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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Your GTM teams likely have models in place:
A lead scoring system in HubSpot.
A forecast rollup in Salesforce.
A churn score in your CS platform.
But if you’re still missing targets, chasing the wrong accounts, or catching churn signals too late, it’s worth asking:
Are your models really broken — or is your data disconnected?
For many SaaS companies, the challenge isn’t building a smarter model.
It’s feeding that model the right signals — from across the customer journey.
And that’s only possible with unified data.
GTM Use Cases That Need Unified Signals — Not More Dashboards
Let’s look at three of the most critical RevOps use cases:
1. Forecasting
The Problem:
CRM-based forecasts rely heavily on rep-entered stages, confidence levels, and historical close rates. But none of that captures silent signals like:
- Product usage drop-offs
- Stakeholder disengagement
- No follow-up after pricing
- Renewal friction hiding in support tickets
Why It Breaks Without Unified Data:
You can’t flag deal risk without product, billing, and CS data — and you can’t model probability accurately if you're blind to behavior.
2. Lead & Account Scoring
The Problem:
Most scoring systems only consider engagement data — like page views, email opens, or form submissions. That leads to false positives and wasted SDR time.
Why It Breaks Without Unified Data:
Scoring without firmographics, product behavior, ICP alignment, and past win/loss data means you’re guessing — not prioritizing.
3. Churn Prediction
The Problem:
Your CS team is often the last to know. By the time a customer is silent or escalates, the risk has already materialized.
Why It Breaks Without Unified Data:
Churn models built only on ticket volume or NPS miss key early signals: declining usage, late payments, renewal terms, lost champions.
The Common Thread: Data Fragmentation
In each of these use cases, failure isn’t due to bad logic — it’s due to bad inputs.
And the reason those inputs are bad isn’t a lack of data.
It’s that the data is scattered across systems, teams, and formats.
According to IDC, only 35% of enterprise data generated is actually used for decision-making — largely because it's siloed, inaccessible, or outdated by the time it’s analyzed.
The Fix: Unify the Right Signals — Without Rebuilding Your Stack
Leading RevOps and CIO teams aren’t solving this by ripping out systems.
They’re doing it by:
- Virtualizing access across GTM systems like Salesforce, Snowflake, HubSpot, and CS tools
- Building lightweight, connected views to power predictions
- Embedding those predictions directly where teams already work
- Aligning around shared intelligence — not disconnected tools
That’s what turns forecasting, scoring, and churn models from lagging indicators into live strategic signals.
Final Thought: It’s Not About Better Models. It’s About Better Data.
Even the best algorithm can’t deliver insight if it’s starved of context.
If your GTM use cases are stalling, don’t start with the model.
Start with the foundation — and ask:
“What signals are missing?”
“Where do they live?”
“How can we activate them — without moving everything?”
Because predictive performance starts with connected context.
And that starts with unifying what you already have.
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