When per-seat breaks, your GTM strategy breaks with it

When per-seat breaks, your GTM strategy breaks with it

SaaStr just spotlighted Willingness to Pay, Ulrik Lehrskov-Schmidt's pricing consultancy. The shop B2B and AI companies bring in when per-seat stops working. What jumped out to me: the founders calling them in aren't confused about pricing mechanics. They're confused about value. And that is a GTM problem wearing a pricing hat.

Post-PMF founders hit this wall in a recognizable pattern. NRR is soft. Expansion is lumpy. Sales cycles stretch as you push upmarket. The reflex is to hire a pricing consultant or run a willingness-to-pay survey. The smarter move is to ask why your positioning is attracting buyers who balk at whatever model you're running.

Pricing is an output. Positioning is the input. If you're selling before you've locked in who you're selling to, what outcome they're paying for, and how you compare to alternatives, you'll cycle through three or four models without solving the actual problem. Willingness to Pay the firm exists because most SaaS founders skip the upstream GTM strategy work that would make the right pricing model obvious.

Per-seat isn't broken; your ICP definition probably is

Per-seat pricing fails when different users extract radically different value from the same product. A 50-person team where 8 people use the tool daily and 42 hold dormant seats isn't a pricing problem. It's a signal that you're selling to the wrong buyer or the wrong use case.

The fix isn't switching to usage-based. The fix is tightening your ICP to segments where the whole team gets consistent value. Drift went deep on sales teams specifically because reps who use the product every day justify per-seat math. The product didn't change. The ICP targeting did. That's a GTM decision, not a pricing decision.

The willingness-to-pay signal hiding in your pipeline data

Most founders go looking for WTP signals in surveys or win/loss calls. The data is already in your pipeline. Look at where deals die. If you're losing at pricing conversations specifically, that's a WTP signal. If you're losing at discovery, that's a positioning signal. The interventions are completely different and conflating them is expensive.

Running $300K/month in paid media at a financial advisory firm taught me to match channel-level CPL against closed-won rates by segment. A segment with $800 CPL and 22% close rate beats a segment with $200 CPL and 4% close rate. The high-CPL segment has higher willingness to pay. Most founders optimize for CPL and miss the real signal entirely.

Why usage-based pricing doesn't automatically fix the problem

Usage-based pricing is having a moment, especially for AI products where cost scales with inference. But it introduces a different set of GTM problems.

Sales cycles get harder. Enterprise buyers want predictability. Finance teams won't sign off on contracts with variable ceilings without serious handholding. You end up needing a more sophisticated sales motion and better usage education just to close deals you'd have closed easily on per-seat. Twilio and Snowflake make UBP work because they built the GTM infrastructure to support it. Most Series A founders haven't.

The question isn't which model is better in the abstract. The question is which model is most legible to your specific ICP given your current sales motion. That answer lives in your GTM strategy, not in a pricing framework.

What happens when you fix positioning first

Here's the pattern I see work. A founder narrows their ICP to two specific firmographic segments. They rewrite positioning around a single measurable outcome per segment. Then they run a pricing test. Close rates go up, not because the price changed, but because buyers now understand exactly what they're buying.

Catalyst Commerce, a Shopify agency, ran this playbook. They went from "growth marketing for eCommerce" to "paid acquisition for DTC brands doing $2M to $10M in revenue." Positioning sharpened. Outbound close rates went from 8% to 23% in one quarter. Pricing didn't move. ICP clarity did.

The sequence is ICP first, positioning second, pricing third. Skipping to pricing is expensive.

The founder trap: running pricing experiments without channel data

If you don't know which channels produce your highest-LTV customers, a pricing experiment will mislead you. You'll raise prices, see close rates drop on the wrong segment, and conclude the market won't support higher prices. The real problem is you measured price sensitivity on buyers who were never your best fit.

A coherent GTM strategy sequences the data gathering correctly. You start by mapping which channels produce which customer profiles. Then you score segments by LTV, payback period, and expansion rate. Then you test pricing on segments with the clearest signal. Running that sequence in reverse costs founders months.

How GTMVP fits in

GTMVP runs eight specialized agents that handle this sequencing. The competitor mapping agent surfaces how rivals are pricing and positioning, which gives you a market baseline before you touch your own model. The channel scoring agent identifies which acquisition channels produce your highest-LTV cohorts. The positioning agent sharpens your angle before you go to market with a new pricing structure. Together, they give you the upstream GTM data that makes a conversation with a pricing consultant like Ulrik actually productive. You show up with data rather than instincts. The GTMVP GTM strategy hub walks through how the agents sequence across a full go-to-market motion.

Running GTMVP before engaging a pricing firm isn't a prerequisite, but it's the difference between a $25K engagement that confirms what you already suspected and one that actually moves the number.

What to do this week

• Audit your pipeline by segment: identify where deals die and whether it's at pricing conversations specifically or earlier in discovery, then separate the signals
• Map your closed-won deals from the last 90 days to their channel source, calculate LTV by channel, and find the segment with the highest willingness to pay before you touch your pricing model
• Score your ICP against three criteria: do most users get consistent daily value, does your positioning make the outcome measurable, and does your current sales motion match the complexity of the pricing model you're considering
• Pull NRR by cohort and match each cohort to the positioning angle that was dominant when they were acquired; pricing problems often trace back to positioning drift 12 to 18 months earlier
• Review how your top three competitors are pricing today using GTMVP's competitor agent; pricing gaps reveal themselves fastest in direct comparisons, and you may find the market has already moved

If your pricing model keeps slipping, run a GTMVP audit of your positioning and channel mix before you change the model. See the output firsthand with a sample report.