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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You’ve got the tools. You’ve got the data. But is your stack built to think?
The average GTM team operates across 10+ platforms — each optimized for its own purpose.
And still forecasts get missed, churn signals arrive too late, and sales and CS operate with different assumptions
It’s not happening because the tools are bad. It’s happening because the stack is fragmented, reactive, and blind to what’s coming next.
That’s why high-performing teams are shifting their strategy.
They’re productizing their data — turning scattered, siloed signals into structured, usable layers that power predictive decisions and scalable growth.
They’re building a GTM stack that thinks.
Why the Old Stack Falls Short
The legacy GTM stack was built for automation and reporting. It pushed emails. Routed leads. Tracked activities. Visualized pipeline health. Helpful? Yes. But passive.
The problem is: revenue decisions happen in real time. And disconnected systems can’t reason, align, or anticipate what’s next.
That’s why teams still:
- Prioritize accounts based on stage, not signal
- Chase MQLs with low conversion probability
- Forecast from rep confidence, not behavioral indicators
What the New Data Stack Looks Like
The next stage of GTM maturity doesn’t rely on more data — it depends on data that’s structured, modeled, transformed into intelligent insights, and embedded where decisions happen. In other words data that's productized to deliver business value.
This requires:
- Unifying cross-functional sources (Salesforce, Snowflake, HubSpot, Gainsight, etc.)
- Applying predictive models on top of these unified signals
- Delivering insights directly into tools where decisions are made (Slack, HubSpot, Salesforce, etc.)
This is the foundation of the GTM brain: a thinking layer that reads signals, reasons across them, and routes insight to the right team at the right time.
And it starts when you treat data as a product — not an exhaust trail. Productized data isn’t just clean and modeled — it’s embedded, maintained, and designed to drive impact.
What This Looks Like in the Real World
Case 1: Forecast Accuracy That Matches Reality
A global SaaS company had every system in place — Salesforce, Gong, Snowflake, Looker — but still missed forecast after forecast. Reps logged activity, managers held pipeline calls, dashboards told a story — but not the real one.
After deploying a predictive GTM layer, they centralized GTM signals and applied real-time scoring across their active pipeline:
- Forecast accuracy increased by 27%
- Pipeline confidence improved
- Revenue leadership began trusting the number again
Case 2: Predictive Lift Without Rebuilding
A fast-scaling SaaS startup wanted to launch lead scoring, churn prediction, and expansion modeling — but was bottlenecked by engineering. Their data lived in Snowflake, Salesforce, and HubSpot. Every request took weeks.
They implemented Forwrd’s virtualized layer, connecting their stack without duplicating or migrating data. Predictive models were up in weeks, and:
- Churn prediction improved 32%
- PQL → SAL conversion jumped 22%
- CS, Sales, and Marketing began acting from shared signal
This is what happens when you productize your data and make the stack think for you.
What CIOs and Data Leaders Should Take Away
This shift isn’t just a RevOps project.
It’s a strategic inflection point for enterprise architecture.
Forward-looking CIOs are asking:
How can we scale GTM decisions without a data warehouse rebuild?
→ Virtualization gives real-time access across systems with minimal engineering lift.
How do we make predictive models useful, not just accurate?
→ Operationalize by embedding scores into workflows and tools teams already use.
Who owns the intelligence layer?
→ Leading orgs form a cross-functional RevOps–Data working group to manage signal flows and model lifecycle.
Ultimately, it’s not just about stacking tools — it’s about building a layer of intelligence that transforms raw inputs into actionable outcomes.
Final Thought
You don’t need to rip and replace your stack.
You need to productize your data — and build a GTM stack that thinks.
The stack of the future won’t just track what happened.
It will anticipate what’s next, align teams before handoffs break, and surface the right signal to the right team at the right time.
You’ve already invested in the tools and the data.
Now it’s time to invest in the architecture that turns it into action.
How Forwrd AI Can Help
Forwrd transforms your fragmented GTM stack into a unified intelligence engine.
Through a virtual data warehouse and predictive modeling layer, Forwrd lets you productize your data — without duplication or rebuilds.
It pulls real-time signals from across your stack, scores outcomes like churn or conversion, and embeds those insights into the tools your teams already use — from Salesforce to Slack.
Whether you want to improve forecast accuracy, prioritize pipeline, reduce churn, or align Sales and CS — Forwrd helps you shift from reactive to predictive, from tool chaos to decision clarity.
