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Automation of Marketing Models
Venkatesan, Rajkumar; Craddock, Jenny; Nagji, Noreen Technical Note M-0965 / Published November 6, 2018 / 9 pages.
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Product Overview

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.


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  • Overview

    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.

  • Learning Objectives