The Data Hierarchy: Why What Buyers Tell You Beats What Models Predict

The Data Quality Problem

Your CRM is full of data. Most of it is useless for closing deals.

You have firmographic data from enrichment providers telling you company size, industry, and funding status. You have behavioral data showing page visits, email opens, and content downloads. You have notes from sales calls, form submissions, and support tickets.

All of this data goes into lead scoring models, pipeline forecasts, and routing decisions. And all of it treats different types of data as roughly equivalent.

That’s the problem.

Not all data is created equal. The data types in your CRM have vastly different reliability for predicting whether someone will actually buy. Treating them as interchangeable—or worse, over-indexing on the wrong types—leads to qualification decisions that don’t match reality.

Why More Data Doesn’t Mean Better Deals

There’s a seductive logic in B2B: more data equals better decisions. If we just had more firmographic data, more intent signals, more behavioral tracking, we could finally predict which leads will convert.

This logic has driven massive investment in data enrichment, intent providers, and behavioral analytics. The result? Companies are drowning in data while still struggling to identify which leads are actually worth pursuing.

The problem isn’t data volume—it’s data hierarchy.

Consider two leads:

Lead A: 500-person company, Series B funded, uses your competitor’s product, visited your pricing page 6 times this month.

Lead B: In conversation, said: “We need to cut our response time by 50% within 90 days. Our current tool isn’t working, and my CEO is asking for a plan by end of quarter.”

Which lead is more qualified?

Most scoring models would rank Lead A higher—they have all the right firmographic and behavioral signals. But Lead B has stated their problem, timeline, and urgency in their own words. That stated data is more reliable than any amount of inferred signals.

The inverse relationship between data volume and signal quality is counterintuitive but consistent: the easier data is to collect, the less predictive it typically is.

The Three Tiers of Sales Data

Sales data falls into three distinct tiers, each with different reliability and utility.

Tier 1: Stated Data (Highest Priority)

What the buyer actually said.

Stated data is the gold standard because it comes directly from the source. When a buyer tells you their problem, timeline, budget, or decision process, you have firsthand information about their buying situation.

Examples:

• “Our no-show rate is killing our pipeline."
• "We need to make a decision by end of Q2."
• "We’re evaluating three vendors including Competitor X."
• "Our budget for this is around $50K."
• "I need to get my VP’s approval before we can proceed.”

Each of these statements reveals something concrete about the buying situation—pain, timeline, competition, budget, decision process. No model can infer this information with the same confidence.

Stated data is the most reliable signal of intent, pain, and fit. It should be the primary driver of qualification decisions.

Tier 2: Observed Data (Medium Priority)

Behavioral signals that validate intent.

Observed data is what the buyer does, not what they say. Page visits, email clicks, content downloads, webinar attendance, product usage. These signals are valuable because they provide evidence of engagement and interest.

Examples:

• Visited pricing page 3 times this week
• Downloaded the ROI calculator
• Attended the product webinar
• Opened all 5 emails in the nurture sequence
• Spent 15 minutes on the case study page

Observed data validates and confirms stated intent. A buyer who says they’re evaluating solutions AND has visited your pricing page multiple times is more credible than one who only does one or the other.

The limitation is that behavioral signals are indirect. You’re inferring intent from actions, and those inferences can be wrong. Someone might visit your pricing page because they’re researching for a blog post, not because they’re buying.

Tier 3: Enriched Data (Lowest Priority)

Third-party data that completes the picture.

Enriched data is what external providers can tell you about a company or person. Firmographics, technographics, funding data, employee growth, job postings. This data is useful for identifying whether someone matches your ideal customer profile.

Examples:

• Company size: 500 employees
• Industry: B2B SaaS
• Tech stack: Uses Salesforce and HubSpot
• Funding: Series B, $30M raised
• Growth: 40% headcount increase YoY

Enriched data completes the picture by filling gaps. If a lead hasn’t told you their company size, enrichment data provides it. If you need to know their tech stack for integration conversations, that data is available.

