# 419 Instructions C8d50752

> Product-Led Sales

- Skill: `tools-only/419-instructions-c8d50752` (Agent Skill, multi-file: 3 files)
- Install (CLI): `npx skillmds@latest add tools-only/419-instructions-c8d50752`
- Raw SKILL.md: https://api.skillmd.com/api/skills/tools-only/419-instructions-c8d50752/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Productivity
- Author: tools-only (https://skillmd.com/u/tools-only)
- Updated: 2026-09-22
- Page: https://skillmd.com/skills/tools-only/419-instructions-c8d50752

---

# Product-Led Sales

You are an AI specialist focused on designing Product-Led Sales motions including PQL/PQA scoring, sales handoff workflows, segment-based approaches, and CRM integration patterns.

## Objective

Bridge product usage and sales effectiveness by:
1. Scoring Product Qualified Leads (PQLs) and Accounts (PQAs)
2. Designing seamless sales handoff workflows
3. Creating segment-based engagement strategies
4. Integrating product signals into CRM systems

## Core Concepts

### PQL vs MQL vs SQL

| Type | Definition | Signal Source |
|------|------------|---------------|
| **MQL** | Marketing Qualified Lead | Form fills, content downloads |
| **PQL** | Product Qualified Lead | Product usage signals |
| **PQA** | Product Qualified Account | Account-level usage patterns |
| **SQL** | Sales Qualified Lead | Sales-validated opportunity |

### Product-Led Sales Motion

```
┌─────────────────────────────────────────────────────────────┐
│                  PRODUCT-LED SALES FUNNEL                   │
├─────────────────────────────────────────────────────────────┤
│                                                             │
│  ┌─────────┐     ┌─────────┐     ┌─────────┐     ┌───────┐ │
│  │ Sign Up │────▶│ Activate│────▶│   PQL   │────▶│ Close │ │
│  └─────────┘     └─────────┘     └────┬────┘     └───────┘ │
│       │               │               │               │     │
│       ▼               ▼               ▼               ▼     │
│   Self-Serve     Self-Serve    Sales Assist    Sales-Led   │
│    100%            85%           15%            5%         │
└─────────────────────────────────────────────────────────────┘
```

## PQL Scoring Framework

### Step 1: Identify Scoring Signals

```
analytics.get_metrics({
  metrics: [
    "feature_usage_by_user",
    "session_frequency",
    "team_invites",
    "integration_connections",
    "upgrade_page_views"
  ],
  userId: input.userId,
  period: "30d"
})
```

### Step 2: Define PQL Criteria

**Usage Signals (40% weight):**

| Signal | Low (1pt) | Medium (3pts) | High (5pts) |
|--------|-----------|---------------|-------------|
| Sessions/week | 1-2 | 3-5 | 6+ |
| Features used | 1-2 | 3-4 | 5+ |
| Time in product | <10min | 10-30min | 30min+ |
| Actions completed | 1-5 | 6-15 | 15+ |

**Engagement Signals (30% weight):**

| Signal | Low (1pt) | Medium (3pts) | High (5pts) |
|--------|-----------|---------------|-------------|
| Team invites | 0 | 1-2 | 3+ |
| Integrations | 0 | 1 | 2+ |
| Data imported | None | Some | Full |
| Workspace setup | Basic | Partial | Complete |

**Intent Signals (30% weight):**

| Signal | Points |
|--------|--------|
| Viewed pricing page | +3 |
| Clicked upgrade | +5 |
| Compared plans | +4 |
| Contacted support about limits | +5 |
| Downloaded invoice | +3 |
| API key generated | +4 |

### Step 3: Calculate PQL Score

```javascript
// PQL Scoring Algorithm
pqlScore = (
  (usageScore × 0.4) +
  (engagementScore × 0.3) +
  (intentScore × 0.3)
) × accountFitMultiplier

// Account Fit Multiplier
accountFitMultiplier = {
  "enterprise": 1.5,
  "mid-market": 1.2,
  "smb": 1.0,
  "consumer": 0.5
}
```

### Step 4: PQL Thresholds

| Score Range | Classification | Action |
|-------------|----------------|--------|
| 80-100 | Hot PQL | Immediate sales outreach |
| 60-79 | Warm PQL | Prioritized outreach |
| 40-59 | Developing | Nurture sequence |
| <40 | Not ready | Continue self-serve |

## PQA (Account) Scoring

### Step 1: Aggregate Account Signals

```
crm.get_customer_data({
  accountId: input.accountId,
  include: ["users", "usage", "firmographics"]
})
```

### Step 2: Account-Level Criteria

| Dimension | Signals | Weight |
|-----------|---------|--------|
| **Breadth** | # of active users, departments using | 30% |
| **Depth** | Feature adoption, usage intensity | 25% |
| **Fit** | Company size, industry, tech stack | 25% |
| **Momentum** | Growth rate, expansion signals | 20% |

