April 07, 2020
4 min read

Price Elasticity and Its Role in Strategic Assortment Analysis

In our last blog post, we explored the concept of price elasticity and the benefits it brings to AI-driven price optimization. Now, let’s explore how price elasticity can be used to create a strategic basis for an AI pricing solution.  

With the help of price elasticity, you can determine exactly how price sensitive and competitive your products are - ergo: which roles individual items play within overall assortment and what this should mean for the pricing of these items. In practice, differentiating items between “Common and Focus Items” versus “Basic and Skimming Items” has proven to be useful.

Common and Focus Items

Customers generally have only a few product prices in mind when shopping. Typically, they remember the prices of the products they buy regularly. In a supermarket, for example, these are basic foods such as bread, butter or eggs.

Based on these items, consumers judge whether a retailer is expensive or not based on their preexisting knowledge of prices. Consumers therefore react strongly, both positively and negatively, to price changes for these items, which makes them highly relevant to switching to a competitor. Therefore, prices of these basic products are crucial to your image. These products enable you to proactively shape your price image, and then sustainably underpin it.

In the context of AI-controlled price optimization, applying a dedicated strategy for the specific requirements of these products allows you to:

  • actively increase customer and purchase frequency;
  • strengthen and expand your brand;
  • optimize target KPIs such as turnover and sales.

Basic and Skimming Items

Comparatively, basic and skimming items are essential for fulfilling KPIs such as gross profit and margin. Consumers are not as price sensitive to these products because they are often “longtail” products. For example, take pots and pans. Customers buy these items infrequently, so they rarely have comparison prices in mind.

The situation is similar with basic items. Although they’re on everyone’s shopping list at some point, they’re bough infrequently, such as spices. Because consumers are less price-conscious toward these items, they are well-suited for optimizing gross profit and margins. It is particularly noteworthy that on average, up to 80% of a product range consists of basic and skimming items, so optimization potential for these products is enormous.

Now, the question that remains is how to use this knowledge to control price optimization by means of an AI, which is what we’ll explore next.