2019 will be a pivotal year for artificial intelligence (AI) in marketing and beyond. Of course, AI is all around us already in our personal lives as consumers and in our professional lives as marketers.
Most of what you watch on Netflix or buy on Amazon comes from a recommendation made by AI algorithms. Google is an AI-first company. If you run a search on Google; if you ask Google assistant for anything; if you use Google Maps; if you accept the smart replies when composing an email in your Gmail account; artificial intelligence (AI) is baked into all of that.
And many sales teams, including ours here at Act-On, use AI-powered Gong.io to record sales calls that are then mined for their “conversational intelligence.” InsideSales.com’s State of AI for Sales and Marketing report found more than 90 percent of insiders expect artificial intelligence to improve sales performance this year.
In an MIT Technology Review Insights survey of 600 executives, 9 out of 10 companies already use AI to improve their customer journeys. And according to the NewVantage Partners 2019 Big Data and AI Executive Survey, nearly 92 percent of Fortune 1000 executives (including those at American Express, Capital One, and Ford Motor Co.) surveyed indicated their companies are accelerating the pace of their big data and AI investments.
What Is Artificial Intelligence?
Paul Roetzer, CEO of PR 20/20, created the Marketing Artificial Intelligence Institute after watching IBM’s Watson beat two record-winning “Jeopardy!” contestants. (IBM was back in the news last week when its Project Debater, a six-year-old artificial intelligence debating system, almost beat a much-decorated human in a debate on subsidized preschools.)
Roetzer was a guest on the Rethink Marketing podcast back in 2017, and we asked him for his definition of artificial intelligence. He prefers the definition given by Dennis Hassabis, the creator of a company called DeepMind, which was acquired by Google in 2014 for about $600 million. Dennis says “AI is the science of making machines smart.”
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Your email inbox is probably filled with companies pitching their AI products and services. Maybe a third of that is simply vaporware, another third is just MarTech with a new name, and the final third is some version of AI and machine learning.
Roetzer said the key to separating SmartTech from MarTech is asking this key question, “does it get smarter on its own?”
“Basically, machines on their own, they don’t know anything. They don’t know a table from a chair,” Roetzer said. “They don’t know how to learn and get better at a task. They’re trained to do this using data and different types of processes to do the training.
“And so AI is that. It’s this big picture idea of enabling machines to get smart. And then underneath that are categories like machine learning, which is the most common one you hear. Lately, you hear a whole bunch about deep learning, which is another level of machine learning — a more intense level of machine learning that actually tries to teach the machine to think more like a human in these what’s called ‘neural networks and neural nets.’”
Use Cases for Artificial Intelligence in 2019
So, how are you adopting AI and machine learning in your marketing activities this year?
The Marketing Artificial Intelligence Institute surveyed more than 200 marketers, asking them the question, “Assuming AI technology could be applied, how valuable would it be for your team to intelligently automate each use case?” Below are their top 10 use cases for AI and machine learning.
- Analyze existing online content for gaps and opportunities
- Choose keywords and topic clusters for content optimization
- Construct buyer personas based on needs, goals, intent, and behavior
- Create data-driven content
- Discover insights into top-performing content and campaigns
- Measure return on investment (ROI) by channel, campaign, and overall
- Adapt audience targeting based on behavior and lookalike analysis
- Optimize website content for search engines
- Recommend highly targeted content to users in real-time
- Assess and evolve creative (e.g. landing pages, email, CTAs) with A/B testing
“In 2019, we’ll start to see a distinction between the successful early adopters of AI technology and the lagging companies who have yet to invest, or have invested hastily without a strategy for increasing ROI,” said Unbounce CTO and co-founder Carl Schmidt in an article on CMO.com predicting AI trends in 2019.
AI-Powered MarTech Is Growing
Roetzer’s team has identified 1,141 companies selling sales and marketing solutions with at least some AI powering them. Together, that group has raised more $5.2 billion in funding, which means we’re seeing more and more AI and machine learning solutions for the many pain points and bottlenecks you encounter in your day-to-day marketing efforts. And that is what AI will do for marketers over the next few years — save you time and increase your revenue.
Gartner predicts AI business value will reach $3.9 trillion by 2022. And according to McKinsey, AI could deliver an additional economic output of around $13 trillion by 2030, boosting global GDP by about 1.2 percent annually.
