Computer wiring.
PHOTO: tony_duell

CMOs had better get accustomed to implementing artificial intelligence (AI) or machine learning into their marketing toolkits. According to the CMO Survey (PDF) released earlier this year, CMOs said their marketing team’s AI implementations were likely to almost triple over the next three years. Currently, CMOs averaged a 1.93 when asked to rate their AI implementations into marketing toolkits (1=Not at all; 7=very likely). But in the next three years they see those implementations at a 3.48.

CMOs will have to cope with AI’s impact on content generation, technology advances, regulations, misuses, challenges, effective use cases, and so on, according to Scott Liewehr, CEO of Digital Clarity Group. “CMOs both want it and fear it, overhype it and underestimate it, misuse and abuse it, and on and on,” Liewehr said. 

AI Should Makes Recommendation Not Decisions

What are CMOs’ current investments, challenges, long-term plans around AI as it relates to marketing processes and tools? For AI to be truly effective, it needs human oversight, according to Alicia Tillman, global CMO of SAP. “While it’s unmatched in its ability to transform a pool of raw data into actionable insights and recommendations, they’re just that: recommendations,” Tillman said. “Marketers are still testing the waters on how to best infuse AI into their decision-making process that preserves a truly empathic connection to what customers want and need.”

In the long run, Tillman said her teams will be looking at the holistic ways AI can support the customer journey. How can it better pick up the emotional cues of customers and proactively build experiences accordingly? “We’re not just looking to improve the operational intelligence of these solutions,” she said, “but their emotional intelligence as well.”  

Related Article: Cutting Through AI Marketing Hype: It's About Machine Learning

Where AI is Providing Value

Heidi Bullock, CMO at Tealium, said AI can provide tangible value in a few areas that are hard for marketers alone to do at scale and with accuracy. The areas she likes the best are:

  • Performance: How can data be used to predict what my buyer wants? How can I be sure I am spending my ad dollars efficiently?
  • Personalization: How can I better engage buyers using personalization across channels like email, ads and my website (including chatbots)?

“In past organizations, the marketing teams I was a part of leveraged next best content offers, optimizing subject lines and personalizing via the website,” Bullock said. “In my current organization, I am fortunate enough to take this a step further and really start at when data is first consumed.”

Collection and Handling of Data is Key

With a lot of AI projects, insights or predictions are limited by the quality of the data being collected, Bullock said. However, she predicted in the future, further adoption of AI as more vendors offer these capabilities to marketers. “It helps marketers focus on their day job versus data wrangling,” she said. “Yet, to make AI a true success, teams should closely understand how they collect and handle data to ensure AI can be applied to a trusted foundation first.” 

Related Article: 6 Steps to Lay the Foundation for AI in Marketing

You Can’t Just Turn on AI in Marketing

Tricia Gellman, appointed as CMO of Drift this month, said in a past role, the first thing she learned when implementing AI is that taking advantage of AI requires new roles on your marketing team as well as planning and coordination. “AI sounds like a thing you just turn on,” Gellman said, “but really you need to inform an AI engine to get the most out of it.” 

Start With a Data Scientist

In past roles, Gellman has used AI to help predict engagement across channels and to expand share of wallet within the customer base. To start these projects she said her team first needed a data scientist to help assess data. “Could we find patterns in the data that aligned to the results we wanted to achieve?” she said. “Understanding this is really helpful and taking these known patterns and applying them to AI tools can have strong results.”

Specifically around expanding share of wallet, her teams looked at customer adoption of products to determine key indicators that lead to adoption of future products. “We were able to apply AI to this data,” she said, “and bring in other third-party, real-time data sources like intent to execute playbooks across marketing touches and sales outreach leading to higher returns in our activities, increased customer engagement and more revenue per customer.”

Related Article: 3 Misconceptions About AI in Marketing

The Future: Building Better Human Connections

What can CMOs expect for the future of AI and machine learning in marketing processes? More implementations, for one, as we initially reported. Also according to the CMO Survey, the top uses for AI in marketing are:

  • Content personalization.
  • Predictive analytics for customer insights.
  • Targeting decisions.
  • Customer segmentation.
  • Programmatic advertising and media buying.

“AI’s greatest impact for marketers will be how it elevates us to build better human connections,” SAP’s Tillman said. “When time isn’t spent on repetitive, administrative tasks, that frees up a tremendous share of mind that can be dedicated to the creative solutions and experiences that stand out for customers. In today’s market, that couldn’t come at a more opportune time. When there’s such a trust gap between brands and their customers, it’s the marketer’s responsibility to champion more authentic and transparent relationships. Marketers will be able to push boundaries further and tell better stories because they’ll have the tools and the resources to do so.”