Transparent scoring: How to build a model sales trusts (and can explain)

Sales ignores prioritization models they can't explain. Marketing can't optimize campaigns when scoring logic is hidden. When your ABM platform tells reps "this account is hot" but can't show why, the data gets ignored.

Transparent scoring means your team sees exactly which signals triggered a priority score, how each factor is weighted, and which specific contacts inside an account matter most. Reps can validate the model against what they know about their territory. Admins can adjust weights when the market shifts. Everyone understands why one account ranks above another.

6sense uses a black box predictive model. You can't see how accounts are scored or adjust the weighting yourself. To build or change the model, you pay their data science team roughly $12,000. UserGems shows you the full scoring logic, lets you adjust signal weights in-product, and drills down to the exact triggers on each account and contact. When sales sees a high-priority buyer, they know exactly why that person matters right now.

Why scoring transparency matters for revenue teams

Most ABM platforms treat scoring as a data science problem. They build a proprietary model, feed it intent signals, and output a number. The problem: sales doesn't trust numbers they can't verify.

When a rep sees "Account Score: 87" with no explanation, they're left guessing. Is this based on website visits? Intent topics? Firmographic fit? Without that context, they can't personalize outreach or prioritize their day. The score becomes noise.

UserGems takes the opposite approach. Every score shows the contributing signals: recent job changes, contact-level intent on specific topics, website visits, CRM activity, past champion relationships, funding events, and more. Reps see the full picture. They understand why this buyer is prioritized and what to say when they reach out.

This transparency drives adoption. When sales trusts the data, they act on it. When they act on it, you get pipeline.

How UserGems scoring compares to 6sense

Limited signals create inaccurate scores

6sense recently added job changes to their account-level intent model, but the signal catalog remains narrow. With only a handful of data points, the model can't accurately predict which accounts are ready to buy. It's scoring based on a tiny subset of what actually indicates buyer readiness.

UserGems uses 600+ ICP fit attributes, 21+ native signals (account and contact intent, website visitors, past champions, job changes, closed-lost context, hiring patterns, funding, M&A activity, and more), plus your first-party CRM and product usage data. The model combines everything that indicates a buyer is in-market.

Black box vs. full visibility

6sense's proprietary model hides the scoring logic. You can't see how signals are weighted, why an account ranks the way it does, or adjust the model yourself. Changes require hiring their data science team at roughly $12,000 per engagement.

UserGems shows you exactly how accounts and contacts are scored. Admins see the weight assigned to each signal. You can adjust those weights in-product when your ICP shifts or a new signal proves valuable. End users drill down to individual contacts and see which specific triggers that person has: intent on "revenue intelligence" topics, recent promotion to VP Sales, website visit to your pricing page, former customer at their last company.

This visibility creates accountability. When marketing and sales can both see the scoring logic, they align on what "high priority" actually means.

Account-level scores vs. contact-level precision

6sense scores accounts. UserGems scores both accounts and contacts.

Knowing "this company is hot" doesn't tell you who to contact first. UserGems identifies the specific buyers showing intent, explains why each person matters (job change, past champion, buying committee member), and prioritizes them based on all available signals. Reps see named people with clear reasons to reach out, not just a company name.

This contact-level precision is what makes scoring actionable. Sales can't send a generic email to "the account." They need to know which VP just joined, which director is researching your category, and which former customer now works there.

What transparent scoring looks like in practice

UserGems surfaces prioritized accounts and contacts with full signal visibility. Here's what your team sees.

Account-level scoring

Firmographic fit score based on 600+ ICP attributes

Signal-based priority: recent funding, hiring velocity, tech stack changes, M&A activity

Aggregate contact-level intent across the buying committee

CRM engagement history and pipeline status

Adjustable weighting for each factor

Contact-level scoring

Individual intent topics (42,000+ keywords available)

Job change triggers (new role, promotion, company move)

Website activity (pages viewed, time spent, return visits)

Past relationships (former customer, champion at previous company)

Buying committee role and influence

CRM and product usage data

Reps click into any contact and see the exact signals that triggered the priority score. They understand the context before they send the first email.

Admins adjust signal weights based on what's converting. If job changes into VP roles are driving 3x more pipeline than intent signals, you increase that weight. The model updates weekly based on your best customers, so scoring stays aligned with what actually predicts revenue.

What does transparent scoring mean in ABM and outbound?

Transparent scoring means your prioritization model shows its work. Instead of a black box algorithm that outputs a number, you see which signals contributed to each score, how those signals are weighted, and why a specific account or contact ranks where it does.

For ABM, this means marketing can explain to sales why certain accounts are prioritized. For outbound, it means reps understand which buyers to contact first and what context to use in their outreach.

UserGems displays the full scoring breakdown at both the account and contact level. Click into any prioritized buyer and you see:

Which intent topics they're researching

Recent job changes or promotions

Website pages they visited

Past relationships with your company

Firmographic fit factors

CRM activity and engagement history

This visibility creates trust. Sales doesn't ignore the data because they can validate it against what they know about their territory. Marketing can optimize campaigns based on which signals actually drive pipeline.

6sense hides this logic inside a proprietary model. You see a score, but not the reasoning. You can't adjust the weights yourself or drill down to contact-level triggers. That opacity creates friction between marketing and sales, and it makes debugging impossible when the model gets it wrong.

