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ACCA Strategic Professional · Strategic Business Leader · Big data and data analytics

Brightmoor Bank holds transaction data for millions of customers but has no pre-defined categories for them. The marketing director wants analysts to let the data reveal natural groupings of customers with similar behaviour, so that tailored products can be designed. Which technique is most appropriate, and why?

Clustering is most appropriate. It is an unsupervised technique that discovers natural groupings of similar customers without needing labelled categories. Classification would require predefined labels, which the bank lacks, while regression and time-series forecasting address numeric outcomes or trends rather than finding customer segments.

  1. AClustering, an unsupervised technique that groups similar records without labelled outcomesCorrect
  2. BClassification, a supervised technique that needs labelled customer categories to learn from
  3. CTime-series forecasting, because transactions occur over time
  4. DRegression, because it quantifies the effect of each variable on a single numeric outcome

Explanation

With no predefined categories, the need is to discover segments, which is unsupervised learning such as clustering. Classification needs labelled examples, so it fails the stated condition. Regression and time-series address a target variable or trend, not discovering groups.

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