Most B2B pricing models ignore the twin forces of purchase frequency and price variance, costing clarity, margin, and loyalty.
This article is meant to provoke thought and offer practical insight. Consider it a pricing masterclass distilled from two decades of experience. I've pushed myself hard to not just share theory, but to codify the patterns, math, and strategies that have shaped real-world pricing transformations across manufacturing and distribution.
We're simplifying B2B pricing reality into two key dimensions purchase frequency and price variance not to oversimplify, but to better navigate the complexity that most pricing strategies avoid.
Quick note: This framework assumes your data reflects at least some structure and guardrails (e.g., strategic price governance, controlled rep behavior). If your historicals are still the Wild West, focus first on reining that in. Otherwise, you're just modeling chaos.
We all know the banana curve aka the Negative Binomial Distribution (NBD) that skewed shape showing how often customers buy. But here's the problem: most pricing strategies only see that one curve.
They ignore the second dimension: price variance what customers actually pay for the exact same product. And when you measure this using real transaction data, you don't get a tidy bell curve. You get a chaotic asymmetric blob across two axes.
Let's be specific:
Purchase Frequency (f): How often customers buy in a given period
Price Variance (v): Measured as % deviation from list or strategic price:
v = |Realized Price - Reference Price| / Reference Price
These aren't independent. Modeling them jointly helps avoid systematic pricing errors.
After analyzing millions of transactions across manufacturing and distribution, here's what emerges:
Most customers are both commitment-phobic and price-sensitive. Over 60% of transactions cluster in zones where customers buy rarely and demand deep discounts.
Your "average price" is a statistical illusion. In most large B2B datasets, fewer than 5% of transactions occur within ±2.5% of the average. It's more myth than metric.
Your high-frequency, high-margin customers are outliers. They may drive 20-30% of profit, but represent under 10% of the base.
Timing and pricing behavior follow probabilistic patterns. Purchase intervals often mirror Poisson or Weibull distributions; price response curves are overdispersed and nonlinear.
Bonus truth: Field behavior distorts everything. Rep overrides, branch policies, and discretionary behavior alter both dimensions locally.
This isn't a niche phenomenon:
In manufacturing, the coefficient of variation (CV) on price often exceeds 0.3, while purchase timing follows long-tail distributions.
In distribution, we've observed transaction frequencies from 2 to 14 times annually, with price swings of 20-40% off list.
These aren't anomalies they're signatures of B2B commerce.
"But we already segment by customer size, vertical, and product line!"
Great. Now you have smaller chaotic blobs instead of one big one.
Even advanced segmentation efforts tend to reveal stubborn truths:
Segmentation brings clarity but unless you incorporate behavioral signals and execution context, you're just categorizing chaos.
Focus on where most deals cluster. Treating frequency and price sensitivity as independent variables leads to compounded error.
P(price_acceptance ∩ purchase_timing) ≠ P(price_acceptance) × P(purchase_timing)
Model the joint probability, not just marginal ones.
Structure matters more than strictness.
Set data-informed pricing bands:
Behavior is more predictive than firmographics.
Incorporate these into models:
P(acceptable_price | frequency_pattern, solution_criticality, usage_depth)
Move from deterministic to probabilistic guidance.
"This price range has a 75% win probability with expected margin between 33-38%"
"This level of discounting usually reduces frequency by 10-15%"
Markets evolve. So should pricing.
If your margin histogram isn't on your dashboard or your resume, fix that.
Most organizations are stuck between Level 1 and 2. The leaders? They're already learning from the noise.
You don't need perfect pricing. You need strategic adaptability.
Stop building models that assume precision in a probabilistic world. Align with the messy, beautiful behaviors your customers actually follow.
Next time someone shows you a clean average or static price list, show them a two-axis plot of transaction frequency and price variance.
"This is what real customers look like. And our pricing strategy should honor that reality."