Is RFM (Recency–Frequency–Monetary) analysis considered a BI reporting application technique?

Introduction / Context:
RFM analysis scores customers by how recently they purchased (Recency), how often (Frequency), and how much they spent (Monetary). The question asks whether RFM belongs to BI reporting.

Given Data / Assumptions:

• RFM uses simple aggregations per customer over a look-back window.
• Results are binned into ordered segments (for example, 1–5 quintiles).
• Outputs are typically delivered as ranked lists, heat maps, or summary reports for marketing actions.

Concept / Approach:
RFM is aggregation-driven and usually implemented with SQL (GROUP BY, COUNT, SUM, MAX) plus bucketing. It is often executed inside data warehouses and surfaced as reports or dashboards. It does not require predictive modeling; it is therefore well within BI reporting/OLAP usage.

Step-by-Step Solution:
Define RFM and the three measures.Recognize implementation via SQL aggregations and window functions.Identify the consumption pattern: marketers view/export segments from reports.Therefore, classifying RFM as a BI reporting technique is valid.

Verification / Alternative check:
Examine common BI suites: many ship RFM templates or sample dashboards; data science tools are optional for RFM.

Why Other Options Are Wrong:
It is not restricted to banks, real-time systems, or OLTP triggers; nor is it inherently a machine-learning algorithm.

Common Pitfalls:
Confusing RFM with predictive churn models; RFM is descriptive segmentation, not supervised learning.

Final Answer:
Valid statement — RFM is a classic BI reporting segmentation