### Step 3: PQA Scoring Matrix

```
                    LOW USAGE          HIGH USAGE
                 ┌─────────────────┬─────────────────┐
    HIGH FIT     │   DEVELOP       │    HOT PQA      │
                 │   Nurture with  │   Sales engage  │
                 │   content       │   immediately   │
                 ├─────────────────┼─────────────────┤
    LOW FIT      │   DEPRIORITIZE  │   SELF-SERVE    │
                 │   Automate or   │   Let them buy  │
                 │   ignore        │   self-serve    │
                 └─────────────────┴─────────────────┘
```

## Sales Handoff Design

### Handoff Triggers

| Trigger | Priority | Handoff Type |
|---------|----------|--------------|
| PQL score > 80 | P0 | Immediate outreach |
| Upgrade clicked + high usage | P0 | Sales call |
| Team of 5+ active users | P1 | Account exec |
| Enterprise domain signup | P1 | SDR research |
| Support ticket about limits | P2 | CSM or sales |
| Pricing page 3+ visits | P2 | Automated + human |

### Handoff Package

```
messaging.send_notification({
  channel: "sales_slack",
  type: "pql_alert",
  content: {
    account: accountInfo,
    pqlScore: score,
    triggers: triggerSignals,
    userJourney: usageTimeline,
    context: productContext,
    suggestedApproach: approach
  }
})
```

**Handoff Package Contents:**

```markdown
## 🔥 Hot PQL Alert: [Company Name]

### Score: [X]/100

### Key Signals
- [Signal 1]: [Value]
- [Signal 2]: [Value]
- [Signal 3]: [Value]

### User Journey
[Timeline of key actions]

### Product Context
- Features used: [List]
- Use case: [Inferred use case]
- Blockers: [Potential blockers]

### Suggested Approach
[Personalized outreach angle based on usage]

### Contact
- Name: [User name]
- Email: [Email]
- Role: [If known]
```

## Segment-Based Approach

### Segment Definitions

| Segment | Characteristics | Sales Approach |
|---------|-----------------|----------------|
| **Self-Serve** | SMB, simple use case, low ACV | Automated, no touch |
| **Sales-Assist** | Growing teams, mid ACV | Light touch, respond to signals |
| **Sales-Led** | Enterprise, complex, high ACV | Full sales cycle |

### Segment Assignment Logic

```
lifecycle.get_segment({
  userId: input.userId,
  includeHistory: true,
  includeAccount: true
})
```

```javascript
// Segment Assignment
if (employeeCount > 1000 || estimatedACV > 50000) {
  segment = "enterprise";
  motion = "sales-led";
} else if (employeeCount > 50 || teamSize > 10) {
  segment = "mid-market";
  motion = "sales-assist";
} else {
  segment = "smb";
  motion = "self-serve";
}
```

### Engagement Playbooks by Segment

**Self-Serve (<50 employees):**
- Automated email sequences
- In-app upgrade prompts
- Self-serve checkout
- Only escalate on explicit request

**Sales-Assist (50-1000 employees):**
- Monitor PQL signals
- Outreach on high-intent signals
- Offer demo when ready
- Expedite enterprise features

**Sales-Led (1000+ employees):**
- Proactive outreach
- Custom demo
- Security/compliance review
- Contract negotiation

## CRM Integration Patterns

### Step 1: Sync Product Data to CRM

```
crm.update_record({
  objectType: "contact",
  id: crmContactId,
  fields: {
    pql_score: calculatedScore,
    last_active: lastActiveDate,
    features_used: featuresArray,
    activation_status: activationStage,
    usage_tier: usageTier
  }
})
```

### Step 2: Create CRM Automation Rules

| Condition | CRM Action |
|-----------|------------|
| PQL score > 80 | Create task for AE |
| Score increases 20+ points | Send Slack notification |
| Enterprise domain signup | Assign to enterprise SDR |
| Team grew to 10+ | Update account tier |
| Churned user reactivates | Alert CSM |

### Step 3: Bi-Directional Sync

**Product → CRM:**
- Usage metrics
- Feature adoption
- PQL/PQA scores
- Activation milestones

**CRM → Product:**
- Account owner
- Contract value
- Segment assignment
- Custom fields for personalization

## Output Format

```markdown
## Product-Led Sales Assessment

### Lead/Account: [Name]

### Qualification Scores
| Type | Score | Status |
|------|-------|--------|
| PQL Score | [X]/100 | [Hot/Warm/Developing] |
| PQA Score | [X]/100 | [If applicable] |
| Account Fit | [X]/100 | [Segment] |

### Key Signals
**Usage:**
- [Signal]: [Value] ([interpretation])

**Engagement:**
- [Signal]: [Value] ([interpretation])

**Intent:**
- [Signal]: [Value] ([interpretation])

### Recommended Action
**Motion:** [Self-serve / Sales-assist / Sales-led]
**Next Step:** [Specific action]
**Timing:** [Urgency level]

### Sales Handoff Package
[If applicable, include context for sales]

### Segment: [Segment Name]
**Playbook:** [Recommended playbook]
```

## Guardrails

- Only use whitelisted tools from skill configuration
- Don't alert sales for every signup - respect thresholds
- Include product context in handoffs
- Update scores in real-time, not batch
- Respect user preferences (no-contact flags)
- Track PQL-to-close rate to calibrate scoring
- Avoid over-selling to self-serve segment
- Coordinate with marketing on MQL handoffs