Sales Pipeline

Sales Pipeline — A Disciplined CRM Out of a Flat File

You run a small team's pipeline from a CSV, a markdown table, or a Notion DB — no Salesforce, no RevOps. Your one rule: the forecast is what the gated stages and current win rates say, not what the rep hopes. Happy-ears deals do not inflate the number on your watch.

This skill does four jobs and refuses to drift into the siblings that own the rest: (1) define a small set of gated stages with objective exit criteria, (2) keep every open deal hygienic (next step + close date + last-touch, flag the stale), (3) run a weekly follow-up sweep, and (4) roll a defensible weighted forecast with a coverage ratio. The heavy statistical modeling, the lead sourcing, the outbound copy, the proposal, the post-close handoff — each belongs to a sibling. Route, do not improvise their job.

When to use / when NOT

Use when the operator already has a list of deals and wants stages, probabilities, hygiene, a weekly sweep, coverage vs quota, or pipeline velocity — or when they describe the symptom "pipeline looks full but nothing closes."

Do NOT use when the ask is one of these — route to the owner:

The ask	Owner	Why it is not here
3-scenario revenue model, seasonality, cohort/time-series projection	forecasting	That is the statistical/scenario layer. This skill only does the simple stage-weighted roll-up that falls out of the pipeline.
Find / scrape / qualify NEW prospect companies to add	lead-gen	This skill operates on deals that already exist in the list.
Write the cold email or follow-up sequence to contact a prospect	cold-outreach	This skill schedules the next step; it does not write the words.
Write the proposal / quote / SOW for a qualified deal	proposals	A stage transition is not a document.
Post-close kickoff, account setup, onboarding	client-onboarding	The moment a deal is Closed-Won it leaves this skill.
Generic dashboard / charting of arbitrary metrics, KPI tree design	dashboard / kpi-framework	This skill emits pipeline numbers, not a charting layer.

The boundary worth memorizing: forecasting owns the math models; sales-pipeline owns the operational CRM. When the request needs Monte Carlo, regression, or scenario trees, hand it over.

The artifact first — the deal-record schema

Everything below lints against one table. Define it before anything else, because hygiene, the sweep, and the forecast are all just operations on these columns. One deal = one row. Required columns:

column	meaning	rule
id	stable deal id	unique, never reused
company	the account	—
value	deal value (one currency, document which)	number, no symbols
stage	current stage	from the allowed set (next section)
win_prob	stage win-probability as a decimal	0–1, owned by the stage, not the rep
weighted_value	value × win_prob	must equal the product (lint enforces it)
close_date	expected close (ISO YYYY-MM-DD)	required on every open deal
next_step	the next concrete action + its date	required on every open deal
last_touch	date of last real activity (ISO)	required on every open deal
owner	who owns the deal	—
forecast_category	Pipeline / Best Case / Commit / Closed / Omitted	a category, not a stage

One good open row (CSV):

id,company,value,stage,win_prob,weighted_value,close_date,next_step,last_touch,owner,forecast_category
D-104,Acme,40000,Discovery,0.30,12000,2026-07-15,"2026-06-09 demo with VP Eng",2026-05-30,Dana,Best Case

Hard rule, no exceptions: no open deal may be missing next_step, close_date, or last_touch. A deal with no next step is not a deal, it is a wish. The sweep and the forecast both treat a missing field as a defect, not a blank.

The stage model — small, gated, stage-owned probability

Default to six stages. Five stages with clear exit criteria beat nine stages with none. Each stage advances only on an objective, verifiable buyer action — never on rep optimism.

stage	default win_prob	exit criterion (the verifiable buyer action that advances it)
Prospecting	0.05	Buyer agreed to a first real conversation (meeting on the calendar).
Qualification	0.10	BANT/MEDDIC documented "yes" — budget, authority, need, timeline confirmed.
Discovery	0.30	Buyer confirmed the problem + success criteria; you have the buying process and review layers.
Proposal/Demo	0.40	Proposal or demo delivered and acknowledged; buyer engaged on it.
Negotiation	0.65	Terms/price under active discussion; verbal intent + a mutual close plan.
Closed	1.0 / 0.0	Signed (Won) or formally lost (Lost).

Rules that make the table hold:

• Probability belongs to the stage, not the deal. A rep who hand-sets one deal to 90% in Discovery is sandbagging or happy-ears; both poison the forecast. The stage carries the number, uniformly. (Why: it removes the single biggest source of forecast inflation in small teams.)
• No stage advances without its exit criterion met AND a calendared next step. "If you don't know the criteria, timeline, review layers and legal steps, the close date is a guess."
• Negotiation 50–80% is a band, not a default 80. Pick within the band on real signal (legal in motion → higher); never max it out reflexively.

Qualification gate: run BANT as a ~60-second screen for small deals; switch to MEDDIC/SPICED above ~$25K ACV. The full six-stage playbook — every exit criterion, required fields per stage, the BANT-vs-MEDDIC pillars, and the forecast-category mapping — lives in references/stage-playbook.md.

Deal hygiene — the required-field gate and the stale rules

Roughly 40–60% of B2B CRM pipeline is stale (no progression in 30+ days). A forecast built on a stale list is fiction. Apply an activity-decay model on every open deal:

condition	action
Missing next_step / close_date / last_touch	Defect — flag, do not forecast until fixed.
No touch for 14+ days	Halve the deal's effective weight in the forecast.
No touch for 30+ days	Exclude from coverage entirely (treat as stale, not pipeline).
Sitting > 1.5× the average time-in-stage	Flag for review — it is stuck.
close_date already in the past, deal still open	Flag — the date is a lie; re-set or disqualify.

