Question

A retail analytics team has a BigQuery table with millions of rows of customer transaction data, including recency, frequency, and monetary value features. The marketing department wants to group customers into an unspecified number of natural segments for targeted campaigns, but there are no pre-existing labels indicating which segment each customer belongs to. The team wants a fully SQL-based, low-code approach without exporting data. Which BigQuery ML approach should they use?

Accepted Answer

Create a k-means model using CREATE MODEL with MODEL_TYPE='KMEANS' and evaluate cluster quality with the Davies-Bouldin index

Answer

Create a logistic regression model using MODEL_TYPE='LOGISTIC_REG' to predict each customer's segment label

Answer

Create a multiclass classification model using MODEL_TYPE='DNN_CLASSIFIER' on the transaction features

Answer

Create an ARIMA_PLUS time-series model to forecast future customer purchasing behavior