What’s the Catchment?

Catchments are more than just shapes on a map, they visualise essential data for location planning. In this blog post we are going to explain what makes a catchment, the benefits of building and analysing catchment areas in location planning, some of the different ways in which you can tackle this common problem and how to choose the right one for the task in hand. 

What is a catchment?

Catchments, sometimes known as Trade Zones are the geographic areas from which retailers, services and organisations draw their customers. Depending on the method used, they can vary significantly in size and shape, influenced by factors such as transport links, population density, and physical barriers.

Determining a location’s catchment is pivotal for location planning. Know where your customers are coming from by mapping and visualising your main service areas.

Knowing about your existing estate can be used to your advantage when planning new locations. Understand gaps and overlaps, and get a sense of the overall market by deep-diving into competitor locations. Streamline operations and optimize strategic planning, ensuring your offering is geographically and demographically tailored.

By combining the catchment areas with other data your business can uncover powerful insights; what demographic characteristics surround your best and worst performing stores, which competitors are also within your catchment, are some of your existing stores already cannibalising each other?

Here’s a handful of common approaches for determining catchment areas

Straight line

Also known as buffers, distance based catchments or ‘as the crow flies’, these are simply a distance radius around the site, and are the most basic approach to catchment area analysis. Buffer catchments can be really useful for quick, simple analysis and opening the door for deeper questions.

Drive Time

Sometimes known as isochrones, the trade area or catchment is defined by the drive time to the point of interest (POI). We can use our novel data sources to look at these during peak, off peak or average traffic conditions. They help paint a clear picture of how accessible your location is to customers, and are especially useful if you are interested in a drive through mission type, or sell large and heavy items. This principle can also be applied to other forms of movement, such as travelling by foot or using public transit.

Nearest Catchment

Nearest catchments assign catchments based on proximity to the point of interest (POI) by assigning every ‘cell’ in a study area to its closest POI. At Geolytix, we use a H3 resolution 9 hex grid which allows for consistent mapping across an entire country and clearly show how catchment sizes change by urbanity. Because of the way hexagons tessellate, they behave more like circular buffers, whilst tiling perfectly across the map.

Gravity Model

The core principle of a gravity model catchment bases the size of the catchment on the attractiveness of the point of interest using market share, assuming that larger, more appealing destinations have a stronger ‘pull’ on the customer. They also use distance decays to capture the relationship between the propensity to spend and distance, accounting for the decline in interaction as distance increases. The rate of decay is not constant across markets or mission types and we can build bespoke decay curves to account for this.

Customer Catchment

Of course, if you collect customer data, you can see where they live and where they shop, meaning it is possible to define ‘primary’, ‘secondary’ and ‘tertiary’ catchments based on variables such as their number of visits or total spend at each location. This is often aggregated up to small local geographies to protect a customer’s privacy - this data is usually highly sensitive! Depending on the desired results there are various ways of defining primary and secondary catchments; for example, while the closest 50% of customers has the advantage of giving you a clean primary catchment, ordering by spend or transaction penetration might give you more accurate insights, but generate a catchment with holes in.

Mobility

Using phone mobility data, it is possible to visualise where people are coming from and going to. Like drive time catchments, this can be done at a number of temporal granularities, enabling differentiation between daytime shoppers, night time economies, workers and residents. It highlights busyness hot spots, and the different patterns generated by pedestrians and vehicles.

Banking Data

Using data from our banking partner we can build out spend contribution catchments based on aggregated home locations at specific outlet levels. This is based on real spend at the retailer and identifies neighbourhood areas that are spending the most at your store (or your competitor's stores) and lets you quantify the value of that area to your asset. Our banking catchments are built at quarter annual frequency so you can even monitor how contributions adjust with your retail strategies.

Whilst some methods are simple and others are more involved, or require a greater depth of available data, no one method is the right one. This is determined by your requirements, the missions you want to target and the speed at which you’d like the answers. There are pros and cons to all, and if this blog post hasn’t demystified them completely then we can help you choose the right one to support your next location planning decision!

Ellen Talbot, Data Scientist at Geolytix

Photo by Martin Sanchez on Unsplash