sales-lead-score

What this skill does

# Design a Lead Scoring Model

Help the user design, weight, and tune a lead scoring model — from defining scoring dimensions and assigning point values through setting MQL/SQL thresholds and implementing in their tools. This skill is tool-agnostic and applies to any CRM (Salesforce, HubSpot), MAP (Marketo, Pardot), or enrichment provider (Apollo, ZoomInfo, Clearbit).

## Step 1 — Gather context


If `references/learnings.md` exists, read it first for accumulated knowledge.

Ask the user:

1. **What do you sell?**
   - Product/service category
   - Approximate ACV (affects scoring complexity — $5K ACV needs simpler scoring than $100K ACV)

2. **What's your sales motion?**
   - A) Inbound-led — most leads come from marketing (content, ads, events)
   - B) Outbound-led — SDRs/BDRs source most leads
   - C) Product-led growth (PLG) — users sign up and self-serve, sales engages at usage thresholds
   - D) Hybrid — mix of inbound, outbound, and PLG
   - E) Channel/partner — leads come through partners

3. **What tools do you use?**
   - CRM: Salesforce, HubSpot, Pipedrive, other
   - MAP: Marketo, HubSpot, Pardot, ActiveCampaign, other
   - Enrichment: Apollo, ZoomInfo, Clearbit, other
   - Product analytics (for PLG): Amplitude, Mixpanel, Heap, Segment, other

4. **Current scoring situation?**
   - A) Starting from scratch — no scoring model exists
   - B) Have a model but it's not working (describe symptoms)
   - C) Have a basic model, want to improve it
   - D) Rebuilding after ICP change or new product launch

5. **What does your funnel look like today?**
   - Monthly lead volume (rough)
   - Current MQL → SQL conversion rate (if known)
   - Current SQL → closed-won rate (if known)

**If the user's request already provides most of this context, skip directly to the relevant step.** Lead with your best-effort answer using reasonable assumptions (stated explicitly), then ask only the most critical 1-2 clarifying questions at the end — don't gate your response behind gathering complete context.

## Step 2 — Define scoring dimensions

Build a scoring model across four dimensions. Default weights are a starting point — tune based on your sales motion.

### Dimension 1: Demographic fit (default 25%)

Score how well the individual matches your buyer persona.

| Attribute | High score | Medium score | Low/negative score |
|---|---|---|---|
| **Job title** | Exact ICP title match (e.g., VP Engineering) | Adjacent title (Director of Engineering, Head of Platform) | Unrelated title (HR Manager when selling to Engineering) |
| **Seniority** | Decision-maker level for your product | Influencer level | Too junior to buy or influence |
| **Department** | Primary buying department | Adjacent department | Unrelated department |
| **Job function** | Direct match to problem you solve | Related function | No relevance |

**Example point values** (25 points max):
- Title exact match: 10 pts | Adjacent: 5 pts | No match: 0 pts
- Seniority match: 8 pts | Adjacent: 4 pts | Too junior: 0 pts
- Department match: 7 pts | Adjacent: 3 pts | Unrelated: 0 pts

### Dimension 2: Firmographic fit (default 25%)

Score how well the company matches your ICP.

| Attribute | High score | Medium score | Low/negative score |
|---|---|---|---|
| **Company size** | Sweet spot (e.g., 100-500 employees) | Adjacent range (50-100 or 500-1000) | Way outside range |
| **Industry** | Primary target industry | Adjacent industry | Industry you don't serve |
| **Revenue** | Revenue range that matches your pricing | Adjacent range | Can't afford your product |
| **Geography** | Primary market | Serviceable market | Unsupported region |
| **Tech stack** | Uses complementary technology | Neutral tech stack | Uses competing product (could be positive for displacement) |

**Example point values** (25 points max):
- Company size sweet spot: 8 pts | Adjacent: 4 pts | Outside: 0 pts
- Industry match: 7 pts | Adjacent: 3 pts | No match: 0 pts
- Revenue fit: 5 pts | Adjacent: 2 pts | Outside: 0 pts
- Geography: 3 pts | Serviceable: 1 pt | Unsupported: 0 pts
- Tech stack fit: 2 pts

