Lead Scoring Model (Product-Led) - Project Details / Task List
Tag: lead-scoring-plg
Total Hours: 45h
Structure: Single Milestone (<=50h)
Milestone: Lead Scoring Model (Product-Led)
Description: A strategic and technical implementation project that develops a systematic scoring model to evaluate and prioritize leads based on product usage signals, behavioral data, and firmographic fit for product-led go-to-market motions.
Task List: (Lead Scoring PLG) 1. Discovery & Data Assessment
Contains: Parts 1-2
| Task | Est | Description |
|---|---|---|
| 1. Define Scoring Objectives and Success Criteria | 2.5h | Align stakeholders on what the lead scoring model needs to achieve and how success will be measured. End state: Documented objectives with specific conversion targets and agreement on what constitutes a "sales-ready" PQL. • Conduct stakeholder interviews with Sales, Marketing, and Product to understand current lead prioritization pain points • Document specific goals (e.g., increase trial-to-paid conversion by 25%, reduce time spent on unqualified leads by 40%) • Define the threshold score that triggers sales engagement (e.g., score of 80+ = PQL for outreach) • Establish baseline metrics for current trial conversion rates, sales cycle length, and lead-to-opportunity ratios • Get executive sign-off on scoring objectives and success criteria |
| 2. Map Ideal Customer Profile to Scoring Criteria | 2.5h | Translate the ICP into specific firmographic and demographic attributes that can be scored. End state: Documented list of fit criteria with relative weightings agreed upon by Sales and Marketing. • Review closed-won deals from past 12 months to identify common firmographic patterns (company size, industry, tech stack) • Document negative fit indicators (company too small, wrong industry, competitor employee) • Assign point values to fit attributes based on correlation with conversion (e.g., ICP industry = +20 points, wrong industry = -15 points) • Validate scoring weights with Sales leadership to ensure alignment with their qualification criteria • Create fit scoring matrix showing all attributes and their point values |
| 3. Identify Product Usage Signals for Behavioral Scoring | 2.5h | Determine which product engagement behaviors correlate with buying intent and conversion. End state: Prioritized list of product usage signals with point values based on predictive value. • Analyze product analytics data to identify usage patterns of converted users vs. churned trials • Identify key activation milestones that correlate with conversion (e.g., first integration, invited teammates, used core feature 3+ times) • Document engagement frequency thresholds (e.g., logged in 5+ times in first week = high intent) • Map feature usage to buying signals (e.g., used enterprise-only features during trial = expansion opportunity) • Weight product signals 3-5x higher than demographic data based on PLG best practices |
| 4. Audit Current Data Sources and Quality | 2h | Assess available data sources and identify gaps in the data needed for scoring. End state: Data audit report showing what data exists, where it lives, and what's missing. • Inventory all data sources: CRM (Salesforce/HubSpot), product analytics (Segment, Amplitude, Mixpanel), enrichment tools (Clay, Clearbit, ZoomInfo) • Assess data quality and completeness for key scoring fields (job title fill rate, company size accuracy) • Identify product usage events currently tracked vs. needed for scoring • Document data flow between systems (how product data gets to CRM) • Flag gaps that need to be addressed before scoring implementation |
| 5. Design Data Integration Architecture | 2.5h | Plan how product usage data will flow into the CRM/MAP to power scoring. End state: Architecture diagram showing data sources, integration methods, and destination fields. • Map product events that need to sync to CRM/MAP (via Segment, native integration, or custom API) • Define custom fields needed in CRM to store product usage data (last login date, feature usage flags, activation status) • Determine sync frequency requirements (real-time vs. daily batch for different signals) • Document any data transformation logic needed (e.g., aggregating events into scores) • Create technical specification for engineering team if custom integration work is required |
Task List: (Lead Scoring PLG) 2. Scoring Build & Integration
Contains: Parts 3-4
| Task | Est | Description |
|---|---|---|
| 6. Build Fit Scoring Rules in CRM/MAP | 3h | Configure demographic and firmographic scoring rules in the marketing automation platform or CRM. End state: Fit scoring rules live and automatically scoring new leads on firmographic criteria. • Create scoring property/field in HubSpot, Marketo, or Salesforce to store fit score • Build positive scoring rules for ICP fit attributes (industry match, company size range, job title/seniority) • Build negative scoring rules for disqualifying attributes (competitor, student, wrong geography) • Configure scoring rules to fire on lead creation and when enrichment data updates • Test with sample records to verify fit scoring calculates correctly |
| 7. Build Behavioral Scoring Rules for Product Usage | 3.5h | Configure product engagement scoring rules that assign points based on usage signals. End state: Behavioral scoring rules live and automatically scoring leads based on product activity. • Create behavioral score property/field separate from fit score • Build scoring rules for activation milestones (completed onboarding, first key action, invited users) • Configure engagement frequency scoring (login frequency, session count, feature breadth) • Build scoring rules for high-intent actions (viewed pricing, started upgrade flow, used premium features) • Implement negative scoring for inactivity (subtract points after 14+ days of no login) |
