“It is a capital mistake to theorize before one has data. Insensibly one begins to twist facts to suit theories, instead of theories to suit facts.”
Every time I’m standing in front of the craft beer cooler at my local bodega, I can’t help but think about the concept of price elasticity.
Buying beer used to be a simple process – there were only a few brands in play, but now with all of the new micro-breweries, IPA selection has become as complex as picking a fine wine. I’ve got a handful of go-to IPA brands I stick with. Which one will I buy on a given day? Probably the one that’s on sale. I’m the poster child for micro-brewery IPA price elasticity.
The simple definition of the price elasticity of demand is the measure of the percentage change in quantity demanded of a product compared to the percentage change in price – (change in demand / change in price). If something has high elasticity, a change in price could have a big impact on demand; with low elasticity, a price change could have less impact on demand. According to the theory, you use a calculated price elasticity factor to model how changes in price impact will impact demand.
Lately, it seems like I can’t spend more than 10 minutes in a Zoom meeting on price optimization before someone brings up the topic of price elasticity. Everyone seems to want to include elasticity modeling in their pricing strategies. Has something been secretly added to our water supply that’s causing this? Price elasticity is a proven concept in beer, consumer products, and retail in general. In the B2B world, it’s not that simple. Here are some things to think about when considering working price elasticity modeling into your pricing strategies.
B2B customers only buy what they need
B2B customers buy out of necessity, not impulse. Even the smallest businesses are using planning systems to manage their inventory and they’re keeping it low. If you need two motors, you buy two motors. A really steep discount might lead you to buy three, but that just means you’ll be putting off buying the third one that you would have bought in the future. From the seller’s perspective you might snatch up some additional orders that you might not have won at your higher price, but how long will the increase in demand last?
Your competition will react to your change in prices
After losing orders on price, your competition is bound to react. Once they lower their prices to match your elasticity market share grab the race to the bottom will be on. The gain that you pick up in volume from lower prices will be short term at best. You have to face reality: over the long term, B2B demand is very price inelastic.
There are some places where price elasticity works in B2B
Think of the craft beer cooler. I’ll buy the discounted brand, but that also means I won’t be buying the non-discounted brand that might have otherwise been my first choice. A great example of cross elasticity of demand – the responsiveness in the quantity demanded of one product when the price of another product changes.
This concept can work in B2B. If you’ve got products that are form/fit/function substitutes (branded vs. private label; good, better, best, etc.) manipulating prices of associated products will clearly show the impact of cross-price elasticity. You’ll see it in your data and you should be able to model it.
So, should you give it a try?
Price elasticity of demand in B2B is a contentious topic. If you’re contemplating rolling out a price elasticity model-driven strategy (or any new pricing strategy) you need to think about measuring cause and effect. Do some analysis first.
Can you find evidence of price elasticity impact in your historical data? What you’re undoubtedly going to find is that, with so many variables to deal with (some in the data, some not), it’s very difficult to measure the true impact of pricing actions on demand.
If that’s the case, how confident are you that you’ll be able to use that historical data to accurately model the impact of price elasticity driven price changes on future demand? And if you develop a strategy you’re comfortable with will you be able to measure true cause and effect? Or will you end up, at best, twisting facts to fit your theory?