Loyalty analytics: measuring loyalty program performance

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The number of loyalty program memberships has increased, but active engagement has declined relative to total membership. As a result, loyalty programs are experiencing competition for customer wallet share.

To be successful, loyalty programs can use data from advanced modeling methods to identify value and predict the future behavior of their member base. This will enable program managers to maintain and potentially grow their market share.

Identifying loyalty program goals

Despite the ubiquity of loyalty programs, each program is unique. It is important to consider a program’s objectives in order to tailor key performance indicators and monitoring dashboards. Generally, a loyalty program’s objectives fall into one or more of the following categories:

  • Collect and use customer information
  • Retain current customer base
  • Increase customer base
  • Build brand affinity
  • Generate additional profit

Growth in membership for select industries

Developing analytic targets

In addition to simply highlighting problem areas, successful analytic processes to identify the root causes of problems; a program manager then can determine potential actions that could improve outcomes. Examples of business processes that companies can examine and monitor include:

  • Testing the impact of program structure changes on performance metrics
  • Testing the impact of changes in the underlying member population mix
  • Providing a standardized basis for understanding and analyzing the different aspects of the program
  • Forecasting future earnings and redemption behavior at the member level
  • Discovering non‐trivial relationships between components of the loyalty program in order to provide a competitive advantage

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Mark Jones

Actuarial Services Advanced Analytics Leader, PwC US

Martin Ménard

Actuarial Director, PwC US

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