As retailers drown in massive seas of data, fierce competition, and shoppers whose behavior changes like the direction of the wind, they are losing touch with the psychological effect price has on shoppers daily. Shoppers’ behavior has become harder than ever to predict and can depend upon many factors. They have unlimited choices across a pervasive number of channels. Study after study has confirmed that price, followed by perceived value, continues to be the primary driver on where shoppers choose to shop and what they buy when they arrive.
One of the most strategic areas of focus for retail executives in today’s volatile industry landscape is price image. Price image places a retailer’s overall pricing position in the mind of shoppers relative to competitive prices. To ensure you are portraying the most optimal price image, you must first understand shopper’s price sensitivities across your assortment.
Shopper sensitivity to price is a complex phenomenon that encompasses many properties related to the product, the store, the channel, the shopper and the competition. Price sensitivity can be identified via price elasticity, which measures a shopper’s sensitivity to the prices charged for goods or services to the quantity purchased.
Understanding price elasticity is the key to building a greater understanding of shoppers’ price perceptions and for developing strong pricing strategies determining optimal prices and promotions that shoppers feel are fair and not perceived as arbitrary. Fortunately, modern price and promotion capabilities leverage AI and machine learning to enable extremely sophisticated understanding and shaping of a retailer’s price image.
The price elasticity of an item is not necessarily an intrinsic or fixed property that spreads across all possible price points, but rather the range of available choices, perception of value, price position relative to other related items, and much more.
Many Factors Influence Price Elasticity
Shopper sensitivity to changes in price can change either gradually or abruptly, crossing psychological price thresholds or creating movement outside the “sacred price points.”
Understanding the many factors involved in determining price elasticity – such as the shopper’s disposable income, ability to trade down, wait for a deal or promotion, go to another store (including a competitor) or channel, or go without completely – is critical. Their ability to defer, substitute, or eliminate a purchase depends upon the type of purchase and available choices.
The various characteristics of an item are one part of the equation of elasticity. In addition to the item, both the store and the shopper have unique attributes that influence sensitivity to price. For example, competitive intensity, store format, and breadth of assortment all influence customer sensitivity and vary by store location. Understanding the variations of elasticity for different shoppers enables fine-tuning of regular prices as well promotional activities to ensure you are delivering the right products, pricing, offers and promotional tactics, leading to increased store traffic, trip frequency, basket size, and profitability.
Predicting Price Sensitivity
The ability to model and predict price sensitivity based upon the store, competition and shopper characteristics gives the ability to predict elasticity. Understanding elasticity is one of the most important components in setting and maintaining an optimal price position. To do this, AI science is employed to leverage vast amounts of data across shoppers, stores, and competitors to establish a full understanding of how price/promotion and quantity sold are related.
Once this is accomplished, retailers can project financial implications and trade-offs of sales quantity, dollars, and gross profit (as well as other metrics such as competitive price indexes, shopper trip frequency, and basket size) for each item across every store. With the understanding of elasticity by product/location across shoppers, it is possible to jointly optimize prices to drive performance improvement and achieve strategic objectives.
Relationship Between Price and Quantity
The economics term for the relationship between price and quantity sold is the price elasticity of demand for a product. AI science can distinguish an item’s level of elasticity and the impact on performance when a price increases or decreases. For example, if an item has an elasticity of 1 you can expect a 10% increase in quantity demand for a 10% decrease in price, meaning that price change is “revenue neutral.” When elasticity is greater than one, quantity demand rises at a faster rate for price decreases and falls off at a faster rate than price increases. For example, for an item with an elasticity of 2 you could expect to see a 20% increase in demand for the same 10% decrease in price. Such an item could be termed as “highly elastic” since the change in price greatly amplifies the demand response. When elasticity is less than one, demand falls at a lower rate than the rate of price increases. For example, an elasticity of .5 means a 10% price increase would result in a 5% decrease in demand.
Long gone are the days of long lead times, loyal shoppers, limited data sources and pure brick and mortar stores. Shoppers today are price sensitive, have complete transparency on an abundance of choices, and demand fair, non-arbitrary prices. Retailers can no longer assume the image they believe they are portraying is actually what the shopper will see. Elasticity is a true, objective measurement of shoppers’ sensitivity to price and the use and adoption of AI science in pricing have become one of the most mature practices of AI in retail. It can monitor and adjust for rapid shifts in shopper and market behavior, and continuously model millions of possible trade-offs across a retailer’s assortment to recommend the optimal prices and promotions.
Price image is a deep concept that goes into the heart of the relationship between a retailer and their shoppers. Ensuring the price image you are portraying to shoppers matches what they are perceiving is essential. Retailers not adopting AI-based price and promotion optimization will miss out on the difference between winning and losing in today’s retail environment.