Customer churn or customer attrition refers to the loss of customers or subscribers for a product or service of a business or organization. Visualizing customer data related to churn can help decision-makers gain meaningful insights to develop engagement and retention strategies. Some key visualizations that can beincluded in a churn prediction dashboard include:
Customer churn rate over time (line chart): This line chart shows the monthly or yearly customer churn rates over a period of time. It helps identify trends in the rates of customers leaving the business. The dashboard can allow selecting different cohorts or customer segments to compare their churn rates. This chart is often one of the first graphs seen on a churn dashboard to give an overview of how churn has changed.
Customer retention rate over time (line chart): Similar to the above chart, this line shows the retention rates of customers (customers who have not churned) over monthly or yearly intervals. It provides an alternative view of how well the business is retaining customers. Both retention and churn charts together give management a holistic view of customer loyalty patterns.
Customer churn by acquisition cohort (horizontal bar chart): This chart segments customers based on the year or time period they were acquired. It shows the churn rate of each acquisition cohort side by side in an easy to compare manner. It can help identify if older customers have higher churn or if certain acquisition periods were more successful at retaining customers. Making informed decisions about re-engaging past cohorts can help reduce churn.
Customer churn by subscription/plan type (pie or donut chart): When the business has multiple subscription or plan types for the product or service, this chart shows the distribution of customers who have churned according to their subscription type. It helps understand if particular plan types have inherently higher churn or if there are engagement issues for customers on specific plans.
Customer churn by various attributes (table or datasource filter): This interactive filtering view shows churn counts and rates according to various customer attributes like industry, region, size of business, etc. Management can select these filters to drill down and understand how churn varies according to different customer profile properties. Insights from this help create churn reduction strategies targeted at specific customer segments.
Customer behavior over time by churn status (dual line chart): This chart compares behavioral metrics of customers who churned (lines in red) versus those who were retained (lines in blue) over a period leading up to their churn/retention time. Behavioral metrics can include usage frequency, purchases made, support requests, etc. This visualization is very effective in identifying differences in engagement patterns between the two customer groups that can be monitored on an ongoing basis.
At risk customers (gauge or meter chart): This view depicts the count or percentage of customers identified as ‘at risk’ of churning by the prediction model in the near future (say 3-6 months). Seeing this number change over time helps assess the effectiveness of any new retention programs or incentives in keeping at-risk customers from real churn. Reducing this number remains a key measure of success.
Prediction accuracy over time (line chart): As the prediction model is retrained over time on new customer behavior data, this chart indicates how accurate it has become at identifying churners vs retainers. A rising blue line showing an increased percentage is ideal. Tracking model accuracy helps confirm it is learning as intended from ongoing customer interactions and past churn behavior.
These are some of the effective visualizations that can be incorporated into an insightful churn prediction dashboard. Proper filters and crosstabs need to be provided to allow drilling down and comparing across different sub-segments of the customer base. With regular monitoring and refinement, such a dashboard becomes a valuable management reporting solution for reducing churn. Key decisions around retention best practices, high-risk customers, acquisition campaign effectiveness and prediction model performance can all be made more data-driven with these visual analytics.