The right assortment to win
over the customer

Product variety increases faster than shelf space. The growing variety of products results in higher operating costs, more frequent discounts and loss of economies of scale. Therefore, frequent optimization of the assortment is essential to ensure long-term success in an extremely competitive and variable commercial environment.
Offering a deep assortment leads to many products within a category. On the other hand, a vast assortment, is the strategy to have one of each category, with a minimum specialization. To attract new consumers, outside their main target, you can rely on an optimization in jargon called “scrumbled”. By using the decision tree grouping, optimization software can effectively balance the width and depth of the shelf based on how customers buy both at the cluster and single-point level.


Bridging the gap with competition

Market data can help you understand where there are opportunities in product categories, brands or price ranges, either by increasing loyalty or by attracting new customers. For example, in health categories, this can create new opportunities by making precise product recommendations to buyers.

Customer-centric assortment

Assortment reviews have been active for several decades, but customer-centric optimization is the priority of the moment. Customer centricity has become a business imperative driven by the wave of consumerisation and e-tailing that offers consumers a myriad of options to choose from.

Emphasis on quality in variety

Determining the scope of the variety to offer affects sales volumes, improves the use of each shelf, allows you to assume how much inventory can be optimized with low selling items. The adoption of models based on the percentage of critical mass (the minimum units per group) helps to ensure the quality of the selected groups, creating an assortment that is an effective representation of purchasing behavior.

The contribution of technology

Optimization engines usually perform at least two steps per store to determine opportunity cost. The first execution considers an optimal theoretical assortment without user constraints, while the second execution shows the results of any applicable constraints. Optimization logics quickly process the best configurations to fill shelf space with “facing”, depth and appropriate positioning.

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