The five biggest misconceptions about B2B pricing optimization

Technological disruptions tend to cause anxiety and elicit misconceptions. Early Americans called trains iron horses and found it dizzying to ride something that went as fast as 30 miles per hour. Telephones were said to deliver electric shocks to anyone who touched them. There are still claims that texting is ruining the English language. 

It’s no wonder, then, that after the pandemic pushed digitization into overdrive, B2B pricing mythologies became more deeply ingrained into the collective psyche. Suddenly losing reliance on human interactions, many businesses had to upgrade their bare-bones websites into eCommerce sites capable of competing with Amazon. Longtime workflows, such as hand-edited pricing spreadsheets and monthly forecasting, suddenly appear woefully slow given the speed and scale of the internet. Businesses started to pay attention to pricing optimization platforms and automated data analytics — and the stories resurfaced. 

Here are five of the biggest misconceptions about pricing optimization and the truths behind them. 

1. Sales is an art, not a science

Selling without a sales rep? You might as well declare that the world is flat. Conventional wisdom is that pricing is an art, selling is an art, and that sales reps who connect customers with products are the ultimate arbiters, and as such, should have the autonomy to negotiate prices.

Sales reps insist, “I know my customer, my market and my product better than anyone.” They develop trusted relationships and understand the intangibles. But data still counts. While a salesperson has one data point, a good pricing optimization platform has a world of data. 

When rolling out new pricing software at sales events, we like to ask a room of salespeople to estimate the cost of a 500 pack of styrofoam containers. They text their estimates, and the numbers are projected onto a large screen. The numbers are all over the place and biased by the reps’ experience and customers (e.g., high-end or low-end restaurants). It’s an eye-opening exercise that demonstrates how, when pricing relies on gut feeling and intuition, the outcomes can vary widely. This allows us to demonstrate that with pricing, AI can think like your best reps think — carefully considering all the moving factors that influence price for each unique situation.

2. B2B purchases are too big for pricing optimization 

B2B purchases are large in nature. You’re not just going to Amazon and buying shoe inserts for your kid, or a new flashlight. Buying in the business world is a complex decision, requiring education about the product, team buy-in, months of selling and years-long contracts. 

Pricing optimization couldn’t possibly tackle such a beast, the thinking goes, so why bother? True, pricing optimization wouldn’t be ideal for a high-stakes purchase like, say,  a Boeing Dreamliner. It would, however, work for something smaller, like air conditioning compressors or connector bolts. Applying AI to smaller products, including those within a larger deal, will get teams closer to the desired margin, profit or other strategic business goals. 

3. AI is a black box that sets its own pricing 

Accustomed to Amazon, social media algorithms and travel aggregators, we often assume that AI will change pricing faster than the blink of an eye, based on opaque calculations, and there’s nothing we can do about it. Any B2B pricing optimization worth its salt doesn’t work like that. Instead, it keeps pricing teams behind the wheel, telling AI where to go, interacting with the model to deploy goal-seeking strategies and testing the outcomes before publishing prices. 

For example, the pricing team is in full control to set a strategy with these goals in mind: “I’d like to maximize profitability on product category A in the Southwest region for small customers, but don’t raise my prices by more than five percent. In a different segment, large customers in the Northeast on product category B, I’d like to take more share and be revenue aggressive, but keep a minimum margin of 30 percent and don’t lower prices by more than seven percent. And, for all other segments in my business, take a more balanced approach to revenue and profitability.”

In B2B pricing optimization, AI does the things that take humans days to weeks to finish. This includes harmonizing pricing across channels to ensure the pricing presented to a customer online is consistent with standing agreements or pricing they are normally given by the sales teams. The smartest pricing AI on the market can ingest competitive data to compare pricing with the competition so pricing teams can adjust in real-time. People remain in charge–we tell the AI what to do and then decide how best to use the analysis it provides.

4. Pricing will become volatile 

B2B pricing optimization is not like high-frequency trading, which creates market volatility. Rather, it contains mathematical models designed to hold up during times of volatility. 

When presented with uncertainty, sales reps tend to quote all over the map. Say you’re selling napkins to restaurants in a certain part of a city, and demand drops significantly. Dropping price to earn back business would be the instinctive response. But that doesn’t take into account unseen factors. Maybe a bunch of restaurants closed due to the pandemic, and volume was simply gone. Maybe a large number of restaurants were concentrated in an area that was bought up for redevelopment, and volume likewise disappeared.

AI that can calculate price elasticity ensures that market-driven shifts in volume won’t cause the pricing model to go haywire. The elasticity model will work reliably throughout the demand shock, observing that, while some categories’ volume is up and some volume is down, the fundamental relationship has not changed and nor should pricing. 

5. Optimization means the same thing to everyone 

Put five people in a room, ask them how they want to optimize their health this year, and you’ll receive five unique answers. One person might want to lose ten pounds; another might want to get better sleep. Similarly, pricing optimization doesn’t mean the same thing for everyone. 

You can set prices to optimize every three years, or every day. You can predict how each of your target markets will respond to a price change. You can change pricing based on margin, profit, revenue, volume or other qualifiers. Pricing optimization is essentially a buffet of options, and each business chooses the best menu items.

Optimizing for our times 

Culturally, the rush to digitize translates to the rapid adoption of tools that crunch data and growing comfort with such tools in day-to-day life. Pricing optimization is becoming the central data tool for B2B organizations everywhere, and it’s worth considering what it will take to understand and adapt. 

Pete Eppele, SVP Products and Science, Zilliant

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