The science behind store layout

Michael Poyser
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As consumers, we’re all aware that when we walk into a store, we are being influenced in some way or another. For years, retailers have been refining the art of keeping us in stores for longer, guiding us to where they think we should go, and encouraging us to buy more.

Think about when you visit a supermarket; the essentials like bread and milk are usually placed at the back of the store, which means you need to pass an array of tempting treats before you get there. Do you ever really walk out with just bread and milk?

Then there is the sensory experience of shopping; walking into a supermarket, your senses are assailed with the smell of freshly baked bread to stimulate your appetite; whilst the impact of the scents, textures and vibrant colours from the fresh produce section put you in a good mood, and the happier you are, the more likely you are to spend more.

The same is true in non-grocery stores; retailers ensure that your first impression is a good one by making sure the entrance is free from clutter, creating a harmonious and welcoming atmosphere.

Of course, there are many other tactics to tempt us to spend more. But in recent years, with the proliferation of data that is generated with every transaction, retailers are moving beyond shopper psychology and augmenting their strategies when it comes to areas such as store layout. Data can be used for so much more than just monitoring stock levels or measuring the effects of a marketing campaign. The wealth of insights generated in real-time at the point of sale can be used throughout the entire store ecosystem, often in ways that retailers may not have thought of before; from the placement of products on promotion to the location of complementary products within the store.

The science of data
Let’s start with basket analytics. Retailers can build an accurate picture of their shoppers, their buying behaviour and their spending habits based on what’s in their basket when they get to the till. While this data is essential in enabling the retailer to improve personalisation efforts and better engage with the customer, it can also be used to influence the set-up of the store itself. Over time, and across millions of transactions, retailers can build up an understanding of which products are complementary and which are competitive.

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For example, customer Y goes to the same supermarket on a weekly basis and buys strawberry yogurt. Sometimes they also buy blueberry yogurt. When strawberry yogurt is out of stock, they buy raspberry yogurt. So, what does this mean? Over thousands of transactions across their customer base, it’s possible to determine that strawberry and blueberry yogurt are actually complementary products, while raspberry is seen as a competitor. In addition, this data also helps retailers understand the customer’s decision-making hierarchy; i.e. what is most important factor when it comes to buying a certain product? In this case, is it brand, flavour, or a single pot versus a four pack?

These insights illustrate the way and frequency in which thousands of brands and products are purchased, enabling the retailer to configure the layout the store, shelves and fridges to optimise purchases — whether this means making things more convenient for shoppers, creating displays to launch new products, or making it simpler to cross-sell other brands.

Now apply this insight to the macro store layout. In a fashion retail example, brands can understand which items are most likely to be purchased together which can influence where they are positioned in the store. Should they be placed together for shopper convenience? Or should they be positioned further apart so shoppers are exposed to a variety of other products while walking around the store searching for the complementary items?

In larger department stores, retailers can even use the data gathered at the point of sale to understand which items are paid for at which checkouts throughout the store. If, for example, the majority of women’s jeans are paid for in the children’s section, then perhaps bringing ladies’ fashion closer to the children’s department makes sense.

Seize the opportunity
For retailers, the potential that data brings is endless — from the micro level, and using real-time data to engage with customers at the point of sale, to the macro level and using longer-term insights and information to optimise store layouts.

In today’s competitive landscape, bricks-and-mortar stores need to use all available tools to make their offerings more relevant and attractive to shoppers. Consumer psychology certainly plays a role here, but can be bolstered through the use of data captured at the point of sale.


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