The Cost-Plus Trap

Cost-plus pricing has been the default for wholesalers and distributors for decades, and for good reason: it's simple, it's defensible to customers ("our costs went up"), and it guarantees a margin on every transaction. But in today's supply chain environment—with input costs fluctuating monthly or even weekly—cost-plus has become a trap.

Here's why: cost-plus ties your pricing to the most volatile variable in your business (input costs) rather than the most valuable one (customer willingness to pay). When steel prices spike 15% in a quarter, a cost-plus wholesaler mechanically passes that increase through and watches as price-sensitive customers defect to competitors who absorbed part of the increase. When prices drop, the same wholesaler lowers prices immediately—even for customers who wouldn't have noticed or cared.

Adding Demand Signals to the Equation

The alternative isn't to ignore costs—it's to layer demand-side intelligence on top of your cost data. For each product in your catalog, you need to know two things: what does it cost you (the supply side), and how sensitive are your customers to price changes (the demand side).

When you have both, pricing decisions become nuanced and profitable:

Cost goes up + Low elasticity: Pass 100% of the increase through. Customers aren't price-sensitive on this product and will absorb the change.

Cost goes up + High elasticity: Pass only 50-70% through and accept a temporary margin compression. Losing volume on elastic products costs more than the margin you'd gain from a full pass-through.

Cost goes down + Low elasticity: Keep prices stable. Your customers aren't comparing prices closely on this item, and the cost reduction goes straight to your margin.

Cost goes down + High elasticity: Lower prices to gain volume share. On elastic products, the volume uplift from a price cut more than compensates for the reduced per-unit margin.

This framework turns the same cost data you already track into a strategic pricing tool rather than a mechanical formula.

Customer Segmentation: One Price Does Not Fit All

B2B wholesalers often have wildly different customer types buying the same products. A large industrial customer ordering on annual contracts has different price sensitivity than a small workshop ordering ad-hoc. A loyal customer of 10 years has different switching costs than one you acquired last quarter.

Yet many wholesalers still use a single price list with ad-hoc discounting handled by the sales team. This approach has two problems: it leaves money on the table with low-sensitivity customers, and it risks losing high-sensitivity customers who don't get offered a competitive price until they threaten to leave.

A better approach: segment your customers into 3-5 tiers based on observable characteristics (order volume, frequency, product mix, relationship tenure) and set tier-specific pricing that reflects each segment's actual price sensitivity. The data to build these segments already exists in your ERP system—you just need to analyze it.

Implementation: A 90-Day Roadmap

Month 1: Data and analysis. Export 18-24 months of transaction data from your ERP. Run elasticity analysis on your top 100 SKUs (by revenue). Build a customer segmentation model using order history.

Month 2: Strategy and simulation. For each product-segment combination, define a target price that balances margin, volume, and competitive positioning. Run scenario simulations to project the P&L impact of moving from your current price list to the new segmented pricing.

Month 3: Phased rollout. Implement new pricing on your highest-confidence products first (those with the most data and clearest elasticity signals). Monitor results weekly. Expand to the full catalog by end of quarter.

The expected outcome: 4-8% gross margin improvement within two quarters, with minimal customer churn on properly segmented pricing. The key is making these changes based on data rather than instinct—and having the scenario modeling to prove the expected impact before implementation.