How Clorox is using Google BigQuery to build Customer Data Depot

What do you do when you have lots of data from legacy systems, online and offline customer interactions and also engagement data from marketing data platforms yet are not able to act on it faster than your competition? You look for ways to integrate and harmonize it faster, at scale and derive actionable insights that data scientists can use to run targeted marketing campaigns for deeper analysis, A/B testing and further business decisions.

All businesses which support users and customers from consumer as well as business segments, face a tremendous challenge – a deluge of “dark” data sitting in silos, shifting consumer preferences that call for integrating internal and external data sources to build solid understanding of customer behaviors and purchasing intents.  As the business scales, it becomes humanly impossible to provide a quick insights from all this big data unless engineered in a fast and efficient manner on the cloud.

Clorox is a Fortune 500 CPG company with 8,100 employees. Besides being the most popular brand in home and business cleaning products, they also have business interests in the beauty and cosmetics market through Burts’Bees brand.  Being data-driven, they seek innovative new ways to capture and expand their consumer base.

The solution comprises of Integration of three marketing data sources containing end consumer data from marketing systems viz BlueKai, MOAT and DCM . This includes data translation and transformation activities as part of the integration to join these data source together to derive detailed insights.

Our project entails:

  • Extensive data preparation for modeling
  • Defining data model for joins and building schema in BigQuery,
  • Building Dataflow components for integration
  • Include error handling and notification for batch integration

“We are building a robust data pipeline – extracting and harmonizing data from three of our major sources for marketing data  on the Google Cloud Platform. We went live in production recently after few agile development sprints and look forward to building more engaging data products for our business to consume in 2019 and beyond”

– Krishnan Vishwananthan,  IT Architect, Clorox

Please contact us at info@springml.com if you have any questions or would like to know more about how to use Google BigQuery to build next generation Datawarehouse.