product-led-sales

Answer these diagnostic questions. If 3 or more are "yes," it is time to layer sales onto your PLG motion.

1. Enterprise demand is emerging: Are companies with 500+ employees signing up but struggling to expand beyond individual teams?
2. Large deal potential: Are there accounts where the product could expand from a $500/year team plan to a $50K+ enterprise contract?
3. Complex procurement: Are potential customers asking about security reviews, compliance, SSO, or custom contracts?
4. Expansion stalling: Are accounts hitting a growth ceiling within one team?
5. Competitor sales pressure: Are you losing enterprise deals to competitors with sales teams?
6. Self-serve conversion plateauing: Has conversion rate flattened despite optimization?
7. Seat consolidation requests: Are multiple teams using separate accounts and asking to consolidate?

Prerequisites (if these are not met, fix them first):

• Working PLG motion with product-market fit
• 1,000+ active free/trial accounts (enough signal volume)
• Average potential deal size > $5K/year
• Product analytics infrastructure for PQL signals
• Activation rate above 20%

---

PQL Signal Categories

Category 1: Feature Usage Signals

| Signal | What It Indicates | Example |

|--------|-------------------|---------|

| Used premium/advanced features | Power user, likely to need paid plan | Used API, custom integrations, advanced analytics |

| High feature breadth | Engaged across the product | Used 5+ distinct features in first 14 days |

| Hit usage limits | Ready for upgrade | Reached free storage limit, API rate limit |

| Exported data | Product creating value they want elsewhere | Downloaded reports, exported CSV |

Category 2: Collaboration Signals

| Signal | What It Indicates | Example |

|--------|-------------------|---------|

| Invited team members | Multi-user adoption beginning | Invited 3+ users within first week |

| Shared content externally | Product output reaching non-users | Shared dashboards, documents, or links |

| Multiple users from same domain | Organic spread within company | 5+ users from @company.com |

| Cross-team usage | Expansion beyond initial team | Users from engineering AND marketing |

Category 3: Velocity Signals

| Signal | What It Indicates | Example |

|--------|-------------------|---------|

| Rapid activation | High intent | Completed onboarding in < 1 hour |

| Accelerating usage | Growing dependency | WAU increasing 3+ consecutive weeks |

| High frequency | Part of daily workflow | DAU/MAU ratio > 0.5 |

Category 4: Intent Signals

| Signal | What It Indicates | Example |

|--------|-------------------|---------|

| Viewed pricing page 2+ times | Evaluating paid options | Visited pricing page multiple times |

| Started upgrade flow, abandoned | Interest but friction | Added card then left, clicked upgrade then left |

| Requested enterprise features | Enterprise procurement | Asked about SSO, SCIM, audit logs |

| Contacted support about billing | Ready for commercial terms | Asked about invoicing, annual plans |

PQL Scoring Model Template

```

PQL SCORING MODEL

== Feature Usage Signals ==

Used premium feature (any): +10

Used 3+ premium features: +15

Hit usage/storage limit: +20

Used API/integrations: +15

High feature breadth (5+ features): +10

== Collaboration Signals ==

Invited 1-2 team members: +10

Invited 3-5 team members: +20

Invited 6+ team members: +30

Multiple users from same domain (3+): +15

Multiple users from same domain (10+): +25

Shared content externally: +10

== Velocity Signals ==

Activated in < 1 hour: +10

DAU/MAU > 0.3: +10

DAU/MAU > 0.5: +20

Usage increasing 3+ consecutive weeks: +15

Used product 5+ of last 7 days: +15

== Intent Signals ==

Viewed pricing page: +10

Viewed pricing page 3+ times: +20

Started upgrade flow, abandoned: +25

Requested enterprise features (SSO, etc.): +30

Contacted support about billing/plans: +20

== Firmographic Signals ==

Company size 50-500 employees: +10

Company size 500+ employees: +20

Company in target industry: +10

Company matches ICP: +15

== Negative Signals ==

Inactive for 7+ days: -20

Declining usage trend: -15

Single-user account, no invites after 30 days: -10

Using only free features after 30 days: -10

THRESHOLDS:

Score >= 80: Hot PQL - Immediate sales outreach

Score 50-79: Warm PQL - Nurture sequence + lightweight outreach

Score 30-49: Emerging PQL - Monitor, product-led nurture only

Score < 30: Not PQL - Self-serve path only

```

Calibrating Your Scoring Model

1. Start with historical data: Look at accounts that converted from free to paid. What signals did they exhibit?
2. Weight by predictive power: Use correlation analysis or logistic regression if you have enough data.
3. Iterate quarterly: Re-calibrate weights as product and user base evolve.
4. A/B test thresholds: Test where sales outreach adds incremental value vs where self-serve would have converted anyway.

