Curn Analysis and Alerting – General

Churn Analysis and Alerting

Executive summary

Screening your existing client population for signs of dissatisfaction and pending attrition can involve a broad range of analysis. Usually the focus is given to transaction pattern analysis. And while this may prove helpful it can be misleading in smaller companies with limited comparative data. We thus integrate a broader variety of less obvious indicators and include an advisor based reinforcement loop to train the models for a company’s specific churn footprint.

Problem statement

When clients close their accounts or cancel their subscriptions, it usually does not come as a surprise to the sales management. But for obvious reasons, the sales manager tries to work against the loss of a client with similar if not the same tools, processes and attitudes that have led to a client being dissatisfied. This is not to say that the sales manager can the sole reason for churn. But often clients do not or not sufficiently voice their issues and simply quit the relationship. To search, become aware and then listen for softer and indirect signs is at the heart of this use case.

Target market / Industries

The described use case can be efficiently applied to any industry that is providing services to a large number of clients and has a large number of transactions. Particularly following industries are benefiting most of this use-case:

  • Retail
  • Entertainment
  • Mass media


Using historical data, client communication and structured interviews with sales people, we create a company-specific churn ontology, that is then used to screen existing clients on an ongoing basis. Creating an interactive reinforcement loop with new churn-cases, this classification, predictor and indicator approach is ever more fine tuned to specific segments and service categories. As direct and ongoing KPIs churn ratios and client satisfaction are measured alongside generated revenues, profitability for the respective clients are classified as “endangered”. Usually a gradual improvement can be monitored within 3-6 months from the start of the use case.

The solution included:

  • Initial typical churn cause analysis based on historical data (client positions, and transactions)
  • Ideally inclusion of CRM notes and written sales to client communication (prior to churn)
  • Sales & Churn Ontology setup & subsequent ontology matching
  • Identification of likely company churn footprint & learning / improvement loops
  • Aggregation of findings, reporting, alerting & action notifications


  • Chief Executive
  • Chief Operations
  • Sales and Marketing

Data elements, Assets and Deliverables

As an Input from the client, the following items were used:

  • Historic data about churned clients
  • Client portfolios - positions / transactions
  • Ideally pre-leave client-advisor coms

Assets & Artefacts:

  • Client Behavioral Model
  • Churn Prediction
  • Action Monitoring

The deliverables included:

  • Sales & Churn Indicator Ontology
  • Use case specific orchestration flow

Tags / Keywords

#churn #churnanalysis #retail #entertainment #massmedia #retention #clientretention #customerretention