The Limits of SKU Rationalization
Markets have increasingly become narrowly segmented along demographic, psychographic and other lines, each with its own unique set of demand variables. This results in an explosion of SKUs as consumer goods companies attempt to supply each narrowcast segment with products specifically aimed at that segment. This in turn presents manufacturers, distributors and retailers with immense challenges in deciding where to allocate each dollar of their marketing budgets for promotions, pricing, assortment mix and other key decisions.
Those Were the Days
Here’s a practical example of SKU proliferation. Way back in the 1950s Neutrogena, a beauty products company, developed a product called the Facial Cleansing Bar, designed to promote clear, healthy skin. The product became wildly popular, and even in the late 1980s, some thirty years after its introduction, it dominated shelf space in retail health and beauty stores in a way scarcely imaginable today. The iconic translucent amber face bar was practically synonymous with “facial cleanser” for a very long time.
By the 1990s, though, increasingly sophisticated customer segmentation was splitting products like the Facial Cleaning Bar into multiple subcategories – dry skin, oily skin, acne, ageing lines, nighttime, daytime, aromatic scents and so on. Eventually the multiplication of segment types outstripped the ability of a single concept such as “cleansing bar” to address demand – leading to all manner of creams, tonics, gels, pore scrubs and more where once a single, simple SKU had sufficed.
Sales Growth creates pressure throughout the industry value chain – from manufacturers to distributors and retailers -- to make tough decisions about SKU assortment across numerous product categories. How do you know which product-customer combinations are working, and which ones aren’t working, when the sheer number of such combinations is so vast? Is there a cannibalization which means true sales ROI is not being achieved due to multiple offerings?
The answer that many marketing decision makers arrive at is simply that ‘you can’t know’, and therefore the only viable solution to facilitate informed market spend allocation decisions is to rationalize the number of SKUs in each product category. A typical rationalization goal might be to eliminate the bottom 25 percent of SKUs, where “bottom” is defined by those transaction histories with the lowest incidence of activity. The least frequent sellers, in other words. By eliminating the underperformers, the thinking goes, marketing dollars for promotions and other activities can then be redirected more profitably.
The Measurement Trap
There is much more to demand than the single metric of transaction turnover. An SKU may have a high reputational value with a certain subsegment of customers, but it may be a niche item or for some other reason be in that bottom quartile of items targeted for elimination. The problem isn’t that straightforward: the cost of not having those high-quality SKUs on hand when they are in demand may well outweigh the imagined benefits of getting rid of them as “underperformers.”
That’s an even bigger problem today than it was just five years ago, thanks to the proliferation of social media. A niche product – perhaps a facial moisturizer designed specifically for the summer months in hot, humid climates where users prefer a light, hydrating gel over a heavier cream – builds up a quiet but active following among engaged customers in a sought-after demographic segment. The growing popularity may not be fully apparent from one summer’s worth of transaction data, so the product gets the axe during an SKU rationalization program. Next summer, those same engaged customers are furious to find their much-awaited moisturizer unavailable, and do not hesitate to share their fury with the rest of the world.
Figuring out how to separate low-frequency products worth keeping around, like the gel moisturizer in the example above, from the genuine losers is not easy. But quantitative solutions based on predictive analytics can help fill in some of the missing pieces. A close analysis of product attributes, customer segments and seasonal factors across all SKUs, for example, can tease out some insights about specific features that customers value at specific times of the year. These findings can then be imputed onto all low-frequency transactions to see which ones are most likely to fall into the valuable niche category.
You Can’t Go Back
SKU rationalization may sound logical, but it does not get to the core of the problem. As nice as it might be to imagine going back to the days of brand dominance for something like the Neutrogena Facial Cleansing Bar, that is clearly not an option. All those customer segmentation efforts over the past several decades really did uncover specific value drivers unique to individual customers. Whilst SKU rationalization is important for the business for an effective distribution of trade spend, on the contrary one in three consumer wants personalized product- potentially meaning more SKUs. The real payoff for sellers up and down the FMCG industry value chain is to meet each individual customer with the right product at the right time in the right place. Intelligent statistical modeling can help provide a coherent, data-driven picture of demand from which to make specific promotional, pricing and assortment activities to grow sales.
Wipro Promax TPO has predictive planning capability – providing users the ability to quickly and efficiently conduct ‘what – if’ analysis to model different promotion scenarios and review systematically generated business outcomes to determine the best combination of promotional inputs. It enables a powerful planning and analysis ecosystem that can be tailored to the needs of each business stakeholder. Our data science and professional services teams are led by experienced domain experts to help you create predictive models to give your business competitive advantage through best practices in trade promotion optimization.
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