<img height="1" width="1" style="display:none" src="https://www.facebook.com/tr?id=2006193252832260&amp;ev=PageView&amp;noscript=1">
5 Insights on AI from Christopher S. Penn That Will Change Your Marketing
Blog Feature

By: Paul Roetzer

Print this Page

April 4th, 2019

5 Insights on AI from Christopher S. Penn That Will Change Your Marketing

Marketing expert Christopher S. Penn knows a thing or two about artificial intelligence. He even wrote an entire book on it, called AI for Marketers.

When he’s not geeking out about AI, Chris is co-founder and chief innovator at Trust Insights, a firm that provides data insights and predictive analytics for marketers.

Chris is a featured speaker at the Marketing AI Conference (MAICON), where he’ll walk you through how AI will change marketing forever.

In my recent video interview with him, he revealed some key insights on AI that marketers need to understand.

See the full interview here—or my top takeaways below.

1. Marketing is getting worse, but AI can help.

“Arguably marketing’s quality is worsening,” says Penn. “Why? We’re faced with more data than ever.” Yet surveys show that shockingly low percentages of CMOs use analytics to make decisions.

We’re getting overwhelmed by data quality and quantity issues. And marketing is not getting faster or cheaper in response.

However, artificial intelligence “promises three things, if we implement it well,” says Penn:

  • Acceleration: reaching results faster than with human or traditional computing processes.
  • Accuracy: reaching better results than with human or traditional computing processes.
  • Automation: reducing repetitive, low-value work for humans, freeing us up to do more valuable, better-suited tasks.

2. Marketers need to do their homework. 

Artificial intelligence presents marketers with serious opportunities to automate, grow, and scale. But marketers face an uphill battle with AI that they didn’t face with prior new technologies, says Penn.

AI runs on great data and superior decision-making models powered by algorithms. Marketers must at least understand what great data, effective models, and actual AI look like.

“For example, vendor companies are slapping ‘AI’ labels on nearly every marketing software product on the market,” says Penn.

The definition of AI can be interpreted very broadly. “The definition is so broad that they can make a truthful claim, even if their software doesn’t help us achieve our core goals of better, faster, or cheaper,” he says.

Who’s the real deal, and who’s faking it? Marketers need to start asking tough questions of AI vendors and the capabilities of their tools.

3. Marketers need good data.

Data is what makes AI work, so marketers need good data to implement AI, says Penn. That’s easier said than done.

“At Trust Insights, we measure data quality by six factors, the 6C Data Quality Framework. Good data should be: clean, complete, comprehensive, chosen, credible, and calculable,” he says.

Companies that have successfully implemented digital transformation with AI have built their capabilities around their data ecosystems. They’ve figured out how to make money on their data, analyze their data effectively, and use it to power machine learning models.

Learn more about the 6C Data Quality Framework here.

4. Decide if you’ll build or buy.

One of the most-asked questions about artificial intelligence and machine learning is whether a company needs to build their own capabilities in-house or whether they can look to vendors and other third parties, says Penn.

The quick answer?

”If you’ve got more money than time, buy it,” he says. “If you’ve got more time than money, build it.”

He advises breaking down the build vs. buy question further into one of core competencies. If your applications of machine learning are central to your core competency, you’d want to build. That way, you have much more control over the machine learning models and how they’re used.

On the other hand, if you’re applying machine learning to your non-core functions, then buying from a vendor might be the best route.

5. Remember, it won’t be easy.

“The first machine learning project to successfully deploy within an organization is always a long slog to victory,” says Penn. “It takes a significant amount of time, effort, people, and budget to bring a project to fruition around anything that’s a core competency of the organization, because the stakes are so high.”

Marketers need to be prepared to assemble the right tech and talent over a longer period of time to make AI pilot projects happen.

But cutting some time off your learning curve is possible, especially when you read Chris’ book AI for Marketers. It’ll teach you more about what questions marketers should ask of vendors and how to succeed in your AI-powered marketing.

About Paul Roetzer

Paul Roetzer (@paulroetzer) is founder of PR 20/20, author of The Marketing Performance Blueprint and The Marketing Agency Blueprint, and creator of The Marketing Artificial Intelligence Institute and Marketing Score. Full bio.

  • Connect with Paul Roetzer
Disclosure: Marketing AI Institute writes about and recommends AI-powered marketing and sales technology. In all cases, content and recommendations are independent and objective. In some cases, Marketing AI Institute may have business relationships with companies mentioned, which may include financial compensation, affiliate compensation, or payment in kind for products or services. View a list of Institute partners here and MAICON sponsors here.