pipeline-visibility

If the evidence is thin, ask three yes or no questions: Is there a segment with 10 or more customers won the same way? Is retention measured monthly? Does someone own the CRM as part of their job? Three no answers means situation 1. One or two yes answers means situation 2. Three yes answers means situation 3 or 4; then ask three more: Is there more than one business unit or region with its own revenue plan and leader? Do go-to-market decisions pass through a formal governance layer? Does finance own the revenue target for board reporting? Two or more yes answers means situation 4; otherwise situation 3.

The full skill applies to both scale-up and enterprise. Not every skill applies at every stage; the table below says what this skill does at each, and "do not run" is a valid answer.

What this skill does per situation:

| Situation | What this skill does |

|---|---|

| Start-up, pre product-market fit | Do not build dashboards or automation yet. Instead, run a quarterly manual pipeline review: list all open opportunities, document which have activity in the past fortnight, and ask each owner 'Will this close in the next 60 days?' Store results in a shared spreadsheet. Capture close dates and loss reasons after each outcome. Skip all dashboard sections, pipeline hygiene automation, pipeline intelligence, and AI patterns. Focus only on understanding which deals close, why, and whether existing customers would buy again. |

| Start-up, product-market fit | Build a single-page sales manager dashboard showing pipeline by stage and stale deals only. Implement stale-deal detection on your Qualification stage; start with a 10-14-day threshold and calibrate to your historical cycle time within 30 days (the skill's recommended ranges at lines 166-174 are practice-based, not prescriptive). Run weekly manual pipeline reviews with your sales manager; document stage, close date, next step, and activity per deal. Calculate your historical stage-to-close conversion rates from closed deals in the past 12 months. Skip executive dashboard, pipeline intelligence, big deal alerts, and forecast accuracy reporting. Run Layer 1 (stage design from your data), basic Layer 2 (manager dashboard only), basic Layer 3 (stale-deal detection only), and Layer 4 (weekly signal review in meetings). |

| Scale-up | Run the full skill as written. All four visibility layers, all dashboard audiences, complete hygiene automation (stale deals, overdue close dates, pipeline quality scoring), pipeline intelligence signals, forecast accuracy tracking, and AI-powered patterns. Layer in AI-Powered Deal Scoring (Section: AI and Automation Patterns) in month 2 once hygiene is stable. Adopt async Slack-native deal review (Pattern 3, same section) to replace synchronous weekly calls. Calibrate all thresholds to your historical win rate and sales cycle length, not to published benchmarks. |

| Enterprise | Enterprise organisations operate multiple business units, regions or product lines with separate P&L ownership, and pipeline visibility scales to a federated architecture. Each business unit runs the full skill with its own stage definitions (unit A: 5-stage B2B model, unit B: 8-stage enterprise model; both report their historical conversion rates). Deploy a consolidated executive dashboard (one layer above unit dashboards) showing pipeline by business unit, cross-unit pipeline coverage ratio (aggregate pipeline against aggregate bookings target), win rate variance across units (flags execution inconsistency), and big-deal escalation matrix (enterprise deals over threshold route through group CFO or steering committee). Implement formal deal quality governance: enterprise deals undergo an additional review gate (legal, finance, product) before deal registration; board-level deals are approved by CRO and CFO before commit. |

Skip before product-market fit: Dashboard Architecture; Pipeline Hygiene Automation; Pipeline Intelligence; AI and Automation Patterns; Forecast Accuracy Reporting.

The Visibility Stack

Pipeline visibility has four layers. Most teams only build the first two and wonder why their forecast is wrong.

### Layer 1: Pipeline Structure (Foundation)

What stages exist, what they mean, and what data is required at each.

**Stage design principles**:

1. Each stage has a **verifiable exit criterion** (not "rep feels good about it")

2. Stages represent **buyer actions**, not seller activities

3. 5-8 stages maximum (more creates friction and reduces compliance)

4. Probability increases monotonically (if it doesn't, stages are wrong)

**Recommended B2B SaaS stages**:

| Stage | Probability | What It Means |

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

> *Common SaaS defaults. Replace with your historical stage-to-close conversion rates within 90 days of implementation.*

| Discovery | 10% | Initial meeting done; pain confirmed |

| Qualification | 20% | Budget, timeline, decision process, champion identified |

| Solution Design | 40% | Requirements documented; demo/POC delivered |

| Proposal | 60% | Proposal delivered; pricing discussed |

| Negotiation | 75% | Verbal yes; contract in legal |

| Closed Won | 100% | Signed |

| Closed Lost | 0% | Documented loss reason |

> *Common B2B SaaS defaults. These probabilities carry no formal research backing. Replace with your historical stage-to-close conversion rates (measure actual close rate per stage from closed deals in the past 12 months) within 90 days of implementation. See "Building Your Historical Baseline" below for how to derive these.*

### Building Your Historical Baseline

Within your first 90 days, calculate your own stage probabilities from closed opportunities:

1. Query all deals closed in the past 12 months (Won and Lost)

2. For each stage, calculate: Deals closed from this stage / Total deals that entered this stage

3. Plot these as your probability curve; use these numbers instead of the common defaults above

4. Update quarterly as your pipeline matures; probability movements signal changes in sales execution or buyer behavior

### Layer 2: Pipeline Reporting (What most teams stop at)

Reports and dashboards that show pipeline state.

###