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Tuesday, May 16, 2017

The Marketing Impact of AI and Machine-Learning: 3 Predictions by 51 ML Marketing Executives |

Photo: Daniel Faggella
"In the last two years, the number of machine-learning (ML) startups has skyrocketed, and companies (in truth or in hype) increasingly predicate their value proposition on artificial intelligence (AI)." notes Daniel Faggella, CEO and founder of both CLVboost, a marketing automation consultancy in Cambridge MA, and TechEmergence, a San Franciso-based market research and discovery platform focused on artificial intelligence and machine-learning.

Photo: MarketingProfs

Although ML and AI in healthcare and finance have garnered a tremendous amount of venture investment and press, other areas, such as marketing and business intelligence, have the potential to more quickly impact profitability, and are less fettered by regulation.

As of last year, there wasn't serious consensus or research into current and future AI marketing trends, so we decided to poll and interview over 50 ML and AI marketing executives. The goal: to determine the industries and applications with the most promise.

The full ML-in-marketing survey, conducted over three months, sheds light on various noteworthy trends and patterns; below, after defining some terms, I've highlighted three major findings.

A Quick Intro to 'Artificial Intelligence' and 'Machine-Learning'
"Artificial intelligence" is a broad term used roughly to describe any task performed by a computer that would normally require human intelligence. This umbrella term covers everything from chess-playing computer programs to Siri to spam filters.

"Machine-learning" is the science of getting computers to learn and improve their learning over time in autonomous fashion, by feeding them data and information in the form of observations and real-world interactions.

Most of the recent buzz about artificial intelligence is due to the incredible breakthroughs in machine-learning, roughly starting with the famous Image Net image recognition competition in 2012. That year, the University of Toronto's Geoff Hinton (now also employed at Google, for good reason) and his team outperformed all pre-programmed "machine vision systems" with a simple machine-learning approach that enabled a computer to recognize images nearly as well as human beings.