CRM Operations skill

CRM Operations

Operational playbook for configuring and maintaining a B2B CRM. The generic patterns below are followed by platform-specific recipes for HubSpot, Salesforce, and Pipedrive. For the revenue metrics layer (ARR/NRR, funnel conversion, pipeline coverage, board reporting) that sits on top of this data, see the sibling revenue-operations skill — this skill owns the CRM configuration; that one owns the analysis.

Workflow

1. Property Architecture

Core contact properties:

Core company properties:

Naming convention (platform-aware — do NOT blindly apply snake_case everywhere):

Rules: pick the API/internal name once — it is permanent in HubSpot and Salesforce and renaming requires re-mapping every integration. Keep labels human-readable Title Case for reps; reserve snake_case for HubSpot internal names and integration payload keys only. Never put a unit or example value in a field name (revenue_usd is fine; revenue_50k is not).

2. Pipeline Design

SaaS sales pipeline:

Required fields per stage transition:

• New → Discovery: pain_point, budget_range, timeline
• Discovery → Demo: champion_name, decision_maker, competitor
• Demo → Proposal: technical_validated = true
• Proposal → Negotiation: proposal_sent_date, contract_value
• Any → Closed Lost: loss_reason (required, dropdown)

3. Lead Scoring

Two-axis scoring: Fit (demographic) + Engagement (behavioral)

Fit scoring (0-50 points):

Engagement scoring (0-50 points, decays 50% per 30 days inactive):

Thresholds — calibrate, never hardcode. The point values and the example 70 = MQL line below are starting placeholders, not universals. A threshold copied from a blog post will flood sales with junk or starve them of leads. Calibrate against your own data:

1. Backtest before launch. Pull the last 6–12 months of closed deals. Compute the score each lead would have had at handoff. Plot conversion-to-SQL (and to Closed Won) by score band. Set MQL at the band where conversion lifts sharply above baseline — that knee, not a round number, is your threshold.
2. Segment the threshold. A single global cutoff is wrong when sources convert differently. Set separate MQL thresholds (or separate models) by motion: inbound demo-request, content download, outbound-sourced, PLG/product-signup, partner referral. A product-qualified lead (PQL: hit an in-app activation event) often outranks any marketing score and should route directly.
3. Govern negative scoring explicitly. Decay (e.g., engagement halves per 30 days inactive) and disqualifiers (competitor domain, student/personal email, unsubscribed, job applicant) must be owned, documented, and reviewed — silent negative rules are the #1 cause of "good leads never reached sales" incidents.
4. QA the false positives. Each week, sample 10–20 leads that crossed MQL but were rejected by sales (HubSpot: "disqualified"; Salesforce: "Unqualified" status). Tag the reason; if one signal dominates rejections, down-weight it.
5. Re-calibrate quarterly. Conversion rates drift with ICP, pricing, and seasonality. Re-run the backtest each quarter and on any major scoring-rule change; version the model and announce changes to sales.

Example starting bands (replace with your calibrated values):

Modern alternative: HubSpot predictive (AI) scoring and Salesforce Einstein Lead Scoring fit a model on your closed data instead of hand-tuned points. Use them when you have ≥ ~1,000 scored leads with enough won/lost outcomes; keep a transparent manual model as a fallback and for explainability. Validate any AI score against the same backtest before letting it auto-route.

4. Lead Routing

Round-robin with rules (MQL_THRESHOLD is your calibrated value from §3, not a literal 70):

IF lead_score >= MQL_THRESHOLD AND arr_potential >= ENT_CUTOFF:
  → Route to enterprise AE (named-account match first, else round-robin within ENT pod)
ELIF lead_score >= MQL_THRESHOLD AND arr_potential < ENT_CUTOFF:
  → Route to SMB AE (round-robin, skip reps who are OOO / at capacity)
ELIF MQL_THRESHOLD > lead_score >= NURTURE_FLOOR:
  → Route to SDR for qualification
ELSE:
  → Nurture automation (no human assignment)

Always territory/named-account match before round-robin so existing-account leads land with the owning AE. Honor rep capacity and OOO so the timer doesn't start against an absent rep.

Speed-to-lead SLA: inbound demo requests should be worked fast — industry studies (InsideSales/Harvard Business Review) show contact within ~5 minutes dramatically lifts qualification odds vs. 30+ minutes. Set the first-touch SLA per motion (e.g., 5 min for demo requests, same-business-day for content leads). If unclaimed within 15 minutes, re-route to the next rep and alert the manager. Measure SLA attainment as a dashboard metric (§6), not just an aspiration.

5. Deal Forecasting

Weighted pipeline method:

Forecast = Σ (Deal value × Stage probability × Rep confidence adjustment)

Monthly forecast review: Compare forecast vs actual for last 3 months to calibrate rep-level accuracy.

6. Data Hygiene

Weekly automated cleanup:

• De-duplicate carefully (see the dedup rules below — email-only matching is unsafe).
• Flag (don't delete) contacts with no activity > 90 days for a re-engagement or sunset review.
• Validate email addresses on send and audit quarterly (hard-bounce rate > 2–3% hurts deliverability; suppress hard bounces immediately).
• Standardize company names with a normalization rule (strip Inc/LLC/Ltd/GmbH/S.à r.l. suffixes for matching, keep the legal name in a separate field).

