The median $15M SaaS company in our dataset is losing $2.1M annually to pricing architecture failures that nobody owns.
That number is not dramatic. It is arithmetic. Six common pricing mistakes, each costing between $200K and $600K per year at the $15M revenue mark, compounding quietly while everyone focuses on pipeline and logos.
The reason nobody owns these failures is structural: pricing in most technology companies sits in the gap between product, sales, and finance. Product sets the list price. Sales negotiates the actual price. Finance reports the result. Nobody measures the distance between what the company should be capturing and what it actually captures — and that distance is where the $2.1M lives.
Every mistake below started as a reasonable decision. That is what makes them persistent. The CEO who approved them was not wrong at the time. They are wrong now.
1. Discounting Without Governance
Annual cost at $15M revenue: $380K–$520K
Every SaaS company discounts. The problem is not that discounting exists. The problem is that in most $5M–$50M companies, discounting is a sales behaviour, not a business decision.
It starts simply enough. A rep has a deal that is close to closing. The prospect asks for 15% off. The rep asks the CRO. The CRO approves because the deal is in the forecast and the quarter is tight. The discount is granted. The deal closes. Everyone moves on.
Now multiply that interaction by every rep, every quarter, for two years. What emerges is a discount culture — not a discount policy. The average discount creeps from 8% to 14% to 19%. Nobody tracks the creep because each individual discount was “approved.” The approval is the problem. When every discount is approved on a case-by-case basis, there is no system. There is only accumulated precedent.
At $15M in revenue, every percentage point of unnecessary discounting costs $150K. The distance between a governed discount policy (average 8–10%) and an ungoverned discount culture (average 15–20%) is five to seven points. That is $380K to $520K per year — in revenue that was already sold, already in the pipeline, and lost at the last moment to a negotiation the company did not need to lose.
The one-sentence fix: Install a discount governance framework with tiered approval thresholds, maximum discount caps by deal size, and quarterly discount audits that report to the CEO, not the CRO.
2. Legacy Pricing Grandfathering
Annual cost at $15M revenue: $280K–$420K
Legacy customers paying legacy prices is the most politically difficult pricing problem in any technology company. These customers were early adopters. They took a risk on the product when it was unproven. They are often the CEO’s personal relationships. Raising their prices feels like punishing loyalty.
It is not punishment. It is alignment.
A customer paying 2019 pricing for a 2026 product is receiving a subsidy. The cost to serve that customer has increased — support costs, infrastructure costs, feature development costs. The value they receive has increased — they are using a product that is four or five generations ahead of what they originally purchased. The price has remained static.
In a $15M SaaS company with a five-year history, legacy pricing typically affects 20–30% of the customer base. The average underpricing is 25–40% relative to current list price. The revenue gap — the difference between what these customers pay and what they would pay at current pricing — ranges from $280K to $420K annually.
The common objection is churn risk: “If we raise prices on our earliest customers, they will leave.” The data does not support this. When pricing increases are communicated with transparency, linked to specific value delivered, and phased over two to three renewal cycles, the churn impact is typically under 3%. The revenue gain is twenty to thirty times the churn loss.
The one-sentence fix: Implement a structured price migration programme that moves legacy customers to current pricing over two to three renewal cycles, with value-based communication at each step.
3. No Monetisation of AI Capabilities
Annual cost at $15M revenue: $300K–$600K
This is the most current pricing failure and the one with the widest cost range, because the opportunity depends heavily on what the company’s AI capabilities actually do.
Most B2B SaaS companies at this revenue mark have shipped AI features in the past eighteen months. Intelligent search. Automated classification. Predictive analytics. Copilot-style assistants. Some of these features are meaningfully differentiated. Some are table stakes. Nearly all of them are bundled into existing plans at no additional charge.
The decision to bundle was rational at the time: the features were new, the company wanted adoption, and unbundling felt premature. But “premature” was twelve months ago. The features now have usage data, adoption metrics, and — in some cases — clear evidence of differential value. Customers who use the AI features retain better, expand faster, and report higher satisfaction. That is the definition of monetisable value.
The failure is not that the company shipped AI without charging for it. The failure is that the company has not revisited that decision now that the data exists to support monetisation. The pricing architecture has not been updated to reflect the product architecture.
At $15M revenue, even conservative AI monetisation — a 5–10% price increase for an AI tier or an add-on module priced at $50–$200/user/month — produces $300K–$600K in incremental revenue. And because these features are already built and deployed, the gross margin on that revenue approaches 90%.
The one-sentence fix: Audit AI feature adoption data, identify the features with measurable differential value, and create an AI pricing tier or add-on module with a dedicated value narrative.
4. Per-Seat Pricing in a Usage-Driven Product
Annual cost at $15M revenue: $200K–$350K
Per-seat pricing made sense when value scaled with the number of users. For many B2B SaaS products, it no longer does.
If the primary value driver is the volume of data processed, transactions completed, API calls made, or workflows automated, per-seat pricing creates a structural misalignment: the customer pays based on a metric (headcount) that does not correlate with the value they receive. This produces two problems simultaneously.
The first problem is revenue leakage. The customer who processes 10x the data volume of another customer on the same plan, with the same number of seats, is paying the same price for dramatically more value. That is a subsidy.
The second problem is expansion friction. When the path to increased spending requires adding seats — which means adding people — the expansion decision becomes an HR decision, not a usage decision. The customer does not need more people. They need more capacity. But your pricing model only allows them to buy people.
