“When I was starting off in my career, marketing analytics and marketing data were very much a back-office function. It was a world dominated by brand management and product management,” says. 

Today, those same analytics functions sit much closer to the front line of marketing. The shift has been steady and significant. “Interpretation of data and rendering of insights has really leapt into the forefront over the course of my career,” Greg says. “In that sense, it has been like riding a wave.” 

This movement from background to center stage has reshaped the skills analytics leaders need. It is no longer enough to be technically strong or statistically precise. Leaders must be able to translate between disciplines, guide non-experts, partner with creative and media teams, and operate in environments where expectations are higher and timelines are shorter. 

Throughout the conversation, Greg returns to one theme again and again. Analytics leaders need to be curious. He describes curiosity as essential in two ways. 

First, it drives experimentation. Greg speaks openly about the need to test and learn in areas like AI and emerging analytics tools. “You have to be willing to try different things, experiment with them, and know when to proceed and when to cut your losses.” He calls this mindset “fast fail,” but makes it clear that experimentation is not just about speed. It is about continuous understanding. 

Second, curiosity shapes interpretation. Greg stresses that teams must ask why something works, not just see that it works. “You have to be willing to ask why, why, why,” he says. That level of inquiry requires an instinct to dig deeper, challenge assumptions, and connect results to human behavior. 

For analytics leaders, curiosity is not a personal trait. It is a requirement for navigating an environment that shifts quickly and produces more data than anyone can process without good judgment. 

One of the most misunderstood pieces of modern analytics is the tension between speed and precision.  

“There is a need to fulfill needs,” Greg says when describing fast insight cycles. But he also points out that “there is going to be some imprecision that exists there.” Analytics leaders must manage that tradeoff thoughtfully. 

This means understanding when a quick read is enough to guide action and when deeper analysis is necessary. It also means being able to explain that balance to partners across the business. 

In Greg’s view, leaders who cling to old standards of perfection will struggle. Those who ignore rigor in the name of speed will create risk. The real skill lies in managing the boundary between the two. 

Earlier in his career, Greg saw how difficult it was to connect different data sources. “There were very well-established silos of information,” he says, and “it was very difficult to connect the dots between them.” 

Modern analytics leaders cannot afford that separation. Marketing decisions now blend insights from digital behavior, brand tracking, media exposure, retail performance, experimentation, creative assessment, and more. Leaders must be able to bridge these worlds, not just operate one of them. 

Greg explains that the expectation today is clear. “You have to find a way of making it happen.” That includes connecting insights from different functions and understanding how they come together to shape overall impact. 

Greg’s view on technology is straightforward. It is essential, but it is not everything. 

He talks about why Coca-Cola embraces technology with energy and enthusiasm. Speed matters. Automation matters. Scale matters. The ability to combine diverse data sources matters. AI adds new layers of power in creative analysis, predictive modeling, and diagnostic work. But technology has limits. 

He says that even the most advanced tools do not replace human understanding. “Good human understanding is still as necessary as ever before,” he notes. And in areas like AI, he believes leaders must understand both the strengths and the risks. That includes being able to evaluate when an AI output is useful and when it requires deeper examination. 

Analytics leaders today need enough technical fluency to work with engineering, modeling, and data science teams. They also need enough research grounding to challenge a dataset, test a hypothesis, or ask whether a source truly reflects the audience. 

This does not mean they need to be coders or model builders. But they must be able to guide a process that depends on both. 

Coca-Cola has a long history with AI. The company was exploring AI techniques in marketing mix modeling two decades ago. Greg worked on AI-based systems for virtual copy testing roughly six years ago. More recently, Coca-Cola partnered with OpenAI to experiment with generative AI. 

Today, the company is developing a system called LearnX that applies AI to creative optimization inside Studio X, Coca-Cola’s partnership with WPP. 

Greg describes AI as something that helps work go “faster,” “deeper,” and produce “wickedly insightful information.” But he also makes an important point. AI becomes more powerful when combined with consumer research. 

“It is very important to incorporate consumer research information in order to take AI up to the next level,” he says. 

For analytics leaders, this means understanding AI in the context of broader insight practice. Leaders must know how to: 

• Choose where AI can add real value 

• Separate signal from noise 

• Combine AI outputs with research inputs 

• Identify when a model is overreaching 

• Support teams in using AI responsibly 

These skills matter more as AI becomes more embedded in marketing processes.

Greg also talks about the importance of experimentation at scale. Coca-Cola uses a mix of digital data, research inputs, and experimentation to understand performance. 

Leaders must be willing to explore new methods, test new models, and run controlled experiments. But they also must know when something is not working and be willing to stop. 

“You have to be willing to try different things,” Greg says. “Know when to proceed, when to cut your losses.” 

This blend of exploration and discipline is another core leadership skill. The goal is not constant reinvention, but thoughtful testing that moves the practice forward. 

When asked about what the next major research revolution will be, Greg looks beyond the current wave of AI to something more distant. 

“Right now, the big revolution we have is the rise of AI,” he says. “If you fast forward another ten years, the next extremely big trend is going to be the melding of biology with computational power.” 

Leaders will need to prepare for a world where analytics touches new types of data, new forms of personalization, and new intersections of science and computation. The environment will keep shifting, and leaders need to be comfortable shifting with it. 

Not every organization will face the same complexity as Coca-Cola. But the skills Greg highlights apply broadly. The modern analytics leader must be able to: 

• Ask better questions 

• Connect data across functions 

• Balance speed with precision 

• Understand technology and challenge it 

• Lead experimentation 

• Interpret results through a human lens 

• Communicate insight clearly 

• Guide teams through change 

• Stay curious 

These skills matter whether you lead a global insights team or a small analytics function inside a growing brand. They determine whether data becomes actionable or simply accumulates, whether AI becomes useful or distracting, and whether insights become part of how decisions get made. 

Delineate supports teams that want to build these skills into the way they work. By providing real-time brand and campaign tracking grounded in strong research practice, Delineate gives teams the clarity, speed, and structure they need to operate with confidence. Get in touch with us today! 

Listen to the full conversation with Greg Pharo on Research Revolutionaries: https://www.research-revolutionaries.com/4-21st-century-research-how-coca-cola-moved-to-real-time-campaign-tracking/