Retention & lifecycle — never silently archive or delete revenue history. Closed-Lost and Closed-Won deals are the training data for forecasting, win/loss analysis, cohort/attribution, sales-cycle benchmarks, and legal/audit trails. Deleting or hard-archiving them at 12 months destroys that. Instead:

Compliance & privacy (2026 baseline — confirm requirements with counsel/DPO for your jurisdiction):

• Consent & lawful basis: store opt-in source, timestamp, and lawful basis on the contact. HubSpot has native subscription types + GDPR consent fields; Salesforce uses the Individual object + Consent Management; Pipedrive has Marketing-status consent fields. Don't email contacts whose consent you can't evidence.
• Right to erasure / deletion request: capture request → verify identity → suppress (add email to a permanent suppression/Do-Not-Contact list so re-imports don't resurrect them) → delete personal data within the legal window (GDPR target ~30 days). Retain a minimal anonymized record for audit and to honor the suppression. HubSpot has a built-in GDPR Delete (permanent, blocks re-creation); Salesforce requires a manual/scripted delete plus an Individual-level opt-out; Pipedrive supports per-person delete via UI/API.
• Enrichment & data sourcing: only enrich with a lawful basis; record the data source/provider on the record. After Clearbit folded into HubSpot as Breeze Intelligence, enrichment is native there; for Salesforce/Pipedrive verify your enrichment vendor's lawful-basis terms.
• Call recording & email tracking: call recording consent is jurisdiction-specific (many US states + EU require all-party or notified consent) — disclose and store consent. Open/click tracking pixels are increasingly defeated by Apple Mail Privacy Protection (opens are unreliable since 2021) and may require consent in the EU; treat opens as a weak signal and lean on clicks/replies/meetings.

Dedup rules (email-only matching is unsafe): people have multiple/shared emails (info@, sales@), and the same email can span subsidiaries, partners, or job changes.

• Contacts: match on a normalized primary email first; if no email or shared/role-based inbox, fall back to fuzzy (first+last name) + company/domain and queue for human review rather than auto-merge. Never auto-merge on name alone.
• Companies/Accounts: match on normalized web domain (most reliable), not company name; account for subsidiaries and franchises that share a parent domain.
• Relationships: preserve the account↔contact link on merge — losing it orphans activity history. Keep the oldest record as the master (or the one with the most engagement), and confirm field-survivorship (which value wins per field) before merging.
• Platform tools: HubSpot surfaces duplicate suggestions (Contacts/Companies) and merges keep both timelines; Salesforce uses Duplicate Rules + Matching Rules (and Potential Duplicates) — review before merge; Pipedrive has Merge duplicates with a side-by-side picker. Always require review for fuzzy matches.

Data quality dashboard:

• % contacts with complete required fields (by owner)
• % open deals with a next_step and a future next_step_date
• Duplicate contact / duplicate account rate
• Hard-bounce rate on email sends; % contacts with unknown consent status
• % contacts with a valid lifecycle stage (no nulls / no skipped stages)
• Stage aging: deals exceeding the expected days-in-stage (see §8 formulas)
• Speed-to-lead SLA attainment % and breach count

7. Automation Workflows

Essential automations:

The generic triggers above map to concrete builders on each platform — recipes follow.

8. Operational Dashboards (formulas & report definitions)

Build these as saved reports/dashboards; the formulas are platform-agnostic, with the report type noted per platform.

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Platform recipe: HubSpot

Objects/terms: Contacts, Companies, Deals (with Deal stages inside Pipelines), Tickets. Lifecycle is the lifecyclestage contact+company property (Subscriber→Lead→MQL→SQL→Opportunity→Customer→Evangelist). Lead handoff also uses hs_lead_status.

Properties & limits (verify current limits at https://knowledge.hubspot.com; they vary by tier):

• Create custom properties under Settings → Properties. Internal name is auto-snake_case and immutable; label is editable.
• Property/field and automation limits scale with tier (Starter/Pro/Enterprise) — don't hardcode a number, check your portal's limits page.
• Use calculated properties for lead_score components and days_in_stage.

Lead scoring: Marketing → Lead Scoring tool (manual fit, engagement, and combined scores on Marketing or Sales Hub Pro+; AI-assisted scoring on Marketing Hub Enterprise). The legacy HubSpot Score property (Settings → Properties) stopped updating on August 31, 2025: migrate any workflows, lists, or reports still referencing it. Fit AI scores on your closed data (see §3 calibration).

