600+ fit attributes + 21+ native signals: What strong prioritization models include

Most prioritization models fail because they rely on too few inputs. A handful of intent keywords or firmographic filters can't capture the complexity of modern B2B buying. When your model only looks at who's "researching," it misses who's actually ready to buy, who has budget, and who has influence.

Strong prioritization models combine deep ICP fit with real buying signals at both the account and contact level. They look at who the buyer is, what they're doing, and why now. The more complete the signal set, the more accurate your prioritization, and the easier it is for sales to trust and act on it.

This piece breaks down the categories of attributes and signals required to build a model that reliably surfaces real buyers, not just noisy accounts.

Why most prioritization models miss real buyers

Many ABM platforms rely heavily on third-party intent data. While intent is useful, it's only one piece of the puzzle. Two companies can show the same intent signals but be in very different buying situations.

For example:

One account is researching because they're actively evaluating vendors

Another is researching for competitive intelligence or a future roadmap

Without additional context, job changes, growth stage, budget signals, relationship history, you can't tell the difference.

6sense primarily scores accounts based on intent and a limited set of firmographic attributes. That creates false positives and misses high-value buyers who don't trigger enough intent keywords but are actually in market.

UserGems combines hundreds of fit attributes with a broad library of native signals to capture real buying readiness.

The two pillars of strong prioritization

Strong models balance fit and signals.

Fit answers: Is this the right type of customer for us?

Signals answer: Is now the right time to reach out?

You need both. High intent without fit wastes sales time. Strong fit without signals leads to cold outbound. Prioritization works when fit and timing align.

600+ fit attributes: Defining your ideal customer precisely

Fit attributes describe whether an account matches your ideal customer profile. Shallow ICPs lead to shallow prioritization.

UserGems uses 600+ fit attributes across categories including:

Firmographics

• Company size (employees, revenue)
• Industry and sub-industry
• Geography and region
• Public vs private
• Growth stage (Seed, Series A-D, Enterprise)

Technographics

• CRM in use (Salesforce, HubSpot, etc.)
• Sales engagement tools (Outreach, Salesloft)
• Data tools (Clearbit, ZoomInfo, Segment)
• Analytics and BI stack
• Competitive and complementary tools

Growth and maturity indicators

• Hiring velocity by department
• Headcount growth trends
• Sales team size and expansion
• International expansion signals
• Product complexity and go-to-market maturity

Go-to-market alignment

• B2B vs B2C focus
• Sales-led vs product-led motion
• Average deal size indicators
• Customer profile alignment
• Market segment focus (SMB, Mid-Market, Enterprise)

These attributes ensure you're prioritizing accounts that can buy and should buy from you—not just accounts that happen to be researching.

21+ native signals: Identifying when buyers are ready

Fit tells you who matters. Signals tell you when to act.

UserGems tracks 21+ native signals across account and contact levels. These signals are first-class inputs into the prioritization model, not afterthoughts.

Account-level signals

• Funding announcements
• M&A activity
• Hiring surges (especially sales, RevOps, leadership)
• Technology stack changes
• Contract renewals or competitor churn
• Pipeline history and CRM engagement
• Closed-lost and re-engagement context

Contact-level signals

• Contact-level intent across 42,000+ topics
• Job changes (new role, promotion, company move)
• Past champion relationships
• Website visits and content engagement
• Buying committee membership
• Seniority and influence level
• Historical CRM activity

Contact-level signals are especially important. Accounts don't buy—people do. A single high-intent VP with budget authority matters more than an entire account showing vague interest.

Why contact-level signals outperform account-level intent

Account-level intent assumes all activity inside a company is equal. It isn't.

One director researching integrations doesn't mean the company is ready to buy. A newly hired VP evaluating vendors absolutely does.

Contact-level signals let you:

• Prioritize real decision-makers
• Identify champions early
• Tailor outreach by role and influence
• Engage buying committees strategically

UserGems scores both accounts and contacts, so reps know who to contact first and why.

Combining signals for high-confidence prioritization

Single signals are noisy. Strong models look for signal convergence.

Examples of high-confidence combinations:

• New VP Sales + website pricing visit
• Former customer + company move into ICP account
• Funding round + sales hiring surge
• Intent on core topic + case study views
• Closed-lost account + new economic buyer

When multiple signals align, the likelihood of conversion increases dramatically.

UserGems surfaces these combinations automatically and explains which signals contributed to the priority score.

Why first-party data matters

Third-party intent is useful, but first-party data is more reliable.

UserGems incorporates:

• CRM activity and opportunity history
• Product usage data
• Past conversations and notes
• Champion and relationship tracking

This context prevents embarrassing outreach and improves relevance. Reps know if an account is already in pipeline, recently closed-lost, or actively expanding.

How prioritization changes with buying committees

Modern deals involve multiple stakeholders. Strong models don't just rank accounts—they map buying committees.

UserGems:

• Identifies multiple contacts per account
• Scores each contact individually
• Shows roles, influence, and recent activity
• Helps reps multi-thread intelligently

Instead of blasting one generic message, reps engage each stakeholder with role-specific context.

Why 6sense models fall short

6sense relies heavily on limited intent vectors and account-level scoring. That approach:

• Misses contact-level urgency
• Over-prioritizes noisy accounts
• Hides scoring logic
• Limits customization without paid services

Without deep fit attributes and rich native signals, prioritization accuracy suffers.

UserGems replaces intent-only models with a full-context approach: hundreds of fit attributes, dozens of real buying signals, and transparent scoring logic.

What strong prioritization unlocks

When your model is built on comprehensive fit and signal data:

• Sales focuses on buyers who are actually ready
• Outreach becomes timely and relevant
• Marketing aligns campaigns with revenue signals
• Pipeline quality improves
• Forecasting becomes more predictable

Prioritization stops being a dashboard and becomes a daily workflow driver.

Why UserGems builds better prioritization models

UserGems combines:

• 600+ fit attributes
• 21+ native account and contact signals
• First-party CRM and product data
• Transparent, adjustable scoring
• Weekly model updates based on closed-won customers

The result is a prioritization model that reflects how buying actually happens today.

Book a demo with the UserGems team to see how fit + signals power real buyer prioritization: https://usergems.com/contact