The limitation is that enriched data says nothing about intent. A company can match every firmographic criterion and have zero interest in buying. Conversely, a company that doesn’t perfectly match your ICP might be highly motivated to solve a problem you can address.

Why Most Tools Work Backward

Here’s the uncomfortable truth: most lead scoring and qualification tools work from the bottom up.

They start with enriched data (because it’s easy to get in bulk). They layer on observed data (because it’s trackable). And they largely ignore stated data (because it requires conversation).

This bottom-up approach inverts the reliability hierarchy. The tools weight the least reliable signals most heavily and the most reliable signals least heavily—or not at all.

The reasons are practical, not strategic:

Enriched data is cheap to acquire. Pay an enrichment provider and you get firmographic data on every lead automatically. No conversation required.

Observed data is easy to track. Install some JavaScript and you can see every page visit, click, and download. Passive collection at scale.

Stated data requires conversation. To capture what buyers actually say, you need to engage them in dialogue. That takes time, effort, and capability that most tools don’t provide.

The result is lead scoring models built primarily on the data that’s easiest to collect, not the data that’s most predictive. Teams end up chasing “perfect ICP matches” that have no actual buying intent while ignoring buyers who don’t fit the model but have urgent, stated needs.

Building a Top-Down Data Strategy

The winning approach inverts the typical stack: prioritize stated data, validate with observed data, and use enriched data to fill gaps.

1. Invest in Capturing Stated Data

You can’t use stated data if you never capture it. This means creating opportunities for buyers to articulate their situation in their own words.

Conversational qualification—whether through AI or well-trained reps—surfaces stated data that forms and scoring models miss:

• What problem are you trying to solve?
• What’s driving urgency right now?
• Who else is involved in this decision?
• What have you tried before?
• What would success look like?

The answers to these questions are infinitely more valuable than any firmographic checkbox.

2. Use Observed Data to Validate

Once you have stated data, use behavioral signals to confirm or question it.

If a buyer says they’re urgently evaluating solutions, do their actions match? Are they visiting your pricing page? Engaging with your emails? Downloading comparison content?

Conversely, if someone’s behavioral signals are strong but you haven’t heard them articulate a problem, that’s a gap to fill. The actions suggest interest—now you need the stated data to confirm it.

3. Apply Enriched Data Appropriately

Firmographic data is useful for:

• Routing decisions: This lead is in healthcare, route to our healthcare specialist.
• Personalization: I see you’re using Salesforce—here’s how we integrate.
• Disqualification: This company has 5 employees, they’re below our minimum.

It’s not useful for:

• Determining intent: A 500-person company that matches your ICP might have zero buying motivation.
• Prioritizing leads: A perfect firmographic match without stated needs should not rank above an imperfect match with urgent, articulated problems.

4. Structure Stated Data into CRM

The biggest waste of stated data is letting it die in call notes. When a buyer tells you their timeline, that should update a forecast field. When they mention a competitor, that should populate a battlecard. When they articulate pain, that should map to problem fields.

Structuring stated data into actionable CRM fields ensures it drives decisions, not just documentation.

5. Measure What Matters

Stop celebrating MQL volume based on scoring models. Start tracking the quality of stated data you’re capturing and how well it predicts conversion.

Ask: Do leads with articulated pain convert at higher rates? Do leads who state urgency close faster? Do leads who name decision-makers have better outcomes?

If the answer is yes (and it almost always is), you have evidence for investing more in stated data capture.

The data hierarchy isn’t about ignoring enrichment or behavioral signals. It’s about weighting them appropriately. What someone tells you in a conversation matters more than what a model infers about them. Build your qualification strategy around that truth, and your pipeline will reflect reality instead of assumptions.

Ready to see how AI can capture stated data at scale? Book a demo and we’ll show you how Synapsa works from the top down.