How to Build an Automated Intent Scoring System

Traditional analytics tools tell you who landed on your site, but not:

• Which visits matter the most

• How engaged an account actually is

• When a company is warming up

• What sales or marketing should do next

Klear, a Dealfront partner, solved this gap by combining Dealfront’s Leadfeeder data, HubSpot, and Make.com automations to build a fully automated intent scoring system. This system tracks, qualifies, and scores every account interaction, turning raw activity into clear, prioritised intent signals.

The outcome of this is that both marketing and sales teams know exactly which accounts are active, warming up, or need re-engagement, all powered by Dealfront data.

With this workflow, you can:

• Automate scoring across every inbound activity

• Identify high-intent accounts in real time

• Trigger timely follow-ups based on real buying behaviour

• Improve sales efficiency and pipeline quality

• Align marketing and sales around shared, data-driven intent

Why Traditional Website Tracking Isn’t Enough

Most analytics platforms stop at passive data collection. You see visits, sessions, and page views, but not whether any of it actually indicates intent. This leads to three core issues:

1. 1.

   No prioritization: Every visit looks the same, regardless of intent, making it impossible to distinguish a casual browser from a sales-ready account.

2. 2.

   Manual, error prone work: Teams spend hours sorting, exporting, and triaging visitor data, often inconsistently.

3. 3.

   Missed timing: If scoring isn’t real-time, reps often reach out late, after the interest curve has dropped.

Klear wanted more. They needed a system that could automatically qualify intent, assign scores, and feed high-value accounts directly into their CRM for follow-up.

How to Build a Dealfront-Powered Intent Scoring Workflow

Using Dealfront (Leadfeeder) as the first-party data source, Klear created an automated scoring engine that detects website visits from target companies, evaluates engagement quality, and syncs scores directly into HubSpot.

Here’s how Dealfront data powers the workflow:

• Visitor Identification – Identify companies spending more than 45 seconds on your site.

• Behavioural Tracking – Capture deeper activity (webinar registrations, email clicks, content journeys).

• Scoring Logic – Assign weighted point values to different intent signals.

• CRM Sync – Push enriched profiles and scores straight into HubSpot.

• Cross-team alignment – Marketing learns which campaigns drive real engagement; sales sees which accounts are heating up.

This system offers dual benefits: from a marketing perspective, it helps organizations understand which campaigns and content are driving the most engaged traffic, while for sales teams, it provides an instant way to identify accounts that are warming up and ready for outreach.

Step-by-Step Workflow

Step 1 – Track Visitor Data Automatically via Dealfront

Klear uses Dealfront (Leadfeeder) to detect companies visiting specific pages or spending more than 45 seconds onsite.

Each qualifying visit triggers an automation event that sends:

• Company name

• Website URL

• Page visited

• Time on page

• Engagement history

This becomes the core data feeding the intent scoring engine.

Step 2 – Send Data to Make.com Via API

Using a Make.com webhook, Dealfront passes visitor data automatically into a central workflow. Klear calls this their “intent signal octopus”; a branching automation that evaluates every incoming activity in real time. Steal our Make.com automation workflow here and replicate the same for your company.

For each event, Make.com determines:

• Whether the company already exists in HubSpot

• What caused the signal (visit, email click, webinar registration, etc.)

• Which score category it matches

• Whether additional enrichment or AI checks are needed

Step 3 – Assign Intent Scores Based on Engagement Quality

Klear’s scoring system differentiates between various activity types to reflect real engagement; they assign different point values to each activity type:

• Website visit > 45s: Score 5 points.

• Email course click: Score 3 points.

• Webinar registration: Score 4 points.

• Newsletter signup: Score 1-2 points.

Scores accumulate at the company level and contact level, providing a more accurate picture of overall account interest.

This avoids overvaluing low-intent actions (like newsletter signups) and ensures highly engaged accounts rise to the top.

Step 4 – Sync Scored Data to HubSpot CRM

Once scored, the system syncs all account data into HubSpot automatically:

If the company already exists:

• Update its intent score

• Add new engagement analytics

• Append behavioural history

If the company is new:

• Trigger an AI ICP-fit check

• If approved → create the company record automatically

• Attach all relevant activity data

Reps can now use intent score filters, activity windows, and account views to prioritise outreach, eliminating manual data entry.

