Six data assets every SaaS company needs before designing an agentic pricing model. Mark your readiness, identify your gaps, and close them before you redesign anything.
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Data assets
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Why this checklist exists
Building a custom agentic pricing model without clean commercial data is guesswork. These six assets form the diagnostic foundation: they determine which pricing structures are viable, which customer segments to prioritise, and where the transition risks are highest. For each item, mark your current readiness. Gaps become your build agenda before any model design work begins.
The six data assets
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ARR decomposition by segment, pricing metric & product line
Building a new pricing model without clean ARR decomposition is guesswork. You need to understand what you're earning, from whom, and on what basis: before designing what comes next. A blended ARR number hides the structural vulnerabilities that agentic pricing is designed to fix.
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Historical usage & consumption data mapped to customer value
The best consumption unit is rarely invented: it's discovered in your existing usage data. Without it, value metric design is based on assumption rather than evidence. Usage logs that have never been analysed through a pricing lens are a hidden asset waiting to be unlocked.
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Customer segmentation by agentic maturity, use case variety & commercial potential
A custom pricing model needs to work across your customer spectrum: from early adopters running complex multi agent workflows to customers yet to deploy a single agent. Firmographic segmentation alone (size, industry, geography) tells you nothing about pricing sensitivity or transition risk.
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Willingness to pay research for agentic features
Willingness to pay data is the difference between a pricing model built on evidence and one built on internal opinion. Even directional research (a handful of structured customer conversations) is significantly better than nothing. What customers say they'd pay for often differs sharply from what they actually value.
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Competitive agentic pricing landscape analysis
Pricing in a vacuum is dangerous. Understanding how competitors are monetising agentic capabilities (and where the market is heading) is essential context for designing a model that wins commercially. Anecdotal awareness from sales calls is not the same as structured competitive intelligence.
These metrics are the diagnostic baseline for any pricing transformation. Critically, you need to know whether they're already showing the strain of agentic displacement, or whether that signal is yet to appear. Blended metrics hide early warning signs until the problem is structural.
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Breakdown
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How to close the gaps
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Start with ARR decomposition
If your ARR is a single blended number, work with Finance to break it down by customer segment, product line, and pricing metric first. Everything else builds on this.
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Mine your usage logs
Don't build a new telemetry layer before analysing what you already have. Most SaaS products log more than they realise: start with event data and map it to customer outcomes.
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Run five WTP conversations
Five structured customer conversations with your most agentic accounts will give you more signal than any internal pricing committee. Focus on value, not mechanics.
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Map three competitors
You don't need a complete landscape: you need to know how your top three competitors are structuring agentic pricing. Check their pricing pages, help docs, and G2 reviews.
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Isolate agentic cohorts
Tag customers by agentic adoption level in your CRM. Even a rough cut (high, medium, none) lets you see whether GRR and NRR are already diverging by adoption tier.
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Bring what you have
Gaps are expected. Partial data is normal. The point of the kit is to know what you have, what is missing, and what to build first.
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The prep kit gets you ready. The full assessment scores all nine layers so you can see where this sits against the rest of the model.
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