The first run of analysis was to use Datasets to join revenue, stock levels and search engine data and then split by product area (mirroring our account structure). We quickly realised that the first areas to address were non-linear relationships as shown in the following basic examples:

We may find for example, that there is high search volume (demand) but a low conversion rate, which immediately suggests that there is an issue with the product or page itself. Is the pricing competitive? Are we missing certain common sizes? Are competitors offering something more suitable? The actions may also be to test adjustments on the client’s site such as pricing or shipping options. If the price of a product is resultantly dropped, we can then continue to monitor the conversion rate at particular prices to discover what the optimal price for that particular product appears to be, based on customer actions.


Once we were able to prove the value in performing this analysis and that we were getting valuable actions, we looked at more complex examples using more granular data. Additional metrics such as new vs. returning customer data, as well as assisted conversions and revenue allow us to make even more informed decisions on how to better target new customers. We can gauge what the full impact of these campaigns would be on other channels by looking beyond simple last-click attribution.


There is also the related ability to monitor impressions and clicks of different product groups side by side, which gives some insight into customer demand for individual products or ranges. This can be fed back to the client to assist them with their future buying strategy.


We have one tool that provides us with data on:

  • Size and Scale of Product Range  
  • Stock Levels
  • Customer Demand
  • Advertising Spend
  • Revenue Generated
  • Direct and Assisted conversions
  • Cost per New Customer
  • Overall ROI