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Geolocation in retail trading
In recent years there has been a boom in the Location Intelligence field due to increasingly broader access to accurate data about the environment and consumers, as well as advances in analytical techniques and spatial visualizations.
The retail sector can benefit the most from these developments, as good location decisions and a better understanding of consumer behaviour are key to gaining a competitive edge in commerce. This will be particularly important in the post-COVID reality, when the former purchasing behaviour of customers will change or will at least be redefined.
Key trends in spatial technologies
When we think about the types of data that have the greatest potential to be used in commerce, it is above all so-called mobile data that comes to mind. More and more providers are offering aggregated data on population movements; this data usually comes directly from telecommunication companies or from mobile applications which allow the monitoring of user locations. Thanks to mobile data it is possible to measure street traffic in locations that are of interest to us, e.g. in front of our own stores, the stores of competitors or candidate sites for the expansion . Some suppliers are even able to profile passers-by in terms of their purchasing preferences and demographic profile. However, you should bear in mind that mobile data is not perfect and individual providers cover only part of the population, so to be able to draw reliable conclusions the data has to be transformed and appropriately extrapolated. Another drawback is that the data is relatively expensive.
Pic. Example of an analysis of changes in the development of pharmacy chains in Sweden.
Source: PwC
Pic. Example of a heatmap showing the volume of traffic in the central part of a district in one of the European capitals.
Source: PwC
Another noteworthy data category which is becoming increasingly popular with the retail sector is satellite data. Access to good quality satellite images is not as expensive as it was several years ago, and companies with their own microsatellite constellations have appeared on the market, which enables the downloading of large numbers of images and scenesfrom any selected area in the world. This data combined with image recognition techniques allow automated analysis of the environment; for example: calculating the number of cars in a parking lot or of passers-by in places of interest to us, to assess the level of traffic in the vicinity of our competitors. This technique is particularly widely used in the DIY industry and forhypermarkets.
Recently, granular data about customer purchasing behaviour has also became more accessible; it is possible to quantify a population’s purchasing portfolios by individual postal codes and to analyse consumer loyalty to particular brands in various types of localities. It is also possible to historically track the dynamics of openings of stores of individual retail chains, which enables recreating the expansion strategy of any competitor.
Application of advanced spatial analyses in commerce
New types of spatial data in conjunction with artificial intelligence methods and growing computing capabilities of servers and cloud solutions enable the solving of increasingly complex business problems.
A classic example of the use of spatial technologies in trade is the support of the expansion process. Correctly constructed geostatistical models are able to accurately estimate the revenue potential of new locations taking into consideration factors such as competition, pedestrian or car traffic, demographic characteristics of the local population, etc. Moreover, these models indicate other criteria for optimal location for individual store formats, which significantly reduces the risk of selecting an unfavourable location. It also allows for the development of an expansion strategy for each individual format and adapting the formats to the nature of particular locations.
Pic. Example of Warsaw citizens segmentation, taking into account demographic factors and consumer habits.
Source: PwC
The combination of the Big Data technology (large data sets) with spatial analyses enables performing advanced analyses of the product mix and pricing. It is possible to make prices for certain products dependent on the types of locations or, going even further, on dynamic spatial data, such as forecast street traffic or weather conditions. We can imagine a scenario in which prices of cold drinks or ice cream in places visited mainly by tourists are increased in advance when the weather forecast predicts an increase in temperature. Such techniques are used by such brands as Starbucks and McDonald’s on the American market.
There are far more use-cases for spatial technologies, in particular in combination with the internal data of a retail chain. At the same time, advanced spatial analysis requires a team of analysts with very broad competences (statistical modelling, analyses in the GIS systems, database engineering, visualization techniques and business intelligence, etc.) and an understanding of a specific branch of business. The key to success is to choose between building internal geoanalytical competences and outsourcing the said activities to a specialized external team. Both solutions have their advantages and disadvantages so we would encourage you to analyse both scenarios in detail.
Pic. Examples of visualizations of modelling results based on geolocation factors.
Source: PwC
Moreover, an expansion strategy based on spatial data does not have to be limited to the brick & mortar channel, more and more frequently geoanalytical techniques are being used to develop omnichannel strategies. It is possible to select optimal locations for click & collect points and showrooms depending on the characteristics of the environment such as the character of the buildings, the number of people working in the vicinity by industry or the demographic profiles of the local residents. Moreover, accurate population profiling from the perspective of purchasing habits and affluence enables advanced marketing campaigns to be conducted based on so-called Geo-fencing or other forms of geo-targeted advertising. Thanks to precise identification of the target group with the use of spatial data, it is possible to optimize marketing budgets and ensure better campaign conversion. In addition, Google’s and Facebook’s marketing platforms offer the possibility of measuring conversion of marketing activities at individual store level (we know how many people who saw an online advertisement actually appeared in the store), which facilitates measuring the effectiveness of omnichannel marketing campaigns.
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