Case Study: How a Fast-Moving SaaS Startup Got Predictive — Without Rebuilding Anything
See how a high-growth startup deployed predictive AI in weeks — no rebuild, no engineers, just real-time scores that drive action.
New mobile apps to keep an eye on
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What new social media mobile apps are available in 2023?
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Use new social media apps as marketing funnels
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Try out Twitter Spaces or Clubhouse on iPhone
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What app are you currently experimenting on?
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When we started talking to this company it was deep in the throes of rapid growth.
They were scaling fast with new customers, a growing pipeline, and big goals.
They knew predictive AI could give them an edge — to score leads, forecast deals, spot churn.
But their biggest blocker wasn’t strategy.
It was engineering.
The Roadblock
Every model they wanted to deploy hit a wall:
- Data was scattered across HubSpot, Salesforce, and product analytics tools
- Stitching it together required custom pipelines
- Engineering resources were already stretched thin
- Building a data warehouse was months away — and they needed action now
Sound familiar?
They didn’t need more data.
They needed a way to make the data they had… usable.
What They Did Instead
They brought in Forwrd’s virtualized predictive layer — and skipped the rebuild.
In a few weeks:
- Data from across their stack was virtually connected
- Churn models and lead scoring were deployed
- Predictive signals were pushed into HubSpot and Slack, where teams already worked
No infrastructure changes.
No migration.
No waiting.
The Impact
Within the first quarter:
- Churn prediction accuracy improved by 32%
- PQL → SAL conversion increased by 22%
- GTM teams started acting on shared, real-time signal — not siloed guesses
Predictive wasn’t just a project anymore. It was operational.
The Takeaway
You don’t have to rebuild your stack to activate predictive AI.
You just need the right layer to make your data think — and your teams act.
This startup didn’t delay their growth goals.
They accelerated them — with a predictive engine built on the stack they already had.
