Marketing Insights Platform

Problem

• Ingesting marketing data from various data sources
• Campaign performance and audience segmentation
• Cross-sell/Upsell, next best action
• Enabling trend discovery to unlock new revenue streams

Challenges

• No centralized Data Science platform for Data Scientists
• Ingesting marketing data from various data sources
• Visualization of cleaned data in R Studio and Tableau
• Backfilling of Campaign Manager data into a consolidated data lake

Solution

• Marketing data from multiple data sources were imported into Google Cloud Storage
• Google Double Click campaign data was transferred to BigQuery using Google BigQuery Transfer Service
• MOAT Campaign viewability data containing ad impressions and content views were bulk uploaded on a daily basis
• Audience Segments collected from Oracle Bluekai pixel tags were bulk uploaded on a daily basis
• Marketing Data Pipeline
• Data from Marketing data sources were pushed into Google Cloud Storage
• Source datasets were stored in Google Cloud Coldline Storage to use it only on requirement basis
• Google Cloud Dataflow transformations were used to enrich the incoming data
• Google Cloud Dataprep was used to cleanse the data
• Google Composer was used as a workflow orchestrator to run the marketing data pipelines. Google BigQuery Transfer Service, Google Dataflow jobs and Google Dataprep Jobs constitutes Marketing Data Pipeline and triggered daily by Composer
• Consolidation of multiple marketing data was done using Google Cloud Dataflow
• Marketing Datawarehouse
• Enriched marketing data were stored in Google BigQuery
• Marketing Data Visualization
• R Shiny App and Tableau read the enriched data from Google BigQuery for creating marketing insight dashboards