Experience TV episode 12: Practical AI for the modern marketer featuring Chris Penn and Stephen Streich

July 14, 2021 | 6 minute read
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Welcome to Experience TV, a live show on social channels about the economic revolution we’re living through—the Experience Economy—where brands compete on the quality of their customer experiences.

Here you’ll find the replay of our latest episode and all resources mentioned within. Follow me, Katie Martell—on TwitterLinkedIn, or the show’s Facebook page—to catch future episodes.

Episode 12 discussed what is possible with artificial intelligence (AI) ­today, and practical ideas for leveraging this technology within your customer experience (CX) or marketing practices.

My guests were Chris Penn, co-founder and chief data scientist at Trust Insights, and Stephen Streich, vice president of product management for Oracle Marketing.

Watch below and read on for a few takeaways.

Trend of the week: AI

Our trend of the week was AI, the intelligence within machines.

In the 70 years since pioneering computer scientist Alan Turing dared to ask, “Can machines think?”, marketers and CX professionals have arrived at a response somewhere between hype and reality.

Research from IDC shows that by 2024, the market for AI software, hardware, and services is expected to break the $500 billion mark.

Experience of the week

Customers calling into Nestlé’s Toll House™ brand customer service line posed more questions about recipe troubleshooting than requests for product information, which created an inconsistent customer experience. Not every employee on the other end of the line was a baker.

Enter Ruth, the company’s new AI-driven Cookie Coach!

AI-driven Cookie Coach

The coach is named after Ruth Wakefield, the founder of the Toll House Inn and inventor of the chocolate chip cookie.

This interactive cookie coach can help customers bake the original Toll House cookie recipe step-by-step, customize the recipe based on dietary restrictions, or answer quick questions about baking cookies. The team calls this “cookie 911.”

Artificial intelligence-driven product placement

An interactive digital Cookie Coach is just one example of AI being used to enhance the customer experience. Another use case puts a spin on a more traditional piece of the marketing toolkit—product placement, now a lucrative $20 billion industry.

Brands can now digitally add products and ads to older movies or TV shows after filming, seamlessly inserting computer-generated images with the help of AI. With the rise of personalization, these ads could be tailored to individual viewers in the future based on viewing activity and behavior.

Research on artificial intelligence attitudes among marketers

My research of the week came from the Marketing AI Institute and Drift, who surveyed marketers in late 2020 about their attitudes and experiences with AI.

34% of marketers are in a pilot phase with AI, piloting some quick-win projects. 56% say they’re in a learning phase, understanding how AI works and exploring use cases and technologies.

The interview: Chris Penn and Stephen Streich

To help understand how AI has the promise to transform our marketing and customer experience efforts, I sat down with two experts on this topic: Chris Penn, co-founder and chief data scientist at Trust Insights, and Stephen Streich, vice president of product management for Oracle Marketing.

Where exactly are we in the adoption of AI among marketers?

Chris Penn: 100% of marketers use AI today, whether they know it or not.

If you’re in your marketing automation software and it tells you, “Hey, your lead scores have gone up by this,” or “These five leads have anomalies,” you’re using machine learning.

Where people are missing out on the value, though, is in the customized implementation of it. It’s good that vendors are incorporating AI into their products, but businesses that are very forward-thinking and willing to make the investments in compute power, people, knowledge, and process can get outsized results from AI.

Stephen Streich: I’ve been with Oracle Eloqua Marketing Automation since 2007, and back then, we worked to convince marketers that marketing automation would make their jobs better, faster, and more efficient through the use of technology.

Really, when you describe AI for marketers today, it has the same benefits. The toolset has changed, but the goals aren’t too different. What is possible is different.

Fifteen years ago, there wasn’t a way to analyze a website and have natural language processing tell you what the content was about or identify a cat in a picture. But we’ve got new tools in the toolkit now.