The transition from per-seat to usage-based or hybrid pricing is not simple. It requires modelling, communication, and careful migration. But the companies that have made this transition in the $10M–$30M band have consistently seen 15–25% revenue uplift within twelve months, driven almost entirely by better alignment between price and value.
The political challenge is real. The sales team has built its compensation model, its negotiation playbook, and its forecasting process around per-seat pricing. Changing the model changes every one of those things. The CEO who attempts this transition without rebuilding the sales compensation structure and re-equipping the team with a new pricing narrative will face internal resistance that kills the initiative before the first customer is migrated.
This is why pricing transitions fail more often from internal politics than from customer pushback. The customer usually welcomes a model that charges them for what they use rather than how many people they employ. It is the sales team that resists — because per-seat pricing is simple, familiar, and easy to forecast, and usage-based pricing requires them to understand value in a way they have never been asked to.
The one-sentence fix: Map your actual value drivers, model a hybrid pricing structure that combines a platform fee with usage-based components, and pilot with new customers before migrating the base.
5. Annual Price Reviews That Do Not Happen
Annual cost at $15M revenue: $180K–$280K
This failure is the simplest on the list and the most inexcusable. The company has no annual price review process.
Costs increase every year. The product improves every year. Market conditions shift. Competitive positioning evolves. And the price list — which is the single most direct lever on revenue — remains unchanged because nobody scheduled the meeting.
In most $5M–$50M technology companies, the last pricing review was triggered by a specific event: a fundraise, a new competitor, a board question, or a customer complaint. It was not triggered by a calendar. This means pricing is reactive, not proactive. It is adjusted when pain becomes acute, not optimised when conditions are favourable.
The cost of this failure is straightforward to calculate. Inflation alone — at 3–4% in most markets — erodes real pricing by 3–4% annually. A company that has not adjusted pricing in two years has given every customer a 6–8% discount in real terms without deciding to do so. At $15M revenue, that is $180K–$280K in captured value that has quietly evaporated.
The one-sentence fix: Schedule an annual pricing review in Q4 every year, with a structured framework that evaluates competitive positioning, cost-to-serve changes, value-delivered improvements, and market willingness-to-pay data.
6. Packaging Architecture That Fights Expansion
Annual cost at $15M revenue: $250K–$450K
The final mistake is architectural, not tactical. The packaging structure — how features, limits, and tiers are organised — was designed when the product was simpler and the customer base was smaller. It has not been redesigned as the product grew.
The symptom is a packaging structure that creates barriers to expansion. The customer on the mid-tier plan hits their usage limit and needs more capacity — but the only available upgrade is the enterprise plan, which costs three times more and includes fifteen features they do not need. The customer’s choice is between staying constrained or overpaying. Many choose a third option: they stay and stop expanding.
The cost of this failure compounds over the life of the customer. Every month a customer remains on a plan that is too small for their usage but too far from the next tier’s price point is a month of expansion revenue lost. Across a customer base of 200–400 accounts, the aggregate cost of poor packaging architecture — measured as the difference between actual expansion rate and achievable expansion rate — ranges from $250K to $450K annually.
The one-sentence fix: Audit your tier structure against actual customer usage patterns, identify the gaps between tiers where customers cluster without upgrading, and introduce intermediate options or usage-based add-ons that create a frictionless expansion path.
The most reliable method for identifying packaging failures is to plot your customer base by current plan against actual usage. The clusters that sit at the top of their tier’s usage limit — using 85–95% of their allocated capacity — without upgrading are the customers being blocked by your packaging architecture. In most $5M–$50M companies, this cluster represents 15–25% of the customer base. Each one is an expansion opportunity that the packaging structure is preventing.
The second diagnostic is time-to-upgrade analysis. How long does it take a customer to move from one tier to the next? If the median time-to-upgrade exceeds twelve months, the friction is architectural. Healthy expansion systems produce upgrade decisions within six to nine months — fast enough to capture the value growth, slow enough to feel natural to the customer.
The Arithmetic
Add the six losses at their midpoints: $390K + $350K + $450K + $275K + $230K + $350K = $2.045M.
That is revenue your company has already earned, already sold, already delivered value for — and is failing to capture because the pricing architecture has not been reviewed, restructured, or governed.
Most CEOs recover 30–60% of this within two quarters once the pricing architecture is correctly diagnosed. The recovery is not evenly distributed. Discount governance and annual price reviews produce results within weeks. Legacy pricing migration and packaging restructuring take two to three quarters. AI monetisation depends on the specificity of the value and the readiness of the product team.
But the starting point is always the same: knowing where the leakage exists, how much it costs, and which fixes produce the fastest return. Without that diagnosis, the CEO is guessing which of the six problems to address first — and the wrong guess costs another quarter of lost revenue.
The question is not whether you are losing this money. The question is whether you know where.
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Before You Commit Capital, Credibility, or Momentum
Technology CEOs are increasingly using decision-grade GTM due diligence before high-stakes commercial bets — not to outsource judgement, but to ensure the decision stands up before it's made.
When a GTM decision is hard to unwind — a senior hire, a pricing change, a market entry — the cost of being wrong compounds quietly. Two quarters slip away before you know it failed.
Commercial Bet Due Diligence (CBDD) is a short, independent review used before commitment. It evaluates a single GTM bet across product, pricing, positioning, sales, and customer growth — and concludes with a clear verdict:
- Review a sample CBDD board memo — the artefact CEOs and boards use to govern these decisions
- Learn how the CBDD process works — and when it's applied
