Automation of Marketing Models

9 Pages Posted: 27 Nov 2018

See all articles by Rajkumar Venkatesan

Rajkumar Venkatesan

University of Virginia - Darden School of Business

Jenny Craddock

University of Virginia - Darden School of Business

Noreen Nagji

University of Virginia - Darden School of Business

Abstract

This technical note gives students an overview of artificial intelligence (AI) and machine learning (ML) in order to help them understand how these fields can contribute to the future of marketing. To provide context, students are first introduced to the history of AI and the basic parameters of AI, ML, and deep learning (DL). The differences between ML and statistical modeling are also described to help students understand that collaboration between these two fields results in better decision-making. The note also provides a description of descriptive, predictive, and prescriptive analytics and how various ML tools span those categories.In order to illustrate AI's applications and the many ways managers can use it to promote their brands, real-world examples are provided, including: (1) 1-800-Flowers' collaboration with the Facebook messenger platform to process orders through chatbots (using DL), (2) Facebook's use of DeepText to determine the meaning of words within their contexts (using DL) and then direct users to related products; and (3) online educator Udacity's use of an ML algorithm to create a bot that advises salespeople on successful words and phrases, but also allows the humans to answer more obscure customer questions, among others. As students consider how AI advances are helping brands such as these market their goods and services to new customers online, students also must consider the ways that AI will continue to shape marketing in the future.

Excerpt

UVA-M-0965

Nov. 6, 2018

Automation of Marketing Models

In 2016, the ongoing tug-of-war between human capability and that of machines reached a critical turning point in the realm of image recognition. As recently as 2010, machine algorithms had a 30% error rate when attempting to identify images from ImageNet, a large database of over 10 million obscure images, lagging well behind the stagnant 5% human error rate. By 2016, however, machines had made such strides in their image recognition capabilities that the error rate had dropped to 4% for the best systems, thus edging out the human eye for the first time in history.

Image recognition provides just one example of how artificial intelligence (AI) has progressed over the past several years and how quickly it will continue to evolve. What do these advances mean for businesses, and, more specifically, how will they help brands market their goods and services to new customers?

Marketing and Artificial Intelligence

. . .

Keywords: artificial intelligence, machine learning, deep learning, marketing analytics, automated marketing

Suggested Citation

Venkatesan, Rajkumar and Craddock, Jenny and Nagji, Noreen, Automation of Marketing Models. Darden Case No. UVA-M-0965. Available at SSRN: https://ssrn.com/abstract=3291191

Rajkumar Venkatesan (Contact Author)

University of Virginia - Darden School of Business ( email )

P.O. Box 6550
Charlottesville, VA 22906-6550
United States

HOME PAGE: http://www.darden.virginia.edu/html/direc_detail.aspx?styleid=2&id=5808

Jenny Craddock

University of Virginia - Darden School of Business ( email )

P.O. Box 6550
Charlottesville, VA 22906-6550
United States

Noreen Nagji

University of Virginia - Darden School of Business

P.O. Box 6550
Charlottesville, VA 22906-6550
United States

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