by Sara-Marie Gansert
Dynamic pricing is the practice of adjusting prices in response to changing conditions: demand signals, inventory levels, competitive moves, timing, and business objectives, rather than holding a single static list price.
It is not a new idea. Airlines have priced this way for decades. So have hotels. What is new is how far the capability has spread, how much noise surrounds it, and how badly the term has been misunderstood in the process.
Most of the public conversation about dynamic pricing is really about something else: surge pricing at concerts, perceived gouging at grocery stores, the Taylor Swift Eras Tour ticket fiasco. Those are legitimate conversations. But they describe a narrow, consumer-facing version of a much broader commercial discipline.
Conflating the two leads companies to either dismiss dynamic pricing entirely or implement it badly.
If you are a pricing manager trying to respond faster to market shifts, a commercial director managing margin across hundreds of SKUs and dozens of customer segments, or a sales leader whose team is quoting deals without consistent guidance, this article is written for you.
It gives you a clean definition, a terminology table, the B2B mechanics, and a practical framework for implementation that does not destroy customer trust in the process.
Dynamic pricing vs. surge pricing, price optimization, and personalized pricing
Dynamic pricing is one of those terms that has become a catch-all for several distinct pricing practices. That matters because the distinctions carry real commercial and reputational consequences.
Using the wrong approach in the wrong context, or calling one thing by another name, creates confusion internally and erodes trust externally.
Here is what each term actually means:
|
Term |
What it means |
Consumer example |
B2B example |
|
Dynamic pricing |
Prices adjust in response to market conditions: demand, inventory, competition, timing |
Airline seat prices rising as departure approaches |
Quote-time pricing that reflects current market index, customer segment, and deal size |
|
Surge pricing |
A specific subtype of dynamic pricing that responds to peak demand |
Uber prices during New Year's Eve |
Less common in B2B; can apply in spot markets or spare parts under supply constraints |
|
Price optimization |
Modeling to identify better target prices, corridors, or deal guidance based on historical data and business objectives |
Retailer setting seasonal base prices |
Identifying the right price floor and ceiling for a product category by customer segment |
|
Personalized pricing |
Prices tailored to an individual or narrow segment using personal data or behavioral signals |
A streaming service offering different prices based on browsing history |
Account-specific pricing based on relationship history, volume, and strategic value |
|
Revenue management |
The broader discipline of using price, capacity, and availability to maximize revenue from constrained inventory |
Hotel yield management across room types and booking windows |
Capacity-based pricing for service contracts or time-sensitive supply |
|
Markdown optimization |
A specific downward-pricing use case for perishable or aging inventory |
Grocery discounting produce nearing expiry |
Distributor clearing end-of-life parts before a product line is discontinued |
The reason these distinctions matter is not academic. Personalized pricing carries privacy and fairness risks that dynamic pricing does not. Surge pricing triggers customer backlash in ways that time-based or segment-based price adjustment typically does not.
And price optimization without dynamic execution is a strategy without an engine.
In B2B, most companies are not trying to change prices by the minute. They are trying to make better, faster, more consistent pricing decisions at the moment a quote is built, a contract is renewed, or a deal is negotiated.
That is a different operating problem, and it requires a different frame.
Dynamic pricing works by connecting data inputs to pricing decisions in something close to real time. The basic sequence is the same whether you are an airline, a distributor, or a chemicals manufacturer: collect signals, apply logic, generate a price, execute it where the decision gets made.
In practice, it breaks into four steps.
1. Collect the signals
Price decisions need inputs. The common ones are demand patterns, inventory or capacity levels, competitor price points, cost changes, and customer or segment data.
In B2B, that list extends to contract status, account tier, channel, and recent deal history. The quality and freshness of these inputs determine how useful the output is.
2. Apply rules, models, or both
Signals alone do not set prices. They feed into either a rules-based engine, a statistical or AI model, or a combination of the two.
Rules are explicit: if inventory drops below a threshold, increase price by X. Models are inferential: given this customer, this product, this market context, what price maximizes margin within acceptable bounds?
