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. Read the rest of this entry »

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Analytics for Intelligent Category Management

Collaboration between distributors and manufacturers is the cornerstone of category management in foodservice. For a given product category a manufacturer is selected to be category captain, with responsibility for improving category performance. This post addresses some key data and analytical issues with which manufacturers should expect to deal as category captains.

So you have been asked by your most important foodservice distribution partner to be a category captain. What happens next? As captain you are tasked with managing the assigned category for optimal performance. That entails the following:

•    Analyze all products across the category (not just your own brands)
•    Augment the data provided by the distribution partner with your own internally generated insights
•    Provide structured, actionable recommendations based on intelligence obtained from the data

These recommendations relate to product assortment, pricing policies, promotional activities and other important demand levers for driving profitability. At the same time you need to educate your distribution partners, both at corporate headquarters and in the field, about the product characteristics that can help increase demand. This requires an intelligent approach to data analytics.

What insights about products can help drive category sales?

What might a good analytics model for category management look like? Let’s consider the key tasks we identified in the previous paragraph. Read the rest of this entry »

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