Clearing Up the Biggest Misconceptions About AI in Digital Marketing

Eva Maria Schmidt
February 13, 2020

Artificial Intelligence continues to be one of the most trending buzzwords, especially in marketing. But as is the nature of new technologies and market segments, not everything lives up to the hype. 46 percent of marketing executives say they’re not where they want to be in terms of delivering personalization. And according to the October 2019 Chief Marketer “2019 Martech Outlook Survey,” AI/machine learning ranks low on the list of planned marketing technology investments, with only 16 percent planning to invest in it.

Why the disconnect? There are a few reasons, not the least of which is the issue of implementation. (You can learn more about these issues in depth in our new eBook, Artificial Intelligence (AI): Powering the Personalized Future of Marketing.) But beyond that is the simple issue of misunderstanding. According to Alexei Kounine, Vice President, Innovation & Solution Consulting and AI Lead at Marigold Engage,

“Artificial intelligence is a transformative technology, but there are currently many different definitions and perceptions of what it is and what it does, especially in marketing.”

AI continues to rapidly spread across multiple industries – and those who get on board now will be the ones reaping the benefits sooner. So let’s help clear up some misconceptions.

What AI is not…

An isolated technology or application. For marketers, it only unfolds its full potential as part of a marketing cloud.

A customer-facing intelligence with a name like “Alexa” or “Siri.” AI in marketing works in the background and adds powerful intelligence to tasks such as micro-transactions, personalized offers, and dynamic content.

A substitute for lack of consumer intelligence. The AI is only as good as the data you put in.

What AI in marketing really is…

We define AI in marketing as how machine-learning algorithms work to understand customer intelligence.

What AI really does for marketers…

Send-Time Optimization. Find the right time and preferred channel for interaction. AI-powered marketing automation helps design better, more responsive customer journey maps based on real-time data points from millions of touchpoints. Instead of being stuck with a static marketing funnel, CMOs define an end goal and watch AI adjust the roadmap to get there.

Audience Segmentation. Segmentation and audience selection based on Universal Consumer Profiles. “With the dynamic nature of marketing, it’s not always just about the recommendations you can spin up, it’s also about identifying the right targets for specific offers,” says John Hernandez, CEO of Selligent. “Smart Audiences ensures that you don’t exhaust your customers by over-delivering messages that are not of interest, and ensuring that all offers are personalized and meaningful. Companies that do not deliver on relevance risk losing their customers for good.”

Live Personalization. AI engines help personalize websites and messages with dynamic content. According to research by Gartner, among marketers in Canada, the UK and the US who are using AI and machine learning to support their marketing activities, 30 percent are already using the technology for personalization.

Customer Service. Chatbots and automated responses to queries. Right now, about 27 percent of customers cannot say with certainty whether their last brand interaction was with a human or chatbot. According to a Juniper Research study, chatbots will help companies save up to $8 billion per year by 2022.

Predictive Offers. AI enables marketers to create predictive offers, providing the right offer for the right customer, based on customer history, retail inventories/warehouse, business logic, etc. According to Forrester Research, 82 percent of international marketing decision makers say that predictive marketing will be key to staying competitive in the future.

Connecting the dots: AI in marketing clouds

21.3 percent of companies say that the quality of existing AI tools is their biggest challenge to adoption. The reality is, most AI solutions “live” isolated in the marketing tech stack, with the expected silos and integration issues associated with that fact.

The marketers who figure out how to connect their AI engines with high-quality consumer data streams will be the ones leading the way into the hyper-personalized future of marketing and customer experience. Their ranks already include clients of Marigold Engage, as our platform solves the challenge of AI data integration out of the box.

Marigold Recommendations is a natively built AI layer that seamlessly blends into our platform, offering the full potential of AI for marketers, by combining all the consumer data available in the platform with state-of-the-art machine learning algorithms, to help you boost conversion, visitor engagement and loyalty.