3 reasons your GTM Stack is falling short on ROI

Even with dashboards, data lakes, and BI tools—why are so many GTM teams still guessing?

If you're a CIO or RevOps leader at a scaling SaaS company, chances are you’ve made significant investments in data infrastructure — Snowflake, Salesforce, HubSpot, maybe even a BI layer like Looker.

And if you’re like most companies we’ve spoken to, you’re probably nodding along as we say that despite all that investment, your GTM teams are probably still struggle to answer basic questions such as, which accounts should we prioritize? Where is churn risk creeping in? Which leads will actually convert?

Did you nod?

If you did then you might also be interested to know that this is what we call the Data-Rich, Decision-Poor Paradox .

Three reasons you're Data-Rich but Decision-Poor

1. Dashboards Aren’t Designed for Action

Dashboards serve a purpose — they inform. But GTM teams don’t just need to know, they need to act. And they need to act in the moment — not after monthly reporting cycles. That’s the gap.

Data exists, but it’s disconnected from where decisions are made:

• Sales relies on CRM activity and anecdotal confidence
• CS teams rely on manual health scoring
• Marketing optimizes for engagement, not conversion probability

And as we discuss in more detail in our article — Predictive AI Isn’t Just About Models:‍

"Insight alone doesn’t drive revenue — operationalizing that insight does"

2. Siloed Systems, Fragmented Signals

Most revenue teams rely on 5–8 disconnected systems across the funnel. Signals exist, but they’re trapped in silos.

• Product usage spikes in Snowflake
• NPS scores sit in a CS tool
• Open rates live in HubSpot
• Sales flags stay in the CRM

No single team sees the full picture. No one acts fast enough.

And as we explore further in our Predictive GTM Blueprint Series — Why a Single Source of Truth Is Your Fastest Path to Predictive GTM:‍

"The biggest cost of disjointed systems isn’t inefficiency — it’s missed revenue"

3. No Product Vision for the GTM Stack

Most revenue teams rely on 5–8 disconnected systems across the funnel. Signals exist, but they’re trapped in silos.

Most GTM stacks evolve reactively. 

Tools are added to solve immediate needs — not to serve a long-term strategy.

The result? — Dozens of siloed systems optimized for individual functions, but disconnected from a shared outcome.‍

What’s missing is a product mindset.‍

Without a unified product vision for how data and decisions flow across Marketing, Sales, and CS, teams can’t capitalize on the full value of the stack.

What’s needed is a productized GTM architecture — one that turns raw, scattered data into a shared, decision-ready layer that delivers real value across the funnel.

What it looks like when ROI is left on the table

Most revenue teams rely on 5–8 disconnected systems across the funnel. Signals exist, but they’re trapped in silos.

One of our customers, a leading CRM provider, experienced this firsthand.

One of their customers began expanding usage rapidly. Five new departments adopted the tool organically. Product usage surged. But no one noticed.

Why?

Because CS lived in Gainsight, product data lived in Snowflake, and sales focused on net-new pipeline in Salesforce.

There was no connective tissue. No system to synthesize those signals into a clear, actionable view.

By the time Sales re-engaged, a competitor had already stepped in and captured the expansion.

The New Imperative for GTM: Productize Your Data‍

This is not a tooling problem. It’s an architectural one.

The issue isn't the solutions you're using — it’s how they’re connected, how data moves between them, and whether that data is usable in real time.‍

Productize your data to power predictive GTM decisions and scalable growth.‍

That means turning scattered, raw data into something structured, modeled, and operational — like a product. Not just for analysis, but to drive live decisions across Marketing, Sales, and CS.

These are three crucial principles for success:

1. A GTM Signal Strategy‍

Establish a cross-functional initiative (often led by RevOps or a “SignalOps” function) to identify which signals matter most — product usage, activity gaps, engagement drops — and how they should flow.‍

2. A Unified, Virtualized Data Layer‍

Skip the costly rebuild. Use virtualization to enable real-time, read-only access across systems — with predictive models layered on top.

In our Predictive Intelligence Blueprint Series: How to Pick the Right Data Strategy for Predictive Success, we offer a practical framework to help CIOs choose the right approach.‍

3. Embedded Analytics‍

Predictive intelligence only works if it reaches decision-makers.

Push scores into operational tools — Salesforce, HubSpot, Slack — so GTM teams can act in context, not in isolation.

We explore all of these strategies further in the Predictive Intelligence Blueprint Series — your roadmap for operationalizing GTM data and AI.

‍Final Thought

If your GTM decisions still rely on instinct, it’s not because your teams aren’t data-driven — it’s because your stack isn’t decision-ready.

The future of GTM belongs to organizations that can align teams, prioritize with precision, and act in time — using systems that do more than report. They reason. The teams that win in 2025 won’t just have visibility — they’ll have precision, alignment, and action baked into every decision.

How Forwrd AI Can Help

Forwrd helps bridge the gap between insight and action by providing a virtualized, predictive intelligence layer that connects your existing systems — no migration required. It pulls live signals from platforms like Snowflake, Salesforce, HubSpot, and Gainsight, models predictive outcomes such as churn risk or conversion likelihood, and embeds those insights directly into your CRM, CS platform, or collaboration tools like Slack.

With Forwrd, your teams operate from shared intelligence — not scattered data. The result? Faster decisions, fewer missed opportunities, and measurable impact. One client secured a $180K expansion opportunity within weeks of implementing Forwrd’s GTM layer — all without disrupting their existing stack.

👉 Discover how Forwrd AI accelerates your predictive GTM with smarter data strategies