Course 107: Introduction to Loyalty Analytics

$225.00

USD

Summary

Moving from reporting to member intelligence — RFM modelling, predictive segmentation, and AI-driven decisions, handled in ways that preserve member trust.

Why Take This Course

You’re collecting loyalty data. It’s the fuel driving your loyalty strategy. But are you measuring what actually matters, and managing that data in ways that build trust with customers?

Course Preview

I want to close Section Two with a discussion of data hygiene. The quality of your analytical outputs is a direct function of the quality of your data inputs. Garbage in, garbage out is the oldest principle in data management — and it’s still true. Let me take you through five key principles. First: always assess the accuracy of self-reported data. We’ve already talked about how people misreport at registration. The follow-on practice is to build ongoing accuracy assessment into your operations — measuring bounce rates, running address validation, testing demographic distributions for statistical anomalies. Second: practice regular data hygiene. This means actively cleaning your database on a schedule. Resolve email bounces. Update postal addresses.

What You’ll Learn

Stop reporting and start analyzing. This course teaches you to move beyond dashboards to true member intelligence: RFM modelling, predictive segmentation, and AI-driven decision-making. Learn to build a measurement culture that drives continuous program evolution and member value realization, all while preserving trust with your customers.

Key Topics

  • Recency, Frequency, Monetary Value fundamentals
  • Member scoring and segmentation
  • Predictive analytics
  • Customer lifetime value modelling
  • Measuring engagement and retention

Featured Framework: RFM Modelling

The analytical bedrock that turns raw data into actionable member segments, and the starting point for prioritizing the members who matter most.

About this Course