Use the cannibalization feature to analyze how neighboring branches and competitors' locations affect a location's catchment area
Using the cannibalization feature
To analyze locations while taking into account the effect of neighboring locations, simply turn on the toggle Cannibalization in the table view as shown in the two images below.
Cannibalization is turned OFF
Cannibalization is turned ON
- The first image, where cannibalization is turned off, shows that My Location C has the biggest catchment area in terms of total population and other demographic groups reached. It therefore ranks No. 1. The travel time and mode are set to 10 minutes by car.
- In the second image, which shows the results when cannibalization is taken into account, we see that Competitor Location B draws most people and has the biggest catchment area. (Biggest means that the number of people that can reach a location is the largest. It does not refer to the square surface of the catchment area on the map.)
- The percentage values in the columns indicate the amount of people lost to other locations. It shows the difference compared to the original value, when cannibalization was not considered.
- The results can also be viewed per location. Simply click on any location in the Table View, and view the figures in the menu, as shown in the picture below. It compares the results for the same place, Competitor Location B, when cannibalization is turned OFF (left) and ON (right).
- In the orange blocks below, we can see how the score and rank changed versus the original values. Location B scored 32 points more and its rank went up (green arrow). In the table, the percentage values indicate the change compared to the original values.
Why is it called cannibalization and why does it matter?
In the retail industry branches of the same chain sometimes compete against one another when they are located in the same area and catchment areas overlap. This effect is known as cannibalization, but the same principle also applies to the impact of nearby branches of competing chains.
By considering the effect, a manager gets a more accurate picture of a location’s true catchment area. If a shop owner wishes to open a new branch, she can immediately see whether that new store would “steal” potential customers from existing shops in the same area and how many. She can also see how many customers she potentially could win from competing shops nearby. Or, conversely, how a competitor’s new location might impact the catchment area of her branches in the same area.
All these surrounding locations have an effect on the number of people that is expected to visit a store, as shown in the image below.
Although reality is more complex, the assumption behind cannibalization analysis is that a person only visits one location and is not counted twice or more when catchment areas overlap. Cannibalization analysis is especially relevant for the uses case where a consumer only buys products in either shop A or shop B. It also applies to the business of micro fulfillment centers, for example, when online grocery shops instantly deliver goods to the buyer’s home, or when restaurants bring their meals to people living in the immediate vicinity.
In all these cases, a business does not want catchment areas of shops, delivery centers or restaurants to overlap, because that would lead to an oversupply of locations and higher operational costs. Or it would lead to a significant loss of potential customers when rival chains are located in the same area, but customers only care for visiting a single chain, for example, Do-It-Yourself Center A or B.
How cannibalization analysis works in TargomoLOOP
For consumers, the travel time to a location and the attractiveness of that location – defined by, for example, the products on offer, opening hours, and the presence of other shops – determine whether they visit that place or a different one.
TargomoLOOP’s cannibalization feature uses travel time to calculate whether a person visits shop A or B, as shown in the picture 2 above. A person is expected to visit the location that he can reach in the shortest amount of travel time, given the selected travel mode. In the future, we also plan to introduce the option to take the attractiveness of a location into account.
The cannibalization analysis is based on a scientific method to predict the likelihood that a person visits a certain location. Newton’s law of gravity, which states, among others, that the gravitational force between two bodies is inversely proportional to the squared distance between the two bodies, is taken as the basis of the calculations. But instead of taking distance, travel time is taken as the variable determining whether a person “gravitates” to location A or B. Anyone interested in the methodology can check our whitepaper Selecting the Ideal Branch Location with Gravitational Models.