In the first half of 2020, we’ve seen consumer behaviors change fundamentally in the wake of the COVID-19 global pandemic. As we move forward, retailers are going to have to critically assess every category and try to understand the “new customer” and what motivates them to make certain purchases.
When you consider that experts in psychology suggest the average person makes up to 35,000 decisions1 in just one day – one every two seconds – it becomes obvious that decisions are made based on a lot of factors: environmental and financial concerns, impulse and excitement, even apathy and impatience.
The “paradox of choice” is a principle that explains how, when faced with too many potential choices, a decision is less likely to be made at all. Some even refer to it as the “tyranny of choice,” suggesting that an abundance of options can overwhelm and confuse, creating a sense that there’s not enough time to research and evaluate each product, ultimately causing an abandonment of the decision altogether.
Essentially, the shopper chooses not to choose. By combining this psychological reality with the work of assortment planning, it’s easy to see why too many similar products in a category translate to less-than optimal sales performance.
We call this the “Rule of 17.” Our research with leading retailers today has taught us that 17% of items in a given category are duplicative in nature. Further, as we world comes out of an unprecedented event that has shifted the consumer paradigm, it’s clear that retailers must come to terms with the Rule of 17, determining that category duplication is an issue to prioritize and resolve in the immediate term.
The average person makes up to 35,000 decisions a day, or one every two seconds.
Up to 17% of items in a given category are duplicated
"The challenge for retailers looking to rationalize SKUs is to do so without negatively impacting shoppers’ perception of choice."
Consumers want choice, and retailers and manufacturers are happy to oblige. But there is a tipping point. This paradox of choice is the reason why, when going down the cereal aisle, customers tend to buy what they always buy, not deviating much, because of the vast array of options available.
To further understand how the paradox of choice translates in a grocery setting, consider how a famous case study2 would play out for a product available on your own shelves. In an upscale food market, shoppers were shown a display with six varieties of jams and jellies. Later, 24 flavors and brands were displayed – four times as many options of jellies and jams as in the first exercise. The results showed an overwhelmingly higher rate of conversion when fewer choices were presented.
Researchers found that with two dozen options presented to passersby, interest in the display was stronger than in the scenario with only six flavors. Yet when it came time to make a selection, those consumers were just one-tenth as likely to actually make a purchase.
This phenomenon extends far beyond the products that make up a customer’s breakfast table, which means there are opportunities for assortment improvement and optimization store wide. When retailers remove duplicative products from a category, consumers purchase more units. By nature, because the number of SKUs are reduced, inventory turns increase, boosting profitability and total revenue as a result.
Further proof of this concept comes from hard discounters such as Lidl and Aldi. Instead of offering 8 to 10 ketchup SKUs, like their competitors, these retailers have strategically opted for just one. The benefits are two-fold: limiting the options to a single type of ketchup helps to keep inventory costs down in the supply chain, and shoppers are released from the process of decision making. Thus, they leave the store satisfied with a purchase that fills a need, not paralyzed by choice.
It is important to note, however, that grocery discounters such as these started with a lean level of category assortments to begin with.
Therefore, the challenge for other retailers looking to rationalize extensive, existing categories is to do so without negatively impacting shoppers’ perception of choice, product availability and loyalty.
So, let’s look at another example that will certainly resonate across the majority of grocery retailers, full service and discounters alike. The beer market is incredibly localized – what sells on one street might not sell around the corner, making it a category in need of careful crafting. There is a huge range of products in the marketplace to choose from, and every store will have a slightly varied assortment.
Whether your market wants mass-produced domestic beers, craft pale ale or prefers stouts and darker brews, managing these assortment decisions manually across all stores to ensure the most effective mix and coverage is difficult, if not impossible.
4x as many choices increases interest but negatively impacts conversion.
When a consumer is faced with too many choices, a decision is less likely to be made or worse abandoned.
Replicating the discounter model of removing choice entirely may negatively impact customer loyalty for other retailers.
If we know that as many as 17% of products in a category are duplicative in nature, and the paradox of choice suggests fewer options increases the likelihood of a purchasing decision, why wouldn’t a retailer take swift action to make appropriate cuts?
If you are going to add, replace or remove a product from your shelves – physically or virtually – you must first understand the customer value the product has within the category, and across your operation. Retailers need to focus on the impact of transferrable demand, or other important metrics, to determine the nuanced way customers make decisions about product assortment. At the end of the day, your customers don’t care what the profitability of an item is – what they’re thinking is, “What’s the value of this item to me?”
The challenge is, retailers must make assortment decisions for every category, at the customer level, for each location, across the enterprise.
Traditionally, given the variety of factors at play as each customer picks one item over another, looking at an item’s profitability seems like a logical place to draw the line. But that isn’t the case.
As has happened in early 2020, environmental, social or financial events can fundamentally change customer behavior across almost every category. Rapid and unpredictable changes in the grocery landscape force retailers to adjust and optimize their entire strategy, making changes to store configuration to accommodate click and collect, and putting increased emphasis on fresh and prepared foods. These shifts – especially fresh with its perishability factor – also create urgency for retailers to improve efficiency by reducing item duplication in a category. But if you don’t rationalize carefully, you’re in danger of having a detrimental impact on sales.
First understand the customer value the product has within a category.
Item profitability, while logical, is misleading.
Pressure on store space utilization is driving urgency to reduce item duplication.
“AI learns shopper behaviors, understanding what a customer buys as a complement to another product, or even as a substitute”
It seems intimidating, even impossible, to determine the right 17% to remove per category, per store. And it’s important to note that a classical, statistical approach to assortment rationalization will not yield the greatest improvement to category performance – a historical context doesn’t always provide accurate indications of the future. But through artificial intelligence (AI) and customer loyalty insights, retailers can identify true duplication in the category.
This will lead to improved forecast accuracy, fewer items to manage, and more predictable demand. But how does it work? Looking at shopping behaviors across sales, basket and loyalty data, AI learns shopper behaviors, understanding what a customer buys as a complement to another product, or even as a substitute. That data is used to drive assortment decisions, helping retailers to understand the motivations, priorities and needs of its customers.
AI can analyze product performance from the customer perspective, knowing which products are most important, irrespective of sales performance. A product might be deemed a “slow mover” in a category, but if that item doesn’t exist, the whole basket might be lost. AI can understand these dynamics. And by better understanding customer segments, at the category and store level, retailers will see improved performance of the category.
AI can do what human brainpower and teams of analysts cannot – it can quickly ascertain, with high accuracy, the predicted outcomes of removing one product over another. “If you delist product A, this is the impact on the category overall. For that reason, product B would have significantly less impact on the customer.” AI learning algorithms, when applied to transferrable demand forecasting, consider the big picture and accurately determine which products are best to eliminate.
Classical, statistical approaches will not yield the greatest improvement to category performance.
AI learns which products are most important to the customer, regardless of sales performance.
By leveraging AI, retailers can identify true duplication in the category.