---

```

PQA Score = Sum of all PQL scores within the account

+ Account-level signals

+ Firmographic fit score

```

Account-Level Signals

| Signal | Weight | Rationale |

|--------|--------|-----------|

| Number of active users from domain | High | Multi-user adoption = organizational value |

| Number of departments represented | High | Cross-functional adoption = harder to churn |

| Growth rate of users within account | High | Expanding adoption = expansion opportunity |

| Executive user detected (by title/role) | Medium | Executive sponsorship accelerates deals |

| Multiple teams/workspaces created | High | Org structure emerging in product |

PQA Tiering

| Tier | Criteria | Sales Action |

|------|----------|--------------|

| Tier 1: Enterprise | 10+ users, 2+ departments, ICP match, score > 150 | Dedicated AE, executive outreach |

| Tier 2: Mid-Market | 5-10 users, score 80-150 | Targeted outreach, custom demo |

| Tier 3: SMB | 2-5 users, score 40-80 | Automated nurture, in-product upgrade prompts |

| Tier 4: Individual | 1 user, any score | Pure self-serve |

---

SMB (1-50 employees) -- Fully self-serve. No sales. Self-serve checkout, credit card. Target ACV < $5K.

Mid-Market (50-500 employees) -- PLG + PQL-triggered sales assist. Inside sales responds to signals. Self-serve for initial purchase, sales for expansion/enterprise features. Target ACV $5K-$50K.

Enterprise (500+ employees) -- PLG + Sales-led expansion. Dedicated AE, solution engineering, executive alignment. Custom contracts, annual invoicing. White-glove onboarding. Target ACV $50K+.

---

Trigger Definitions

High-Signal (Immediate outreach):

• User requests enterprise features (SSO, SAML, audit logs)
• User selects "Enterprise" or "Contact Sales" in upgrade flow
• Account has 10+ users approaching plan limits
• User asks support about volume pricing or custom plans

Medium-Signal (Within 24-48 hours):

• PQL score exceeds hot threshold
• Account adds 5+ users in a single week
• User views pricing page 3+ times without converting
• Account matches ICP with multiple active departments

Low-Signal (Automated nurture first):

• PQL score enters warm zone
• Single high-engagement user at target account
• Steady usage growth over 4+ weeks

Handoff Process

```

Step 1: SIGNAL DETECTION

Product analytics detects PQL/PQA trigger

Step 2: ENRICHMENT

Auto-enrich: company size, industry, tech stack, existing contacts

Pull product usage summary

Step 3: ROUTING

SMB: Automated email sequence

Mid-Market: Inside sales rep

Enterprise: Named AE

Step 4: CONTEXT DELIVERY

Provide sales rep with:

- Product usage summary (features, frequency, team size)

- PQL score breakdown (which signals fired)

- Current plan and potential expansion value

- Recommended talk track based on usage patterns

Step 5: PERSONALIZED OUTREACH

"I noticed your team at [Company] has been using [Feature] extensively.

Teams at this stage often benefit from [Premium capability]. Would it be

helpful to discuss how [Company-similar] teams have scaled their usage?"

Step 6: OUTCOME TRACKING

Track: response rate, meeting booked rate, pipeline created, deal closed

Feed outcomes back into PQL scoring model

```

---

Individual / Solo User

```

Signup -> Minimal form -> Immediate product access ->

Guided first-task -> In-product education -> Self-serve upgrade

```

Small Team (2-10 users)

```

Signup -> Team creation prompt -> Invite teammates ->

Collaborative first-task -> Team tips -> Self-serve team plan

```

Mid-Market (detected via domain or self-reported)

```

Signup -> Enrichment lookup -> Richer onboarding (import, integrations) ->

Optional: "Want a 15-minute walkthrough?" ->

Product-led activation + parallel sales nurture

```

Enterprise (detected via domain, self-reported, or referral)

```

Signup -> Enrichment -> Flag to sales immediately ->

Product access (never gate!) -> Offer dedicated onboarding call ->

Assign CSM/AE -> Enterprise deal pipeline

```

Key Principle: Never require a sales conversation to use the product. Sales is an accelerator, not a gate.

---

Data Flow

```

Product Database -> Data Pipeline (Census, Hightouch, or custom) -> CRM

```

Sync to CRM:

• User-level: signup date/source, activation status, plan, key feature usage, PQL score, last active
• Account-level: user count, total usage, PQA score/tier, departments, growth trajectory, enterprise feature requests
• Real-time triggers: PQL threshold crossed, new user from target account, pricing page visit, enterprise feature request, user count threshold

Tooling Options

| Approach | Tools | Best For |

|----------|-------|----------|

| Reverse ETL | Census, Hightouch, Polytomic | Syncing warehouse data to CRM |

| Product analytics integration | Amplitude -> Salesforce, Mixpanel -> HubSpot | Teams already on these platforms |

| Custom pipeline | Segment + dbt + custom sync | Maximum flexibility |

| PLG-specific platforms | Pocus, Endgame, Calixa | Purpose-built PLS workflows |

