Map an End-to-End Agency Sales Process

You are a fractional VP of Sales who has built repeatable sales processes for 30+ digital marketing agencies billing $1M-$25M annually. You think in stages, exit criteria, and forecast accuracy.
Agency: [AGENCY_NAME] — [SERVICES]
ICP: [ICP_DESC]
Average deal size: [AVG_DEAL_SIZE]
Average sales cycle: [SALES_CYCLE_DAYS] days
Current pipeline stages (if any): [CURRENT_STAGES]
Team: [SALES_TEAM_STRUCTURE]
Lead sources: [LEAD_SOURCES]
Design an end-to-end sales process for this agency. Specifically:
1. Define 6-8 named pipeline stages from first touch to closed-won, with a one-sentence purpose for each.
2. For each stage, list the buyer behaviors and seller actions that define it.
3. Specify the single CRM field that proves a deal belongs in that stage (no vibes).
4. Recommend stage probability % weighted to this agency's historical cycle.
5. Flag the two stages where deals stall most for service businesses and how to mitigate.

- Use agency vocabulary (discovery, audit, proposal, SOW, retainer) — not generic SaaS terms like MQL/PQL unless relevant.
- Stages must be mutually exclusive and collectively exhaustive.
- No stage exists without exit criteria the rep can prove.
- Keep total output under 900 words.

Markdown with one H2 per stage. Under each H2, render:
- **Purpose:**
- **Buyer signals:**
- **Seller actions:**
- **Exit criteria (CRM field):**
- **Probability:** X%

Close with a 'Common stall points' section (2 bullets) and a 'Forecast call' table mapping stages to commit/best-case/pipeline.

• [AGENCY_NAME] — Your agency name
• [SERVICES] — Core service lines you sell (e.g. SEO, paid social, web design retainers)
• [ICP_DESC] — Ideal client profile — industry, revenue band, role of buyer
• [AVG_DEAL_SIZE] — ACV or first-year contract value
• [SALES_CYCLE_DAYS] — Median days from first touch to closed-won
• [CURRENT_STAGES] — Stages you use today, or 'none — starting fresh'
• [SALES_TEAM_STRUCTURE] — Who sells — founder-led, AE+SDR, etc.
• [LEAD_SOURCES] — Top 3 channels driving pipeline today

Agency: Northbound Digital — SEO + content for SaaS
ICP: Series A/B SaaS, $5M-$30M ARR, VP Marketing buyer
Average deal size: $84K/yr
Average sales cycle: 47 days
Current pipeline stages: 'New, Working, Qualified, Proposal, Closed'
Team: 1 founder + 2 AEs, no SDRs
Lead sources: inbound SEO, partner referrals, LinkedIn outbound

## 1. New Inquiry
**Purpose:** Capture intent before scoring.
**Buyer signals:** Form fill, referral intro email, LinkedIn reply.
**Seller actions:** Respond <2 hours, send qualifier link.
**Exit criteria (CRM field):** `discovery_booked_at` populated.
**Probability:** 5%

## 2. Discovery Scheduled
**Purpose:** Confirm fit before spending AE time.
**Buyer signals:** Calendar held, pre-call form completed.
**Seller actions:** Pre-call research, send agenda.
**Exit criteria (CRM field):** `pre_call_form_complete = true`.
**Probability:** 15%

## 3. Discovery Complete
...continues through Proposal Sent, Verbal, Contract Out, Closed-Won...

### Common stall points
- **Proposal Sent → Verbal:** Buyer goes dark when economic buyer wasn't in discovery. Mitigation: require multi-threading exit criteria.
- **Verbal → Contract Out:** Procurement surprises. Mitigation: ask 'who signs?' in discovery.

### Forecast call
| Stage | Forecast bucket |
|---|---|
| Verbal, Contract Out | Commit |
| Proposal Sent | Best case |
| Discovery Complete | Pipeline |

• Run this once per service line — SEO retainers and one-off web builds need different stage probabilities.
• Paste the output into Notion before configuring HubSpot/Close so non-sales teammates can debate it without breaking the CRM.
• Re-run quarterly with updated cycle and win-rate numbers — a process built on last year's data lies to your forecast.