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Store sales form the lion's share of MediaMarkt's revenue across Europe. Yet, all most online advertising was only measured online sales. With the online-to-offline effect become more pronounced in other research, MediaMarkt was looking for a way to tie these two together.
Getting online sales data generated via performance marketing was straightforward via Google Analytics 360.
For offline sales, we used a methodology called Store Sales Direct (Project Pegasus back in 2019). This methodology is similar to customer match. A user clicks on an ad, and Google captures their logged-in credentials via their Google account.
MediaMarkt then had a loyalty card program to register the purchase in the store. If the same email/name was found, a match can be made to the actual ad click and the sale in the store.
However, the main limitation was that not everyone is a logged-in Google user. So another layer was added on top of that, by extrapolating the remaining % that we couldn't match.
This data was then ingested into Google Bigquery where the online sales dataset and the offline sales dataset was joined. In both of these datasets, we had the product ID that was sold in that transaction as a parameter. Now we could see offline and online sales per product and per product category
It became evident that some high performing categories and products were not necessarily the best performers in-store. And actually, when looking at the total performance across online and offline it became clear that some product categories and products were performing much better than the ones that performed only well in online.
As a consequence, a huge shift in ad spend happened. By pushing products that had great omnichannel performance, and decreasing ad spend on products with low omnichannel performance, the the entire category mix and product mix in performance marketing changed subsequently.
Online Profit on Ad Spend moved to Omnichannel Profit on Ad Spend. Omnichannel Profit on Ad Spend increased by more than 50% without decreasing spend.