Which tasks are commonly supported by data mining applications in customer analytics and decision support? Select the single best answer.

Introduction / Context:
Data mining and advanced analytics systems go beyond simple transaction processing to derive insights and simulate outcomes. In marketing and CRM, two widely used activities are RFM-based segmentation and what-if (scenario) analysis for planning promotions, budgets, or pricing.

Given Data / Assumptions:

• Use case: customer analytics.
• RFM: Recency, Frequency, Monetary scoring and segmentation.
• What-if: changing inputs/assumptions to estimate outcomes.

Concept / Approach:

While pure OLTP systems “process transactions,” analytics platforms apply algorithms and simulations to historical data to discover segments and forecast impacts. Many data mining suites integrate segmentation tools (including RFM-like ranking or clustering) and support what-if modeling through decision trees, regression, or integrated OLAP scenarios.

Step-by-Step Solution:

Verification / Alternative check:

Commercial analytics tools and open-source stacks (SQL + Python/R) routinely implement RFM scoring and scenario forecasting for marketing experiments.

Why Other Options Are Wrong:

Process transactions only: transactional, not data mining. RFM only: too narrow. What-if only: too narrow.

Common Pitfalls:

Confusing OLAP pivoting with predictive what-if modeling; treating RFM as a substitute for true clustering or predictive scoring when richer methods are needed.

Final Answer:

Do both RFM and what-if analysis