Customer Lifetime Value

The power of Customer Lifetime Value (CLV) is that it clarifies key strategic considerations for our clients. At Mather, we have developed a CLV analysis that can accurately predict changes in retention due to certain events such as: transition to EZ pay, promotions, upgrades and price changes. Using our econometric model, CLV allows our clients to conduct certain Marketing analyses with increased rigor. How the model is built – and implemented – is crucial to making CLV scores actionable and statistically significant.

Key questions that we answer for our clients:

  • What is the likelihood a specific customer will leave?
  • How is a customer’s value affected by various attributes or targeted incentives? What will happen to my bottom line if those attributes change by 10%?
  • What customers should we acquire? What incentives should we use?
  • What specific conditions optimize profitability for a given level of risk?
  • How do we operationalize CLV to impact retention and drive increased customer satisfaction through targeted customer experiences?

The CLV metric allows you to allocate acquisition and retention resources to their most profitable use through rigorous fact-based analysis. CLV is the risk-adjusted operating margin for an individual customer within 36 months of acquisition.

CLV = [(Circ Rev + PP rev – Del Cost – Prod Cost)*(Expected Lifetime)] PV – Acquisition Cos

Once the source data is identified and available in a central location, the CLV model can be implemented in a scientific way

  • Construct a customer service history
    • Start date
    • Usage statistics (e.g., Prices, Payments, Complaints, Classified usage)
    • Service changes
    • End date
  • Attach indicators to the service history
    • Demographics (e.g., income, age, gender, children, education)
    • Marketing-specific metrics (e.g., preprint revenue, ROP adv. value)
    • Organization-specific metrics (e.g., payment methods, costs, credits)
  • Develop a retention (survival) model
  • Calculate a “best fit” (regression) model correlating the customer variables to retention rates
  • Calculate the CLV using the retention rates, revenue and cost metrics

Our strategic pricing analysis and actions provide many of the components of CLV. Retention has been modeled using survival analysis and A/B testing has validated price elasticity for customer segments.

Quantified variances in price elasticity by income, age, service. In addition, direct costs and preprint revenues by customer are integrated with retention analysis. Finally, targeted upgrades use analytics to increase circulation and revenue by account, which will improve CLV for those customers.

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