Revenue Operations skill

Revenue Operations

Workflow

1. Revenue Funnel Definitions

Align ALL teams on the same definitions:

Conversion benchmarks — segment before you compare. Public "B2B SaaS averages" are nearly useless because conversion is dominated by motion (PLG vs sales-led), ACV, channel (inbound vs outbound), ICP fit, and market maturity. Treat the table below as order-of-magnitude priors, not targets — then compute your own baselines (next).

Outbound-sourced opps usually convert at a higher win rate but lower top-of-funnel volume than inbound; PLG replaces MQL with PQL (product-qualified lead — hit an activation/usage threshold) and SAL with a sales-assist trigger.

Calculate your own baseline (do this before setting any target):

-- 90-day trailing stage-to-stage conversion, segmented by motion + source
-- (assumes an opportunities table with stage-entry timestamps and a deals/leads source)
WITH cohort AS (
  SELECT
    o.opportunity_id,
    o.acv_band,                         -- '<5k' | '5-50k' | '>50k'
    o.source_channel,                   -- 'plg' | 'inbound' | 'outbound'
    MAX(CASE WHEN h.stage = 'SQL'          THEN 1 ELSE 0 END) AS reached_sql,
    MAX(CASE WHEN h.stage = 'Opportunity'  THEN 1 ELSE 0 END) AS reached_opp,
    MAX(CASE WHEN h.stage = 'Closed Won'   THEN 1 ELSE 0 END) AS reached_won
  FROM opportunities o
  JOIN stage_history h USING (opportunity_id)
  WHERE o.created_at >= CURRENT_DATE - INTERVAL '90 days'
  GROUP BY 1, 2, 3
)
SELECT
  acv_band, source_channel,
  COUNT(*)                                                AS opps,
  ROUND(100.0 * SUM(reached_opp) / NULLIF(SUM(reached_sql), 0), 1) AS sql_to_opp_pct,
  ROUND(100.0 * SUM(reached_won) / NULLIF(SUM(reached_opp), 0), 1) AS opp_to_won_pct
FROM cohort
GROUP BY 1, 2
ORDER BY 1, 2;

Recompute quarterly, watch the trend (your own series) over the absolute number, and segment any rate you report by ACV band + source. A single blended funnel rate hides the segments that actually need fixing.

2. Forecasting Models

Weighted pipeline (standard):

Deal forecast = Deal value × Stage probability
Total forecast = Σ all deal forecasts

Historical conversion (more accurate):

Expected revenue = Current stage count × Historical stage-to-close rate × Average deal size

Bottoms-up / category roll-up (most accurate, most work):

Rep forecast = Commit + (Best case × historical best-case close rate) + (Pipeline × historical pipeline-create-to-close rate)
Team forecast = Σ rep forecasts × per-rep calibration multiplier (see below)

Use your own historical close rates per category, not 0.5 / 0.15 magic numbers — derive them from the last 2–4 quarters by rep and segment.

Define forecast categories explicitly (the #1 cause of bad forecasts is undefined categories, not bad reps):

Forecast hygiene signals to inspect weekly (per deal):

• Close-date push rate — count of times close date moved out. ≥2 pushes ⇒ deal is at risk regardless of category.
• Stage aging — days in current stage vs your segment median. Flag deals >1.5× median (going stale).
• Next-step quality — is there a scheduled, mutual next step (meeting/MAP milestone), not "follow up"? No next step ⇒ not a commit.
• Coverage gap — Commit + weighted pipeline vs target; if short, the fix is new pipeline this period, not pressure on existing deals.

-- Deals that should be challenged: pushed twice OR stale OR no next step
SELECT opportunity_id, owner, amount, stage, close_date, push_count,
       date_part('day', now() - stage_entered_at) AS days_in_stage,
       next_step_at
FROM opportunities
WHERE forecast_category IN ('commit','best_case')
  AND ( push_count >= 2
     OR date_part('day', now() - stage_entered_at) >
        1.5 * (SELECT percentile_cont(0.5) WITHIN GROUP (ORDER BY days_in_stage)
               FROM stage_durations s WHERE s.stage = opportunities.stage)
     OR next_step_at IS NULL )
ORDER BY amount DESC;

Forecast accuracy tracking:

Track both accuracy (|forecast − actual| / actual) and bias (signed, to catch consistent over/under-calling). Target ±10% accuracy and near-zero average bias. A persistent miss is a diagnosis to run, not a verdict on reps — check, in order:

1. Stage/category definitions — are "commit" and "best case" applied consistently across reps?
2. CRM hygiene — stale close dates, missing next steps, amounts not updated.
3. Slippage / push rate — are deals real but landing a period late? (fix close-date discipline, not the number).
4. Pipeline creation — was enough new pipeline created early enough to hit coverage?
5. Seasonality / deal-desk & legal / procurement delays — late-stage drag outside the rep's control.
6. Product or pricing changes, churn/expansion timing — shifts that move close dates.
7. Rep calibration — only after the above: some reps are reliably optimistic, others sandbag. Build a per-rep calibration multiplier from their trailing 4-quarter forecast-vs-actual, coach with the data, and apply the multiplier in the roll-up rather than assuming intent.

