Churn is a term used in business to describe customers who have stopped using a product or service. Churn analysis is studying customer data to identify and understand why they are churning. This information can be used to improve customer retention rates and increase profits.
Here the readers will learn what customer churn analysis says and how they can do it themselves using the data.
What Does Churn Analysis Say?
Like any analysis, it has several key performance indicators, and what it tells one depends on KPI, and the data one is looking at. So here are some important KPIs of this analysis.
Client Engagement and Usage
One of the key performance indicators is client engagement and usage. By studying client data, a person can identify which customers engage with their product or service and which are not. This information can help one improve retention rates and increase profits.
Customer Lifetime Value
Another key performance indicator is the lifetime value. These metric measures the total value a client will bring to one’s business throughout their relationship. By understanding such lifetime value, one can identify who are most valuable to the business and take steps to keep them from churning.
Likelihood to Upgrade
One of the key performance indicators of churn analysis is the likelihood to upgrade. These metric measures how likely a client is to upgrade their subscription or product plan? By understanding this KPI, one can identify who are most likely to increase their spending with them and take steps to keep them from churning.
Customer Behavioral Patterns
These are another key performance indicator of churn analysis. By studying customer data, a business personnel can identify who will likely churn in the future. This information can help the personnel improve the client retention rates and increase profits.
How to Do Such Analysis?
Customer churn analysis is also relatively easy, especially if a business representative has access to customer data. Here, people will know how to conduct their own analysis using custom data.
The first fact in conducting a churn analysis is to gather data. This data can come from various sources, such as client surveys, transaction records, and their service logs. Once a person has this data, they must identify which clients have churned. To do this, one can look for patterns in the data that indicate when a client has stopped using their product or service.
Once a personnel has identified which customers have churned, the next step is understanding why they have left. It can be done by examining the data to look for trends and patterns. For example, if you see that many customers who live in a certain area are leaving, you may want to look into why that is. Maybe there are problems with your product or service in that area or competition from another business.
By understanding why customers are leaving, businesses can make changes to improve retention rates. Churn analysis is a useful tool for any business and relatively easy to do if you have access to customer data.
Now that you know how to address customer churn, you can choose the right AI platform offering predictive analytics to reduce it.
Remember losing customers means losing business and strengthening your competitor. Hence, you must take timely steps to prevent customers from leaving your business.