A 3-step guide to unlocking marketing ROI with causal AI

Marketing has always had the potential to be a powerful business multiplier, but its true impact is often misunderstood — or underestimated. 

The key to changing that? A shift from reactive strategies to proactive, data-driven decisions powered by causal AI. 

Why marketing’s impact deserves a second look

Marketing teams are tired of playing defense. With many companies missing revenue targets and struggling with data challenges, the need for change is clear. Tech-forward marketers are shifting from reactive to proactive decision-making using causal AI — and seeing actual results.

Business performance depends on a complex interplay of external events, like market trends, competitor actions and internal dynamics. Traditional forecasts fail to capture these connections, leaving marketing’s true impact underappreciated. Yet, marketing is a powerful business multiplier — unlocking value in ways many leaders have yet to fully realize.

What sets causal AI apart?

At its core, causal AI reveals what drives marketing success, going beyond basic analytics and attribution. Unlike traditional methods that only provide surface-level insights, causal AI uncovers the deeper cause-and-effect relationships between campaigns, brand strength and market conditions. It shows how these elements work together to deliver results, providing a clearer understanding of marketing’s impact.

This deeper understanding doesn’t just clarify past performance. It fundamentally evolves how teams invest in go-to-market (GTM) strategies, enabling more intelligent, more confident decisions that drive measurable outcomes.

While genAI finds patterns and correlations in data, causal AI goes much further by:

• Identifying cause-and-effect relationships across marketing and GTM efforts.
• Distinguishing correlation from causation, enabling more accurate insights beyond traditional machine learning.
• Testing “what-if” scenarios with statistical confidence, enabling teams to plan for multiple outcomes.
• Validating marketing investments with comparative forecasts, showing how marketing impacts revenue, market share and growth.

Dig deeper: It’s time for B2B marketing to understand its GTM role

Where do you start?

While the potential of causal AI is transparent, adopting it requires a strong foundation. Marketing teams must assess readiness and align on what causal AI can deliver. From there, building momentum becomes a matter of focus and prioritization. Here are three practical steps to guide your journey.

Step 1: Assess readiness and get clear on causal AI

Start by using this eight-question framework to evaluate your organization’s readiness for causal AI:

Score each question from 1-3:

• 1 = No/Not Yet.
• 2 = Partially/In Progress.
• 3 = Yes/Fully Implemented.

Scoring guide 

• 20-24: Advanced readiness.
  • Ready to apply causal AI across marketing and expand to comprehensive GTM adoption.
• 17-19: Moderate readiness.
  • Begin building causal AI models to demonstrate marketing’s networked impact on GTM performance.
• 14-16: Early-stage readiness.
  • Use what-if analysis to show how marketing initiatives influence broader business outcomes.
• Below 14: Foundational challenges.
  • Focus on strengthening marketing data foundations while building bridges with the GTM team.

Most organizations score between 14 and 16. Don’t be discouraged by a lower score. Start by improving one area of marketing analytics. 

Build momentum through small wins. First, strengthen your marketing foundations, then expand across the GTM team. Even minor improvements in data quality and team alignment drive meaningful change.

Here’s what the questions measure:

Data access and integration (Questions 1-2) 

• Marketing data is scattered across systems, creating incomplete stories. The good news: Causal AI works with focused data sets that answer key questions — no complex infrastructure is needed.

Metrics and analysis (Questions 3-5) 

• Basic funnel metrics aren’t enough. What-if analysis shows how marketing activities and market conditions drive tangible outcomes.

Team alignment (Questions 6-7) 

• Different metrics and definitions of marketing and sales create confusion. The GTM team needs one shared language to make better decisions.

RevOps integration (Question 8) 

• Connect marketing success to GTM performance through shared metrics, creating one source of truth for all teams.

Step 2: Fast-track to causal AI readiness

Data challenges run deep. Most companies struggle with data silos and outdated systems. But the real problem isn’t technical — it’s how teams work. When each team uses different data and definitions, communication breaks down.

Here’s your 12-week plan to get started while keeping your current programs running.

Progress beats perfection. Small, consistent steps toward better data practices create the foundation for advanced analysis. 

Step 3: Define your destiny: Embrace advanced analysis and the power of what-if scenarios

Data quality concerns are real — and costly. But you have a choice. Instead of letting imperfect data hold you back, take control with causal AI to run what-if analyses.

For instance, Proof Analytics’ Scenario Planning dashboard connects marketing data with economic trends to model outcomes and identify key drivers.

Combining your existing data with market intelligence (economic data, employment data, etc.) allows you to model business scenarios while others remain paralyzed, waiting for perfect data.

From pattern-spotting to power moves 

As you embrace advanced analytics with causal AI, forecasts alone don’t deliver the depth of insights needed. This new approach helps you provide meaningful metrics that resonate with executive stakeholders. When structuring your initial proof of concept, consider your executive’s underlying questions:

• For CEOs: Show causal links between marketing and GTM success via ARR, deal volume, velocity and market share. Move beyond forecasts to reveal key growth drivers.
• For CFOs: ROI frameworks linking marketing investments to GTM impact on bookings and cash flow. Go beyond acquisition costs to connect customer journeys to financial outcomes.
• For CMOs: Turn KPI tracking into actionable intelligence. Link pipeline contribution to marketplace dynamics to identify top-performing investments.

Dig deeper: An open letter to CEOs and CFOs about GTM

How to make 2025 your year

The 2025 marketing landscape will divide leaders into two groups: those who let data challenges define them and those who use causal AI to break through them. The question isn’t whether to embrace AI but how you’ll use it to shape your future.

Your next move will define your path. Which type of leader will you be? Your destiny is waiting to be written.