Accurately measuring the impact of marketing campaigns is a high priority for brands. It allows them to understand the customer journey or sales cycle, to optimise the media mix, and to justify ad spend.
Demand for solutions that enable brands to attribute sales back to specific activities is driving massive investment, with the global multi-touch marketing attribution software market expected to be worth over $1.6 billion by 2023.
Unveiling the path to purchase
When the customer journey takes places exclusively online, attribution is relatively simple. While there may be some challenges in tracking the user across multiple platforms and devices, there is a tight set of data used for attribution purposes to understand the impact of marketing activity. A user may see a display ad for a department store, click through to the store’s website and view a pair of shoes. They may then be retargeted with an ad for those shoes before ultimately making a purchase. This process is entirely traceable, with the eventual sale attributed to multiple advertising touch points.
But when we cross into the real word, attribution becomes more difficult. Imagine if the same user decided they wanted to try the shoes for size and headed for their local store to make the purchase. It is much harder for the department store to attribute that sale back to their digital marketing efforts.
This is where data around people movement comes into its own. Using vast geospatial data combined with secondary data sets showing points of interest, marketers can analyse in-store footfall and understand offline shopping behaviour. They can even use additional data sets such as loyalty programme information to link actual sales with exposure to marketing messaging, closing the attribution loop.
Offline to online: connecting the dots with data
Spatial data is particularly effective at measuring the impact of offline advertising formats, such as out-of-home (OOH) digital billboards. Using the GeoSpock spatial big data platform retailers can run complex queries, combining raw data around people movement with other data sets showing the location of advertising billboards and their stores. They can filter the data to show people located within a specific radius of a billboard at the time their ad was shown, and then follow that group’s subsequent movements, seeing which ones visited a store within a set timeframe after being exposed to the billboard.
In this way the retailer can assess the value of individual billboards within the context of outcomes which happen in store. They can determine whether billboards are effective or not, which locations perform the best, and which ads generate the best response. When different data sets such as spatial data, sales data, and loyalty data are hooked together they can provide real attribution of the impact of that OOH advertising.
Measuring the true impact of their marketing activities is a high priority for brands and spatial data provides the answer to marketing attribution in the real world.
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Further reading
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