Enter a retail store and that arresting first impression is made by the assortment of products siting on the shelves. Not every product is of that fly-off-the-shelves type and not every product has that ‘presence’ felt on the shelf space. Then there are those new products that have won their shelf space through the ‘pay-to-stay’ mode. And the walk down the aisle offers the view of varied products on the shelves.

What pushes the retailer to use specific shelf space areas for specific products and categories or why some products gain prominence in terms of shelf space allocation and some languish on the shelves?

From a brand’s perspective, shelf space is a battle ground for capturing customer eyeballs and attention. But for the retailer, it is more about using shelf space planning, facing and positioning to increase demand, accelerate stock turnover and sales, and increase profits in the bargain.

There’s more to this shelf space story than just using facings and positioning to increase product demand – it is also about shelf optimization to make best use of shelf space to identify and sell profitable products, eliminate stock-outs, reduce inventory, increase stock turnovers and optimize replenishment cycles to meet customer demands.

How to create the winning link between shelf optimization and sales, profits and customer experience?

Conquering shelf space allocation challenges

For retailers, challenge lies in using the limited shelf space to accommodate ever increasing product and categories. Then conquering challenges means finding answers to space planning questions to establish sync among the right quantities of products, appropriate shelf space and replenishment cycles. Some critical queries include.

  • How to improve product turnover?
  • How products need to be displayed?
  • How to allocate shelf space?
  • What’s the optimal number of facings?
  • What’s the restocking frequency?
  • How to eliminate stock-outs?

Factoring consumer demand

Let’s take the case of a crowded beer market wherein a host of players compete for retail shelf space. Established brands have their fair share of shelf space while newer varieties and entrants make a beeline for the coveted shelf space at retail outlets. From the retailer angle, what’s important is the shelf allocation and assortment to make sure that right beer brands are being stocked on shelves to meet the changing customer demand.

And with retail chains having more branches and outlets, shelf allocation based on consumer demand can vary from one store to another. It boils down to knowing what’s happening on the shelf, how consumer demand is driving sales across branches and outlets to maximize value from shelf space planning and shelf space allocation.

Articulating shelf space planning in terms of customer experience

Judy enters a retail store only to find a product missing on the shelf – Judy had plans to buy the product. When she turns to the store associate, she isn’t convinced with the service either. From unavailability of items on the shelf to poor customer service and difficulty in finding required items, customer experience becomes a salient feature of the shelf optimization equation. Optimized usage of shelf space in ensuring right products are available at the shelves paves way for enriched customer experience

Focusing on factors influencing shelf optimization

Retailers wanting to maximize returns from their shelf space allocation move beyond the ‘stocking, stacking and displaying’ needs pertaining to shelf space planning. The other critical areas attracting retailers’ attention and areas that can impact stock turnovers, sales and profits include:

  • What’s the ideal replenishment cycle?
  • What’s the logistics cost for shipment to a store?
  • Are there ‘lost sales’ owing to stock-out positions?
  • Are pricing errors and theft causing shrinkage?

With price playing a significant role in influencing customer buying decisions, there is also the need to balance shelf space based on product pricing. Though it is not the ‘low price’ that always wins amid customers, retailers ought to take note of categories that are sensitive towards price and optimize shelf space taking the price factor into account.

Connecting category management to shelf space planning

Space allocation, on display or on shelves, from a category perspective is about shelf space planning at an individual SKU level. This boils down to answering two critical questions

  • Where to position the SKU on the shelf?
  • How to allocate space for a specific SKU?

Tracing the category-shelf optimization link, positioning of the SKU depends on various factors like target customer segment, market share, promotions, logistics and impact of a categories on the assortment. Different products demand different shelf levels depending on customer pull, stock turnover, market share and target audience.

When it comes to allocating space for product SKUs, ‘facing’ is the most important feature that matters – since more the number of facings, more the chances of influencing consumer demand. For this reason, allocating shelf space hinges on the productivity of space, product turnover and impact of product on store sales.

Letting machine learning drive shelf optimization

Making the right shelf space allocation decisions hinges on insights mined to identify and place the most profitable products on the shelves and maximize sales. That said, machine learning powered shelf space optimization also guide retailers into making decisions around display orientations, facings, categories, pricing and replenishment.

Formulating robust shelf optimization models also calls for increasing the granularity in terms of looking at the stochastic demand, number of shelves, capacity of shelves, sales of a product for a predetermined time, promotional area, seasonality, profit margin pertaining to SKUs, total area allocated for a specific category and replenishment period. And the retail objective of using the available shelf space to generate more profits from products and categories, reduce inventory and optimize replenishments begins with the shelf optimization model.