A client has a business goal of increasing units sold by 10 percent. Which two data mining objectives would be consistent with their business goal? (Choose two.)

Question

Options

• ACreate a profile of customers most likely to default on their account.
• BCreate a profile of current high value customers' purchasing patterns.
• CReduce marketing costs.
• DDetermine which products should be grouped together on the shopping Web site.

• A
  5% (1)
• B
  81% (17)
• D
  14% (3)

Explanation

Heads-up: the stated answer of B and C appears to contain an error. The more defensible correct answers are B and D.

Why B and D are correct:

• (B) Profiling high-value customers' purchasing patterns directly supports selling more units - you identify what drives purchases and replicate that targeting toward similar prospects.
• (D) Grouping products together on a website is a classic cross-selling/upsell strategy that directly increases units sold per transaction - it's one of the most common data mining applications for boosting sales volume.

Why A and C are wrong:

• (A) Profiling customers likely to default is a credit risk objective - it protects against losses but has no direct link to selling more units.
• (C) Reducing marketing costs is a cost-reduction objective, not a sales-volume objective. A business could slash marketing spend and sell fewer units. It doesn't logically align with "increase units sold by 10%."

Memory tip: Map each answer to the goal metric. The goal is units sold - ask yourself, "Does this data mining objective directly move that needle?" Profiling buyers (B) and optimizing product placement (D) both directly influence purchase behavior; cost reduction (C) and default risk (A) do not.

If this is from a study guide or practice exam, I'd recommend flagging the answer key - B and D is the logically sound pair here.

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