Marketers can leverage AI to help them better understand their customers’ multi-channel path across the funnel toward a purchase. With AI tools and services, you will be able to answer these questions and then optimize for improved performance:
- Which marketing channels are driving revenue?
- What kinds of content help retain customers?
- At which stage of the customer journey does our content perform best?
- Where are customers falling out of the funnel?
The 5 P’s of Artificial Intelligence
Most marketers are familiar with the 5 Ps: product, price, place, people, and promotion. But Roetzer has developed the 5 Ps of AI: planning, production, personalization, promotion, and performance. Here’s how the sequence works.
Planning: Predicting consumer behavior, defining strategies, prioritizing activities, and determining how to allocate marketing resources.
This type of AI-powered marketing product would help you with discovering keywords and topic clusters, constructing buyer personas and segmentation, analyzing content for gaps and opportunities, predicting customer churn, and allocating your paid advertising budget and channels among other uses.
“Planning is one of the least developed areas,” Roetzer said. “The tools aren’t that intelligent yet, because it’s pretty hard to build those things.”
Production: Creating, curating, and optimizing content — including blog posts, emails, landing pages, video, and advertisements.
Roetzer said the Associated Press is using AI to create earnings reports, going from producing 300 human-written earnings reports each quarter to 3,000 written by machines. His company, PR 20/20, started using AI to create Google Analytics reports for its clients.
A few other use cases for production AI include:
- Email subject lines
- Social media updates
- Converting voice to text
- Creating nurture or sales email workflows
“You have to create the templates and train [the machine] the different branching logic,” Roetzer said. “But once you do that, you can tell a data-driven story at scale hundreds or thousands of times instantaneously if you’ve created the templates for it.”
I regularly use Zubtitle to create captions for videos that are being shared on LinkedIn and other social platforms, especially those where most people view them with the sound muted. Zubtitle uses AI to convert the voice to text in creating the captions.
Personalization: Personalizing consumer experiences through intelligently automated emails, content and product recommendations, AR/VR, and web experiences.
According to Roetzer, this is where most venture capital is being invested.
“Almost every existing marketing technology platform, automation player, CRM system, CMS system — they’re all trying to do variations of personalization,” he said. “That could be recommending content on a website, changing out calls to action on a site. Again, things that, right now, a human has to set rules for. The machine can absolutely do that better than a human if it has enough data.”
Use cases include:
- Product recommendations (think Netflix)
- Adaptive email send time for each individual
- Personalized content, offers, and web experiences
AI empowers marketers to build Adaptive Journeys™ that adapt to each individual and their preferred channel and send time while delivering the most relevant message in real-time as the customer goes about their research and discovery process.
Promotion: Managing cross-channel and cross-device promotions to drive engagement and actions — including audience targeting, social publishing, and digital paid media management.
Use cases include:
- Adjusting digital ad spend in real-time by channel and audience
- Testing headlines, landing pages, images, and creative
- Scheduling social posts
- Improving email deliverability
- Better targeting and retargeting
“You just give it the budget and the creative, and it runs all the infinite variations and makes all the changes itself based on performance data,” Roetzer said.
You can use AI-powered platforms to help automate bidding on paid ads and to pause poorly performing ads. Google Ads allow you to also tap into dynamic search ads that automatically generate ad headlines based on a user’s search intent. The platform also has ad suggestions based on machine learning that use models of prior performance to suggest changes to your ads to boost results.
Performance: Turning data into intelligence through automated narratives and insights — and using that intelligence to optimize performance.
Use cases include:
- Lead scoring
- Forecasting performance
- Discover insights
“We mainly look at [performance AI] as taking analytics data and finding insights out of it, and then figuring out what to do next,” Roetzer said.
Google’s Ask Analytics Intelligence tool is an example of Performance AI. You can ask Google Analytics questions about your data, ranging from basic performance to measuring goals and understanding visitor behavior.
Putting AI to Use in Your Daily Marketing Efforts
More and more, artificial intelligence (AI) and machine learning solutions are coming to the market in our personal and professional lives. In fact, you are likely already using AI in some marketing activities you perform each day.
As you consider taking a deeper dive into AI, practice the same tips we suggest for considering your adaptive marketing platform. First, identify the use case for the technology. And, second, ask your current MarTech vendors which AI solutions they are working on. From there, you can begin experimenting with AI to better understand the possibilities of this emerging technology.