3. GTM Alignment

Weekly GTM standup (30 min):

• Marketing: pipeline contribution this week, upcoming campaigns
• Sales: deal updates, blockers, competitive intel
• CS: churn risks, expansion opportunities, product feedback
• RevOps: funnel health, forecast update, process issues

Monthly revenue review (60 min):

• Funnel conversion rates vs targets
• Pipeline coverage (3x target = healthy)
• Win rate trends by segment, source, rep
• Churn and expansion ARR
• Forecast vs actual analysis

4. Quota & Territory Planning

Capacity model (build this bottoms-up — Company target / #AEs × 1.15 is too naive: it ignores ramp state, attainment distribution, attrition, overlays, and the new-logo vs expansion split). Plan capacity and quota separately, then reconcile.

Order of operations:

1. Split the number. Board target → new-logo bookings + expansion bookings (NRR-driven, often owned by CS/AM, not AEs). Only assign the new-logo portion (plus any AE-owned expansion) to AE quota.
2. Quota per ramped AE is set so that expected attainment, not full quota, covers the target. If reps historically attain ~70% on average, gross-up the quota: quota = (target per head) / expected_attainment. Carry quota above the number on purpose (typical aggregate over-assignment 15–25%) so that median attainment still hits plan.
3. Convert headcount to ramped-equivalents using the ramp curve, not a raw count — a rep in month 3 is ~0.25 of a ramped AE.
4. Discount for attrition over the period (ramped capacity lost mid-year is rarely backfilled in time).
5. Apply seasonality — distribute quota by historical bookings-by-month, not 1/12 per month.
6. Check coverage — pipeline needed = quota / weighted win rate; if marketing+outbound can't create it, the quota is fiction.

# Worked example — capacity in "ramped-AE equivalents"
new_logo_target          = $12.0M                  # AE-owned slice of the board number
expected_attainment      = 0.72                     # trailing median, NOT 100%
attrition_haircut        = 0.90                     # ~10% ramped capacity lost in-year
ramp_curve (% of full quota by tenure month) = {1-2:0, 3:0.25, 4:0.50, 5:0.75, 6+:1.0}

# Sum ramped-equivalents across the roster (each AE weighted by their month in-period):
ramped_equiv = Σ ramp_curve[ae.tenure_month]        # e.g. 9 fully-ramped + 4 ramping = 10.0 equiv
effective_capacity_heads = ramped_equiv × attrition_haircut          # 10.0 × 0.90 = 9.0

# Quota grossed-up for expected attainment, then over-assigned for safety:
quota_per_ramped_AE = (new_logo_target / effective_capacity_heads) / expected_attainment
                    = ($12.0M / 9.0) / 0.72  ≈  $1.85M

aggregate_quota      = quota_per_ramped_AE × ramped_equiv  ≈ $18.5M   # ~1.5× the $12M target
expected_bookings    = aggregate_quota × expected_attainment ≈ $13.3M  # cushion above $12M target

Territory design principles:

• Balance by potential, not count — score each territory's TAM (target accounts × ICP fit × whitespace/expansion headroom) and aim for similar expected pipeline, not equal account counts.
• Account for existing relationships — don't reassign active opportunities; carve around in-flight deals.
• Minimize disruption from churn — keep at-risk renewals with the owning rep/CSM through the renewal.
• Geographic/segment clustering only where it reduces real friction (timezone, language, field travel); for inside sales, cluster by vertical or persona instead.
• Review quarterly — territories drift as markets, headcount, and product change; rebalance with the TAM score, not gut feel.

Ramp schedule:

5. Handoff Processes

Marketing → SDR (MQL handoff):

Trigger: Lead score ≥ MQL threshold
Data passed: Lead source, content consumed, pages visited, company info, score breakdown
SDR action: Research (5 min) → personalized outreach within 1 hour
Feedback loop: SDR marks SAL accepted/rejected with reason → Marketing adjusts scoring

SDR → AE (SAL handoff):

Trigger: Discovery call completed, BANT confirmed
Data passed: Pain points, budget range, timeline, decision process, competitors
AE action: Review notes → demo prep → schedule demo within 3 days
Handoff format: Warm intro email (SDR introduces AE + summarizes conversation)

AE → CS (Closed Won handoff):

Trigger: Contract signed
Data passed: Contract terms, use case, success criteria, stakeholders, technical requirements
CS action: Onboarding kickoff within 48 hours
Handoff format: Internal doc + joint call (AE + CS + customer)

6. Tech Stack Audit

Core RevOps stack (mid-2026 naming — verify current product names/pricing at each vendor's site before standardizing):

AI/data hygiene & privacy (2026):

• AI-assisted CRM hygiene — conversation-intelligence and CRM-AI features (Gong, Breeze, Einstein, Clari) auto-fill next steps, contact roles, and competitor mentions from calls/emails. Treat them as assistive: spot-check accuracy and keep a human owner for stage/forecast-category changes.
• Privacy/consent — enrichment and intent data are subject to GDPR/CCPA and the EU AI Act. Keep a lawful basis + suppression list for enriched contacts, honor opt-outs across all tools, and don't sync sensitive personal data into systems that don't need it.

