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How the right assortment benefits brands, categories and shoppers

Thekla Berger

Senior Consultant Commerce, Insights Division


Bernd Grosserohde

Global Director Portfolio Management, Insights Division


Every brand is striving to be “Meaningfully Different” from its competitors – not just in the minds of consumers, but also on the shelf at retail. Products are Meaningful if they deliver what consumers want. They are different if they pick up on or set trends in the category. Meaningfulness without difference is boring; Difference without Meaningfulness is pointless.

But Meaningful Difference is increasingly difficult to accomplish in fragmented markets where consumers expect a broad variety of benefits. Natural cosmetics are a case in point: most brands achieve only a very small market share and speciality is part of their identity. Yet the segment as a whole is attracting a growing number of consumers. Every tenth Euro spent on beauty products today goes to a natural cosmetics brand. Manufacturers in the category are challenged to reconsider their portfolios in the light of shifting consumer preferences and price sensitivities.

Retailers, too, are struggling to offer meaningfully different assortments to buyers. Limited shelf space is available for products that meet the increasing variety of consumer needs. And although a large number of new products are newly listed in supermarkets every month, less than 1 percent survive for more than a year. Retailers need to develop a point of view on what buyers want now, and what they will want next.

How can manufacturers and retailers create Meaningful Difference with their assortments?

Let us look at this through the eyes of the consumer: How do buyers find the right product in the clutter of stores and shelves? A purchase decision matrix is an extremely useful representation of how shoppers organize a category. Based on their search and choice behavior, we learn what matters most to them, which products they find substitutable, and why.

The graphic below presents a fictitious example of how buyers make decisions in the deodorant category. The vertical (Meaningful) - axis shows the order in which buyers reduce complexity; format followed by brand are the most important choice criteria. The horizontal (Different) axis positions the various product characteristics according to how often they are considered together by the same buyers.

 Axe and Nivea are perceived as more different from one another than Axe and adidas. In the same fashion, we learn that Axe products are more often sought by spray-buyers than by stick-buyers.

A decision matrix like this also provides clarity on which products provide the most incrementality based on where their profiles fall into the structure: Any given product can be visualized as a line from top to bottom connecting characteristics such as format, brand and benefits. The closer two lines are to one another, the more likely buyers are to substitute the two items on the shelf. This information is most useful for new product development. Knowing in advance to what degree a new product will be incremental to the brand will significantly reduce the risk of failure.

Retailers also benefit from the concept of incrementality underlying the decision matrix. If a given item is unavailable and buyers easily find substitutes on the shelf, then the product is not contributing unique revenue to the category and can be delisted.

Another useful category management guide can be found in the brackets below each product characteristic. The numbers indicate the ideal share of features on a shelf; from a buyer perspective, the perfect shelf would have 45 percent sprays, 25 percent roll-ons, and so on.

Assortment check box:

  • Do you know what makes products in your category Meaningfully Different to buyers?
  • If a retailer plans to delist your products, will you have a defense strategy?
  • Will your strategy be based on recent data? In most categories, consumer decision data should be updated at least once a year.

A thorough understanding of category purchase decisions can be directly linked to sales data analytics. With modern cloud-based computing software it is possible to simulate the revenue impact of new assortment scenarios in real time. A purchase decision matrix will then inform optimization algorithms, and fill in information where data where necessary, such as when data on existing products is scarce, and in simulating new product launches.

This approach generates win-win situations for manufacturers and retailers. And since shoppers will be more satisfied with the assortments they find, it will actually create win-win-win situations.