RFM scoring practice: is it common to bucket customers into five groups and assign scores 1–5 for each of Recency, Frequency, and Monetary?

Introduction / Context:
RFM analysis ranks customers on three axes: how recently they purchased, how frequently they purchase, and how much they spend. A widely taught approach bins each axis into five ordered groups (quintiles) and assigns scores from 1 to 5.

Given Data / Assumptions:

• The dataset includes customer transactions over a chosen timeframe.
• Scoring uses quantile bucketing or business-defined thresholds.
• Scores are combined into an RFM code (e.g., 5-1-3) for segmentation.

Concept / Approach:
Quintile scoring balances granularity with simplicity, yielding 125 possible RFM cells (5 * 5 * 5). Alternatives exist (terciles, deciles), but 1–5 quintiles are common in practice and pedagogy, making the statement valid.

Step-by-Step Solution:
Choose look-back window; compute recency (days since last purchase), frequency (number of orders), and monetary (total spend).Bin each metric into 5 ordered groups; assign 1–5 scores (direction adjusted so “5” is best).Combine into an RFM code and analyze segments.

Verification / Alternative check:
Marketing analytics references commonly present 5-bin RFM as a default; many BI templates implement it.

Why Other Options Are Wrong:
RFM does not require deciles, z-scores, a specific industry, or minimum purchase counts beyond reason.

Common Pitfalls:
Failing to invert recency (lower days = higher score); using uneven buckets that skew segments.

Final Answer:
Valid statement — quintile scoring 1–5 is a common RFM method