Are We Underpricing or Leaving Money on the Table in B2B SaaS?
Direct answer: Most B2B SaaS companies are underpricing, but not for the reason founders assume. The issue is rarely the headline number — it's weak pricing power: the ability to raise prices, hold discounts, and monetize expansion without losing customers. To find the money you're leaving on the table, run a Pricing Power Analysis that measures how much your customers actually depend on you, how differentiated your offer is, and how much of the value you create you're capturing. If your net revenue retention is strong, churn is low on price increases, and discounting is habitual, you almost certainly have room to charge more.
What "Pricing Power" Actually Means in B2B SaaS
Pricing power is not "can we charge a high price?" It's "can we change our price in our favor and make it stick?" Warren Buffett's framing — the ability to raise prices without losing business — translates cleanly to SaaS, where the mechanics are visible in your metrics.
For a B2B SaaS company, pricing power shows up in five places:
- Willingness to pay vs. your list price. How far below the ceiling of perceived value are you priced?
- Switching costs. Data lock-in, integrations, workflow embeddedness, and retraining costs all raise your power.
- Differentiation. Are you a category-defining tool or a feature that a platform could absorb?
- Value metric alignment. Does your pricing scale with the value the customer receives (seats, usage, outcomes)?
- Discount discipline. How often does sales cut price to close, and by how much?
The reason this matters more than a competitor benchmark: two SaaS companies can charge identical prices and have wildly different pricing power. One raises prices 12% and loses nobody; the other tries and watches churn spike. The first is underpricing. The second is priced correctly and needs to build power before it prices up.
A Concrete Pricing Power Analysis Walkthrough
Here's how to run the analysis on your own book of business. Work through it in order — the later steps only make sense once the earlier ones are answered.
Step 1 — Map your value metric. Ask: does the customer's cost grow as they get more value? If a customer 10x's their usage but pays the same, you have a value-metric leak. Seat-based pricing on a product used by a whole department, or flat pricing on a high-usage account, are classic sources of left-behind money.
Step 2 — Measure retention under stress. Look at net revenue retention (NRR) and gross retention separately. High NRR (expansion outpacing churn) is the single strongest signal of pricing power. Then look specifically at churn following price increases — if past increases caused negligible logo loss, you have proven headroom.
Step 3 — Quantify switching costs honestly. List what a customer loses by leaving: historical data, custom integrations, trained users, embedded workflows. Rate each account High/Medium/Low. Low-switching-cost segments are where you're most exposed — and where you should not aggressively raise prices.
Step 4 — Audit discount behavior. Pull realized ACV vs. list ACV across the last 12 months of deals. A persistent gap means either your list price is aspirational or your sales team lacks the confidence (or ammunition) to hold it. Habitual, unstructured discounting is money on the table you're handing away deliberately.
Step 5 — Test willingness to pay. Use Van Westendorp price-sensitivity surveys, win/loss interviews, and segment-level analysis. The goal is to find the price ceiling per segment, not a single blended number.
Step 6 — Decide the move. The output isn't "raise prices." It's a segmented plan: raise where power is high, hold and build moat where power is low, re-architect the value metric where it's misaligned, and tighten discount governance everywhere.
What "good" looks like: NRR above 110%, price increases that stick with minimal churn, a value metric that scales with customer outcomes, and a realized-to-list ACV gap under control by segment. If you have those, you can price up confidently. If you don't, fix the fundamentals before touching the number.
How Percision Helps — and When It Doesn't
Full disclosure: I write for Percision, an AI-powered strategic intelligence platform, so weigh this accordingly.
Percision runs your business context through structured reasoning steps to produce a board-ready Pricing Power Analysis — mapping your differentiation, switching costs, and value-capture gap, then translating the findings into a segmented pricing plan with scenario analysis. Because it also produces DCF-style financial intelligence and Excel-exportable models, you can see the revenue impact of a proposed price move before you commit, and hand the deck to your board without three weeks of slide-building. It's positioned as a co-pilot, not an autopilot — your leadership team makes the call. This fits founders and CFOs who want consulting-grade structure in minutes rather than an 8–12 week engagement.
