Value Analysis
Ross Sylvester, Co-Founder & CEO, Adrata | Feb 2026 | ~9 min read
I started my career in market research. Specifically, I spent years running conjoint analysis — the statistical technique that decomposes what people actually value by analyzing how they make tradeoffs.
Here's how it works: instead of asking someone "What's important to you?" (which produces polite, useless answers), you present them with forced choices. Would you prefer Product A at $50K with great support and average features, or Product B at $30K with great features and no support? By analyzing hundreds of these tradeoffs across thousands of respondents, you can mathematically decompose the utility each person assigns to each attribute.
Conjoint analysis is the most powerful tool in market research. It's what Netflix uses to decide which shows to greenlight. What automotive companies use to configure option packages. What pharmaceutical companies use to price drugs. It's what reveals the gap between what people say they value and what their behavior reveals they value.
And it occurred to me: we can apply the same framework to understanding what buyers in enterprise deals actually value — using AI to analyze their signals instead of survey responses.
The Stated vs. Revealed Problem
In every enterprise deal, the buyer tells you what they want. They send an RFP. They describe their requirements. They ask about features and pricing. This is stated preference — what the buyer says matters.
But stated preferences are unreliable. Research from Harvard Business School and the Journal of Consumer Research consistently shows a 40-60% gap between stated preferences and actual decision-making behavior.^1^
What actually drives the decision? Revealed preference — what the buyer's behavior shows they value.
The signals are everywhere:
- What they click on in your materials — features page or pricing page?
- Who they bring to meetings — technical evaluators or business sponsors?
- What questions they ask — implementation timeline or ROI model?
- How they engage with competitors — are they comparing features or negotiating price?
- Which internal stakeholders they involve — procurement (cost-focused) or the CEO (strategic)?
- How fast they respond — instant on security questionnaire, slow on business case?
Each of these signals reveals utility — the relative importance the buyer assigns to each attribute of the purchase decision.
The Conjoint Framework for Enterprise Deals
Traditional conjoint analysis decomposes buyer utility across product attributes. Our Value Analysis framework decomposes buyer utility across decision drivers:
The Six Decision Drivers
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Risk Reduction — Does the buyer primarily value reducing downside risk? Signals: asks for references first, leads with security/compliance questions, involves legal early, requests SOC 2 reports before demo.
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Outcome Achievement — Does the buyer primarily value specific business outcomes? Signals: asks ROI questions early, wants case studies with metrics, builds a business case with specific targets, maps features to KPIs.
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Strategic Alignment — Does the buyer value how the product fits their broader strategy? Signals: involves the CEO or strategy team, asks about roadmap, wants to understand the company's vision, evaluates partnership potential.
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Operational Efficiency — Does the buyer value ease of implementation and use? Signals: asks about integration complexity, timeline to value, training requirements, IT overhead.
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Political Safety — Does the buyer value making a "safe" choice? Signals: asks "who else uses this?", wants to know market position, gravitates toward established vendors, involves many stakeholders in evaluation.
-
Innovation Edge — Does the buyer value being ahead of competitors? Signals: asks about unique capabilities, interested in beta features, wants to know what's not available elsewhere, fewer stakeholders (fast mover).
How AI Changes the Analysis
Traditional conjoint analysis requires surveys. You have to ask people to make choices, collect responses, run statistics. It's slow, expensive, and the sample is limited.
Value Analysis is passive conjoint. Instead of asking buyers to make tradeoff choices, we observe their natural behavior and infer their utility functions from signals.
The math is conceptually similar:
Utility(buyer) = β₁(Risk) + β₂(Outcome) + β₃(Strategy) + β₄(Operations) + β₅(Politics) + β₆(Innovation)
Where each β coefficient represents how much that buyer weights each decision driver, estimated from their observed signals.
A buyer who:
- Asks for references on the first call (Risk: +2)
- Brings the CISO to the second meeting (Risk: +3)
- Wants a security assessment before pricing (Risk: +4)
- Has 8 stakeholders involved (Politics: +3)
- Asks about competitive differentiation (Innovation: +1)
Would have a value profile heavily weighted toward Risk Reduction and Political Safety. This tells the seller exactly how to frame the conversation: don't lead with innovation or outcomes. Lead with proof, stability, and social proof.
