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Posted: August 20, 2014
James Walker
Rutger Von Post

James Walker is a partner with Strategy& based in London. He specializes in demand analytics strategies.

 

 
 

Price Elasticity Has Snapped

Price elasticity has occupied a prime spot in marketing theory for a long time now, in part because its simplicity and elegance are so appealing. It is comforting to recall the basic principle from undergraduate economics: an x-percent increase in price leads to a y-percent decrease in sales volume. If you know the ratio between x and y for a given product, you can calculate an optimal price.

Unfortunately, that concept is useless in the real world of business—especially for marketing strategies. Just as we have left behind our childhood toys (and our econ textbooks), it’s time to leave price elasticity behind.

There are four reasons that the concept of price elasticity is obsolete:

1. It obscures the precise sales impact of price changes. An e-commerce marketing leader I know recently talked about her frustration with the price elasticity model she was using. It had been programmed with a price elasticity ratio of -5. In other words, a 10 percent cut in overall prices would increase sales volume by 50 percent. But the model said nothing about how this sales increase was going to actually happen. Why should we believe it?

Most businesses, these days, need pricing models that do more than predict sales volume. They need to break down the impact of any given sale into its component parts. When prices go down, for example, how many new customers are acquired? How many trial buyers switch to becoming repeat purchasers? What happens to the frequency of purchases? Are customers likely to spend the money they save elsewhere, perhaps on categories they haven’t bought before? Does a price decrease make them less likely to buy from competitors, and do different customer segments (based on age, gender, or socioeconomic brackets, for instance) respond in different ways?

2.  Relative prices matter, not just changes in price. Price elasticity models tell you only how price changes affect behavior. In many categories, this is irrelevant. For example, in the United Kingdom, where I live, we are currently in the midst of a price war on milk. This is, in effect, a race to the bottom of the supermarket cooler. Price elasticity calculations are useless here, because sales accrue only to the brand with the lowest price. Similarly, having the lowest-priced cigarette pack in a retail store can drive huge increases in sales, regardless of how much the price has changed since last week. These effects are also common when people are purchasing auto insurance, airline tickets, and hotels—indeed, in any milieu where buyers can easily compare prices.

3. Retailers often control the outcome. When a CPG manufacturer decreases its prices, retail chains often have the clout to demand that their cash margin remain untouched. The manufacturer covers the entire burden. And when the manufacturer raises its prices, the same retailers take a percentage of the increase. In both cases, regardless of whether a pricing move is up or down, the CPG company is worse off. A price increase that produces only a theoretical profit gain for the CPG company may lead to a backlash from retailers; they can demand promotions that push the price back down, making the manufacturer pay both a margin compensation and a subsidy of the promotion. The aggregation of discounts, compensations, and margins, known as the “pricing waterfall,” can dramatically reduce profitability for manufacturers.

4. There is no such thing as a pure “price.” The price of a product reflects many factors, depending on the context. In e-commerce, for example, the price incorporates such things as the minimum order value required for free delivery, the mix of products offered by the manufacturer, and the use of promotions and loyalty incentives (reward points). Sometimes a company undermines its own pricing effectiveness. Recently, at a big data conference, a soft drink company marketing leader quipped that his company’s greatest competitor was in the next cubicle, launching another product. In other words, his biggest concern was cannibalization of his stock keeping unit (SKU) from others in the same company. The price is also affected by consumer perceptions, emotions, and confusion—for example, when consumers have to sort out comparable products from different companies that are offered with varying sizes and weights.

In short, price is neither a simple input variable for a model, nor is the outcome as simplistic as just aggregate sales. Price can no longer be managed by a price elasticity model. The elastic has snapped.

What then do we replace it with? The details will vary from one company to the next, depending on industry, scale, and whether it’s a consumer or a B-to-B enterprise, but the basic principle is the same. To improve overall revenues and profitability, you need to design a holistic analytic model that takes into account not just price changes and absolute price, but other considerations. The factors you consider should meet the following criteria: You have some control over them, they are relevant to the price, and changes in them have a clear impact (that you understand) on new customer acquisition, the size of purchases, the likelihood of repeat purchases, and so on. Examples of specific factors include the promotion schedule, the mix of SKUs, and other aspects of pricing such as the minimum order value required for free delivery and rewards programs.

These factors have their own complexities that should also go into the models. For instance, to create the optimal rank order of SKUs, you need to be aware of margins, sales levels, market basket analysis (which products are often purchased together), and the potential for other products to cannibalize this one. You need to avoid the kind of promotional price cuts that increase sales on one product, while decreasing sales on the other SKUs you didn’t promote.

Avoid the kind of promotional price cuts that increase sales in one product while decreasing sales on others.

In short, for a pricing model to be useful, both the input “price” and the output “sales” need to be dramatically more sophisticated than they are in most price elasticity models today. Think of all these factors coming together in an ecosystem of pricing. Add a gift for baskets above a certain value (such as a set of steak knives for anyone who spends more than US$120), or initiate a rewards or loyalty card, and you affect the components of a sale (customer acquisition, repeat purchase, and advantage against competitors’ offerings). Fortunately, the analytics tools exist to model all these factors, and to track what happens when you make specific changes. But you can only use those models if you’re willing to graduate from simple metrics like price elasticity; the comfort they provide is illusory.

 

 
 
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