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Kinky (Pricing) Curves

Demand curves are not smooth; finding the inflection points (kinks) where price does influence demand is key to uncovering profit improvement opportunities. 

Economists believe in nice, smooth demand curves. Many research tools do as well, based on the output they create. But that is not the reality: real demand curves have kinks, that is, steep upward parts connected to shallow, relatively flat parts. Knowing where these kinks are can help you set better pricing.

This was brought to life for us first when we did work looking at ketchup pricing. The client used Nielsen output to assess price elasticity and, therefore, to set the prices of their different sizes. But this output had two flaws: it was based on smooth demand curves and there was little variation in the price of ketchup over time, making the model output further suspect.

Our solution was to use a technique called brand price trade-off or BPTO. In BPTO you show a respondent a shelf set with the relevant competitive brands and prices. The respondents looks at the shelf set and then picks the product they would buy. We then increase the price of the chosen item. The respondents again picks a product to buy. This time we find that they may choose the same product (they are brand and size loyal) or they choose a now relatively cheaper product, which could be a different size  (still brand loyal), or they choose a different brand (a switcher).

Often, this information is input into a model that creates smoothed demand curves, which we believe to be a mistake. In this case, because each respondent went through more than two hundred choices (which went quickly; this was not a case of task overload) we did not need to model; we had the purchase intent for every brand at every needed price combination. So there was no modeling used.

Did this matter? Absolutely. We identifed where the kinks were in the demand curve and set prices appropriately. This meant for one size if you increased the price by 10 cents, there was a dramatic decrease in sales, but as the price increased further, there was a limited drop-off in sales. The client should either keep the price as is or increase it substantially, but to choose the middle ground is uneconomical.

 

What made this work valuable is that the findings suggested big changes in the client’s pricing. Previously their models supported small decreases in price per ounce as the sizes grew larger, that is, it rewarded consumers for buying more. Their new pricing was much more flexible, reflecting kinks in the demand curve, so the price per ounce differences became much more pronounced. For example, the large bottles actually charged a premium over the smaller bottles. While this appeared to make no economic sense (one could just buy more of the smaller ones), the results held up in the real world when implemented, where we believed, first, few people are constantly scanning ketchup prices and second, if you use a lot of ketchup, you find larger bottles convenient, and so worth a premium.

We also demonstrated the opportunity to raise the price of the smallest bottle, which was price inelastic until it rose above that of the smallest competitive brand. We interpreted this to suggest that a segment was brand insensitive and would simply buy the cheapest bottle, knowing they used little but wanted some in the house. This was important because grocery stores often carried only one brand’s smallest bottle, stocking only larger sizes of competitors’ brands. Again, by identifying the kink (the lowest competitive price), profitability was improved.

We also were able to show that there were clear packaging loyalties, with some consumers preferring glass (recyclers) while others liked plastic (mom’s with kids who liked to drop bottles). This suggested considering packaging preferences in setting prices, something that had not happened before.

We have had repeated success using BPTO in the form discussed above to show companies how to set more profitable prices—both in heavily promoted categories where price knowledge is likely high (like soda and bread) and in others where price knowledge is more sporadic. In all cases, we identified kinks that led to capturing pricing opportunities, sometimes by lowering price to grow unit sales despite lower margins, sometimes to lower sales but capture higher margins.

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This Post is an extract from the book “52 Things We Wish Someone Had Told Us About Customer Analytics”, by Mike Sherman (pictured above) and his son, Alex, available exclusively from Amazon. Used with permission.

If you’d like to talk Marketing, Insights, Big Data, Voice of the Customer and Pricing, book a call with Mike here.

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