From a Messy Salesforce to a Pipeline You Can Trust
A fast-growing B2B company had a Salesforce nobody trusted and forecasts nobody believed. **DevCommX overhauled their RevOps** — clean data, routing, and reporting that hold up.
Customer Intro
A B2B scale-up past Series B had done the hard thing — grown fast — and hit the problem that growth always brings: the systems hadn’t kept up. Salesforce had become a patchwork of inconsistent fields, abandoned custom objects, and half-finished automations layered on by different people over years. Reps didn’t trust it, so they worked around it. Leadership didn’t trust the reports that came out of it, so every forecast turned into an argument about whose numbers were right.
At their stage, that’s not a nuisance — it’s a strategic risk. A board that can’t trust the forecast can’t plan hiring, spend, or the next raise. The company needed a RevOps foundation that produced numbers everyone believed.
Problem/Goal
The dysfunction came down to a few root causes:
- Unreliable pipeline data — duplicate records, missing fields, and inconsistent entry meant no two reports agreed.
- Stages that meant different things to different reps — without a shared definition, “pipeline” was subjective, so the forecast was too.
- Hours lost to manual reporting — RevOps spent its week stitching spreadsheets together instead of improving the system.
- No trust — the compounding effect of all the above was that leadership simply didn’t believe the CRM.
The goal: a RevOps foundation that produced trustworthy pipeline and forecasts without constant manual cleanup — one the team actually used.
The DevCommX Solution
DevCommX re-architected the revenue stack, treating clean data as an engineered outcome rather than something to nag reps about.
1. Clean the foundation
We cleaned and de-duplicated the CRM, fixed the field structure, and removed the dead automations and objects that were creating noise — giving the system a reliable base to build on.
2. Standardise the model
We defined a single stage and qualification model, so “pipeline” meant the same thing to every rep and every report. Shared definitions are what make a forecast defensible.
3. Automate hygiene and routing
Automated lead routing and enrichment made sure records were complete and in the right hands from the start, and guardrails kept data clean going forward — so quality didn’t depend on rep discipline. The work follows our RevOps system design playbook.
4. Reporting on a single source of truth
Dashboards were rebuilt on the cleaned, standardised data, giving leadership one source of truth for pipeline and forecast — and ending the weekly reconciliation scramble.
Results & Benefits
The re-architected stack produced forecasts leadership could trust:
| Metric | Result |
|---|---|
| Forecast accuracy | within ~10% quarter over quarter |
| Manual reporting | hours eliminated each week |
| CRM data | clean, de-duplicated, auto-maintained |
| Pipeline view | a single source of truth the team trusts |
The cultural shift mattered as much as the metrics: reps started trusting and using the CRM because it was finally clean, which kept the data clean in a virtuous loop.
Insight
Reps won’t fix CRM data by being asked — data quality is an engineering problem, not a discipline problem. You fix forecasting by engineering the system — standard stages, automation, and guardrails — so clean data is the path of least resistance rather than a chore. For the signals layer that sits on top, see real-time sales signals & lead scoring.
Callout Quote
“For the first time, our forecast and our pipeline tell the same story.” — VP RevOps, B2B scale-up
Explore revenue operations and fractional RevOps, or book a GTM assessment.
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Related reading: real-time sales signals & lead scoring and managing multiple lead-gen vendors.