When you don't need Percision: If you have one product, one segment, and clean NRR data, a well-built spreadsheet and a Van Westendorp survey will get you 80% of the answer for free. If your pricing problem is deeply political — sales, product, and finance disagreeing on strategy — a human consultant who can facilitate that room is worth more than any tool. And if your data is a mess, fix that first; no analysis outruns bad inputs. (Broader research, like BCG and Harvard Business School's 2023 study on generative AI and knowledge work, suggests AI tools lift performance on well-scoped analytical tasks — but that assumes a clear question and clean context, which is on you to provide.)
Use the tool to accelerate the analysis and the deck. Keep the judgment human.
What this looks like when the analysis is actually run
The question "are we underpricing?" has no single answer, because willingness to pay moves in opposite directions inside the same product. Here is a pricing run splitting it.
The subject is TechNova Solutions, a sample company profile we use for testing rather than a customer: a $45M ARR DevOps platform, 280 employees, Series B.
Excerpt from a real Percision run · Pricing Strategy (T2) · sample company profile
Willingness to pay, by direction of travel. WTP premium expands 50–100% for agentic AI + compliance needs (Job 3,5 intensify to 60–100% WTP); commoditizes 70–90% for classical CI/CD onboarding/scaling (Job 4,7 drop to 5–10% WTP); new LLM security/sovereignty needs command 40–70% premiums — regulated verticals 2–3x LTV precedent × emerging AI risk multiplier.
Return on repricing. 3.5–5x — $35–50M incremental ARR ÷ $12M avg investment; LTV/CAC expands 4.2x → 6–8x.
What it costs to get there. $10–14M over 24 months — 80 FTE × 18 months × $75K/month loaded (20% existing bench, 80% new hires) + $2M infra/partners.
The sizing it refused to trust. TAMs directionally reasonable but inflated 20–40% vs conservative benchmarks. Total $15.3–24B across segments vs stated $25–35B DevOps TAM implies 60–100% coverage (implausible). Adjusted TAMs: total $9–14B, 40–50% of total DevOps.
What repricing is worth if it works, and if it does not. Bull case: AI-powered Enterprise DevOps captures 15–25% premium via telemetry data flywheel and 108% NRR, growth accelerates to 40–50% YoY by 2027 on $10–15M AI investment, hitting $120–150M ARR — probability 20. Base case: growth continues at slowed 32% YoY, SMB/mid-market (70% ARR) faces commoditization from free tiers, steady-state $75–85M ARR by 2027 — probability 45.
| Element | Quantification |
|---|---|
| Current advantage | Telemetry data flywheel from 108% NRR and 6% churn in $180K+ ACV enterprise base ($4.5M ARR ), enabling AI upsell (15-25% ) into autonomous pipelines and anomaly prediction. |
| Quantified advantage | Enterprise segment: 108% NRR vs industry 90-95% ; 6% churn vs 10-15% ; $180K ACV supports 15-25% AI premium . SMB: scale advantage eroding (32% YoY ). |
| Switching costs | 108% NRR, 6% churn = 24-36 month durability . So what: enables 15-25% AI upsell pricing power ; reversal if NRR<105% by Q4 2026 kills moat. |
| Experience curve | Negligible signals no meaningful scale/learning advantage at $45M ARR] |
| Strategic implication | Double down on Differentiation via AI Enterprise BU ($10M invest from $22M Series B ) to 3-5x LTV ($250-400K ACV ) and $150M ARR by 2027; cost leadership unviable at $45M ARR with slowing 32% growth . |
The answer is not "yes, raise prices." Two thirds of the product is being commoditized at the same time one third is gaining pricing power, and a single across-the-board increase would harvest the wrong half. Pricing power here is a feature-level property, not a company-level one.
Note that the return is quoted against an investment and a headcount build — 80 FTE over 18 months — rather than as a margin percentage. And the upside case carries a 20% probability against the base case's 45%. A pricing recommendation that states how likely it is to work is a different object from one that states only what it would be worth.
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FAQ
How do I know if we're underpricing rather than just cheap? Cheap is a strategy; underpricing is a mistake. You're underpricing if your NRR is high, price increases stick without churn, and you discount habitually — that combination means customers value you more than you charge.
What's the fastest signal of pricing power? Net revenue retention. If existing customers spend more over time without you fighting churn, you have expansion pricing power you can lean into.
Should we raise prices across the board? Almost never. Pricing power varies by segment. Raise where switching costs and differentiation are high; build moat before pricing up where they're low.
Want a structured Pricing Power Analysis and a board-ready plan for your SaaS book? Run your company through Percision and pressure-test the numbers before your next pricing decision.