### Dimension 3: Behavioral signals (default 30%)

Score what the lead is doing — this is the most predictive dimension for most teams.

| Signal | Points | Decay |
|---|---|---|
| **Requested demo/trial** | 15 pts | None — this is a hard conversion event |
| **Pricing page visit** | 10 pts | Decays to 5 after 14 days |
| **Multiple website visits (3+ in 7 days)** | 8 pts | Decays to 4 after 14 days |
| **Content download (ebook, whitepaper)** | 5 pts | Decays to 2 after 30 days |
| **Email engagement (open + click)** | 3 pts per engagement | Decays to 0 after 30 days |
| **Webinar/event attendance** | 8 pts | Decays to 4 after 30 days |
| **Intent data — researching your category** | 10 pts | Decays to 5 after 14 days (intent is perishable) |
| **G2/review site comparison views** | 8 pts | Decays to 4 after 14 days |

**For PLG/product-led motions**, add product usage signals:

| Signal | Points | Decay |
|---|---|---|
| **Signed up for free tier/trial** | 10 pts | None |
| **Completed onboarding** | 8 pts | None |
| **Hit usage threshold (e.g., 100 API calls, 5 team members)** | 15 pts | None |
| **Invited team members** | 10 pts | None |
| **Used premium feature (paywall hit)** | 12 pts | Decays to 6 after 30 days |
| **Daily active usage (5+ days in last 14)** | 10 pts | Rolling — recalculated weekly |

### Dimension 4: Timing signals (default 20%)

Score recency and urgency signals.

| Signal | Points | Decay |
|---|---|---|
| **New in role (<90 days)** | 10 pts | Decays to 5 after 90 days, 0 after 180 |
| **Recent funding** | 8 pts | Decays to 4 after 90 days |
| **Hiring for roles your product supports** | 6 pts | Decays to 3 after 30 days (job postings are time-sensitive) |
| **Company growth (20%+ headcount in 6 months)** | 5 pts | Decays to 2 after 90 days |
| **Competitor contract renewal window** | 10 pts | Decays to 0 after the window passes |

### Tuning weights by sales motion

| Motion | Demographic | Firmographic | Behavioral | Timing |
|---|---|---|---|---|
| **Inbound-led** | 20% | 20% | 40% | 20% |
| **Outbound-led** | 25% | 30% | 20% | 25% |
| **PLG** | 15% | 15% | 50% | 20% |
| **Enterprise/ABM** | 25% | 25% | 25% | 25% |

## Step 3 — Set thresholds & stages

### MQL/SQL threshold calibration

Start with these defaults, then tune based on conversion data:

| Threshold | Default | What it triggers |
|---|---|---|
| **MQL** (Marketing Qualified Lead) | Top 20% of scored leads | Marketing nurture intensifies, SDR notification |
| **SQL** (Sales Qualified Lead) | Top 5% of scored leads | SDR outreach, AE handoff, or sales follow-up |
| **PQL** (Product Qualified Lead, PLG only) | Usage threshold + firmographic fit | Sales outreach to active free users |

**How to set initial thresholds**:
1. Score your last 100 closed-won deals retroactively
2. Find the median score — this is roughly your SQL threshold
3. Set MQL at 60-70% of the SQL threshold
4. Adjust after 30 days of live data

### Scoring decay rules

Behavioral signals lose relevance over time. Implement decay to prevent score inflation:

- **Fast decay (7-14 days)**: Intent data, pricing page visits, comparison shopping
- **Medium decay (30 days)**: Content downloads, email engagement, webinar attendance
- **Slow decay (90 days)**: Job changes, funding events, hiring signals
- **No decay**: Demo requests, trial signups, firmographic/demographic fit

### Negative scoring

Subtract points for disqualifying signals:

| Signal | Points |
|---|---|
| Unsubscribed from emails | -20 pts |
| Competitor employee | -50 pts (or auto-disqualify) |
| Student/educational email (.edu) | -30 pts |
| Personal email (gmail, yahoo) for B2B product | -10 pts |
| Job title contains "intern" or "student" | -20 pts |
| Company size way below minimum | -15 pts |
| Bounced email | -10 pts |
| Marked as "do not contact" | Auto-disqualify |

## Step 4 — Implementation guide

### HubSpot