Vendors have woven AI into common jobs to make them easier, faster, and better—and that value is always on and continuous. For example, it’s being used to optimize the send time of an email and running A/B tests. You can see the benefits right away.

At Oracle, our customers are benefiting from some form of machine learning or advanced algorithms that are driving mechanisms inside of Oracle Eloqua, such as making orchestration decisions on the canvas or more complex scoring algorithms.

From a maturity level, pretty much all of them are benefiting from it, whether they realize it or not!

Where adoption has been slower is on less obvious benefits, such as understanding your ideal customer profile. Trust in AI is a big issue here.

What should more marketers take advantage of with AI?

Stephen Streich: Something that’s less obvious but has a lot of traction for us lately is what we call fatigue analysis, understanding somebody’s level of interest in communicating with you.

When you look at the volume of messages you’re sending across different channels and their engagement with those messages, you can put them into cohorts automatically and identify those that are starting to become fatigued.

We stamp that value onto a contact’s record so that it can be used for segmentation, personalization, and orchestration.

You may even choose to withhold someone from a campaign because they’re not really all that active with you. If you keep spamming people who are not engaging with you, you’re going to decrease your open rates and possibly hurt your deliverability.

Instead of sending them an email, consider targeting them on a different channel. Put them into a LinkedIn audience, for example.

Anything that helps with automating decisions, that’s where we’re really trying to focus.

Chris Penn: Attribution analysis is the most common request we get from clients.

Read how Trust Insights used natural language processing to improve PPC ads with Google for AAA. 

What will prevent success in AI for marketers?

Stephen Streich: Within companies, structural issues around people and processes can be very problematic. Teams and applications are often siloed from each other, but I think the technology is well ahead of that.

Ultimately, it’s a bit of a garbage in/garbage out problem. If you don’t have the connected data set necessary to drive the right calculations or the right sort of training, then you’re serving someone at a standstill—or you’re at least going to get substandard results.

Determine which pieces of your tech stack have the most impact with marketing, service, sales, and commerce data, and have that in your own data lake, or in an off-the-shelf customer data platform (CDP).

I think CDPs are having a moment because they promise to bring things together. It’s a way to traverse identities, a way to pull together signals from different types of sources. But more importantly, most CDPs also then provide a way to actually drive decisions or have intelligence on top of the data.

Breaking down departmental silos so that people can actually orchestrate their activities, share the data, and coordinate campaigns together is a big challenge we see with some of our customers. It’s not necessarily the technology that’s holding them back.

Learn more about the Oracle Unity Customer Data Platform with an interactive demo.

Chris Penn: AI is like a blender; it’s a tool. If you’re making steak, it’s not going to be helpful. It’s all about the ingredients, the recipe, the person, and the outcome you’re after.

Very few teams do data cleansing very well. There’s a whole slew of great data, like behavioral data, that nobody’s doing anything with.

The biggest challenge we’ll have with AI in general is that it’s trained from human data, which means all of our biases and misconceptions are baked into our systems. Our systems have to be fair, accountable, values-based, and explainable. Right now, most AI systems are none of those things.

AI is nothing but software that machines write. What goes into that are all the things we provide. One big blockage to success is when the system does something antithetical to our brand.

Interview has been condensed and edited.

Resources from Chris Penn for marketers who want to learn more about AI:

  1. AI for Marketers: An Introduction and Primer, Third Edition
  2. Data Science 101 for Marketers Workshop.

Katie Martell

Katie Martell is the host of Experience TV, a show about the economic revolution we’re living through, the Experience Economy. She is known as an “unapologetic marketing truth-teller,” a LinkedIn Top Voice in Marketing, and "one of the most interesting people in B2B marketing.” Her forthcoming documentary and book, "Woke-Washed," examines the collision of social movements and marketing, and she is the author of "Trust Me, B2B," a short book about building long-term trust. Follow her on Twitter @KatieMartell and subscribe to The World’s Best Newsletter at Katie-Martell.com.

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