The CRM SaaS founders ship into production.

At $1M ARR, the spreadsheet that worked stops working. You have 30+ paying accounts, 200+ trial users, 5+ MAUs trending toward expansion, and 2-3 reps trying to keep their books straight. Generic CRMs don't survive this transition because your growth levers live inside your product, not in a contact database.

PLG signals live in your product, not your CRM

Activation events, MAU trends, feature adoption — these are the signals that matter. A generic CRM has Companies and Deals; it doesn't know that Account A just crossed 50 MAU, or that Account B's power users switched plans last week. So your reps are flying blind on accounts about to expand or churn.

MQL → SQL has too many rules for HubSpot workflows

PQL routing depends on combinations — feature usage, team size, plan tier, support ticket history. Generic CRM workflows hit their ceiling fast. You need to say: "If activation within 2 days AND feature_x_used AND invites > 0, then route to expansion team."

Expansion + churn use the same data, opposite direction

A "low MAU + high pricing tier" account is a churn risk; "high MAU + lower tier" is expansion-ready. Generic CRMs don't pull product data, so expansion lives in spreadsheets and Slack.

We've configured Attio for 7 SaaS teams between $1M and $30M ARR. The data model that holds up is below.

Most SaaS founders build dashboards in Mixpanel or Amplitude and treat the CRM as a separate system. Bad split. The CRM is where reps work; if it doesn't have product signals, reps make decisions on stale data.

Six signals to land in Attio for every account:

1. Activation status — Has the account hit your activation milestone? Single boolean, computed weekly.
2. MAU trend — Last 30 days, last 90 days, direction (growing / flat / declining).
3. Feature adoption breadth — How many of your top 10 features are being used.
4. Seat utilization — Paid seats vs active seats. Low = churn risk; high = expansion candidate.
5. Support ticket volume + sentiment — Tickets per active user. High = friction; low + declining = disengagement.
6. Plan tier vs ICP fit — Big company on Starter = expansion; tiny company on Enterprise = churn.

These signals come from your product DB, Mixpanel, Amplitude, or PostHog. We wire them into Attio as scored attributes — formula attributes compute the score in-CRM. No manual dashboards, no Slack polls, no Friday spreadsheet reviews.

The MQL-to-SQL handoff is where SaaS revenue leaks. Marketing has criteria, sales has criteria, and they don't match. Result: 60% of MQLs never get worked, and 30% of SQLs are accounts marketing didn't qualify.

The fix is technical, not political. Codify both criteria as Attio formula attributes:

• MQL signal: form fill OR demo request OR pricing-page visit + ICP-fit score over 70
• PQL signal (product-led variant): activation + 3+ active users + plan tier matching ICP
• SQL signal: MQL OR PQL + worked-by-rep flag + last-touch within 7 days

Workflows then route each:

• MQL → SDR queue, Slack ping with full context
• PQL → AE direct, no SDR layer (PQLs convert better when AE owns)
• SQL → opportunity created in Attio Deals, owner auto-assigned via round-robin

We've shipped this for 7 SaaS teams. Conversion from MQL/PQL to opportunity averages 30-40% post-rebuild, vs 8-15% on generic CRM workflows.

Expansion and churn use the same product data, just read in opposite directions. A CRM that does this well shows you both at once.

Expansion-ready

Signals: high MAU + high feature adoption + plan tier below ICP. The account is using the product hard but hasn't moved up. Workflow: monthly review surfaces expansion candidates; AE owns outreach.

Churn-risk

Signals: declining MAU + low feature adoption + support ticket volume + champion left. Workflow: 14-day-out churn-risk alert to CSM; intervention play runs.

Both run as Attio formula attributes computing scores from product data. Slack alerts fire when scores cross thresholds. Reviews happen weekly, not quarterly.

Expansion + churn turning into revenue is usually how the Attio implementation pays for itself. A typical $1M ARR SaaS sees 3-5 expansion conversations and 2-3 churn saves in the first 90 days that wouldn't have happened on a generic CRM.

Week 1: Foundation. Day 1 audit — pull every signal you track in Mixpanel/PostHog/Amplitude. Days 2-3: Attio data model — 4 objects, attributes, views, permissions. Days 4-5: workflows — MQL→SDR routing, PQL→AE routing, expansion alerts.

Week 2: Activation (optional). Days 6-7: product-data sync via Clay or direct webhook (PostHog → Attio nightly job; Mixpanel → Attio daily activation push). Day 8: formula attributes for activation score, churn risk, expansion-ready. Days 9-10: testing with real accounts; runbook + Loom walkthroughs delivered.

Pricing: $4,500 for Attio implementation only (greenfield or HubSpot Sales Hub migration). $7,500 for full Migration + Wire including Clay enrichment + product-data sync. Custom for >$10M ARR with multi-product orgs. See full implementation scope or Clay + Attio details.