A Series B SaaS company at $15M ARR had accumulated 12 disconnected tools and no source of truth. We designed a unified revenue data model, consolidated the stack, and migrated the data. Reporting went from two days to two hours, and the forecast started landing where leadership said it would.
Context
The company was a Series B B2B SaaS business that had grown fast to roughly $15M ARR. Growth like that leaves a trail: over five years they had bought 12 different tools one problem at a time, including Salesforce, HubSpot, Intercom, Gong, and Tableau, plus seven more point solutions.
The Problem
Each tool was a reasonable purchase. Together they were chaos, with no single place anyone could trust.
- Sales could not find the information they needed across the scattered systems.
- Marketing could not measure ROI because the data lived in silos that did not reconcile.
- Every board meeting meant scrambling to pull numbers from five different dashboards that rarely agreed.
- Duplicate data and conflicting sources of truth made every report suspect.
Every one of those twelve tools was a sensible purchase on the day it was bought. The problem only existed in aggregate.
What We Did
We consolidated the stack in three phases, without disrupting day-to-day operations.
Phase 1: Revenue data architecture (weeks 1 to 2)
- Audited all 12 tools and mapped how data actually flowed between them.
- Identified duplicate data and the conflicting sources of truth causing the mistrust.
- Designed a unified revenue data model, and selected HubSpot plus two integrated tools as the consolidated stack.
Phase 2: Data migration and integration (weeks 3 to 8)
- Migrated and deduplicated 45,000 contacts.
- Built custom integrations for the remaining tools and automated the data-sync workflows.
- Stood up a real-time revenue dashboard on the clean, unified data.
Phase 3: Team training and optimization (weeks 9 to 12)
- Trained sales, marketing, and CS on the new system.
- Created documentation and playbooks so the setup would hold.
- Implemented automated reporting and optimized workflows from real user feedback.
The Result
The company traded 12 disconnected tools for one trustworthy system. Reporting that used to eat two days now takes two hours, the forecast started landing inside the range leadership had committed to, and every team finally reads the same numbers. Just as important, the clean, unified data foundation set them up to automate and layer on AI safely, instead of building on a mess. That is the sequence behind every GTM engineering project.
Frequently Asked Questions
How do you consolidate tools without losing data or history?
We map the data model first, migrate and dedupe deliberately, and validate before retiring anything. Nothing gets turned off until its data lives safely in the new source of truth.
Won't consolidating tools mean losing capabilities we rely on?
Rarely. Most 'capabilities' are overlapping or unused. We keep what earns its place and cut the redundancy, you usually end up with more capability, not less, because the data finally connects.
Dealing with scattered revenue data?
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