Which tasks are commonly supported by data mining applications in customer analytics and decision support? Select the single best answer.
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AProcess transactions only
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BDo RFM analysis only
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CDo what-if analysis only
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DDo both RFM and what-if analysis
Answer
Correct Answer: Do both RFM and what-if analysis
Explanation
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:
1) Exclude options limited to transactional processing—those are OLTP responsibilities.2) Recognize that RFM segmentation and scenario analysis are both standard analytics tasks.3) Choose the option that includes both RFM and what-if analysis.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