LimeLight CRM Support 

February 07, 2017 21:13


Churn is the number of customers who are no longer active customers with you. Said another way, Churn Rate is simply the percentage of lost customers compared to total customers at the beginning of the date range.

  • The higher the churn, the more customers lost.
  • The higher the Net Churn, the more customers gained.
  • Customer Churn = Customers Lost ÷ Customers at Start Date
  • Net Churn = (Customers at End Date - Customers at Start Date) ÷ Customers at Start Date

Analyze churn to understand the customers you are losing, so you can develop strategies to reduce churn rate. The purpose of this dashboard is to help you to identify trends (positive and negative) related to Churn ratio - specifically, if you are gaining or losing customers.

When Churn is examined as a trend, amongst other findings, one can assess the effectiveness and efficiency of the employed marketing methods. Use this information to reduce churn rate. You can leverage the filters to isolate specific marketing channels, campaigns and more to isolate various characteristics of your marketing mix.

The Customer Churn measure can provide an insight into several operational and/or marketing activities:

  • How clear is the offer
  • How effectively is customer service handling customers
  • How quickly is the business growing or shrinking from a pure customer quantity perspective - are customers being acquired quickly enough

Net Churn is a second Churn measure to leverage when considering Customer Churn. Net Churn Rate, which goes further than Churn Rate, takes into consideration customers gained within the period being analyzed - not just customers lost.

If a business's Net Churn is increasing, it means that it is gaining more customer's than it is losing; its customer base is growing. Said another way, positive Net Churn increases a customer base. Negative Net Churn indicates that the customer base is shrinking.

Additionally, the rate at which customers are churning is vital to the Customer Lifetime Value (CLTV) measure since the longer a customer is retained, (typically) the higher the CLTV. Since CLTV is one of the more important measurements, indicating the highest cost a company should pay to acquire new customers, one can understand that Customer Churn is a vital indicator of business success or failure.

To help improve understanding, we have created some standard language.

Ending Unique Customers = EUC
 Starting Unique Customers = SUC
 Lost Unique Customers = LUC
 EUC - SUC = Net Change

Using the above, the formulas used to calculate Customer Churn Rate and Net Churn are:

  • Customer Churn Rate equals LUC ÷ SUC
  • Net Churn Rate equals Net Change ÷ SUC

Potential Actions

Use the Churn dashboard to:

  1. Consider the impact to Customer Churn Rate that can be achieved by making modifications to campaign and/or operational procedures (i.e. modify customer service scripts on how cancellation calls are handled, alter fulfillment procedures to decrease time in home (how long it takes a package to get to the customer), modify internal refund protocol to inspire a larger percentage of partial refunds and save sales, etc).
  2. Perform deep channel evaluation of campaign traffic (i.e. affiliate, paid search, email marketing, etc.) to improve the efficiency of new customer acquisition - measured through the eCPA and MER measures.
  • Data presented in this dashboard is based on "Transaction Date" unless otherwise noted. Transaction Date is the date that the order was placed without an association between the Re-bill and Recurring transactions (Cycles 1…X) and the Initial transaction (Cycle 0) that initiated the order.
  • All orders marked as "test" within the platform are automatically removed from the Analytics data.

Data Elements & Measures

The following data elements and measures are shown on the Churn dashboard. For comprehensive definitions of data elements and measures, please reference the 

Glossary of Measures.

Additional Resources

You may find the following Help Center articles relevant to Analytics helpful.

  1. Glossary of Measures
  2. Glossary of Terms
  3. How to use Filters
  4. Analytics Vs. Reports - Data Calculation Methodology
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