any churn.
The most important thing you need to do is analyze it using the proper tools. You can also use some predictive analytics software to look into past trends and try to find any patterns that could indicate future outcomes. By looking at these patterns, you can make customer churn predictions using machine learning, if any issues could be causing churn or not.
Once you have identified these issues, you can take the necessary actions so that the customers do not leave your business because of these problems or concerns.
To prevent churn from happening in your business, you need to ensure that all your customer’s needs are met and that they are happy with their experience with your company.
Machine learning use
We have seen that machine learning has a high potential to grow your business. It can get used in many different areas, but one of the most critical areas is customer churn prediction.
If you have an extensive database of customers, it will not be feasible to track each customer’s data manually; it is best to use the customer churn prediction with the help of AI for the study. Many companies are using the best resources for building the proper framework that will work the best for using machine learning to analyze the customer’s actions and know the customer churn immediately.
The first thing that you need to do is gather all of your information about customers and their activities. That includes what they buy, how much they spend on each item or service, how often visit your store or office, how often they call or email for support or assistance, etc. It would help if you also considered other factors like demographics and location when creating this profile of your customers (or potential customers).
Conclusion
Churn predicting with machine learning looks excellent on paper, but it is not easy at all. The customer churn issue is complex, and so are its multiple factors. Customer churn can get estimated only by looking at the historical data. However, customer behavior is dynamic. In other words, you will have to continuously improve and remain ahead of your competitors to get more of your customers to spend time with you.
