What is cannibalisation?
When a retailer grows its store footprint, it will eventually run into cannibalisation, reducing the incremental returns of new stores. This happens because the customer base of two or more stores may overlap – meaning customers who used to shop at location A now shop at location B.
How can we measure the impact of cannibalisation?
Which stores are cannibalising each other? How much more revenue could be achieved? Is it worth revising the stores’ location? Market mix modelling can help – more specifically store-level modelling, which allows us to efficiently build a model for each and all stores of a retailer.
Therefore, this means we can identify and measure factors that only affect specific areas or even individual stores. To measure cannibalisation then, all we need is data that represents distance between each store. Including distance within MMM will show what relation there is between a store’s performance and its proximity to other stores.
What insights does store-level MMM give us?
This relation is what will tell us the distance at which stores start to impact each other as well as how much revenue is being cannibalised. Once we have this information, we get an idea of how future store openings will impact your business – how incremental will new stores be to your portfolio?
Turning this insight into an actionable recommendation can be tricky. Below we can see a simplified representation of cannibalisation where two, “Store A” and “Store B”, cover an overlapping area.
The overlap in red, as explained above, represents the potential increase in revenue that would be gained from by revising the new store’s location, for instance.
Knowing there is this opportunity, how can we take advantage of it? Should we move future stores further away from existing stores or even close underperforming stores? If so where would those sales go?
At Brightblue we can accurately analyse retailers’ stores and deliver an actionable recommendation using MMM and cannibalisation as a starting point. In addition to this, the analysis optimises the coverage of the market territory and boosts significantly the performance of a retailer’s points of sale.
As a result, adopting geographic information in analytics, like in the above case of distance and cannibalisation, can reveal untapped opportunities for growth. Above all, its applications are not limited to cannibalisation. In fact, this approach can be applied to a number of a business’s aspects including pricing, media, and competitors. In conclusion, any inherent variation across different locations can be measured and used to generate insights and growth.
The Brightblue team have decades of experience helping clients understand media efficiency by truly digging into the drivers of sales and revenue. We have experience across automotive, retail, travel, entertainment, telecoms, FMCG, white goods, financial services, health and many other sectors. Our unique way of modelling the entire client journey truly helps marketeers understand what they can do to shine in their organisation by driving business and making a real difference to their bottom line. Get in touch if you have any questions on this article or any of the ways Brightblue can help.