Implementation playbooks for revenue operations.
Long-form, operator-written guides to the systems behind scalable revenue: CRM architecture, governance, forecasting, attribution, lifecycle design, and AI workflows. Built from the engagements we run.
How these work
What is inside a RevPal playbook
Not think pieces. Each playbook is the same material we hand an in-house team at the start of an engagement.
The operating model
Every playbook opens with the framework: the layers, who owns each one, and where most teams stop short.
The build steps
Source taxonomy, CRM fields, form logic, scoring, routing rules, and SLAs written so an operator can implement them.
The reporting spec
Dashboard definitions, evidence types, and governance rules so the numbers hold up in a board review.
Library
Published playbooks
The RevPal AI Intent-to-Pipeline Playbook
A six-layer operating model for capturing AI referral traffic, resolving accounts, scoring intent, routing Sales, and attributing pipeline and revenue.
More playbooks are being written
We publish a playbook once we have run the system enough times to stand behind every step. Until the next one lands, the field notes below cover the same ground.
Related reading
Field notes on the same systems
How long should HubSpot last-touch attribution last in B2B SaaS? How to choose an expiration window
HubSpot last touch needs an expiration rule. Compare window lengths and reset options, and settle first-touch handling before building reports.
Why Salesforce campaign influence shows zero revenue for events (and what to report instead)
Salesforce gives the full revenue share to one primary campaign, so events show zero. Learn why and what to report so event impact stays visible.
Why HubSpot attribution data goes missing without an error: 4 failures to test
Tracked links, hidden forms, unpublished workflows and ad pixels can drop HubSpot attribution data with no error. Four failures, and how to test each.
Why your Salesforce revenue field doesn't match finance (and how to fix it without breaking reports)
A hand-built estimate field can drift so far from reality that two people on the same team recalculate it by hand and get two different answers. Here's the sequence that actually fixes it.
Why domain matching keeps merging the wrong two companies
A duplicate-record cleanup fixes the backlog for a week. The matching logic that created the backlog keeps running the day after, and it will make more duplicates by Friday. Here is how to decide which signals your matching logic should actually trust.
When an AI agent creates a CRM record, who's checking its work?
A human who skips a required field gets stopped at the form. An AI agent writing to the same object, through the API instead of the UI, often doesn't. Here's the validation gap that opens up, and what closing it actually requires.
Want the playbook run in your stack, not just read?
RevPal operators implement these systems inside your Salesforce, HubSpot, analytics, and automation stack, then run them with your team.