Some details
Problem: The customer actively used marketing traffic channels to promote their services and goods. Data on channels is presented in Google analytics (GA), Big Query (BQ), and in offline databases without integration with GA. The functionality offered by GA didn’t permit full analysis of the data through the sales channels.
For marketing expense optimization, the customer decided to expand and modify
its analytical tools to be able to deal with complex data structures – integrating more sophisticated attribution algorithms such as Markov chains was a top priority.Solution: Create a web solution with rich functionality. Create an infrastructure for interaction between the BQ and the data warehouse for attribution and implemented mathematical models of channel attribution. In the web interface, the user can choose from a wide range of parameters to build multi-channel report.
Outcome:
- With the help of the custom reports, the customer can now choose a specific multi-channel attribution model
- Reduced budget for marketing and increased its effectiveness of marketing campaigns
- Optimised return on expenditure due to corrected data flows
- Our client was happy to endorse the quality of our work
Technological stack: Python 2.7, Python 3, Django, PostgreSQL, SQLite, Big Query, Google Analytics.
