Marketing and Artificial Intelligence: Personalization at Scale

Marketing and Artificial Intelligence: Personalization at Scale

Sujata Ramnarayan (Notre Dame de Namur University, USA)
DOI: 10.4018/978-1-7998-5077-9.ch005

Abstract

Technologies are changing marketing due to the amount of information available to consumers, along with information being generated by consumers. Marketers face a challenge with greater volume and variety of data generated at a faster rate than ever before along with fragmentation of channels. This data when combined with artificial intelligence presents an opportunity to marketers to provide value add at a granular level and a personalized customer experience round the clock 24/7/365. Treating customers as individuals by offering an optimized personalized offering, sending the right personalized message at the right time through their preferred channel is the promise of data fed into AI algorithms. Artificial intelligence has the potential to transform companies by making sense out of an insanely voluminous variety of data being generated with its ability to serve customers more effectively and efficiently, personalizing at scale.
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Introduction

The successful marketer today needs to be conversant in new technologies transforming businesses overall and marketing in particular. One such transformative technology is artificial intelligence (AI). The objective of this chapter is to help the reader understand the impact of AI on marketing. The first part of the chapter covers what AI means and why AI is becoming more important in marketing. This is then followed with an overview of how AI works, its numerous applications in marketing, and its current and future impact on marketing.

What Is AI?: Through the Lens of Intelligence

Discussion of AI would be incomplete without an understanding of the concept of intelligence itself. In the simplest terms, intelligence involves intake of information, followed by processing, leading to other types of actions as a result. Intelligence has been defined as the capacity to reason validly about information (Mayer, 2004). Intelligence has also been referred to as the ability to “face problems in an un-programmed manner (Gould, 1981)”. Simple though the definition sounds, intelligence is not uni-dimensional.

The multiple intelligences theory views intelligence as a multi-dimensional concept with seven different types of intelligences (Gardner, 1983). These include logical mathematical reasoning, musical intelligence, bodily kinesthetic intelligence, spatial intelligence, interpersonal intelligence, intrapersonal intelligence, and linguistic intelligence. These intelligences could also be treated as representing the multi-dimensional nature of the concept of intelligence itself (Morgan, 1992). As Morgan (1992) states, an element that is common to intellectual functioning is the ability to solve problems through information processing. However, the granular distinction of such intelligences (Gardner, 1983) or as referred to as cognitive capabilities by Morgan (1992), is relevant to the field of artificial intelligence.

The purpose of AI, from the time the term was coined, has been to emulate the human mind and brain which is complex in its functioning. Artificial intelligence refers to such intelligence emulated by computerized machines, with the term attributed to John McCarthy, although envisioned earlier by Turing (Horvitz, 2016).

Key Terms in this Chapter

Unsupervised Learning: A machine learning technique that involves providing a machine with data that is not labeled, instead allowing for the machine to learn by association.

Supervised Learning: A machine learning technique that involves providing a machine with data that is labeled.

Deep Learning: Another term for unsupervised learning that includes reinforcement learning in which the machine responds to reaching goals given input data and constraints. Deep learning deals with multiple layers simulating neural networks with ability to process immense amount of data.

Algorithm: Rules that allow AI to learn patterns in the data, classify, and to predict.

Natural Language Generation AI: The ability of AI technologies to create and generate unstructured data such as voice and text.

Computer Vision AI: Also known as machine vision, the ability of AI technologies to process data in the form of images acquired using cameras, to be able to recognize, and attach a description to the image similar to human vision processing.

Intelligence: Ability to take in information, followed by processing, leading to a specific action based on the information

Strong AI: General AI that is capable of learning and making decisions with a higher level of context awareness

Machine Learning: Ability of a machine to learn from the data it is presented using different techniques that are supervised or non-supervised.

A/B Testing: A form of experimentation applicable to websites and digital marketing to evaluate the effectiveness of messages and content by changing one parameter while keeping others constant.

ChatBot: An AI technology-based software program that uses a set of rules to hold a conversation either through text or voice.

Natural Language Processing AI: The ability of AI technologies to process unstructured data such as voice and text directly.

Artificial Intelligence: Intelligence emulated by computerized machines.

Weak AI: AI technologies that involve learning in a narrow domain of knowledge. Such technologies can only be applied within a specific field as experts in an area.

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