Large US-based Cosmetics Brand
Client
Goal
To fix and improve their existing Google Analytics implementation and connect Web and Mobile data to get a 360 view of their users for advanced analysis by their product and marketing teams.
Services
- Analytics Strategy (Web + Mobile)
- Analytics Implementation
- Audience Activation Consulting
- Tag Management Support
- Data Visualization
Technologies Used
- Google Analytics Universal Analytics
- Google Analytics 4
- Google Firebase
- Google Tag Manager (with Data Layer Design)
- Google BigQuery
- Google Data Studio
Project Outcome
- Build reporting tables in Redshift using Redshift’s version of Postgres 8 and feed those tables back into the cluster using Python and S3 buckets. An atypical design pattern at the data warehouse layer; yet this allowed the marketing and data teams, and the C-suite to use the same tools they were familiar with – such as Tableau Server and GSuite (now Workspace) – without configuring another connector.
- Build custom API clients for marketing platforms and tools to automate reporting.
- Added data compression middleware when moving data into document storage, and keeping data in the same AWS region. This resulted in huge cost savings for sparse tables.
- Set the schema with distribution keys and sort keys optimizing for query execution time at scale across shards in the cluster. Size of the data lake made this truly Big Data, and the optimizations improved some query speeds by over 1000x.
- Validated data with custom code for reconciling marketing data between the website, Google, Facebook, and other platforms.
- Democratized data access across the company by automating export of sales and marketing data to familiar tools, including Google Sheets and Slack bots, as well as published Tableau dashboards.
- Automated the marketing data pipeline, from connecting to vendors to reporting. Went from over a dozen office workers manually collecting and entering data, to 0; all with a cost-effective, fast, and accurate system hosted on AWS.
- Slashed data warehousing costs with Redshift optimizations, S3 Glacier, and data compression. Huge savings on EC2 instances and EBS storage migrating processes to serverless with Lambda and S3.
- Unified data previously siloed in the (many) marketing platforms used by a modern ecommerce company.
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