Workflow recipe — MQL routing with speed-to-lead SLA:

Workflow: "MQL → Route + SLA"
Enrollment trigger: lead_score >= MQL_THRESHOLD AND lifecyclestage is any of (Lead, MQL)
Actions:
  1. Set property: lifecyclestage = "marketingqualifiedlead"
  2. Branch on ICP / arr_potential:
       - Enterprise → Rotate record to owner (Enterprise AE team)
       - SMB        → Rotate record to owner (SMB AE team)
  3. Set property: hs_lead_status = "NEW"; stamp mql_timestamp = now
  4. Create task "Call within 5 min" assigned to deal/contact owner (due in 5 min)
  5. Send internal Slack/email notification to owner
  6. Delay 15 min → IF hs_lead_status still "NEW" (unworked):
        Rotate to next owner + notify manager   // SLA re-route

Use Active lists for dynamic segments (auto-update) and Static lists for point-in-time snapshots. Hide stale records from sales views with list filters rather than deleting.

Privacy/AI: native GDPR delete + subscription types; Breeze Intelligence (formerly Clearbit) for native enrichment, Breeze Copilot/Agents for AI assist — record enrichment source and respect consent.

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Platform recipe: Salesforce

Object model: Lead (pre-conversion) → on qualification, Convert to Account + Contact (+ Opportunity). Opportunities have Stages (with Probability and a Forecast Category). This Lead→Account/Contact/Opportunity split is the biggest difference from HubSpot/Pipedrive — design around conversion, not a single lifecycle field.

Fields & naming: custom fields get an auto __c suffix and an immutable API name; labels are editable. Use Record Types + Page Layouts (or Dynamic Forms) to show the right fields per process/segment.

Automation — Flow-first (2026). Salesforce has retired Workflow Rules and Process Builder; build new automation in Flow (record-triggered for create/update, scheduled for time-based, screen flows for guided UI). Use Validation Rules for required-field-at-stage enforcement, and Assignment Rules (native Lead Assignment Rules) or a Flow for routing.

Validation rule — block stage advance without required fields (Opportunity, runs on save):

AND(
  ISPICKVAL(StageName, "Proposal"),
  OR( ISBLANK(Proposal_Sent_Date__c), Amount == 0 )
)
// Error: "Set Proposal Sent Date and Amount before moving to Proposal."

Record-triggered Flow — MQL routing + SLA (on Lead create/update):

Trigger: Lead, A record is created or updated
Entry:   Lead_Score__c >= MQL_THRESHOLD AND Status != "Working"
Path A (Enterprise: Annual_Revenue__c high / target account):
  - Assign OwnerId = matched named-account AE (else round-robin via assignment Flow)
Path B (SMB): assign via round-robin (skip OOO using a User capacity flag)
Then (all paths):
  - Update Status = "Working"; set First_Assigned__c = NOW()
  - Create Task "Call within 5 min" (due NOW + 5 min) for OwnerId
  - Scheduled path +15 min: IF Status still "Working" with no completed task → reassign + email manager

Forecasting: use Collaborative Forecasts with Forecast Categories (Pipeline / Best Case / Commit / Closed) mapped from stages — align these to the categories in §5.

Privacy/AI: Individual object + Consent Management for consent/erasure; Einstein (Lead/Opportunity scoring, Einstein Activity Capture) and Agentforce agents for AI — keep a manual scoring fallback for explainability. Salesforce has no soft-archive; use a Record_Status__c flag + list views, or Big Objects for reportable cold storage.

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Platform recipe: Pipedrive

Objects/terms: Leads (inbox, pre-deal) → Deals moving through Stages within a Pipeline; plus Persons and Organizations. Activities drive the "next step." Pipedrive is activity-centric — its strength is forcing a scheduled next activity on every open deal.

Custom fields: add per object; field key is a hash (not human-readable) — reference by API key in integrations. Use required fields per stage (web app: pipeline settings) to enforce data capture on stage change.

Automation recipe — new-lead routing + rotten-deal alert:

Automation 1: "Route inbound deal"
  Trigger: Deal created
  Condition: Deal value >= ENT_CUTOFF
     True  → Update owner = Enterprise rep; create Activity "Call" due in 5 min
     False → Update owner = next SMB rep (round-robin); create Activity "Call" due in 5 min
  Then: send internal notification to the new owner

Automation 2: "Stale next step"
  Trigger: Activity marked done OR daily scheduled check
  Condition: Deal is open AND has no scheduled future activity
     True → Create Activity "Schedule next step" for owner + notify

Set "rotten" deal flags per stage (days until a deal rots) so the pipeline view surfaces aging deals automatically. Use Insights dashboards for the §8 metrics (conversion, duration, win rate).

Forecasting: Pipedrive's Forecast view weights deal value × stage probability and groups by expected close date — set per-stage probabilities to match §2.

Privacy: per-Person consent/marketing-status fields; honor erasure by deleting the Person (UI/API) and adding the email to a suppression list so re-imports don't resurrect it. Archive deals (don't delete) to keep them in reports/exports.

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Scope note: This skill covers CRM configuration and operations. For revenue analysis on top of this data — ARR/NRR, funnel/cohort conversion, pipeline coverage ratios, sales-capacity and quota planning, and exec/board dashboards — use the sibling revenue-operations skill. Keep the CRM the system of record; do the heavy metric math in the RevOps layer to avoid duplicating (and diverging) definitions.