Most mature B2B implementations use both. Rules enforce guardrails. Models optimize within them.
3. Generate a price recommendation
The output is rarely a single number handed directly to a customer. In B2B it is more often a price corridor: a floor, a target, and a ceiling.
The floor protects margin. The ceiling reflects what the market will bear. The target is what the business wants to realize. Sales teams work within that corridor, with visibility into where a given deal sits and why.
4. Execute at the point of decision
This is where most implementations fail. A price recommendation that lives in a spreadsheet or a separate tool is not dynamic pricing. It is a suggestion.
Execution means the price reaches the salesperson building the quote, the ecommerce platform serving the customer, or the ERP processing the order, at the moment the decision is made.
Speed and integration are what separate a dynamic pricing capability from a periodic repricing exercise.
In B2B, that point of decision is almost always a quote, a contract renewal, or a deal negotiation, not a public price board. Prices are not changing every few minutes on a screen somewhere. They are being calculated, governed, and delivered at the moment a commercial decision is made.
Most articles on this topic list demand, competition, and time as the factors that drive dynamic pricing. Those are real, but they describe a consumer or e-commerce reality.
In B2B, the factor set is considerably more complex, and getting it wrong in either direction — pricing too aggressively or too uniformly — costs margin.
The factors that matter most break into two groups: market-level signals and account-level signals.
Market-level factors
Demand patterns. In manufacturing and distribution, demand signals often come from order velocity, backlog data, and macroeconomic indices rather than real-time web traffic.
Supply and inventory position. Scarcity justifies higher prices. Excess inventory justifies movement. The dynamic pricing logic in spare parts, chemicals, and commodity-adjacent products is often driven more by supply position than demand.
Competitor pricing. Not just what competitors charge, but how frequently they move, where they hold firm, and which segments they prioritize. In B2B this intelligence is harder to collect than in retail, but it is still a material input.
Input cost changes. Raw material costs, logistics costs, and energy prices all affect margin realization. Dynamic pricing in process manufacturing and chemicals often starts here, with cost-plus logic that adjusts automatically as input indices move.
Account-level factors
Customer segment and tier. Not every customer should see the same price. A pricing model that ignores segmentation is not dynamic; it is just volatile.
Contract and agreement status. A customer on a long-term contract has different price expectations than one buying spot. Dynamic pricing logic needs to know the difference and respect it.
Deal history and margin performance. What has this account paid historically? What discount patterns have been approved? What is the realized margin on this relationship?
Channel. Direct, distributor, and marketplace pricing often need to reflect different economics. A price that makes sense in a direct negotiation may create channel conflict if it appears in a different format elsewhere.
McKinsey's research on B2B pricing transformations consistently identifies the integration of these signals — not the sophistication of the model — as the primary driver of whether dynamic pricing delivers or disappoints.
The short answer is no. Taylor Swift specifically refused to use dynamic pricing for the Eras Tour, telling her team she did not want to do that to her fans, even if it meant taking less income. AEG Presents chairman Jay Marciano confirmed this in a published interview.
Ticketmaster's Platinum ticket program prices tickets in advance based on anticipated demand and seat location. When millions rushed to buy simultaneously, the result was system meltdowns, canceled sales, and widespread outrage, with average resale prices exceeding $700 — more than four times face value.
The backlash was fierce. But what actually drove it points to something more specific than "dynamic pricing is bad."
The anger was not purely about high prices. It was about opacity and a sense that the system was operating against fans rather than for them. People arrived expecting one price, encountered another, and had no clear explanation of why.
That experience sits at the heart of what makes any variable pricing system go wrong, regardless of industry.
What the Ticketmaster story does not tell us is what dynamic pricing looks like for a manufacturer adjusting prices across 50,000 SKUs, a distributor responding to a raw material cost spike, or a chemicals company recalibrating contract prices at renewal.
In B2B, there is no anonymous buyer refreshing a browser watching prices move. There is a salesperson, a named account, a negotiation history, a contract, and a relationship that both sides want to protect.
The Ticketmaster story is a useful entry point for understanding why variable pricing provokes strong reactions. It is a poor guide for how to implement it inside a complex selling environment.