Audit checklist:

• One clear system of record per object (account, contact, opportunity); no duplicate sources of truth
• Data flows are integrated/automated (or warehouse-synced via reverse ETL) — minimal manual re-entry between systems
• Reporting pulls from one governed dataset (not multiple conflicting dashboards)
• Routing + speed-to-lead automation actually enforces the SLA (measure, don't assume)
• Enrichment/intent credit burn is metered and capped to ICP-fit records
• Consent/suppression is honored across every tool that stores contact data
• Tooling cost is benchmarked against the right denominator, not a flat % of ARR. A flat "<15% of ARR" rule is misleading: early-stage teams run high (small ARR base), efficient scale-ups land far lower, and enterprise stacks vary widely. Evaluate stack spend against gross margin, S&M efficiency (CAC payback, magic number), headcount leverage (ARR per GTM head), and measurable pipeline impact — and kill tools with no attributable usage or pipeline contribution.

7. RevOps Metrics Dashboard

Metric definitions (be explicit — most disagreements are definitional, not numeric):

Reference SQL (Postgres/warehouse flavor — adapt table/column names):

-- (a) Pipeline coverage for the current quarter (weighted), by owner
SELECT o.owner,
       SUM(o.amount)                                   AS raw_pipeline,
       SUM(o.amount * s.stage_win_prob)                AS weighted_pipeline,
       q.quota,
       ROUND(SUM(o.amount * s.stage_win_prob) / NULLIF(q.quota, 0), 2) AS weighted_coverage_x
FROM opportunities o
JOIN stage_probabilities s ON s.stage = o.stage          -- your own historical win prob per stage
JOIN quotas q ON q.owner = o.owner AND q.period = date_trunc('quarter', CURRENT_DATE)
WHERE o.is_open
  AND o.close_date >= date_trunc('quarter', CURRENT_DATE)
  AND o.close_date <  date_trunc('quarter', CURRENT_DATE) + INTERVAL '3 months'
GROUP BY o.owner, q.quota;

-- (b) Sales velocity + median cycle for last 90 days of closed-won, by segment
SELECT acv_band,
       COUNT(*) FILTER (WHERE stage = 'Closed Won')                         AS won,
       percentile_cont(0.5) WITHIN GROUP (
         ORDER BY (close_date - created_at)) FILTER (WHERE stage = 'Closed Won') AS median_cycle_days,
       AVG(amount) FILTER (WHERE stage = 'Closed Won')                      AS avg_deal,
       ROUND(100.0 * COUNT(*) FILTER (WHERE stage = 'Closed Won')
             / NULLIF(COUNT(*) FILTER (WHERE stage IN ('Closed Won','Closed Lost')), 0), 1) AS win_rate_pct
FROM opportunities
WHERE close_date >= CURRENT_DATE - INTERVAL '90 days'
GROUP BY acv_band;

-- (c) NRR / GRR for a fixed starting cohort over the trailing 12 months
WITH base AS (
  SELECT account_id, arr AS start_arr
  FROM account_arr_snapshot
  WHERE snapshot_date = CURRENT_DATE - INTERVAL '12 months'
),
now_arr AS (
  SELECT account_id, arr AS end_arr
  FROM account_arr_snapshot
  WHERE snapshot_date = CURRENT_DATE
)
SELECT
  ROUND(100.0 * SUM(LEAST(COALESCE(n.end_arr,0), b.start_arr)) / NULLIF(SUM(b.start_arr),0), 1) AS grr_pct,
  ROUND(100.0 * SUM(COALESCE(n.end_arr,0))                    / NULLIF(SUM(b.start_arr),0), 1) AS nrr_pct
FROM base b
LEFT JOIN now_arr n USING (account_id);   -- accounts that fully churned have no row in now_arr

-- (d) New-pipeline / bookings sourced by channel (attribution), last quarter
SELECT source_channel,
       COUNT(*)                                              AS opps_created,
       SUM(amount) FILTER (WHERE stage = 'Closed Won')       AS won_arr,
       SUM(amount) FILTER (WHERE is_open)                    AS open_pipeline
FROM opportunities
WHERE created_at >= date_trunc('quarter', CURRENT_DATE) - INTERVAL '3 months'
  AND created_at <  date_trunc('quarter', CURRENT_DATE)
GROUP BY source_channel
ORDER BY won_arr DESC NULLS LAST;

Govern these in one place (warehouse + dbt, or your BI semantic layer) so every team reads the same number; conflicting dashboards are the most common RevOps failure mode.

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Related skills: for CRM data model, object hygiene, and automation see crm-operations; for top-of-funnel demand generation and channel strategy see customer-acquisition.