The study involved 149 ads across 35 brands and 22,334 people in six countries. Realeyes measured how people felt while they watched the ads by using artificial intelligence to analyse their facial expressions through their webcams (with their consent). The study was designed in collaboration with the Mars Marketing Laboratory at the Ehrenberg-Bass Institute for Marketing Science. Realeyes' emotion data was cross-referenced with Mars, Incorporated's known sales lift data for each ad to investigate the relationship between emotions and sales performance. This created the largest emotional dataset linked to real business outcomes currently in existence.
Suppose that, someday in the future, a data-besieged marketer could use menu commands or plain English text to ask questions about any data. That future, according to a company called Equals 3, is here, and it's called Lucy. Launched recently, she is the first marketing-focus portal built on services provided by IBM's now-legendary cognitive supercomputer, Watson. "There is nothing in the marketplace like Lucy," Equals 3 Managing Partner Scott Litman told me. "She is a user interface for all my marketing systems."
For years, marketing was considered more art than science. But more recently, as marketing automation software has proliferated, marketers have had to blend the art of storytelling with the science of data. Then along comes artificial intelligence (AI) and machine learning, which promise to help marketers make sense of all that data. Some experts believe AI's impact on marketing will be hugely significant, that it could even change the nature of marketing entirely -- enabling brands to break through the noise and deliver a more personalized experience to customers. Not surprisingly, though, there are challenges ahead for organizations seeking to add AI to their marketing technology stack.
Overview: Artificial Intelligence is a technology that uses machine intelligence and human like thinking ability to process historical, and increasingly, real-time data to make predictions, recommendations, and decisions. AI is not a single technology but a convergence of various technologies, statistical models, algorithms, and approaches. Machine Learning is a subfield of computer science that evolved from the study of pattern recognition and computational learning theory in AI. Cognitive Computing involves self-learning systems that use data mining, pattern recognition and natural language processing to mimic the way the human brain works. AI is increasingly integrated in many areas including Internet search, entertainment, commerce applications, content optimization, and robotics.