NCA-GENL Question 117
Select 3A retail company is analyzing customer purchase data from a large dataset to identify trends and improve product recommendations. They decide to use data mining and data visualization techniques. Which of the following steps would be most appropriate for extracting actionable insights in this scenario?
- A
Identify patterns in customer purchase behavior using clustering algorithms
- B
Visualize customer demographics and purchase trends using heatmaps or bar charts
- C
Manually review each transaction to detect anomalies in the data
- D
Use association rule mining to find relationships between frequently purchased items
- E
Remove all outliers from the dataset without understanding their context
Show answer and explanation
Correct answers: A, B, D
Explanation
Extracting insights from large datasets involves using scalable and efficient techniques like clustering, data visualization, and association rule mining. These methods help identify patterns, trends, and relationships that can drive actionable recommendations. Manual review or arbitrary data removal are not practical or effective approaches for handling large-scale data.
- A. Correct.
Clustering algorithms are effective for identifying groups of customers with similar purchase behaviors, which can be used to create targeted recommendations.
- B. Correct.
Data visualization methods like heatmaps or bar charts are essential for presenting trends and patterns in an intuitive, easy-to-understand format.
- C. Incorrect.
Manually reviewing each transaction is not feasible for large datasets and is not a scalable approach for extracting actionable insights.
- D. Correct.
Association rule mining is commonly used to discover relationships between items, such as identifying items frequently bought together, which is highly relevant to enhancing product recommendations.
- E. Incorrect.
Blindly removing outliers without understanding their context could lead to the loss of valuable or meaningful data, which could distort the insights derived.