Mark Stouse is the Chairman and CEO of Proof Analytics, a SaaS-based AI platform that helps companies measure and optimize their go-to-market (GTM) strategies using causal analytics. With more than 25 years of leadership experience at HP, BMC Software, and Honeywell Aerospace, he was one of the first B2B CMOs to use causal analytics to understand, plan and optimize GTM performance globally. A recognized innovator, founder and board director, Mark has received multiple industry awards for his contributions.
We sat down with Mark to talk about why PR has long been misunderstood, how companies can finally measure PR’s real impact on revenue and sales velocity, and what CEOs, CMOs and CFOs should be doing differently in 2025 (and 2026) to make smarter marketing investments.
PR Vibes: Why do companies struggle to understand the impact of PR on business outcomes?
Most companies rely on activity metrics — impressions, press release volume, media placements, advertising equivalency — and mistake them for indicators of success. But those outputs are not results. They don’t tell you if PR is driving revenue, accelerating deal velocity, or building the kind of brand trust that reduces friction in the sales process.
PR has traditionally been seen as a black box. Without causal analytics and counterfactual modeling, it’s very hard to draw a direct, defensible line between PR and communications activity and business results, particularly in the context of what’s happening in the marketplace.
PR Vibes: Why have traditional measurement models failed PR and marketing?
Traditional models like simple measurement and multi-touch attribution (MTA) confuse correlation with causation and overlook key variables like the time lag between PR successes and market forces. Causal analytics addresses these gaps and reflects how B2B buying decisions really happen — not in a straight line, but across months and multiple touchpoints.
Unfortunately, there are leaders on both the client and agency sides who understand the logic gaps but choose not to address them.
PR Vibes: Can you give an example?
Sure. Think about when a customer finally signs a deal in Q3. That decision may have been influenced by a PR campaign from Q1 or even the previous year, not by what happened last week. That’s why trying to measure PR’s impact in the same quarter is misleading.
Causal analytics shows you the real drivers across time, not just what’s recent. MTA, on the other hand, gives partial credit to every touchpoint a customer had before buying — like giving everyone who shook their hand some credit for the sale. But just because something came before a sale doesn’t mean it caused it.
Counterfactual modeling takes this one step further. It answers the question, “What would have happened if we hadn’t run that PR campaign?” By simulating the alternative scenario — the business outcome without the campaign — you can isolate the true, incremental impact of PR with a much higher degree of confidence. This is essential in today’s fiduciary environment.
PR Vibes: What is causal analytics, and how is it different from traditional PR and marketing measurement?
Causal analytics is a statistical approach that models the true cause-and-effect relationships between actions and outcomes. Unlike correlation-based methods — which only show surface-level associations between two variables — causal analytics accounts for:
• Time lag
• External market forces
• Interdependent variables across the GTM system
It leverages techniques like lag-normalized regression, directed acyclic graphs (DAGs), Bayesian modeling, and counterfactual simulation to isolate causality. So, if you launch a PR campaign today, causal analytics helps determine how much that effort contributes to sales pipeline growth or Brand lift three, six, or even twelve months later — and what the outcome would have been had you done nothing.
PR Vibes: If PR results take time, how can companies justify the investment?
Understandably, some CEOs may wonder, “Why invest in PR if it takes so long to show results?” But that perspective overlooks how marketing and sales function in different time horizons. Sales performance in Q3 has almost nothing to do with marketing performance in Q3 — it was probably impacted by marketing performance from a previous quarter or year.
Causal analytics proves that strong brand Awareness, Confidence, and Trust — or Brand ACT — don’t just support revenue, they accelerate it.
ACT is a decision-making psychographic structure originating in research conducted in the early 2000s by Princeton University researchers. High ACT scores are like tailwinds, resulting in more sales deals, bigger deals, and faster sales velocity once the brand foundation is in place. Counterfactually, if ACT scores were low or unchanged, deal velocity and conversion rates would have been materially weaker — a conclusion provable through simulation.
PR Vibes: How does causal analytics help companies make better decisions about their GTM strategy?
Traditional analytics often deliver insights after the fact. By the time you get the report, the market has moved on.
With causal analytics — especially when AI-enabled — you can update your models in near real-time. That means you can spot shifts in effectiveness early, identify what’s changing and why, and pivot accordingly. It’s not just retrospective analysis — it’s a forward-looking, strategic tool.
And because the models are causal and counterfactual, they don’t just describe the past — they can predict what will happen if you take (or don’t take) a certain action.
PR Vibes: Can you give an example?
Absolutely. Sales teams often assume that current quarter results are tied to current quarter PR and marketing. But that’s rarely true. Causal models show that Q3 revenue is usually influenced by marketing done two or even three quarters earlier.
When paired with counterfactual modeling, you can simulate what would have happened if your marketing mix or timing had been different — and quantify the opportunity cost of inaction or misallocation. That changes how you evaluate success and course-correct your PR and marketing.
PR Vibes: Can causal analytics help reduce the risk in PR and marketing spending?
Yes — and dramatically. If you can’t prove what’s working, you’re the first budget to get cut. But when you can show that a PR initiative improved trust or shortened sales cycles — even after accounting for all the external noise — you’re not just defending your budget, you’re optimizing it.
You’re reallocating spend based on evidence. That’s exactly what the C-suite needs: clear confidence in ROI — not just from attribution reports, but from counterfactual proof of what would have happened otherwise.
PR Vibes: Can causal analytics measure the impact of brand awareness, confidence, and trust on revenue?
Yes, and that’s where the model really shines. These aren’t just soft metrics — they’re statistically linked to sales deal velocity and size. Strong ACT scores increase sales volume, making everything else in your GTM machine run smoother. Weak ACT scores create drag.
With the right survey data and modeling, you can isolate how improvements in confidence or trust correlate with sales acceleration and revenue lift — and simulate the inverse scenario where ACT levels were flat or declining.
PR Vibes: How do you advise companies on getting started?
Start by aligning on business goals: revenue growth, margin improvement, shorter sales cycles. Then map the internal and external variables that influence those outcomes, like brand trust, time lag, market conditions, and your channel mix.
Identify what you’re doing across PR and marketing to move those needles. That becomes the dataset for your causal model.
Once you have that, it’s not just about reporting. It’s about asking:
“What would we do differently if we knew exactly what moved the needle — and exactly what didn’t?”
That’s the power of causality and counterfactual simulation.
PR Vibes: What would you say to CEOs or CFOs who still think PR can’t be measured?
I tell them this: It’s not about gut feel anymore. PR can be measured — meaningfully, responsibly, and with rigor.
And in a world where fiduciary responsibility is under new scrutiny, you need to be able to show that every dollar you spend is defensible.
Causal AI gives you that power. If you want to make better bets — smarter, faster, and with less risk — that’s the way to do it.
Mark is a prolific creator of very high-quality content on LinkedIn and Substack. To read his material, go to www.linkedin.com/in/markstouse or www.substack.com/markstouse.
Want to learn how to elevate your PR strategy into a revenue driver?
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