Catchment areas are essential for commercial entities such as retailers, wholesalers and other businesses since they draw most of their business from them. When you talk about the retail industry there has been a marked shift in recent years especially in how brick and mortar stores have been functioning, in fact, many of them have been facing closure. This disruption has been causing what can only be called a ‘retail apocalypse’ – and many businesses have arrived at a crucial point in their decision-making process regarding where a new store should open or which stores should be closed or kept open. When you think about these important considerations spatial data science can help in the identification of catchment areas via mobility data – which can help in tackling the challenges effectively.
Calculation of catchment areas is really essential for booming business sectors such as retail and real estate, as it helps businesses in better understanding its customers and then developing business strategies accordingly. Ranging from identifying circular trade areas through isochrones, to gravitational models to the usage of more advanced techniques that look at location-specific variables – calculating catchment areas generally depends upon the kind of information and data that is available.
Historically speaking, two approaches have been commonly used for the identification of catchment areas. The first being circular catchment area, which is used to define the attraction on the basis of a fixed radius around the store; and the second one being Isochrones area which is calculated on the basis of the maximum reach that the store has – that is through transportation such as walking, driving, cycling, etc. and the time range. Isochrones areas generally depend on cartographies and mapping of all the available streets and roads in given geography, which obviously helps in reaching a more accurate definition for the retail space in question.
Although these methods have worked traditionally one also has to factor in now the consideration that people who are visiting these retail spaces, could actually at a point be living, working or spending time in other places as well, which is outside the target catchment area. Hence, customer profiling needs many more considerations apart from these, and mobility data has proved to be quite helpful in doing so. Identification of human mobility with the help of GPS data tells you about footfall and origin-destination matrix as well as the various points of interest.
Additionally, one has to also calculate catchment areas for locations which may not have historical consumer data. Why it is so important to have the said data is because it will help in modelling any potential cannibalisation that might happen amongst branches of a business.
Hence, in order to identify optimal locations for a commercial space, generally, the following methodology is considered:
- Data Discovery that will help in the identification of relevant data to collate.
- Grid Selection and Cell Enrichment based on the data that has been collected in the discovery phase. Generally, this data concerns human mobility, commercial aspects, as well as socio-demographics.
- Calculation of Catchment Area for the subset selected. The subset is selected generally by the application of a filter for the identification of cells that have high commercial potential – as gauged by human mobility data collected.
- Optimisation of Algorithms that will help in the identification of the best locations. An approximation algorithm is generally developed using a greedy approach in linear programming on every step.
Mobility data that has been obtained from mobile devices, is a powerful tool and the catchment areas that are identified by the cleaning and aggregation of this data offers retailers and investors a much clearer picture regarding their target audience. Adding this data to location intelligence toolbox can help in modernising business decision making and help businesses in staying agile.
As witnessed by the collapse of the global economy due to the current pandemic, it has become even more imperative for retail and other sectors to use techniques and tools that will help them in making informed decisions that will allow them to get a competitive advantage. Mobility data when enriched with other data streams and helped by optimisation and other spatial techniques can aid in making a difference and tackling the challenges that the sectors are facing today as well as those that might arise in the future.
Authored by: Ashwani Rawat, Co-Founder & Director, Transerve. Originally published here.