How do I show reps why an account or contact is prioritized?

Give them the specific signals that triggered the priority score, not just a number.

UserGems displays signal-level detail for every prioritized buyer:

Intent signals: "Researching 'sales engagement platform' and 'revenue intelligence' topics in the past 14 days"

Job change triggers: "Promoted to VP Sales 30 days ago at a Series B company in your ICP"

Website activity: "Visited pricing page and case studies, 3 return visits in the past week"

Relationship signals: "Former customer at previous company, worked with your champion Sarah Chen"

Account context: "Company raised $50M Series C, hiring 12 sales roles, uses Salesforce and Outreach"

Reps see the full picture in one view. They know exactly why this person is prioritized and what to reference in their outreach. That context drives response rates.

6sense shows account-level intent stages but doesn't connect those to named buyers or explain the specific triggers. Reps get "this account is in Decision stage" without knowing who to contact or why. That gap between insight and action is why intent data often gets ignored.

UserGems closes that gap. The signal detail is right there, ready to copy into an email or reference on a call. Reps spend less time researching and more time engaging real buyers.

What signals should be weighted highest for outbound prioritization?

It depends on your sales motion, but job changes and contact-level intent consistently outperform account-level signals for outbound.

Job changes

New role, promotion, or company move creates immediate buying windows. A new VP Sales has 90–120 days to show impact. They're evaluating their stack, looking for quick wins, and open to conversations. UserGems tracks these moves in real time and flags buyers who match your ICP.

Contact-level intent

When someone searches "revenue intelligence" or "sales engagement platform," you know they're in-market. UserGems monitors 42,000+ intent topics and ties that activity to named people, not just anonymous accounts.

Past champion relationships

Former customers convert at 3–5x the rate of cold outbound. When someone who bought from you at their last company moves to a new role, they already trust your product. UserGems tracks these moves and alerts you the day they start.

Website de-anonymization

Website visits identify buyers who are researching but haven't filled out a form. You see their name, title, company, and which pages they viewed. Reps can reach out the same day with context about what they were researching.

The right weighting depends on what drives pipeline for your business. UserGems lets you adjust signal weights in-product and see how changes affect prioritization. If job changes into VP roles are converting at 40%, you increase that weight. If intent on specific topics correlates with closed-won deals, you prioritize those keywords.

The model updates weekly based on your best customers, so the weighting stays aligned with what actually predicts revenue.

How often should scoring models update?

Weekly, at minimum. Markets shift, ICPs evolve, and buyer behavior changes. A static model gets stale fast.

UserGems updates scoring models weekly based on your closed-won customers. The system analyzes which signals and attributes correlate with revenue, then adjusts the model to prioritize similar buyers. This keeps scoring aligned with what's actually working in your market right now.

You can also adjust weights manually when you spot a pattern. If you notice that job changes into VP roles are driving more pipeline than other signals, you increase that weight immediately. The change takes effect across all accounts and contacts, and you see the impact in real time.

6sense models are static unless you pay their data science team to rebuild them. That creates lag between market changes and model updates. By the time you notice the model is off and schedule a rebuild, you've already lost weeks or months of prioritization accuracy.

Dynamic scoring matters because buyer behavior changes faster than annual contracts. A signal that predicted buying intent six months ago might not work today. Your model needs to adapt.

How do I debug scoring when sales says the model is wrong?

Look at the signal-level detail and adjust the weights.

When a rep says "this account shouldn't be prioritized," you need to see exactly why the model scored it high. UserGems shows the contributing signals: maybe the account has strong firmographic fit but no recent engagement. Or maybe one contact showed intent, but they're not the decision-maker.

With full visibility, you can validate the score against reality. If the model is over-weighting a signal that doesn't actually predict revenue, you adjust the weight. If a high-priority account turned out to be a bad fit, you refine the ICP criteria.

This feedback loop is how scoring models improve. Sales tells you what's working and what's not. You adjust the weights. The model gets more accurate over time.

6sense doesn't give you this visibility. When sales says the model is wrong, you can't see why it scored the account that way or adjust the logic yourself. You're stuck with a black box that requires a paid engagement to change.

UserGems makes debugging fast. Click into the account, see the signals, adjust the weights, and move on. The model learns from real sales feedback, not just historical data.

Why UserGems delivers transparent, trustworthy scoring

Scoring transparency isn't a nice-to-have. It's what makes prioritization models actually work in practice.

When sales understands why a buyer is prioritized, they trust the data and act on it. When marketing can see which signals drive pipeline, they optimize campaigns around what works. When admins can adjust weights in-product, the model stays aligned with your ICP and market reality.

UserGems gives you full visibility into scoring logic, signal-level detail on every account and contact, and the ability to adjust weights yourself. The model updates weekly based on your best customers, so prioritization stays accurate as your business evolves.

6sense hides the scoring logic, limits customization, and requires paid services to make changes. That opacity creates friction between teams and makes it impossible to debug when the model gets it wrong.

If you want a prioritization model your team actually trusts and uses, transparency is the foundation.

Book a demo with the UserGems team to see transparent scoring, signal-level detail, and Gem-E's AI-driven execution in action: https://usergems.com/contact