The shopping area will be an important factor for many retailers to properly assess a shop’s expected revenue. The size of the shopping area and the type of shopping area (retail park, shopping centre, superstores on approach roads) will often determine a shopping area’s strength. In addition, the shopping area’s composition also plays an important role. A shopping area with an attractive retail mix will draw more customers than a shopping area with an unbalanced mix.

But your strength as a retailer in this shopping area may also depend on the chains that have an outlet in the shopping area. A shoe retailer may not necessary find the same mix attractive as a DIY retailer. We have added this decisive factor to our software in a smart way, with our new “perfect neighbour” functionality.

The “perfect neighbour” functionality starts from the idea that good neighbours will contribute to more footfall, with the right customer profile for your shop. The closer this neighbour is located to your shop, the greater the chances of more people passing through your shop.

But there is much more to this than you think. A perfect neighbour doesn’t necessarily have to be a retail concept. Do you sell sweets? Then perhaps a school is your perfect neighbour? Or do you have a network of newsagents’? Then a hospital and a station may be your perfect neighbours. Every geographical aspect that may influence your shop’s revenue – whether positively or negatively – can be factored in.

So how does it work?

STEP 1: Together we determine which chains attract the same customer profile as YOUR concept. 
As a clothing retailer, I prefer to have a shoe retailer as my neighbour rather than a shop that sells pet supplies. Are you a retailer that offers goods that fulfil different needs and match different customer profiles? Then you can determine your “perfect chains” for each individual segment.

STEP 2: All the POS of the chains that we determined in step 1 are displayed on the map of Belgium.

STEP 3: Drag the pointer to the exact location where you wish to open a store.

STEP 4: The software calculates a score (the “perfect neighbour” score) for the location of your choice. It takes the following into account:

• The number of perfect neighbours in the vicinity: you can indicate at which maximum distance you stop feeling the impact of a perfect neighbour.
• How “perfect” this neighbour (chain) must be: you can use weightings to indicate how well a chain’s customer profile matches your customer profile and apply this in a segment-specific manner, where applicable.
• The distance from the new location to each of these perfect neighbours: the closer your shop is located to the shop of a perfect neighbour, the more potential customers it can generate for you, with the right customer profile.
• The floor surface of the perfect neighbours: the greater the floor surface of your perfect neighbour, the greater the number of passers-by this neighbour will generate for you.
• The general strength of perfect neighbours on the chain level: based on publicly available data, you can implement a correction for general strength on the chain level. A Primark shop will generate huge footfall, of customers with the right profile, for a low-end clothes retailer.

Based on all these factors, the software will calculate to which extent this location will attract customers with the same customer profile.

STEP 5: As soon as we have the “perfect neighbour” score for every POS, we will examine the strength or weakness of your neighbours’ impact on the revenue of your points of sale.

This will prove whether our assumption is correct, namely do perfect neighbours really contribute to generating a higher revenue for the point of sale? We then include this relationship in our predictive model.

So what does this all mean?

When you test the impact and revenue of new locations with the Retail Sonar Platform, the revenue will now be corrected upwards or downwards, depending on the perfect neighbours score. The higher the perfect neighbour score, the higher the estimate of additional revenue for the POS.