Managing the Category Beyond SKU Rationalization

SKU proliferation has been a fact of life in foodservice much as it has been in other industries in recent years. Proliferation creates considerable pressure throughout the value chain to make tough decisions about SKU assortment across numerous product categories. In foodservice the problem is not shelf space as it is in retail; rather, it is the limited amount of product information that a sales representative can manage in his or her head in order to match the right products with the right customers on a daily basis in real time. As managing assortment has grown more complex, manufacturers and their downstream partners have looked to SKU rationalization to reduce streamline product offerings and manage inventory costs for improved category performance. While SKU rationalization can address these challenges to some extent, it does not get to the core of the problem. The most effective way to improve category performance is to increase demand for products in that category. In turn, the best way to grow demand is to seamlessly match unique customers with the products whose attributes they most highly value. This requires a holistic category management approach, supported by robust data analytics that can take into account the key levers of demand – assortment, promotions, pricing and purchase timing.

The Importance of Collaboration

In foodservice, manufacturers and distributors are the logical partners for a collaborative category management venture. Manufacturers possess deep insights into the product attributes that drive demand for specific customer types, and have a strong understanding of how to manage assortment. On the other side, distributors have the benefit of daily transaction data at a very granular level – what quantities of products in the category are being sold to what locations with what frequency. Combining these insights – ideally through a single integrated data management system able to process inputs from multiple sources and generate insights and actionable recommendations to the relevant decision makers – can create a coherent, unified picture of demand that provides a basis for specific assortment, pricing and promotional activities to grow sales.

Reducing the Guess Factor

A traditional SKU rationalization program may analyze aggregate transaction histories for all the SKUs in a category and mark for elimination some subset of those that occupy the so-called “long tail” – products with sparse data records due to infrequent activity. A typical goal in this regard may be to eliminate 20-25% of all SKUs in the category. The problem with this approach is that without an appropriately detailed level of analytical insight, managers are left to guessing what the resulting effects will be on sales. Transaction frequency is only one variable in presenting a composite picture of demand. For example a certain product may transact on an infrequent basis only, but it may also be a popular niche product with attributes highly valued by major customers. What will the sales impact be of not having this niche product available when a major customer wants to add it to his or her market basket? How can decision makers recognize and differentiate between niche products and other long tail denizens that really deserve to be eliminated from the active product line?

A holistic category management solution, driven by advanced predictive science, can supply answers to these questions. By integrating product attribute knowledge possessed by the manufacturer with quantity and purchase timing data known by the distributor, the system can make recommendations about when to stock the low-frequency but desirable niche items with a higher likelihood of coincidence with the customer’s purchase decision. Techniques such as Hierarchical Bayesian modeling help overcome the analytical challenges typically presented by sparse data. Rather than losing all or part of the customer’s market basket for the sake of an incremental SKU reduction – in most likelihood a losing proposition – the result is retaining a satisfied customer.

Focus on Growing Demand

This approach to category management program shifts attention away from simple cost reduction through inventory rationalization and focuses instead on the revenue side of the equation – growing demand in the category. There are two critical requirements for this to be successful. First, the data management platform must be sufficiently granular to provide meaningful insights at the level of every customer and every product (for example as in the long tail analysis described above). Second, the platform must seamlessly transform into a practical tool which sales representatives can use in the field. This is a particularly important requirement. Foodservice sales & marketing representatives as a rule have very little time for incremental effort above and beyond their existing selling and administrative responsibilities. They certainly do not have sufficient time to juggle multiple sales tools offered by multiple manufacturers acting in the role of category manager. The ideal tool is one with which the representatives have existing familiarity (to avoid time-consuming learning curves for new processes) and which can seamlessly integrate data from multiple input sources.

Manufacturers and distributors need more than just a rationalization program to optimize performance at the category level. A holistic approach, supported by robust analytics delivering actionable real-time guidance to sales professionals in the field, can improve category performance all along the foodservice value chain.

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