Press Club Communicators explore the intersection of business analytics and communications ROI

Communications team members explored the use of business analytics to demonstrate return on investment and glimpsed into the future of predictive analysis using artificial intelligence machine learning concepts during a virtual “Lunch and Learn” event hosted by the National Press Club Communicators Team on Sept. 21.

Scholar-practitioner Professor Beth Egan, associate professor of advertising at the Syracuse University S.I. Newhouse School of Communications, explained how business analytics is used to evaluate communications and how to best communicate such analyses to the C-suite. She also discussed her academic research on predicting audience retention of advertising messages using machine learning.  

The event was moderated by NPC Communicators Team member Mitchell Marovitz, director of the communications, journalism, and speech program at the University of Maryland Global Campus.

Egan, citing the work of Market Motive, Inc. co-founder and author Avinash Kaushik, defined analytics as “the discipline that applies logic and math to data to provide insights for making better decisions.” She noted that gathering and analyzing information isn’t enough. Analysis must lead to decisions, which lead to actions, which starts the analytics process all over again, until the business goals are met. 

“I hear from my colleagues that PR and advertising is manipulative [that] we’re trying to persuade people to do things that they normally wouldn’t do…that we’re creating these products that nobody needs. In my experience, nothing could be farther from the truth,” Egan said.

“We are problem solvers,” she continued. “People have problems and we’re creating products, services and messages that help them solve those problems.” 

Citing a 2014 case in which “The Atlantic" published a native advertising piece from the Church of Scientology that was perceived as “inauthentic,” Egan said that when we get our communications wrong, it can damage the reputation of the organization involved. Return on investment (ROI) often involves the degree to which people are brought into a conversation, she said.

Egan noted that we should consider evaluation once we have established our communications objectives not at the end of the project. 

Once you’ve settled on your goal and objectives, Egan said, it’s easier to determine what kinds of metrics you want to track and analyze. For example, if you are a new organization, you may want to create awareness and that could mean you want to measure impressions. If you’re an established organization but notice you’re not getting your ideas across, you may want to measure click-through rates. If your focus is on conversions or behaviors, identify the activities that drive the action you want.

“If your research question is really well-informed, then it’s much clearer what data you need to answer the question,” she said. 

Egan’s current research focuses on how people perceive content in the moment. Using functional near infrared (fNIRS) brain imaging technology, her team looks for activity in the parts of the brain that register suspicion or distrust. So far, advertising content has higher distrust than informational content from the university website. 

Similarly, in another ongoing experiment, Egan has identified predictors of advertising retention. She said she was surprised to discover how important originality in content was to retention: First run programs had better retention than reruns.

While noting that machine learning provides better predictive data, she says the “why” answer is “…often enough to drive our strategic communications plans. We don’t always need to predict in order to create that strategy.”

Egan summarized the session by returning to the beginning: Know the goal, determine the metrics that will demonstrate how well you are moving towards the goal, collect it, analyze it, tell the story of your analysis to the C-suite so they will allow you to act on it, then measure the results of your actions.  

Egan closed by citing Einstein: “If I had an hour to solve a problem, I’d spend 55 minutes defining the problem and five minutes solving it.”