Christopher S. Penn is an authority on digital marketing and marketing technology. A recognized thought leader, author, and speaker, he has shaped four key fields in the marketing industry: Google Analytics adoption, data-driven marketing and PR, modern email marketing, and artificial intelligence/machine learning in marketing. Known for his high-octane, here’s how to get it done approach, his expertise benefits companies such as Citrix Systems, McDonald’s, GoDaddy, McKesson, Toyota, and many others.
Mr. Penn is a highly-sought keynote speaker thanks to his energetic, informative talks. He is an IBM Champion in Watson Analytics, co-founder of the groundbreaking PodCamp Conference, and co-host of the Marketing Over Coffee marketing podcast.
He is the author of over two dozen marketing books including bestsellers such as AI for Marketers: A Primer and Introduction, Marketing White Belt: Basics for the Digital Marketer, Marketing Red Belt: Connecting With Your Creative Mind, Marketing Blue Belt: From Data Zero to Marketing Hero, and Leading Innovation.
—The Measurement Standard: Welcome back! We’re thrilled to have you back with us on The Measurement Standard.
Our focus this month is on Data and Doubt. From fake news to Adobe’s recent estimate that nearly 28% of website traffic shows “non-human signals,” how much trust can we really put in the data we are collecting? Do you see this as a problem—and, if not, why?
—TMS PR professionals, marketers, and communications pros are getting more acclimated to looking at data, such as Google Analytics. They are still a long way off, generally speaking, from doing analysis of large data sets. Is this a skill set that you see as becoming more important to these professionals, or will it give rise to a new profession within communication’s ranks: the data specialist with communications skills (rather than the comms specialist with data skills)?
—TMS: What should we look for in a data set to provide us with some point of reference that the data are “clean”? Are there some data sets that are more suspect (with respect to accuracy) than others, either because of the collection process or the data set size? How should communicators and marketing professionals assess data that might be useful, but might also not be “perfect”?
—TMS: One other aspect of data distrust is the information provided to marketing, advertising, and PR professionals from platforms, such as Facebook and Twitter. Should we trust information provided from platforms if there isn’t transparency in the collection and analysis? If we do use these numbers, are there any cautions we should take when reporting on these—and, are there any tools, tips, or tricks we can use to provide verification or a “gut check” on the info provided?
—TMS: If you could invent one magical measurement or evaluation tool to accomplish anything, what would it be?