Member Only Access
As the consumer journey becomes increasingly complex, many marketers are turning to artificial intelligence to decipher it. But how does it actually help them be more effective at understanding and reaching consumers at different stages?
What makes the purchase journey so complex is the endless supply of consumer-curated data. “Consumers express their needs and wants, attitudes, and values in various forms (through search, comments, blogs, Tweets, ‘likes,” videos, and conversations) and across many channels (web, mobile, and face to face),” Jan Kietzmann (University of Victoria), Jeannette Paschen (KTH Royal Institute of Technology, Stockholm, Sweden), and Emily Treen (Beedie School of Business at Simon Fraser University, Burnaby, Canada) wrote in the Journal of Advertising Research.
Marketers face the task of transforming this flow of big data into valuable consumer insight, and AI offers the tools to do so. The challenge starts with understanding the types of input data that AI deals with:
- Structured data, the traditional standardized datasets, such as basic customer demographics, transaction records, or web-browsing history. “AI, with its enormous computing power,” the authors noted, “runs complex computations on large volumes of such structured data and often produces results in real time.”
- Unstructured data: “about 80% of the approximately 2.5 billion gigabytes of daily user-generated data are unstructured and provided as written texts, speech, and images….AI’s ability to process large volumes of this type of data—and to do so very quickly—is what distinguishes it from traditional computing systems.”
AI takes these inputs and processes them into various components, or building blocks, the results of which “vastly outperform our natural intelligence—to advertisers’ benefit.”
Jan Kietzmann is an associate professor at the University of Victoria, Canada, focused on organizational and social perspectives related to emerging technologies. His research can be found in the Journal of Advertising Research, Industrial Marketing Management, California
Management Review, and Business Horizons.
Jeannette Paschen is a doctoral candidate in industrial marketing and entrepreneurship at the KTH Royal Institute of Technology, Stockholm, Sweden. Her work is published in Business Horizons, Online Information Review, and IT Professional.
Emily Treen is a doctoral candidate in marketing at the Beedie School of Business at Simon Fraser University, Burnaby, Canada. Her research specialty is the interface between marketing strategy and entrepreneurship. Her work can be found in Business Horizons, GfK Marketing Intelligence Review, Journal of Product and Brand Management, and Journal of Public Affairs.