Google introduces BigQuery ML machine learning templates for predictive marketing analytics
Posted on 24 June 2019
Enterprises are collecting and generating more data than ever. As a result, data scientists and analysts increasingly need to build robust machine learning models that can forecast business trajectories and help leaders plan for the future.
Google has announced BigQuery ML, a capability inside BigQuery that allows data scientists and analysts to build and operationalize machine learning models in minutes on massive structured or semi-structured datasets. BigQuery ML democratizes predictive analytics so that users unfamiliar with programming languages like Python and Java can build machine learning models with basic SQL.
To make it even easier for anyone to get started with BigQuery ML, they have open-sourced a repository of SQL templates for common machine learning use cases. Each template is tutorial-like in nature, and includes a sample dataset for Google Analytics 360 and CRM along with SQL code for the following steps of machine learning modeling:
- data aggregation and transformation (for feature and label creation)
- machine learning model creation
- surfacing predictions from the model on a dashboard
Here’s more on the three templates:
Customer segmentation—By dividing a customer base into groups of individuals that are similar in specific ways, marketers can custom-tailor their content and media to unique audiences. Users can implement a BigQuery ML k-means clustering model to build customer segmentations.
Customer Lifetime Value (LTV) prediction—Many organizations need to identify and prioritize customer segments that are most valuable to the company. This template implements a BigQuery ML multiclass logistic regression model to predict the LTV of a customer to be high, medium, or low.
Conversion or purchase prediction—There are many marketing use cases that can benefit from predicting the likelihood of a user converting, or making a purchase. This template implements a BigQuery ML binary logistic regression model to build conversion or purchase predictions.