Read on to learn why B2B leaders are choosing price optimization to ‘normalize’ their business.

When we hear a term like ‘new normal’ we might immediately conclude it relates to the aftermath of the pandemic. Yet striving for a ‘new normal’ could have a different meaning for B2B leaders who are looking to accelerate revenue growth and expand margins.

When considering the range of prices customers will pay for any given product in B2B sales, a ‘normal distribution’ might seem a likely outcome. A ‘normal distribution’ is simply a family of distributions of the same general form: where the frequency of occurrences result in no or little strict bias left or right of the mean, median, or average occurrence.

What’s a ‘new normal’ for pricing?

In the world of B2B pricing, a normal distribution might look like Example A below with the majority of transactions for a certain product or service within a given segment grouped toward the middle with less frequent transactions grouped toward each end.

The range between the Floor (F) and the Start (S) is the range of prices a company might charge for a product or service in a given segment - which, in practice, can be quite broad. It’s important to note that in these examples, the ‘y’ axis labeled ‘frequency’ does not equate to the order size or the number of units purchased in a given order, merely the ‘frequency’ in which these transactions occur relative to the total number of transactions for that segment.

The Target Price (T) is ideally where most transactions would be grouped in the distribution: toward the middle. This is the price you hope the majority of customers will pay, and thus the price you would quote most frequently and where the majority of transactions would therefore cluster.

'S' is often referred to as full price or list price, which few if any customers pay. ‘F’ is the lowest price or the highest discount from the list price you would offer on a given product or service. In both cases, you would expect if not hope to see less frequency if your Target price is appropriately set.

On occasion, some customers might pay below the margin floor or above the list price. There could be valid reasons for this: a below-floor price on one item compensated by a better price on another. Or a price above list due to additional services or to compensate for a below-floor price on another item.

‘Not normal’ is often what often seems ‘normal’

A normal distribution might be called a ‘paranormal phenomena’ in some or perhaps even many segments of B2B companies. Without the right technology, segmentation and pricing guidance in place, pricing distributions can look more askew - leaning left, leaning right, or in a much more jumbled pattern like Example B below.  In example B, this experience could be attributed to poor segmentation, particularly missing segmentation attributes.

In a given customer segment, you might expect your smallest customers or those who purchase infrequently to pay a higher price - a price closer to the list price, or published price. Your largest customers who generally buy in bulk, in large orders, or customers in certain competitive situations might only accept the lowest price for a specific quote, a price closer to the floor. It’s a fact that most large companies bargain harder for lower prices because they can. They know that if they take their business elsewhere the seller will hurt more and will do almost anything to avoid that. But you would expect the majority of customers would wind up in the middle of the histogram or distribution curve.

Typical distributions that Zilliant might see before applying pricing science and world-class software to the problem might look something like the examples below, showing distributions for a product for an important segment, again, based on the number of transactions at various pricing levels.

Example C above shows prices skewed slightly to the right toward a higher list price. In this case, a higher percentage of customers paying above your target price for this segment may indicate you could be sacrificing revenue by overcharging, even at higher margins.

In the next case (Example D, above), the reverse is true: more customers in this segment are getting discounts from the target price indicating margins may be impacted negatively even if revenue is higher. In a case of extreme discounting from the target price, this might require the most immediate consideration and action to help stop margin leakage. Important questions to ask in this scenario are: Is pricing set below a minimal acceptable margin for market conditions? Do I have too many transactions priced too low or below minimum margin levels?

In Example E above, there is barely any bias at all, which might indicate you haven’t properly segmented your customers and everyone regardless of profile, purchase history, propensity to pay, or other circumstances has an equal probability of paying a price that is too high or too low, resulting in lost revenue or reduced margin depending on each customer.

In Example F we see transactions occurring at an abnormally high frequency at certain price points. This is known as ‘groove pricing’. In this example, prices are set by sellers, somewhat out of convenience, at predictable values at predictable intervals, say $5, $10, $15 and $20. But would the business paying $5 pay $5.95? Or the $10 customer pay $10.35. If the answer is ‘yes’, ‘grooved pricing’ is leaving money on the table.

Narrower is the New Normal

When looking at similar transactions, is the spread of prices too wide?

Another way price optimization can help B2B companies is to ‘tighten up the envelope’ between the Floor Price and the Start Price in your targeted segments, shown in Example G below. This has the effect of ‘Lift and Shift’, which not only compresses the range of prices into a more narrow grouping but increases the frequency, creating a ‘tighter and taller’ grouping.

This result means you have even greater predictability in the prices you establish in the market and the resulting revenue and margins attendant to those transactions.

What if my pricing curves aren’t normal?

Great question. If you think your product and customers are not properly segmented or prices are not apportioned accurately, consistently, and predictably, you are not alone. Even when you rely on the intuition of experienced pricers and sellers, over any considerable period of time, without proper policies, disciplines, segmentation and science applied to each and every transaction, things can get out of whack.

This is why you need an experienced partner that leverages software technology and science to help ‘normalize’ your pricing distributions within your rational segments. This allows you to generate extraordinary results while treating customers more equitably based on performance rather than promises.

Is my ‘Pricing Envelope’ even optimal?

Do the Start-Target-Floor prices represent the optimal pricing? Also, are your current Go-to-Market pricing rules stale? Is the guidance you’ve set so dated as to be irrelevant to current market conditions and customers’ willingness to pay? How can you address that as well? Prices are sometimes, if not often, ‘set and forgotten,’ not reviewed or developed with any rigor around margin goals or market conditions, nor based on supply and demand for a product change when its price changes. This is yet another way price optimization can help: by establishing appropriate guardrails, lower and upper bounds for establishing price, using data science and rules, along with input from pricing teams to help establish an optimal range of prices for a given product or segment.

Beyond the Distribution

The goal of price optimization is to find your perfect balance of profit, value, and desire that will influence a distribution that is optimal for each segment. Price optimization uses your market, customer, product and transaction data to segment customers and find the most effective price point for your product or service for each type of customer and each customer transaction.

The optimal price point is the price at which your customer buys from you at a price near to or at the price you quote, and where you best meet your objectives, whether that means increased profit margins, customer growth, or a blend. This results in maximizing sales and profitability.

In order to execute on these outcomes, Zilliant loads, analyzes, and groups like transactions and then determines which factors your best sellers and pricers consider when determining the price. Zilliant Price IQ®, powered by AI and machine learning algorithms, analyzes all the factors to identify which ones have the most explanatory power when segmenting customers and recommending prices. Zilliant is able to combine the art from your best knowledge workers with our state-of-the art scientific approaches to create precise, effective, scalable, and repeatable results.

And speaking of ‘art,’ we enable pricing teams to infuse the art of pricing with the science of pricing. While Zilliant analyzes large data sets (customer, product, historical transaction data, for example), we also know there are certain factors that aren’t found in the historical data, and these factors can often drive your Go-to-Market objectives, and subsequently, can be inputs into the science capabilities you bring to your pricing and sales teams.

Aligning to Win

Price optimization, done correctly, results in a more normal and narrowly distributed curve in all of your relevant segments, once prices are optimized for each segment. This aligns your sales practices with your strategy and aligns prices with your desired outcomes, which spells better results for your business.

And speaking of obtaining better results, Zilliant can show you how better technology can help your business grow and thrive. To schedule a demo and discussion about how we do this, simply fill out a brief form so we can show you how to create a ‘better normal’ for your business.