pipeline-strategy

When to use

• Pipeline stages are named but have no exit criteria — reps move deals on gut and forecasts ride on opinion, not evidence.
• Coverage is being computed from a flat multiple ("3x quota") without per-stage conversion rates, so the multiple flatters or punishes the team at random.
• A board ask names "why did pipeline coverage look fine and the quarter still missed?" — a leak is hiding inside a healthy-looking total.

Do NOT use to qualify a single deal (route to deal-qualification-meddic), construct the forecast call from a locked pipeline (route to forecast-accuracy), or configure CRM stage fields in a specific vendor tool (out of scope — this skill is strategy, not tooling).

Cognition cluster

• Mental model 6 — Theory of constraints. A pipeline has one binding stage at any time; rates upstream of the constraint are inventory that never ships, rates downstream cannot exceed the constraint. Find the constraint before changing anything else. See docs/contracts/mental-models.md § 6.
• Mental model 16 — Leading vs. lagging indicators. Stage-to-stage conversion is leading; closed-won is lagging. A coverage call built on lagging signals can only confirm the miss after it lands. See mental-models.md § 16.
• Mental model 3 — Pareto (80/20). ~20 % of segment × stage cells carry ~80 % of revenue risk. Coverage uniformly applied across cells is theatre; coverage weighted by cell-level conversion is reasoning. See mental-models.md § 3.
• Context-spine — product + customer-segment + channel-stage. Read the product slot for what is actually sellable this quarter, the customer-segment slot for which segments belong in pipeline (and which are pre-pipeline education), and the channel-stage slot for where each segment enters. See context-spine.

Procedure

Step 0: Inspect — inventory the current pipeline shape

Pull stage counts, $ value, age in stage, and stage-to-stage conversion for the trailing two quarters. Inspect whether each stage has a written exit criterion; if not, write one now (one bullet per stage, falsifiable). A pipeline without exit criteria cannot be audited.

Step 1: Lock stage definitions with exit criteria

Each stage gets three lines:

1. Definition — what the deal looks like in this stage (one sentence).
2. Entry trigger — the buyer event that moves a deal in (not a rep action).
3. Exit criterion — the artefact or signal that proves the deal earned the next stage (one bullet, falsifiable; "meeting booked" is not falsifiable, "economic buyer named and confirmed" is).

Reject any stage whose exit criterion is a rep activity ("call made") rather than a buyer signal ("buyer confirmed budget owner").

Step 2: Compute per-stage conversion rates with bands

For each stage transition, compute the trailing-quarter rate and a 95 % confidence band. Segment by customer-segment (and channel-stage if the channel mix changed). Report the band, not the point — a 30 % rate on 12 deals and a 30 % rate on 300 deals are different signals.

Step 3: Find the binding constraint

The constraint is the stage whose conversion rate is furthest below its segment-historical median, weighted by $ value flowing through it. Add deals upstream of any other stage and watch them queue; add deals upstream of the constraint and they queue twice as fast. A pipeline-coverage call that ignores the constraint multiplies inventory the team cannot ship.

Step 4: Compute coverage cell by cell, not by total

Coverage = (pipeline $ at stage s) ÷ (target closed-won $ in window) ÷ (cumulative conversion from s to closed-won). Compute per segment × stage cell. A 3× total can hide a 0.8× cell — the 0.8× cell is the quarter's risk.

Step 5: Name three leaks with falsifiable hypotheses

For the three lowest coverage cells (or the three steepest conversion-rate drops vs trailing-quarter median), write one sentence per leak: "<cell> leaks at <stage> because <cause>; falsified if <test> shows <expected signal>." If a cause is not testable in under two weeks, the hypothesis is not yet sharp enough — sharpen before recommending a fix.

Step 6: Hand back

Hand the locked stages, the per-cell coverage table, and the three leak hypotheses to forecast-accuracy for commit / best-case categorisation, and to deal-qualification-meddic when the leak is upstream qualification, not late-stage execution.

Related Skills

WHEN to use this

• Designing or auditing pipeline stages and per-stage rates.
• Computing coverage by segment × stage instead of by total.

WHEN NOT to use this

• Single-deal qualification — route to deal-qualification-meddic.
• Forecast call construction from a locked pipeline — route to forecast-accuracy.
• Product-led conversion-funnel diagnosis (signup → activation → paid) — route to funnel-analysis.

When the agent should load this

• "Tighten our pipeline stages — exit criteria are vibes."
• "Coverage looks 3× and we still missed. Where is the leak?"
• "Audit per-stage conversion by segment."
• "Welche Stage ist das Bottleneck im Quartal?"

Output

1. stage-definitions.md — one block per stage: definition · entry trigger · exit criterion (buyer signal, falsifiable).
2. coverage-by-cell.md — table of segment × stage cells with $ pipeline, cumulative conversion, and computed coverage. Cells under 1× flagged.
3. leak-hypotheses.md — three leaks with falsifiable test + expected signal + 2-week deadline.

Gotcha

• A pipeline with exit criteria written as rep activities ("call made") is unauditable — reps move deals on activity, not on buyer signal, and forecasts inherit the noise.
• Total-pipeline coverage is the wrong unit. A 3× total made of 5× early-stage and 0.5× late-stage will miss the quarter even though the dashboard looks healthy.
• Per-stage conversion rates without confidence bands are gossip dressed as evidence. Twelve deals give you a band so wide the rate is uninformative.

Do NOT

• Do NOT invent stages to flatter the dashboard. Each stage must have a buyer signal earning the transition.
• Do NOT compare the quarter's coverage to a fixed historical multiple if segment mix changed — recompute per cell.
• Do NOT recommend a fix to a leak before naming the falsifiable test that proves the cause.

Runnable example

Mid-market SaaS, total coverage 3.1×, quarter missed by 18 %.

• Stages with exit criteria — Discovery → Qualified (economic buyer named) → Proposal (pricing in writing) → Negotiation (terms in redline) → Won.
• Per-cell coverage — Mid-Market × Proposal: 0.7×. Mid-Market × Discovery: 6.4×. Enterprise × Negotiation: 2.1×.
• Constraint — Proposal → Negotiation conversion 22 % vs 41 % trailing-quarter median (band 14–31 %). Constraint is Proposal exit, not pipeline volume.
• Leaks — (1) Mid-Market proposals stall at pricing-page review; falsified if a pre-proposal pricing-walkthrough call lifts Proposal → Negotiation to 35 %+ within four weeks. (2) Enterprise Negotiation drags > 60 days; falsified if procurement-checklist shipped at Proposal stage cuts median age by 20 days. (3) Discovery overflow is unqualified — route to deal-qualification-meddic.
• Hand-off — coverage table + leaks → forecast-accuracy for commit / best-case rebuild on the corrected denominator.