# analytics-componentized-patterns

**Repository Path**: mirrors_GoogleCloudPlatform/analytics-componentized-patterns

## Basic Information

- **Project Name**: analytics-componentized-patterns
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: Apache-2.0
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2020-08-08
- **Last Updated**: 2026-02-21

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

[![License](https://img.shields.io/badge/License-Apache%202.0-blue.svg)](LICENSE)

## Analytics Componentized Patterns

From sample dataset to activation, these componentized patterns are designed to help you get the most out of [BigQuery ML](https://cloud.google.com/bigquery-ml/docs) and other Google Cloud products in production.

### Retail use cases
* Recommendation systems:
  * How to build an end to end recommendation system with CI/CD MLOps pipeline on hotel data using BigQuery ML. ([Code][bqml_mlops_code] | [Blogpost][bqml_scann_guide])
  * How to build a recommendation system on e-commerce data using BigQuery ML. ([Code][recomm_code] | [Blogpost][recomm_blog] | [Video][recomm_video])
  * How to build an item-item real-time recommendation system on song playlists data using BigQuery ML. ([Code][bqml_scann_code] | [Reference Guide][bqml_scann_guide])
* Propensity to purchase model:
  * How to build an end-to-end propensity to purchase solution using BigQuery ML and Kubeflow Pipelines. ([Code][propen_code] | [Blogpost][propen_blog])
* Activate on Lifetime Value predictions: 
  * How to predict the monetary value of your customers and extract emails of the top customers to use in Adwords for example to create similar audiences. Automation is done by a combination of BigQuery Scripting, Stored Procedure and bash script. ([Code][ltv_code])
* Clustering:
  * How to build customer segmentation through k-means clustering using BigQuery ML. ([Code][clustering_code] | [Blogpost][clustering_blog])
* Demand Forecasting:
  * How to build a time series demand forecasting model using BigQuery ML ([Code][demandforecasting_code] | [Blogpost][demandforecasting_blog] | [Video][demandforecasting_video])


### Gaming use cases
* Propensity to churn model:
  * Churn prediction for game developers using Google Analytics 4 (GA4) and BigQuery ML. ([Code][gaming_propen_code] | [Blogpost][gaming_propen_blog] | [Video][gaming_propen_video])

### Financial use cases
* Fraud detection
  * How to build a real-time credit card fraud detection solution. ([Code][ccfraud_code] | [Blogpost][ccfraud_techblog] | [Video][ccfraud_video])


[gaming_propen_code]: gaming/propensity-model/bqml
[gaming_propen_blog]: https://cloud.google.com/blog/topics/developers-practitioners/churn-prediction-game-developers-using-google-analytics-4-ga4-and-bigquery-ml
[gaming_propen_video]: https://www.youtube.com/watch?v=t5a0gwPM4I8
[recomm_code]: retail/recommendation-system/bqml
[recomm_blog]: https://medium.com/google-cloud/how-to-build-a-recommendation-system-on-e-commerce-data-using-bigquery-ml-df9af2b8c110
[recomm_video]: https://youtube.com/watch?v=sEx8RwvT_-8
[bqml_scann_code]: retail/recommendation-system/bqml-scann
[bqml_mlops_code]: retail/recommendation-system/bqml-mlops
[bqml_scann_guide]: https://cloud.google.com/solutions/real-time-item-matching
[propen_code]: retail/propensity-model/bqml
[propen_blog]: https://medium.com/google-cloud/how-to-build-an-end-to-end-propensity-to-purchase-solution-using-bigquery-ml-and-kubeflow-pipelines-cd4161f734d9
[ltv_code]: retail/ltv/bqml
[clustering_code]: retail/clustering/bqml
[clustering_blog]: https://towardsdatascience.com/how-to-build-audience-clusters-with-website-data-using-bigquery-ml-6b604c6a084c
[demandforecasting_code]: retail/time-series/bqml-demand-forecasting
[demandforecasting_blog]: https://cloud.google.com/blog/topics/developers-practitioners/how-build-demand-forecasting-models-bigquery-ml
[demandforecasting_video]: https://www.youtube.com/watch?v=dwOt68CevYA
[ccfraud_code]: https://gitlab.qdatalabs.com/uk-gtm/patterns/cc_fraud_detection/tree/master
[ccfraud_techblog]: https://cloud.google.com/blog/products/data-analytics/how-to-build-a-fraud-detection-solution
[ccfraud_video]: https://youtu.be/qQnxq3COr9Q




## Questions? Feedback?
If you have any questions or feedback, please open up a [new issue](https://github.com/GoogleCloudPlatform/analytics-componentized-patterns/issues).

## Disclaimer
This is not an officially supported Google product.

All files in this repository are under the [Apache License, Version 2.0](LICENSE.txt) unless noted otherwise.