Since 2013, EBP has been developing and refining Walkalytics, an approach to data analytics for business-relevant questions regarding pedestrian mobility. At the heart of the approach lie isochrones, which are calculated for every square meter of an area-of-interest. In the early stages, we successfully applied Walkalytics mainly in urban and transportation planning. In this blog post, however, I want to demonstrate how Walkalytics can help you in geomarketing on very small scales.
Example 1: Narrow the audience of a direct mailing campaign
As a first example, let’s assume you are big transportation agency with a large customer database that also features a postal address for each of your customers. Let’s further assume you want to send some customers a special offer by mail – but only to the segment of customers who are most likely to accept your offer. In other words, you want to narrow your target audience, if only to save printing and postage costs. A sensible criterion to optimize your campaign’s target audience could be the time that customers take to reach the next transit stop (or any other customer contact point of your liking) on foot.
With Walkalytics, we have the solution to your task: We’ve taken all Swiss addresses from the federal register of buildings and calculated the walking time from each address point to the closest transit stop. You can use this massive dataset to narrow the segment of customers that will be targeted in your campaign. You don’t even have to ask your in-house geodata expert to help you with your filtering: everything is done directly in your customer database based on our augmentation of your CRM data!
Example 2: Find the optimal location for your customer contact points
Let’s assume you are a manager in a retail company which wants to find the optimal locations of new service points. As you have a business with lots of walk-in customers (i.e. pedestrians), this means you want to find locations that serve many non- or under-served people within a sensible walking distance or time – say within 5 minutes walking time.
For addressing this need, we took advantage of another government data set: The population and household statistics (STATPOP) and the business demography statistics (STATENT) have a number of indicators that are measured in 100×100 meter units all over Switzerland. For each of the effectively 360,000 units, we calculated a walking isochrone and aggregated relevant indicators such as the reachable residential population or reachable number of, e.g., third sector employees. After completion of our analysis, we know for each 100×100 meter square in Switzerland how many people can reach this location within 5, 10 or 15 minutes of walking. Since your business relies on walk-in customers, this informs your choice of where to open your next service point(s).
Did these examples whet your appetite for geo-augmenting your customer and site data? Are you, for example, interested in filtering your customer database according to the reachibility of your service points? Do you want to optimize locations based on socio-economic statistics? Let’s have a talk, e.g. during GEOSummit in Bern or online using e-mail or Twitter!