The Value Analysis in Practice
Example: Analyzing a VP of Engineering
Signals observed:
- Spent 14 minutes on the API documentation page (Operational Efficiency)
- Forwarded the architecture whitepaper to 3 colleagues (Strategic Alignment)
- Asked 6 questions about data migration in the first call (Risk Reduction)
- Requested a sandbox environment before seeing pricing (Operational Efficiency)
- Asked "what happens if the product goes down during peak?" (Risk Reduction)
AI Value Profile:
| Decision Driver | Weight | Confidence |
|---|---|---|
| Risk Reduction | 34% | High |
| Operational Efficiency | 28% | High |
| Strategic Alignment | 18% | Medium |
| Outcome Achievement | 12% | Medium |
| Political Safety | 5% | Low |
| Innovation Edge | 3% | Low |
Recommended Approach: This buyer values reliability and ease of implementation above all. Lead with uptime SLAs, migration support, and integration simplicity. Don't waste time on visionary roadmap slides — they want to know it works, it's easy, and it won't break.
Example: Analyzing a CFO
Signals observed:
- First question was "What's the 3-year TCO?" (Outcome Achievement)
- Asked for ROI case studies segmented by company size (Outcome Achievement)
- Wanted to understand revenue impact quantitatively (Outcome Achievement)
- Asked about payment terms and contract flexibility (Risk Reduction)
- Only involved after VP of Sales championed internally (Political Safety)
AI Value Profile:
| Decision Driver | Weight | Confidence |
|---|---|---|
| Outcome Achievement | 45% | High |
| Risk Reduction | 25% | High |
| Political Safety | 15% | Medium |
| Operational Efficiency | 8% | Low |
| Strategic Alignment | 5% | Low |
| Innovation Edge | 2% | Low |
Recommended Approach: This buyer wants numbers. ROI models, TCO comparisons, payback period analysis. Every conversation should quantify the impact. Don't talk features — talk dollars.
From Individual to Committee
The real power of Value Analysis isn't profiling one stakeholder. It's profiling the entire buying committee and understanding the value tensions between them.
In a typical enterprise deal with 7 stakeholders:
| Stakeholder | Primary Driver | Secondary Driver |
|---|---|---|
| VP Sales (Champion) | Outcome Achievement | Innovation Edge |
| CFO (Economic Buyer) | Outcome Achievement | Risk Reduction |
| VP Engineering | Operational Efficiency | Risk Reduction |
| CISO | Risk Reduction | Risk Reduction |
| VP Strategy | Strategic Alignment | Innovation Edge |
| Procurement | Risk Reduction | Political Safety |
| CEO (Exec Sponsor) | Strategic Alignment | Outcome Achievement |
Now you can see the deal dynamics:
- VP Sales and CFO align on Outcome Achievement — they'll be allies
- CISO and VP Engineering align on Risk Reduction — they'll form a bloc
- VP Strategy and CEO align on Strategic Alignment — they're the vision buyers
- The tension: Risk vs. Innovation. The CISO wants proven. The VP Sales wants cutting-edge.
The winning strategy: Build the risk case strong enough to satisfy the CISO, then let the VP Sales champion the innovation angle to the CEO. Don't try to satisfy everyone with one message. Map the committee's value profile and deliver different messages to different personas.
The Conjoint Insight
Here's what conjoint analysis taught me that applies perfectly to AI-driven sales:
People don't choose the "best" option. They choose the option with the least unacceptable tradeoff.
A buyer won't pick the product with the most features, the best price, or the strongest brand. They'll pick the product that doesn't violate any of their high-weight decision drivers. The CFO can live with fewer features (low weight) but can't live with unclear ROI (high weight). The CISO can live with higher price (low weight) but can't live with a security gap (high weight).
Your job as a seller isn't to be the best at everything. It's to know each buyer's non-negotiables and make sure you pass every threshold.
Value Analysis gives you that map.
^1^ Hausman, J., "Contingent Valuation and Stated Preference Methods," Journal of Economic Perspectives. Also: Kahneman, D., "Thinking, Fast and Slow" — on the gap between experienced and stated utility.
^2^ Orme, B., "Getting Started with Conjoint Analysis," Research Publishers LLC, 4th edition. The standard reference on conjoint methodology.
^3^ Gartner, "B2B Buying Behavior Study," 2024-2025. Analysis of buying committee signal patterns across 500+ enterprise deals.