The Customer Retention Dashboard

An Essential Tool to Lower Customer Churn


  • A good customer retention dashboard focuses on the most essential key performance indicators.
  • Better information delivery shows which customers are at risk of churn, identifies the top causes of turnover, displays forward looking analytics, and shows the financial impact of both action and inaction.
  • The best information delivery permits real-time predictive modeling to show how changes in the causes of attrition impact business outcomes. It's this level of reporting that shifts information value from delivering bad news too late, to delivering the foresight that prevents bad news.
Johnny Grow Revenue Growth Consulting

You can't manage what you can't measure.

If you intend to improve customer retention, you will need a customer churn dashboard to bring real-time visibility to progress, highlight variances in need of quick course corrections, and map your continuous improvement program.

Below is a customer retention dashboard, built within CRM software, that we routinely use to lower customer attrition.

Customer Retention Dashboard

See the Customer Churn Dashboard that delivers real-time visualization of customer attrition prediction and prevention.

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Good information delivery is grouped into a hierarchical order. Here's how these groups work.

The headline metrics deliver the top measures that show real-time status. These are the factors that most impact the overall customer retention rate (CRR). While each industry is different, almost all industries should recognize that Customer Satisfaction (CSAT), Net Promoter Score (NPS) and trends in Customer Lifetime Value (CLV) are extremely influential in CRR results.

The Cost of Churn panel delivers financial information that generally goes uncalculated and unnoticed. By bringing this information to the forefront stakeholders can assess the need and priority of an improvement program, and know how much they should invest.

Note that the Churn Trend shows the cost of inaction. If attrition is not improved, this panel shows the forecasted cost of continued loss. The problem is put into an attention getting perspective when stated as a recurring financial loss.

The Causes of Customer Loss are illustrated as a treemap. Our research shows the reasons for customer attrition vary by industry and company maturity, but regardless of industry we've found this format provides an immediate and simple view to understand the analysis. This data is essential for any company that is going to shift from a reactive to proactive program.

A data transformation process is needed to achieve these types of insights and proactive efforts that lead to forecasted outcomes.

Customer Churn Prevention Framework

As you can see from the above diagram, the information is actually the output of an interactive model. It can be used to visualize outcomes from improving or adding new customer services, performing What-If analysis on the causes of attrition, or modeling different measures of attrition.

It's these types of predictive interactions that allow managers to examine tradeoffs, identify the low hanging fruit or find the improvement opportunities that align with their time, resources or investments.

The below Customer Churn Dashboard is an alternative model and illustrates customer at risk of attrition. It also shows the financial impact of lowering this critical metric.

Customer Churn Dashboard

The Annual Customer Retention panel uses industry benchmarks to deliver predictive analytics. This predictive analytics forecast the financial impact of reducing customer loss, and essentially answers the question, is the benefit significant enough to justify the efforts and investments? In the above example, improving this company's annual from 85 percent to the industry median benchmark of 91 percent delivers a $594K annual revenue increase.

The Customers at Risk panel is part of an early warning system. It allows immediate action to be taken and can suggest which retention levers to apply. While some customers can be saved, much of the time actions taken at this point are too little too late.

That's why a shift from a static forecast report that predicts which customers will defect to an interactive and predictive model that shows not just who, but why customers leave, with measurability, is so important. This later approach surfaces the root causes that can be resolved before they create customer churn.