| 8. Configure Score Decay and Recency Logic | 3h | Implement time-based decay rules to ensure scores reflect current intent, not stale engagement. End state: Decay rules automatically reduce scores for inactive leads, keeping the pipeline fresh. • Configure decay rules to subtract points after defined inactivity periods (e.g., -10 points per 30 days of no engagement) • Weight recent actions higher than older actions (demo request this week > demo request 30 days ago) • Build engagement velocity bonuses (e.g., +25 points if 5+ key actions in 48 hours) • Set floor scores to prevent decay from pushing scores negative • Test decay logic with historical leads to verify it properly surfaces active prospects |
| 9. Create Combined Lead Score and PQL Threshold | 3h | Combine fit and behavioral scores into a single actionable lead score with defined PQL thresholds. End state: Combined lead score field that triggers PQL status at defined threshold. • Create combined lead score formula (e.g., Fit Score + Behavioral Score = Total Lead Score) • Define PQL threshold based on historical conversion data (score at which leads convert at target rate) • Build PQL flag/status that triggers when threshold is reached • Configure notifications to sales when leads reach PQL status • Document scoring model logic in a reference sheet for stakeholder visibility |
| 10. Connect Product Analytics to CRM/MAP | 3.5h | Implement the data pipeline to flow product usage events into the scoring system. End state: Product usage data syncing to CRM in near-real-time, populating the fields that drive behavioral scoring. • Configure Segment/CDP to send key events to CRM/MAP destination • Alternatively, set up native product analytics integration (e.g., HubSpot product tracking, Salesforce Engage) • Verify events are populating correct fields in CRM • Test event latency to ensure acceptable delay between product action and score update • Document the event mapping for ongoing maintenance |
| 11. Build Sales Alert and Assignment Workflows | 3h | Create automation that notifies sales reps when leads become PQLs and assigns them appropriately. End state: Sales reps receive real-time alerts for new PQLs with context on why the lead qualified. • Build workflow triggered when lead reaches PQL threshold • Configure lead assignment logic (round-robin, territory-based, or account-based) • Create email/Slack notification to assigned rep with key context (score, top behaviors, fit details) • Build CRM task creation for PQL follow-up with suggested messaging • Test end-to-end flow from product action to sales notification |
Task List: (Lead Scoring PLG) 3. Testing & Enablement
Contains: Parts 5-6
| Task | Est | Description |
|---|---|---|
| 12. Validate Scoring Model Against Historical Data | 3h | Test the scoring model against historical closed-won and closed-lost deals to verify predictive accuracy. End state: Validation report showing correlation between lead scores and actual conversion outcomes. • Apply scoring model retroactively to leads from past 6-12 months • Compare score distribution of converted vs. non-converted leads • Calculate conversion rate by score bucket (0-25, 26-50, 51-75, 76-100) • Verify PQL threshold produces acceptable precision (not too many false positives) • Adjust scoring weights if validation reveals weak correlation |
| 13. Conduct Pilot Test with Sales Team | 2.5h | Run a limited pilot with subset of sales team to validate scoring effectiveness in real sales workflows. End state: Pilot feedback incorporated and scoring model refined based on frontline input. • Select 2-3 reps for 2-week pilot test • Brief pilot reps on scoring methodology and how to interpret scores • Collect daily feedback on lead quality at different score ranges • Track pilot reps' conversion rates on PQLs vs. non-PQLs • Document refinements needed based on pilot learnings |
| 14. Train Sales and Marketing Teams | 2h | Enable all teams to understand the scoring model and how to use lead scores effectively. End state: Teams trained on interpreting scores, with documentation for ongoing reference. • Develop training deck explaining fit vs. behavioral scoring, PQL definition, and score interpretation • Conduct live training session (45-60 min) for Sales, Marketing, and RevOps • Walk through example PQL profiles and recommended outreach approaches • Create one-page quick reference guide for sales reps • Record training for onboarding new team members |
| 15. Launch Scoring Model to Full Team | 2h | Go live with the scoring model across all leads and full sales team. End state: Scoring model live in production, all new leads being scored, sales using scores for prioritization. • Activate scoring for all leads (new and existing) • Backfill scores for active leads in pipeline • Enable PQL notifications and workflows for full sales team • Communicate launch to all stakeholders with links to documentation • Set up monitoring dashboards to track scoring health |
| 16. Document and Hand Off to Client | 3h | Transfer ownership of the scoring system to client team with complete documentation. End state: Client self-sufficient with admin playbook, tuning guidance, and support contacts. • Deliver scoring model documentation (all rules, thresholds, decay logic, field mappings) • Create admin playbook for making scoring adjustments • Document troubleshooting guide for common issues • Transfer access credentials and ownership to client RevOps • Schedule 30-day and 90-day check-in calls for optimization review |
Summary
- Total Task Lists: 3 (consolidated from 6 Parts)
- Total Tasks: 16 (one per Step)
- Total Hours: 45h
- Generated from: playbook_lead-scoring-model-product-led.md
- Generated on: 2025-12-31