Case study
How our digital media client adapted systems to understand the attribution data they held
We had previously worked with the team at a different media company, when they moved on they called us to help update their data systems.
The Situation
Our client has grown very successfully over the past few years. Gathering a vast amount of data from a high volume web site with over 500k visits monthly, they realised their legacy system could not give the insights they required.
The legacy system was not flexible and did not have the functionality to allow the business to grow. Although money had been provided for new data technology, the real challenge was to produce a system before competitors gained a hold on their market. The Google stack was chosen as the most appropriate technology for this project.
The Challenge
Our client faced a common issue, with a pot of money allocated for sales expenditure, they were unable to attribute which sales campaigns were providing sales. With their own website, white-label websites, television campaigns, and social media campaigns, it was not clear where to invest further money for the biggest impact.
Senior Management desired a 360-degree view of their business and they no longer wanted to wait for daily data feeds. The business required modern near real-time analytics which would provide a self-service function enabling a larger pool of users to interrogate the data held and attribute return on investment (ROI).
The Results
We built a Big Query data lake pulling data from Google Analytics Premium, Social Media, Display, TV, Pay Per Click, Bing Ads, Offline Telephone Channels and Affiliate Network data. The data was re-modelled and presented back to the business through Tableau tying up complex sales with a last non-direct attribution model.
Senior Management and the business rapidly gained a clearer understanding of their attribution data. This enabled decisions to be made regarding which campaigns worked and should be expanded and those which were no longer required. Data was then further utilised to drive a machine learning algorithm that enabled the business to target match their offering with clients on more attributes than just price, driving up sales by 8%.
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