The power of Geoblink-BBVA location-based spending data
The Geoblink tech team has been working hard to create insights and drive value for our customers following the recent exclusive licensing of BBVA spending data. This data set is unique and offers three particular advantages to its users. First, it features data such as average and total spend, number of cards, and movement of consumers for a range of different industries. This data allows users to track spending patterns in retail in combination with location. Second, the data is updated and integrated into the Geoblink app weekly. This enables retailers to follow changing spending behaviours almost in real time so they can quickly react with data to inform decisions. And third, metrics are available for several levels of geographic granularity to provide different lenses to analyse the data. These characteristics have already proven useful in understanding changing consumer behaviours during Covid-19 across different industries. For more information, check out this link for the impact on retail in the UK, or here for Spain.
Range
The data set features spending data for various industries, including fashion, bars and restaurants, groceries, gas, pharmacies, and more. Each of these categories comprises more specific subcategories, totalling over 70 subcategories. We have insight into important indicators like the average spend amount:
Here we see, for example, that the average spending per transaction on pharmacy and opticians is ~5 times that of the other four categories.
We can also see the movement of consumers: where spending on a certain category in an area originates, and where consumers from a certain geographic area spend. Typically, we use an isochronous catchment area to gather data on demographics, spending, and footfall.
Here the purple isochrone marks a 12 minute walk catchment area and the yellow denotes the boundary of the postcode around the POI. From this isochrone we can make inferences about average footfall, income, population and more. However, defining a catchment area in this way smooths out important nuances in where consumers that drive spending in an area are actually located, the percentage contribution of these areas to the total spending in a category, and the different demographics associated with these areas.
This is more relevant in some industries than others. The decision on where to buy groceries, for instance, is largely driven by convenience. The map below, shaded to show the relative spending on groceries per postcode, illustrates this: more than 60% of the spending on groceries in the postcode shaded in yellow is generated by customers originating from that geographic area. The next 20% of spending is derived from adjacent postcodes (shaded as darker blue areas). Retailers can hereby gear their marketing initiatives to these target areas.
Looking at spending on fashion in the same postcode (shaded yellow), the data tells a different story. Only 40% of the spending on fashion comes from customers originating from this postcode, and customers from distant postcodes are willing to travel the distance to spend on fashion there.
As such, fashion retailers may want to expand their focus to include these distant areas to get an understanding of the customer profiles located here.
In a previous blog post, we describe how aggregated and anonymised GPS signals can serve as a basis for an improved catchment area methodology. Still, we recognise that areas of high mobility do not necessarily equate to areas of high spending. We see the future of accurately mapping out consumer behaviour to arise from the use of both spending and mobility data.
Speed & Granularity
Two further facets of the data makes it particularly unique and valuable. First, the speed at which the data is received and processed; data is updated weekly so we are able to capture consumer behaviour almost in real time. Second, the level of geographic granularity that is available, within GDPR restrictions; at its most granular level we can visualise spending patterns in ~500 x 500 meter quadrants. This allows our customers to react to rapidly changing market conditions across their network of stores, armed with up-to-date information.
Recently we have been harnessing these strengths to understand how Covid-19 is impacting consumer behaviour. For example, taking a municipality level view of the country, we can compare weekly total spending before and during Covid-19 with the same week last year. The dark red shades that start covering the whole country starting in March shows how significant the change in total spending has been in comparison to the same period last year.
Not all industries are impacted in the same way. Spending on groceries (Figure 6), for instance, has surged compared to last year, with a peak in the week following the announcement of the state of alarm in Spain.
The facets of the data investigated here, including range, speed, and granularity are extremely valuable, especially given the uncertainty and volatility during Covid-19. This data set has boosted our predictive and analytical models, has allowed retailers to guide their decisions on where to (re)open stores, and has provided more clarity on consumption patterns and areas in the haze of Covid-19 uncertainty. The Geoblink tech team is very excited to be working with this exclusive data set, and we look forward to sharing more insights in future posts!
