Case Study: How a SaaS Leader Brought Forecasting Back to Reality
Discover how one global SaaS team boosted forecast accuracy by 27% using predictive scoring layered on their existing stack.
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One of our long time customers, a global SaaS company, had everything in place: Salesforce, Gong, Looker, Snowflake — the full GTM stack.
And still… forecasts kept missing.
Pipeline reviews turned into guesswork. Sales managers leaned on rep confidence. And revenue leaders couldn’t trust the number.‍
The problem wasn’t effort — it was signal.
The Root of the Issue
Like most growing SaaS companies, their data was everywhere — but nowhere unified.
- Product usage data was in Snowflake
- CRM data sat in Salesforce
- Gong housed call notes
- Forecasts were stitched together in spreadsheets or stitched manually in BI tools
There was no shared intelligence layer.
Just tools. And gaps.
What They Did Differently
They brought in Forwrd to add intelligence on top of the stack — not rebuild it.
Using Forwrd’s predictive GTM layer, they:
- Connected data across systems — virtually, no migration
- Applied real-time scoring on open pipeline
- Embedded those scores directly into Salesforce
Now reps, managers, and RevOps all operated from the same model — one grounded in live data, not gut feel.
The Results
Within a quarter:
- Forecast accuracy jumped by 27%
- Pipeline confidence improved across teams
- Sales and RevOps aligned around one version of the truth
Leadership finally trusted the forecast again — because it wasn’t just a projection.
It was a productized, predictive model grounded in the full GTM signal layer.
The Takeaway
It wasn’t a data problem. It was a decision problem.
By layering predictive intelligence on top of their stack, they turned GTM from reactive to proactive — without lifting and shifting a thing.
