What should I include in my Pardot scoring model?

Focus on behaviors that correlate with opportunities, such as pricing or demo page views, high-value content downloads, webinar registrations and attendance, and key product page views. Assign fewer points to low-intent actions like generic blog views or email opens.

How do I choose an MQL score threshold in Pardot?

Start by reviewing existing leads that became opportunities and note their scores at handoff. Use that range as your initial threshold, then refine based on MQL-to-SAL and SAL-to-SQL conversion performance, adjusting the threshold as needed.

How do I prevent bad data from inflating Pardot scores?

Use filters and automation rules to exclude internal users, bots, competitors, students, and personal email domains where appropriate. Apply negative scores for unsubscribes, spam complaints, and job-seeker activity that does not represent real buying intent.

How do you set up lead scoring in Pardot?

To set up lead scoring in Pardot (Account Engagement), you define which behaviors signal real buying intent—like pricing-page views, form fills, and webinar attendance—then map those actions to points, thresholds, and alerts that match your Salesforce lead lifecycle. Effective Pardot scoring focuses on fit + intent, aligns with MQL criteria, and is governed by a simple process for testing, tuning, and communicating changes to sales.

What Do You Need Before You Configure Scoring?

Defined MQL Criteria — Clear agreement with sales on what makes a lead “sales-ready,” including minimum score, required fields, and disqualification rules.

Fit vs. Intent — A simple model where grading (profile fit) and scoring (behavior) work together, so A/B leads with high scores get to sales first.

Salesforce Alignment — Standardized fields and stages (Lead, MQL, SAL, SQL, Opportunity) so score thresholds tie directly to routing and SLAs.

Channel Taxonomy — A consistent way to tag assets (forms, pages, emails, webinars) so you can apply and analyze scores by campaign or motion.

Data Hygiene — Rules to suppress bots, competitors, students, and personal emails from inflating scores or cluttering sales queues.

Governance & Reporting — A cadence to review conversion by score band, adjust weights, and communicate updates to SDRs and AEs.

A Practical Pardot Lead Scoring Playbook

Use this sequence to go from “out-of-the-box scores no one trusts” to a shared, data-backed scoring model that improves MQL quality, speed-to-lead, and pipeline.

Align → Design → Configure → Activate → Integrate → Optimize

Align on definitions: Document MQL/SAL criteria with sales and marketing. Capture minimum score, required profile fields, and what does not qualify.
Map behaviors to value: Rank key actions—form fills, pricing and demo page views, webinar registrations, email clicks, product usage—and assign draft point values.
Design your scoring model: Decide on default scoring vs. custom scoring rules, score decay, negative scores (unsubscribes, job seekers), and channel-specific boosts.
Configure in Pardot: Update the global scoring model, set up automation rules and completion actions, and test scoring changes in a sandbox or test segment.
Connect to Salesforce: Map score fields to Salesforce, set thresholds for MQL conversion, and configure assignment rules, queues, and alerts.
Launch and monitor: Roll out to a pilot region or segment, track conversion rate by score band, and gather feedback from SDRs on lead quality.
Optimize quarterly: Adjust weights based on outcomes (opportunities, revenue) rather than opinions. Keep a change log and notify sales when the model changes.

Pardot Lead Scoring Maturity Matrix

Capability	From (Ad Hoc)	To (Operationalized)	Owner	Primary KPI
MQL Definition	Loose, channel-specific rules; score threshold unknown to sales	Documented global MQL definition tied to score & grade, with exceptions	Marketing + Sales Leadership	MQL→SAL Conversion %
Scoring Model	Default Pardot scoring; rarely reviewed	Custom model by asset type and intent, with negative scores and decay	Marketing Ops / RevOps	Pipeline per 100 MQLs
Salesforce Integration	Score not visible or trusted in Salesforce	Score & grade on all records; used in views, reports, and routing rules	CRM Admin / Sales Ops	Speed-to-Lead, SLA Attainment
Governance & Testing	Changes made ad hoc without communication	Quarterly review against opportunity data with documented updates	Marketing Ops	Win Rate by Score Band
Segmentation & Nurture	One-size-fits-all nurtures	Nurtures and plays tailored to score range and lifecycle stage	Demand Gen	Engagement Lift, Re-MQL Rate
Sales Enablement	SDRs ignore scores; work leads manually	Playbooks and views that prioritize high-intent leads for outreach	Sales Enablement	Meetings per Rep, SQOs

Client Snapshot: From “Noisy” Leads to a Trusted Score

A B2B SaaS team using Pardot had strong volume but low conversion from MQL to opportunity. After aligning MQL criteria, redesigning their scoring model by asset type, and syncing score bands into Salesforce views, they saw 20–30% higher MQL→SAL conversion and faster qualification cycles. Explore how we approach this kind of work: Comcast Business · Broadridge

When lead scoring is wired into The Loop™ customer journey and your revenue marketing operating model, every campaign has a clear purpose: move buyers into the next score band that correlates with pipeline and revenue.

Frequently Asked Questions About Pardot Lead Scoring

What is lead scoring in Pardot?

Lead scoring in Pardot (Account Engagement) is a points-based model that measures a prospect’s level of engagement with your marketing. Actions like email clicks, form submissions, and key page views add points so you can prioritize follow-up and know when a prospect is ready for sales.

What is the difference between Pardot scoring and grading?

Scoring measures interest based on behavior; it changes frequently. Grading measures how well a prospect matches your ideal customer profile (industry, title, company size, region) and changes less often. High score + high grade usually equals a strong MQL.

What should I include in my scoring model?

Prioritize behaviors that correlate with opportunities: pricing or demo pages, high-value content (eBooks, ROI tools), event/webinar engagement, and key product pages. Give fewer points to low-intent behaviors like generic blog views or email opens.

How do I choose an MQL score threshold?

Start by examining existing leads that became opportunities and note their scores at the time of handoff. Use that range as your initial threshold, then adjust based on MQL→SAL and SAL→SQL conversion performance over time.

How often should I update my Pardot scoring model?

Review at least quarterly. As you launch new campaigns and offers, confirm which behaviors correlate with opportunities and revenue. Adjust weights, decay, and negative scores based on data—not opinions—and communicate those changes to sales.

How do I prevent bad data from inflating scores?

Use filters and automation rules to exclude internal traffic, bots, competitors, students, and one-off personal emails. Apply negative scores for unsubscribes, spam complaints, and job-seeker behaviors that don’t reflect real buying intent.

Make Pardot Lead Scoring Work for Revenue

We’ll help you align with sales, design a data-backed scoring model, and connect Pardot to Salesforce so every rep sees a prioritized, trusted queue of high-intent leads.

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Pardot (Account Engagement) Services Revenue Marketing Transformation (RM6™) Customer Journey Map (The Loop™)

How Do You Set Up Lead Scoring in Pardot?

What Do You Need Before You Configure Scoring?

A Practical Pardot Lead Scoring Playbook

Align → Design → Configure → Activate → Integrate → Optimize

Pardot Lead Scoring Maturity Matrix

Client Snapshot: From “Noisy” Leads to a Trusted Score

Frequently Asked Questions About Pardot Lead Scoring

Make Pardot Lead Scoring Work for Revenue

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