Define a target outcome variable for lead scoring, choosing between opportunity creation, closed-won conversion and projected deal value.
Marketing & Sales
Predictive Lead Scoring Models for Sales Prioritisation
Build predictive lead scoring models that combine firmographic, behavioural and engagement data to rank prospects by conversion likelihood, so sales teams prioritise the accounts most likely to buy.
🗓️Duration5 training days
📚Content4 modules · 8 sessions
🎓On completionAccredited attendance certificate
About the programme
Course Overview
Sales teams routinely spend their week working leads that were never going to buy, while a smaller set of genuinely ready accounts wait too long for a call. Manual, points-based lead grading cannot keep pace with the volume and variety of firmographic, technographic and behavioural signals now available from a CRM, web analytics and enrichment tools, and it rarely tells a rep why one lead outranks another. This course builds predictive lead scoring from first principles: choosing a target outcome such as opportunity creation or closed-won revenue, engineering features from company data and engagement history, and comparing rules-based grading against logistic regression and gradient-boosted models. You will validate a model with holdout data, lift charts and calibration curves before it ever reaches a sales queue, then configure routing rules, score thresholds and SLA alerts inside a CRM so prioritisation happens automatically. The course closes with monitoring for score drift, a retraining schedule, and the data governance needed to keep scoring both accurate and defensible. The result is a lead scoring model that sales teams trust enough to change how they plan their day.
Expected Learning Outcomes
Engineer firmographic, technographic, intent and behavioural features from CRM, web analytics and enrichment data sources.
Compare rules-based point scoring against logistic regression and gradient-boosted tree models for ranking prospect quality.
Validate a scoring model using holdout samples, lift charts and calibration curves before releasing it to sales teams.
Configure lead routing, SLA alerts and score thresholds inside a CRM so sales reps act on the highest-priority records first.
Monitor score drift and conversion feedback loops, retraining the model on a defined schedule as buyer behaviour changes.
Align marketing and sales teams on shared scoring definitions through a service-level agreement covering handoff criteria.
Who Should Attend
Marketing operations specialists building or maintaining lead scoring rules inside a CRM or marketing automation platform.
Revenue operations analysts responsible for lead routing, data quality and pipeline reporting.
Demand generation managers who need to prove which leads convert into qualified pipeline.
Sales development representatives and managers who triage inbound and outbound leads daily.
Data analysts embedded in marketing or sales teams asked to build predictive scoring models.
B2B marketing leaders redesigning the marketing-to-sales handoff process.
Course Modules
Select any module to see its sessions and points.
01Defining What a Qualified Lead Means for the Business
2 sessions · 8 points
Session 1Setting the Target Outcome and Success Metric
- Distinguish between scoring for opportunity creation, closed-won conversion and projected deal value, and select the target variable that matches the sales motion.
- Interview sales leaders to capture the qualification criteria they currently use informally before encoding them into a model.
- Audit historical CRM records to check whether outcome labels such as closed-won and closed-lost are consistently and accurately recorded.
- Define the scoring window, deciding how much lead history to include before an opportunity is created or a lead goes cold.
Session 2Mapping Data Sources for Scoring Inputs
- Catalogue firmographic data such as company size, industry and revenue band available from CRM records and enrichment vendors.
- Capture technographic signals including installed software and job postings that indicate buying readiness.
- Track behavioural and intent data such as website visits, content downloads, email engagement and pricing-page views.
- Assess data completeness and freshness across sources, flagging fields with high missing-value rates before modelling begins.
02Building and Testing the Scoring Model
2 sessions · 8 points
Session 1Choosing a Modelling Approach
- Compare a manually weighted points-based grading scheme against a statistically derived logistic regression model.
- Apply gradient-boosted tree methods such as XGBoost to capture non-linear interactions between firmographic and behavioural features.
- Engineer features including recency, frequency and momentum of engagement to strengthen model predictive power.
- Address class imbalance between converted and non-converted leads using resampling or weighted loss functions.
Session 2Validating Model Performance Before Release
- Split historical data into training and holdout sets to test whether the model generalises to unseen leads.
- Read lift and gains charts to confirm the model concentrates conversions in the highest-scoring deciles.
- Check calibration curves to ensure a score of, for example, eighty corresponds to a consistent real-world conversion rate.
- Run the new model in parallel with the existing scoring rules for a trial period to compare routing outcomes.
03Operationalising Scores Inside Sales Workflows
2 sessions · 8 points
Session 1Routing, Alerts and Sales Adoption
- Configure CRM routing rules that assign high-scoring leads to the appropriate sales rep or queue within a defined SLA.
- Design real-time alerts that notify account executives when a tracked account crosses a scoring threshold.
- Translate numeric scores into grade bands such as hot, warm and cold that sales reps can act on without interpreting raw numbers.
- Train sales teams on how the score is built so they trust it enough to change daily prioritisation habits.
Session 2Aligning Marketing and Sales on Scoring Definitions
- Draft a service-level agreement that defines handoff criteria, response times and feedback obligations between marketing and sales.
- Establish a shared glossary distinguishing marketing-qualified leads, sales-accepted leads and sales-qualified opportunities.
- Run joint review sessions where sales feedback on lead quality is logged and fed back into scoring criteria.
- Resolve disagreements over scoring weight by testing disputed criteria against actual conversion outcomes.
04Governance, Monitoring and Continuous Improvement
2 sessions · 8 points
Session 1Monitoring Drift and Retraining the Model
- Track score distribution and conversion rates over time to detect drift caused by changing buyer behaviour or market conditions.
- Set a retraining cadence and trigger criteria, such as a defined drop in model accuracy, that prompt an early refresh.
- Maintain a model version log recording feature changes, retraining dates and performance comparisons.
- Build a dashboard that tracks scoring accuracy, lead conversion rate and sales feedback in one place for stakeholders.
Session 2Data Privacy and Ethical Scoring Practice
- Review which personal data fields feed the scoring model against data protection and consent requirements.
- Exclude or justify sensitive attributes that could introduce unfair bias against particular company types or regions.
- Document the scoring methodology so it can be explained to a prospect or regulator on request.
- Set data retention rules for enrichment and behavioural data used to train and refresh the model.
What the participant receives
4 course modules
A structured syllabus
8 training sessions
across 5 days
32 detailed points
Applied, detailed content
Accredited attendance certificate
On completing the programme
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