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Answer: Create a k-means clustering model using BigQuery ML. Allow BigQuery to automatically optimize the number of clusters.
The correct answer is A. Creating a k-means clustering model using BigQuery ML is a highly efficient way to segment customers by their purchasing habits. This approach utilizes BigQuery's built-in machine learning capabilities, which can automatically optimize the number of clusters based on the data. K-means clustering is an unsupervised learning algorithm that is ideal for identifying natural groupings or clusters in your data, making it well-suited for customer segmentation. Other options, such as using Dataprep (B), Data Labeling Service (C), or getting a list from the Marketing team (D), are either less efficient or not as directly suitable for the task at hand.
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You are a data scientist working for a large retailer. The management has tasked you with segmenting the customer base based on their purchasing habits to inform targeted marketing strategies. They have provided you with the purchase history of all customers in a BigQuery database. You believe that multiple distinct customer segments exist, but the exact number and specific behaviors defining these segments are unknown. Given the need for an efficient and scalable solution, what approach should you take?
A
Create a k-means clustering model using BigQuery ML. Allow BigQuery to automatically optimize the number of clusters.
B
Create a new dataset in Dataprep that references your BigQuery table. Use Dataprep to identify similarities within each column.
C
Use the Data Labeling Service to label each customer record in BigQuery. Train a model on your labeled data using AutoML Tables. Review the evaluation metrics to understand whether there is an underlying pattern in the data.
D
Get a list of the customer segments from your company’s Marketing team. Use the Data Labeling Service to label each customer record in BigQuery according to the list. Analyze the distribution of labels in your dataset using Data Studio.