---

Roles

Growth / PLG Team (owns product-led funnel):

• Growth PM: Owns activation, conversion, expansion in-product
• Growth Engineers: Build and test growth features, instrumentation
• Growth Analyst: PQL scoring, conversion analysis, model calibration

PLS Sales Team (acts on product signals):

• PLS Account Executives: Mid-market and enterprise PQL outreach
• PLS SDRs (optional): High-volume warm PQL follow-up
• Solutions Engineers: Technical sales support for enterprise

Customer Success (post-sale):

• CSM: Enterprise accounts, adoption, expansion
• Technical Account Manager: Complex implementations

Organizational Alignment

• Weekly PQL review: Growth presents top PQLs; Sales provides signal quality feedback
• Quarterly PQL model calibration: Analyze which signals predicted conversion; adjust weights
• Shared dashboard: Both teams see the same data

---

Primary Metrics

| Metric | Definition | Benchmark |

|--------|-----------|-----------|

| PQL Volume | Accounts crossing threshold per month | Track trend |

| PQL-to-SQL Rate | % of PQLs accepted as qualified | 30-50% for well-calibrated models |

| PQL-to-Close Rate | % of PQLs that become paying | 15-30% |

| PQL Sales Cycle | Days from trigger to close | 50-70% shorter than outbound |

| PQL ACV | Average deal size, PQL-sourced | Compare to self-serve and outbound |

| Incremental Revenue | Revenue that would NOT have happened self-serve | True PLS value measure |

| Expansion Revenue Rate | % of PLS revenue from expanding accounts | Target: 30-50% |

Counter-Metrics

| Counter-Metric | Warning Sign |

|---------------|-------------|

| Self-serve conversion decline | Sales intercepting users who would have converted alone |

| PQL response rate declining | Outreach quality degrading or threshold too low |

| Time-to-first-contact increasing | Sales team overwhelmed |

| NPS of contacted vs non-contacted | Sales creating negative experiences |

---

1. Over-Gating Features: Locking self-serveable features behind "Contact Sales" creates resentment. Gate only what genuinely requires sales (custom contracts, dedicated infrastructure, compliance).

1. Premature Sales Hiring: Underutilized reps revert to cold outbound, undermining PLG culture. Add headcount proportional to PQL volume.

1. Generic Outreach: PQLs expect product-aware messaging. Reference features they use, teammates they invited, value they received.

1. No Feedback Loop: PQL models degrade without calibration. Weekly sales feedback on quality; quarterly deep calibration with conversion data.

---

```markdown

# Product-Led Sales Playbook: [Company/Product Name]

• Current PLG metrics: [signup volume, activation rate, self-serve conversion rate]
• PLS trigger signals identified: [Yes/No, list top signals]
• Data infrastructure readiness: [product analytics, CRM, data pipeline status]
• Readiness verdict: [Ready / Need prerequisites / Not yet]

Signals and Weights

[Customized scoring model with signals, weights, and thresholds]

PQA Tiering

| Tier | Criteria | Volume Estimate | Sales Action |

|------|----------|-----------------|--------------|

| [Tier] | [Criteria] | [Estimate] | [Action] |

SMB Motion

[Self-serve design]

Mid-Market Motion

[PQL-triggered sales assist]

Enterprise Motion

[Sales-led expansion]

Trigger Definitions

[Triggers and routing rules]

Outreach Templates

[Personalized templates by signal type]

Feedback Loop Process

[Weekly/quarterly calibration]

Data to Sync

[Fields and sync frequency]

Tooling

[Selected tools and implementation]

Roles Needed

[Roles with descriptions]

Hiring Sequence

[Order of hires tied to PQL volume milestones]

| Metric | Current Baseline | 90-Day Target | Tracking Method |

|--------|-----------------|---------------|-----------------|

| [Metric] | [Baseline] | [Target] | [Method] |

Month 1: Foundation

• [ ] Instrument PQL signals
• [ ] Build scoring model v1
• [ ] Set up CRM data sync
• [ ] Define handoff triggers

Month 2: Pilot

• [ ] Assign 1-2 reps to PQL follow-up
• [ ] Run personalized outreach
• [ ] Collect signal quality feedback
• [ ] Track conversion metrics

Month 3: Iterate

• [ ] Calibrate scoring model
• [ ] Adjust thresholds
• [ ] Expand team if metrics support
• [ ] Document playbook

```

---

• plg-strategy -- Broader PLG strategy and hybrid model design
• feature-gating -- Free vs paid vs sales-gated features
• expansion-revenue -- Net revenue retention through PLS
• plg-metrics -- Full PLG metrics stack
• activation-metrics -- Activation signals feeding PQL scoring