What are the drawbacks of dynamic pricing?
The risks of dynamic pricing are real, they are well-documented, and understanding them is what separates implementations that build commercial advantage from ones that create customer relations problems the sales team spends months cleaning up.
There are four drawbacks worth taking seriously.
1. Unfairness perceptions when demand is exploited
Research by Kahneman, Knetsch, and Thaler established that people judge price increases driven by demand spikes as fundamentally unfair in a way that cost-justified increases are not.
The distinction matters: a price rise explained by higher input costs is accepted. A price rise explained by "you want it more right now" is not.
In B2B, where price changes are visible to named accounts and discussed in renewal conversations, that distinction has direct commercial consequences.
2. Trust erosion when buyers do not understand the logic
Research on price fairness perceptions shows that buyers judge prices through both outcome and process. A price can look unfair not only because it is higher, but because the buyer cannot understand why it is higher, who else paid what, or how the price was calculated.
In B2B, where a procurement team may be comparing quotes across multiple suppliers and multiple periods, unexplained price movement creates suspicion that is very difficult to walk back.
3. Privacy and discrimination risk when pricing becomes personalized
There is a meaningful legal and ethical line between pricing that responds to market conditions and pricing that responds to personal data. The FTC's 2025 surveillance pricing study found that firms using individualized pricing practices were drawing on a wide range of personal data to set prices, raising serious concerns about fairness.
Dynamic pricing that stays at the market or segment level avoids most of this risk. Pricing that moves toward individual behavioral targeting does not.
4. Organizational misuse when execution moves faster than governance
The most common failure mode in B2B dynamic pricing is not a bad model. It is an organization that deploys price changes faster than it can explain or defend them.
When sales teams do not understand the logic behind a price recommendation, they either ignore it or cannot defend it to the customer. The technology is rarely the bottleneck. The operating model almost always is.
These are not reasons to avoid dynamic pricing. They are reasons to implement it with guardrails, explainability, and a clear governance model.
The instinct when customer perception comes up is to treat it as a communications problem. Get the messaging right, explain the pricing clearly, and customers will come around. That instinct is mostly wrong.
Customer perception of dynamic pricing is not primarily shaped by the size of the price change. It is shaped by whether the change feels fair.
Kahneman, Knetsch, and Thaler's foundational research on price fairness found that people evaluate prices through two lenses: the outcome and the process. A price can feel unfair because it is higher than expected. But it can equally feel unfair because the buyer does not understand the procedure that produced it, even when the price itself is reasonable.
What this means in practice is that the same price, for the same product, can be accepted or rejected depending entirely on how it is explained.
Further research on price fairness frameworks identifies three factors that consistently predict whether a buyer will accept or reject a variable price: whether they understand the reason for it, whether they believe the same rules apply to other buyers, and whether they feel they had any agency in the process.
In B2B, all three are addressable. In consumer ticketing, almost none of them are.
Research on reference prices and loss aversion shows that prices above a customer's reference point generate a disproportionately negative reaction compared to the positive reaction generated by an equivalent discount below it.
In practical terms: if you have trained a customer to expect a certain price level, moving above it carries more commercial risk than the margin arithmetic alone would suggest.
For B2B companies, the implication is clear. Dynamic pricing does not require customers to accept unpredictability. It requires the explainability and governance infrastructure that makes price movement understandable, consistent, and defensible.
The pricing is dynamic. The logic is not.
What are the legal considerations for dynamic pricing?
Dynamic pricing is legal in most markets and most industries. But the legal landscape is shifting, and the direction of travel is toward greater scrutiny, not less.
The clearest current risk is not dynamic pricing itself. It is what happens when dynamic pricing crosses into surveillance pricing.
The FTC's 2025 surveillance pricing study found that firms offering surveillance pricing services were collecting and using a wide range of personal data, including location, demographics, browsing behavior, and device information, to set individualized prices. The FTC's concern is not that prices vary. It is that prices vary based on personal characteristics in ways that buyers cannot see, understand, or contest.
