"Many may simply not want to learn the truth of what's happening in the PR and comms world." With that warning, host Tan Sukhera opens one of the most consequential conversations in The Piar Podcast series—a deep dive into why correlation is not causation, and how causal AI is solving the measurement problem that has plagued PR and communications for decades.
Dr. Frank Buckler is an author, researcher, and CEO of SUPRA whose work is built on a simple idea: growth comes from understanding what truly drives success, not from copying generic best practices. With a background spanning marketing, engineering, management consulting, and corporate leadership, he's helped some of the world's largest organizations solve problems that big agencies and big consulting couldn't. He began researching AI in 1994, driven by one question: why do people buy? His PhD work developed a causal AI methodology that remains SUPRA's foundation today—evidence first, informed by knowledge, not assumption.
What You'll Learn:
Why Causality Matters – In business, PR, and communications, we're driving actions. Actions that work build on truth—not facts, but the hidden mechanisms between action and outcomes. Frank uses powerful analogies: scanning a computer's circuit board shows where electrons flow, but not what the processor does. Scanning the brain shows activity, but not how thinking works. "You cannot measure causality. You can only infer it."
The Complexity Problem – Technology is complicated but not complex. Complex problems have many drivers that interact unpredictably, with effects that emerge at unknown times. Social science lags behind technology precisely because human behavior is exponentially more complex than engineering problems.
The Lazy Why Trap – Business leaders invest enormous sums mapping business performance but weirdly accept the first answer to "why" things happen. As Rory Sutherland calls it, "the lazy why." Frank shares devastating examples of spurious correlations that cost companies millions.
The Lottery Company Revelation – Everyone believed older people were the target audience because older people play more lottery. Causal AI revealed the opposite: younger people are more likely to play when all else is equal. The real drivers are habituation and winning experiences that accumulate over time. Time correlates with age, but age doesn't cause lottery playing. The same pattern appeared with charitable donations.
Marketing Mix Modeling Failures – Traditional models create false confidence by showing correlations between spending and sales. But they miss the mechanics. Without understanding true causality, companies optimize for the wrong things.
Counterfactual Reasoning – Causal AI builds an "inner mechanics machine" of PR and communications. Once built, you can simulate scenarios that never happened in reality—war gaming future outcomes, predicting impacts of investment changes, testing campaign components before launch. Like a weather forecaster narrowing a hurricane's path, causal AI constrains possibilities based on true mechanisms.
The Kitchen Sink Confession – Tan admits his own past failures: "I used to layer earned media coverage on top of sales data, add HR data, business performance data, hoping to prove causation. But I was just creating spurious correlation. There's a time lag to impact. Unless I test it, I wouldn't know it."
What This Means for PR Leaders – The holy grail of PR measurement is being found. This changes everything: career pathing, strategic counsel, trust and confidence in the boardroom. As execution gets relegated to AI, human leaders must develop decision-making, critical thinking, and problem-solving skills to interpret results, communicate findings, and inspire action.
