Data Analytics: Foundations to Practice · Segmentation and Cohort Analysis
Combining Segmentation Approaches
RFM and cohort analysis answer genuinely different questions, and combining them, along with other segmentation dimensions, often reveals patterns that neither approach would surface fully on its own.
RFM segmentation identifies which customers are currently valuable based on their recent transactional behavior, while cohort analysis reveals how behavior evolves over time depending specifically on when a customer first joined; these are genuinely complementary rather than competing or redundant approaches, and combining them, such as examining whether customers acquired through a specific channel and cohort period end up disproportionately represented in a valuable RFM segment later on, can reveal patterns neither approach would fully surface working entirely on its own.
- RFM identifies current customer value; cohort analysis reveals how behavior evolves by signup timing; combining them can reveal patterns neither surfaces alone.
- Segmenting by acquisition channel often reveals different retention and value patterns, since customers from different channels differ systematically in intent and familiarity.
- Layering too many segmentation dimensions produces increasingly narrow segments that eventually stop adding insight and become too small or too numerous to act on.
- The right segmentation combination should be driven by the specific business question being asked, not an open-ended instinct to segment as many ways as technically possible.