AI-based decision automation is particularly beneficial when a large number of decisions must be made in near real time and need to take a wide range of signals into account. Additionally, these decision-making components have to be aligned with the pricing strategy and integrated into the price management process. In this post, we will describe an example of how a pricing strategy can be mapped to intelligent decision-making components.
In retail pricing, the price strategy is often shaped by two opposing forces: the internal economics of the company, and consumer price perception. Economic considerations, such as revenue and margin targets, are clearly the key drivers and constraints of pricing decisions. At the same time, consumer price perception is also a critically important consideration that is directly linked to company competitiveness, customer loyalty, and sales volume.
One of the most popular techniques for addressing this trade-off between margins and price perception is key value item (KVI) pricing. The basic idea of this approach is to quantify whether a product or product category significantly influences the consumer value perception, and then differentiate the price setting strategy based on this insight. Virtually all retail sectors have some standard items whose prices determine whether a consumer decides a store is expensive or not. These items are referred to as KVIs, with examples including socks and t-shirts in apparel, bananas in grocery, and batteries in electronics. The KVI approach assumes different price setting strategies for KVIs and non-KVIs, and these strategies are typically designed based on the following considerations:
In practice, there is no sharp boundary between these two categories, but rather a continuous spectrum of value perception. This spectrum ranges from products with reference prices that are well known to the customer (KVIs), to products for which a customer has some understanding of their reference prices, to products for which a customer has little or no idea about their reference prices (non-KVIs). Consequently, a retailer can have more than two KVI levels, possibly segmented by channels or store zones, or even use a continuous KVI score.
The separation of KVI and non-KVI price setting decisions is only one part of the retail price strategy. The other parts include the introductory price setting, promotion strategy, and clearance sales strategy. Figure 1 illustrates how these parts come together in the context of one particular product lifecycle:
The pricing strategy described above requires multiple types of pricing decisions. The quality of these decisions is extremely important, because all of them are directly linked to profitability and customer loyalty. Predictive analytics and advanced automation can help to improve virtually all types of pricing decisions in the KVI strategy example, as each element of the strategy can be mapped to a software component, as shown in Figure 1:
Figure 1. The relationship between decision-making components and pricing strategies.
This is the second post in a series of articles on the impact of AI on retail price and promotion management. In the first post, we focused on industry trends and the assessment of risks and opportunities in algorithmic pricing. In this post, we zoomed into the relationship between decision automation and pricing strategies in the scope of an individual enterprise. In the final post in the series, we will discuss the technology strategy for all these components.