AI to Pull in Galore of Insights on Customer Experience

A wave of new AI technologies will revamp marketing strategies, generating gobs of particulars about consumers.

Chief marketing officers will likely develop future strategies based on the transformational influence of artificial intelligence such as emotion AI, influence engineering and generative AI.

According to a recent report from technological research and consulting firm Gartner, the accelerated fragmentation of digital media poses challenges to marketers in advertising, and AI can be deployed to help target audiences, measure impact and assist with identity resolution, as well as consent and preference management.

Mike Froggatt, senior director analyst in Gartner’s marketing practice, said based on the company’s analysis, up to half of marketers are already using AI in some way.

Marketers can optimize their campaigns by directing users down a customer journey with an orchestration platform or by using natural language processing for things like chatbots, he said.

“Marketers are working on things like identity resolution and consent and preference management,” he said. “There are AI-driven tools out there that are helping marketers produce and use alternatives.”

Froggatt said all marketers who build a roadmap to deploying AI tools are going to have their unique challenges. For example, a legacy consumer packaged goods (CPG) brand is going to have a ton of historical data that it can model from, while a newer direct-to-consumer or business-to-business (B2B) brand will have a smaller data set.

“In that case, a generative AI can artificially create segments or content to speak to more niche types of targets within their marketing practice,” Froggatt said.

Influence Engineering to Steer Customer Experience

As it develops, influence engineering will use AI to better guide customers toward what they’re seeking and what they may not even realize they’re seeking.

“If deployed responsibly and effectively, I think it can have tremendous impacts on digital marketing,” said Raj De Datta, CEO and co-founder of Bloomreach, a digital customer experience provider. “Consider, as a consumer, how frustrating many digital experiences still are today. This extends from the time it takes trying to complete a single task, difficulties in finding the right brand, to how many products you sift through before finding what you want.

“AI has the ability to simplify your digital life, guiding you on a journey that is relevant to you as a consumer and personalized to your preferences,” he added. “From a digital marketing perspective, simplifying that journey — and ultimately, making it more enjoyable — can not only delight customers but can also drive real business value.”

Related Article: Why Marketers Should Explore These 4 Emerging Technology Trends

The ‘Virtuous Circle’ of Digital Marketing

Caroline Carruthers, CEO of business consulting firm Carruthers and Jackson, said CMOs have recognized the benefits of using AI, and the key to successfully implementing these tools is to ensure using data and AI in marketing is a “virtuous circle.”

“The data and the AI inform them about customers’ wants, needs and moments where they can have an impact — predictions on their behavior,” she explained. “Then you need to be able to test: ‘Well, if I did X, Y and Z, did what I want to happen happen?’”

The goal is to create this circle of knowing enough to get consumers to do what you want them to do, or otherwise figure out what you need to do. CMOs should be constantly fueling their knowledge base on how they can improve, she said.

She pointed out that no AI tool or technology, no matter how sophisticated it may be, is going to be much help without first having the right data to feed it.

“If the CMO is in an organization that already has a chief digital officer or a director of data, then they should form a very symbiotic type of relationship with that person because they can let them deal with the input problem,” Carruthers said.

The key is not to try to solve all the perceived data problems and all the data quality issues, but rather to focus on the minimum amount of data needed to be able to fuel the tool to do what it’s been adopted to do.

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