Bodystore – Increasing yearly revenue growth by 100%
By completely re-doing the entire Search setup focusing on maximising all areas of machine-learning revenue skyrocketed to new levels.
The main goal for the test was to increase the sales volume as much as possible while retaining the strong ROAS levels, and this was fully accomplished.
With the completion of this case it became clear that there’s an issue with historical best practices of retaining control and that the main objective for the future of search is to work together with and not against algorithms. It’s about finding creative ways to give the algorithms a better foundation for their decisions as well as tailoring the accounts towards each client to better enable their business goals through the structure.
1. Data aggregation based on value per click
By grouping keywords in ad groups based on their value per click, it meant that even though smart bidding tries to cluster similar performing queries we could hold a strategic advantage by easing this process, especially in low data situations like the always highly valuable long-tail keywords.
2. Automated adgroup and campaign level search query analysis
Themed grouping of search query data allowed for intelligent use of N-gram logic by turning big data into actionable data. By having different themed levels to apply the automated negative keyword set up on, we were able to limit wasteful spend, while retaining as much revenue production as possible in an automated manner.
3. Tailored campaign specific audiences
Smarter segmentation and grouping of campaigns opened up the ability to tailor audience lists towards certain segments specific to the given campaigns. This allowed us to feed the smart-bidding algorithm, with extremely high-value audience signals.