Bad → Good on a single row:

BAD  (open, but a wish dressed as a deal — fails the gate):
  D-220,Globex,60000,Proposal,0.80,48000,,,,Sam,Commit
  ^ win_prob hand-set to 0.80 in Proposal, no close_date, no next_step,
    no last_touch, Commit category on zero evidence.

GOOD (gated, hygienic, stage-owned probability):
  D-220,Globex,60000,Proposal,0.40,24000,2026-08-01,"2026-06-12 send revised SOW",2026-06-02,Sam,Best Case
  ^ win_prob = stage default, weighted_value recomputed, dated next step,
    fresh last_touch, category demoted to match the evidence.

The weekly follow-up sweep

Build hygiene into a ~45-minute WEEKLY pipeline review, not a quarterly cleanup — stale deals compound fast and a quarterly purge always finds the rot too late. The sweep is a fixed checklist; run it and emit a prioritized follow-up list:

• Every open deal has next_step + close_date + last_touch — list the defects first.
• Stale: no touch 14+ days (halve) and 30+ days (drop) — surface both buckets.
• close_date in the past on an open deal — re-set or disqualify.
• Slipped: close_date pushed two weeks in a row — flag; two slips is a pattern, not noise.
• Stuck: time-in-stage > 1.5× the average for that stage.
• Output: a ranked follow-up list (highest weighted_value × most stale first), each with the one concrete next action and its date.

Track coverage as a 4-week rolling trend, not a single snapshot. Two consecutive weeks of declining coverage with no closes is a red flag — escalate, do not wait for quarter-end.

The forecast roll-up

Three numbers fall out of a clean pipeline. Compute them; do not model beyond them (that is forecasting).

Weighted forecast = Σ over open deals of value × stage win_prob. Apply the stale decay first (halve at 14d, drop at 30d) so the number reflects live pipeline, not the wish list.

Pipeline coverage ratio = Total Qualified Pipeline Value ÷ Revenue Target. Read it against the segment, because win rates differ:

segment	ACV / win rate	coverage min	coverage target
Enterprise	$100K+ / 15–20% win	5×	6–7×
Commercial	$25–100K / 20–30%	3.5×	4–5×
SMB	<$25K / 30–40%	2.5×	3–4×

Pipeline velocity = (Open opps × Avg deal size × Win rate) ÷ Sales-cycle length (days) → dollars/day. Cycle length has the most leverage: a ~20% cut in cycle length lifts velocity ~25%.

Forecast categories are not stages — map them separately: Pipeline / Best Case / Commit / Closed / Omitted. As a sanity expectation, roughly ~25% of "Pipeline", a third-to-half of "Best Case", and near-all of "Commit" typically lands in-quarter.

The current-win-rate caveat — non-negotiable. 2025 benchmarks moved against sellers: B2B win rates fell ~21% → ~18% and cycles lengthened ~12% YoY. Forecast with the current ~18% win rate and your real cycle length, never last year's optimistic numbers. A 24-month-old win rate is the quietest way to over-forecast.

Worked examples — weighted forecast on a 5-deal list, coverage by segment, velocity, and the exact column contract verify.sh enforces — are in references/forecasting-math.md.

Anti-patterns

Anti-pattern	Why it fails	Do instead
Rep hand-sets win_prob per deal	Happy-ears/sandbagging inflate the forecast; the number stops being comparable	Probability is owned by the stage, applied uniformly
Stages with no exit criteria	"Discovery" becomes a place deals go to die; no one can verify progression	Every stage gates on an objective buyer action
Counting stale deals (30+ days no touch) in coverage	40–60% of pipeline is stale; coverage looks healthy while nothing moves	Drop 30d+ from coverage, halve 14d+, flag stuck deals
Forecasting off list price, not weighted value	Treats a Qualification deal like a signed one; massive over-forecast	value × stage win_prob, decayed for staleness
Quarterly cleanup instead of weekly	Rot is found three months too late; the quarter is already lost	45-min weekly sweep; 4-week rolling coverage trend
Using a 24-month-old win rate	2025 win rates fell to ~18%, cycles +12%; old numbers over-forecast	Use the current win rate and your real cycle length
forecast_category set to "Commit" on a Discovery deal	Category drifts from evidence; the commit number becomes a fantasy	Category must match documented evidence, not hope
Nine micro-stages "for granularity"	More stages, less discipline; reps can't tell them apart	Six gated stages beat nine ungated ones
Open deal with no next_step	It is a wish, not a deal; it silently ages into staleness	No next step → it is a defect, surface it in the sweep

References and siblings

• references/stage-playbook.md — the six stages in full, exit criteria, required fields per stage, BANT-vs-MEDDIC, forecast-category mapping.
• references/forecasting-math.md — worked weighted-forecast / coverage / velocity examples and the verify column contract.

Siblings that own the adjacent jobs — route to them by name: ../forecasting/SKILL.md (the math models), ../lead-gen/SKILL.md (sourcing), ../cold-outreach/SKILL.md (the outbound copy), ../proposals/SKILL.md (the quote/SOW), ../client-onboarding/SKILL.md (post-close). For wiring the list to Sheets/Notion or bulk edits, ../spreadsheet-ops/SKILL.md.

To lint a pipeline file you produced — required columns, required fields on open deals, stage names in the allowed set, win_prob in range, weighted_value == value × win_prob, and a coverage line present — run scripts/verify.sh path/to/pipeline.csv (read-only; a clean or empty file exits 0).