With this level of automated insight immediately available, it has freed up a huge amount of our analysts time. This is time that can now be spent optimising the campaigns and improving the numbers off the back of this data insight, rather than simply measuring the metrics required to gauge success.
Taking into account stock thresholds, demand and ROI, we can make intelligent decisions as to how to best spend a client’s budget and continually monitor for potential trends. Because we store this data on a daily basis, over the course of our relationship with our clients we build up significant amounts of historic data and can perform this analysis for any time period. We could use this to analyse the changes in demand and performance that we saw over the previous year for example, and then use this insight to inform the buying and marketing strategy for the coming year.
For clients, this analysis has turned PPC from simply an advertising channel into a highly quantifiable, measurable and scalable look into the online behaviour of their customers. Looking at these reports, buying teams are also able to base wider decisions on the insights gained from PPC.
The above examples were all born through analysts performing custom analyses for their clients. Once certain patterns and repeatable actions are identified, these can be scheduled as regular pieces of optimisation or reporting. This automation frees up the analysts to start looking for the next opportunity for the client, meaning we are constantly improving performance and innovating for our clients.
The examples contained here are also all retail specific. A strength of our Datasets system and Stage as a whole is the ability to perform this same analysis using completely different data, as long as there is that common thread between the data sources. For example a hotel client could replace the product stock levels for room occupancy, an airline could use ticket availability, etc.