Step 5 – Filter and Analyze Active Companies

Klear uses HubSpot filters to create dynamic segmentations, such as:

• “Active in the past 14 days” → Sales-ready or warming accounts

• “Inactive for 30–60 days” → High-value accounts needing remarketing or nudges

This provides a clear, real-time view of:

• Pipeline movement

• Account momentum

• Campaign effectiveness

• Content impact on different buying stages

Step 6 – Take Action Based on Engagement Trends

For Marketing

• Re-engage accounts that went cold after recent activity

• Prioritise campaigns targeting high-score accounts

• Use signals to test message-market fit

For Sales

• Focus on accounts showing sustained activity in the past few weeks

• Time outreach based on surges in engagement

• Add intent-triggered tasks or sequences to your workflow

Klear currently prefers a blended approach of human review + automated scoring, but the same architecture can easily trigger:

• Automated nurture sequences

• ABM campaigns

• Sales notifications or tasks

• Targeted retargeting audiences

Best Practices & Pro Tips

Differentiate engagement types clearly - and map them to buying stages

Not all activity signals carry the same weight. A 60-second product page visit suggests much more intent than a homepage skim, and a webinar registration is a far stronger signal than a newsletter signup.

To maximise accuracy:

• Group signals into Awareness → Consideration → Decision stages.

• Assign higher scores to behaviours that indicate evaluation (e.g., pricing page, case studies, product tutorial views).

• Use lower scores for surface-level engagement (e.g., blog reads, social clicks). This ensures your intent scoring system reflects true progression through the buyer journey, not just raw activity.

Keep scoring simple, clear, and scalable - but document your rules

A common pitfall is over-engineering: 20+ scoring rules, overlapping signals, and unclear definitions.

Instead:

• Start with 4-6 high-quality signals that correlate strongly with revenue.

• Add new rules only when data shows they improve accuracy.

• Maintain a shared scoring playbook so your revenue teams understand:

  • what each signal means

  • why it matters

  • how it fits into your go-to-market motion

This clarity prevents confusion, enables better sales–marketing alignment, and makes the system easier to maintain as you scale.

Use AI to enrich, validate, and categorise new companies automatically

AI can dramatically reduce CRM bloat and improve data quality. Use it to:

• Evaluate whether a new account fits your ICP criteria (industry, size, tech stack, region).

• Automatically categorise companies into segments such as High Fit, Moderate Fit, or Low Fit.

• Flag “high intent but low fit” accounts to deprioritise outbound effort.

• Enrich company records with public data (e.g., number of employees, funding stage).

This ensures reps only spend time on accounts that are both engaged and relevant, boosting pipeline efficiency.

Analyse time-based trends weekly - look for momentum, not just scores

Intent scoring is most powerful when viewed as a timeline, not a snapshot.

Weekly reviews help you spot:

• Accounts gaining momentum → now moving into outreach territory

• Accounts slowing down → perfect candidates for marketing re-engagement

• Signals clustering together → indicating a broader account-level buying cycle

Tracking time windows (7, 14, 30, 60 days) reveals patterns that individual events can’t. This approach helps teams act at the peak moment of interest.

Iterate and optimise your scoring model using real pipeline data

The biggest mistake revenue teams make is treating intent scoring as “set and forget.”

Instead, revisit your scoring model monthly or quarterly to identify:

• Which signals appear most frequently in deals that close

• Which signals triggered sales conversations that didn’t convert

• Whether high-intent accounts are consistently rising to the top

Examples of optimisations:

• Increase scores for signals that correlate with pipeline creation (e.g., pricing page visits)

• Reduce scores for vanity metrics that don’t predict conversions

• Add negative scoring for signals like “bounced email” or “long periods of inactivity”

This ensures your model becomes more accurate over time, not less.

Expected Results: What You’ll Achieve with This Workflow

1. A real-time view of buying intent With automated tracking and scoring in place, you gain a continuous, real-time understanding of which companies are actively engaging with your website, content, and campaigns. Instead of guessing who might be interested, you can see exactly how long they spend on key pages, what actions they take, and how frequently they return.

This clarity allows your team to prioritise outreach around accounts showing the strongest and most recent intent signals, improving timing, relevance, and ultimately, conversion rates.

2. Automated, data-driven scoring The days of subjective or inconsistent lead qualification are gone. This workflow replaces manual guesswork with a consistent, rules-based scoring model that evaluates behaviour across every inbound channel: web visits, email interactions, webinar registrations, content downloads, and more.

Because the criteria are automated and standardised, every account is scored fairly and accurately, giving your revenue teams a reliable, objective measure of engagement and buying readiness.

3. Smarter sales & marketing alignment By working from the same data set, both marketing and sales benefit from a unified view of account activity and intent. Marketing gains clarity on which campaigns and assets are driving genuine engagement, while sales receives timely insight into which accounts are warming up or moving closer to a buying decision.

With shared definitions of what “ready to talk” looks like, handoffs become smoother, follow-up becomes more targeted, and both teams operate with far greater confidence and efficiency.

Steal Our Free Template: Intent Scoring Blueprint

✅ Value: Use this scoring matrix as a foundation for your own AI or automation workflows.