Most retail business is regular-price sales. Regular price optimization allows retail science to concentrate on the overall price presentation of products and stores to consumers. We can earn a lot optimizing markdowns and promotions, but we should start with the foundation, regular sales.
In this paper, we’ll discuss the opportunity in regular-price optimization. From there we will discuss the factors that affect the opportunity, namely- demand elasticity, price image, and finally business rules.
While the retail-science community is buzzing about markdown and promotion optimization (and I have written markdown and promotion optimizers myself), the lion’s share of most retail business is at regular price. The difference between pretty-good regular prices and scientifically-optimized regular prices can increase revenue by several percent and double grocery profit without jeopardizing a retailer’s customer base.
How can we double grocery profit with retail science? How can we make such a profound improvement in the bottom line when experienced category managers are already doing a good job? Because retail margins are slim, even a small margin improvement makes a major difference. Retail science can align prices so they pull profit and revenue in precisely the same direction to maximize their support of business needs.
Retail category managers already know how to set good prices with good trade-off decisions for profit, revenue, and price image. Science adds the ability to tune these trade-offs to get the extra one percent. Think of it as the difference between all your prices going the same general direction and all of them flying in formation. Retail science can do an elasticity optimization while maintaining a retailer’s price image and following the business rules.
COMPETITION AND ELASTICITY
Elasticity is the Economics buzz-word for the response of consumers to price changes. We know higher prices diminish unit sales of a product, the question is how much less do shoppers buy? If we raise a price, say, 10%, then we might see a small change in unit sales like 2% or a huge change like 40%. We write ΔP for the change in price, %ΔP for the percent change in price, and %ΔUS for the percent change in unit sales, The ratio of %ΔUS to %ΔP is called price elasticity. Retail science tells us how unit sales respond to price of a product or, more precisely, how elasticity response to price.
There are two immediate drivers of elasticity, the willingness of consumers to pay for a product and their ability to go elsewhere to buy it. If a grocer raises the price of cigarettes, for example, then we know there are a million other places shoppers can go, some of which are convenience stores on their way home. On the other hand, raising the price of shoe polish is likely to have less of an effect on units sold because most shoppers can’t be bothered going to another store that sells shoe polish just to save a few cents.
When everybody in the market raises prices, during gasoline shortages for example, consumers buy less. They may not cut back right away, but over time they will find a way to buy less as the price rises.
There is a more-subtle point on elasticity, a critical point for retail science. Elasticity tends to increase as price increase. This means not only do shoppers buy less at higher prices, but the rate at which they buy less increases at higher prices. By understanding the relationship between price and elasticity, retail scientists can get a firm handle on trade-off decisions for profit, revenue, and price image.
Once we understand this more-subtle relationship between price and elasticity, we can find the price that maximizes revenue and the higher price that maximizes profit. Prices between these two define the frontier price range for revenue versus profit.
Ask a retailer why prices aren’t raised to maximum profit levels and he or she will answer immediately, “Nobody will shop here if I raise most of my prices.” The retail-science concept we’re talking about here is called price image, consumer perception of the price level. Some prices affect price image more than others, products shoppers buy more count more and products with high demand elasticity count more.
The trick here is managing individual-product prices within the totality of price image. This is where science can make a big difference because a mathematical model can find product combinations where we can raise one price and lower the other to increase profit or revenue while maintaining price image. We can go even further and optimize many prices for profit or revenue within a price-image constraint. This is the trade-off tuning I wrote about earlier.
There are practical limitations to price optimization. We have to recognize that consumers have certain expectations about prices. They expect almost all products in The Dollar Store to be priced at one dollar, for example. Think about people buying plastic, two-liter Coke or Pepsi bottles. Pricing those below 99 cents or above 1.49 makes people suspicious.
Another source of shopper discomfort is changing prices. Each price on a retailer’s shelf is like a promise and too many price changes makes shoppers feel those promises are being broken. Our science limits the number of price changes.
There are price relationships we need to respect. Consumers expect the larger size of the same stuff to cost more but less per unit. Consumers expect the name brand to cost more than the house brand. Consumers expect a retailer’s prices not to be too far from the competition. (The comparison shopping with competitors works both ways. As a retailer, consider a detergent sold at other stores for 5.99 that you have on your shelf for 2.49. Shoppers will wonder why it’s so cheap. Even if they buy it, the low price creates a atmosphere of confusion.)
Some products are priced differently in different seasons. Most products have some variation in sales at different times of year but most of them do not require price change to follow these trends. We can account for most seasonal change by carrying more inventory during high-sales seasons, promoting products when appropriate, and marking them down at the end of their seasons.
Other rules may be dictated by vendor contracts, the competitive landscape, and other business constraints.
For most retail enterprises, regular price optimization is the big slice of the retail-science-benefits pie. It keeps the basic business aligned in a unified direction consistent with the consumer market (price image) and consistent with price relationships required (business rules). While the improvement may be a small fraction of total sales, it is a large share of the profit, especially in grocery where margins are very slim.
Retailers define direction, set strategy, and build a regular-price framework for products and stores. Retail science finds the small price adjustments and tradeoffs that get the extra profit or revenue the business requires.