Price discrimination law
In the United States, the Robinson-Patman Act prohibits selling the same product to competing buyers at different prices when the effect is to harm competition.
Segment-based pricing, channel pricing, and volume-based pricing structures are generally defensible. Documented pricing logic and consistent policy application are the practical safeguards.
Personalization and data privacy
If your dynamic pricing model draws on personal data, behavioral signals, or third-party data enrichment to individualize prices, it enters territory governed by GDPR in Europe, CCPA in California, and an expanding set of state-level privacy laws.
Sector-specific regulation
Financial services, healthcare, utilities, and government contracting all have specific frameworks that restrict how and when prices can change. If you operate in any of these sectors, dynamic pricing strategy needs legal review before implementation, not after.
The safeguard is not avoiding dynamic pricing. It is building pricing logic that is transparent, documented, consistently applied, and explainable to the customer on the other side of the deal.
What does good dynamic pricing look like in B2B?
Everything in this article points to the same conclusion. Dynamic pricing is not primarily a technology problem. It is an operating model problem.
The companies that see the margin improvement that McKinsey's B2B pricing research cites — between 4 and 8 percent in successful transformations — are not the ones with the most sophisticated models. They are the ones that built the organizational capability to use price data consistently, govern decisions across the business, and give their sales teams the confidence to defend a price in a conversation.
Segmentation before sophistication
The first question is not "how do we make prices more dynamic?" It is "which customers, products, and channels actually warrant differentiated pricing, and what logic should drive those differences?"
Segment-level pricing that buyers can understand and that sales teams can explain is more commercially durable than a model that optimizes margin in ways nobody in the business can articulate.
Guardrails that protect the relationship
Dynamic pricing without floors and ceilings is not dynamic pricing. It is price volatility. Every implementation needs hard floors that protect margin, soft ceilings that reflect what the market will bear, and the governance rules that determine when exceptions are justified and who can approve them.
Explainability at the point of the deal
A price recommendation that a salesperson cannot explain to a customer is not a pricing capability. It is a liability.
The sales team is the last mile of every pricing decision in B2B. Good dynamic pricing gives sales teams the context alongside the number: what drove this price, where it sits in the corridor, and what flexibility exists within policy.
Feedback loops that make the model smarter
Win rates, deal outcomes, margin realization, discount patterns, and customer pushback are all signals that should flow back into the pricing model and the segment logic.
Without that feedback loop, dynamic pricing becomes a static system that was built once and slowly degrades. With it, the model improves continuously and the business builds genuine pricing intelligence over time.
Integration into the workflow, not alongside it
A price recommendation that lives outside the workflow is a suggestion. The commercial value of dynamic pricing is realized at the moment a quote is built, a contract is renewed, or a deal is negotiated.
That means the pricing logic needs to be inside the tools where selling happens. Pricefx integrates with the ERP, CRM, and CPQ systems your teams already use, so pricing decisions happen in workflow, not around it.
The platform enforces governed, explainable pricing logic with the guardrails, audit trails, and sales-facing context that make dynamic pricing something a business can actually operate at scale.
Most companies we work with see measurable impact in weeks, not months, because the bottleneck was never the data. It was connecting the insight to the decision at the moment it matters.
If you are exploring how other pricing leaders are approaching this, the Margin Makers webinar series brings together practitioners working through real pricing transformation challenges.
Watch previous sessions on demand to hear how manufacturers, distributors, and chemicals companies are building dynamic pricing capability in practice. New sessions are scheduled regularly — check the page for upcoming dates.
Head of Solution Strategy EMEA, Pricefx
Sara-Marie Gansert leads the Solution Strategy team at Pricefx across EMEA, a group of pricing and presales specialists who guide complex enterprise sales cycles from discovery through value case to solution design. Over five years at Pricefx she has moved from solution strategist to senior solution strategist to her current role leading the region. Her background spans both the practitioner and presales sides of pricing, including pricing manager roles at Algeco and Michelin. Based in Germany and a Bonn-Rhein-Sieg University graduate, Sara-Marie writes on pricing software and dynamic pricing.
