Book Review: Seven Ways that AI Will Transform Customer Engagement

Mukul Pandya
The Wharton School, University of Pennsylvania

Artificial intelligence, or AI, is transforming every aspect of business and society, including the very nature of customer engagement. This point of view is shared by several authors of books about AI and marketing who believe that AI will dramatically change customer engagement in the future, at least in the following seven ways:

• First, through the use of AI, companies will be able to redefine customer experience by making it possible to personalize products and services at scale.
• Second, AI will help companies to move away from short-term transactional interactions with their customers and build long-term relationships that can help them maximize customer lifetime value (CLV).
• Third, AI will allow firms to offer more immersive, impactful experiences to their customers, especially in conjunction with emerging technologies such as the metaverse.
• Fourth, AI will sharply lower the cost of making predictions and enable companies to use new techniques to build customer engagement.
• Fifth, AI will make companies more mindful that, if they do not consciously introduce new experiences, customizing user interactions can trap people in silos.
• Sixth, AI will help companies make better and smarter use of data to improve customer engagement.
• Seventh, AI will transform customer engagement by changing what products and services are offered to customers and also how companies create and deliver these products and services.

Understanding the impact of AI on customer engagement is crucial for the entire C-suite. It matters, of course, to marketing managers who are at the front lines of the battle to win customers, but it is equally important for leaders who are involved with business strategy, R&D and innovation, and finance. Understanding customer engagement can also shed light on human relations, which is central to human resources and talent management. These books are valuable guides for executives in each of these roles.

1. Personalize products and services at scale: As the chief measurement strategist at Google, Neil Hoyne has led thousands of projects for companies that want to engage effectively with customers.¹
   Asked to suggest a book that will help readers understand how AI is reshaping customer engagement, he recommended The AI Marketing Canvas: A Five-Stage Road Map to Implementing Artificial Intelligence in Marketing² by Raj Venkatesan,³ who teaches marketing and analytics at the University of Virginia’s Darden School of Business, and Jim Lecinski,4 a clinical associate professor of marketing at the Kellogg School of Management at Northwestern University. “Jim spent twelve years at Google leading these types of efforts for customers. You’re not going to find many authors with that level of depth intersecting both research and practice,” Hoyne notes.
   In The AI Marketing Canvas, Venkatesan explains that AI can “supercharge the customer experience through personalization at scale.” Explaining this philosophy in Forbes in August of 2021, Venkatesan spoke about Peter Chang, a Chinese restaurant that he often visits near the Darden campus. The restaurant staff greet him by name, know where he likes to sit and what he likes to order, and are familiar with his family.
   In other words, for the staff at Peter Chang, Venkatesan is not a nameless, faceless customer; he is a human being with whom they have built a relationship. That allows them to offer him a personalized service. “Brands like Chick-fil-A and Starbucks are trying to achieve this personalization using AI in their mobile apps and translating the insights from the app into personalized in-store customer experiences also,” Venkatesan said to Forbes.
   AI, with its ability to crunch vast amounts of data at phenomenal speed, will allow companies to apply this degree of granular personalization to every aspect of customer engagement, according to Venkatesan. This includes acquisition, retention, growth, and advocacy, which is the term he uses for word of mouth. Venkatesan and Lecinski recommend a five-step progression for top executives who want to use AI to build customer engagement.
   • Foundation: lay the foundation by collecting the first-person data that can be used to train the AI algorithm.
   • Experimentation: conduct experiments to learn how to personalize a single aspect of customer engagement.
   • Expansion: personalize more than one aspect of the company’s engagement with the customer.
   • Transformation: every aspect of customer engagement is personalized.
   • Monetization: use AI to generate a fresh revenue stream.
     
     As Venkatesan points out, companies in industries ranging from consumer packaged goods to banking and retail now use AI to personalize engagement with customers.
2. Build long-term relationships to maximize customer lifetime value (CLV): Hoyne’s Converted: The Data-Driven Way to Win Customers’ Hearts⁵ has several nuggets of information about the use of AI to build customer engagement. Though he has written this book in his personal capacity, Hoyne draws extensively upon his experience at Google and as a senior fellow with Wharton Customer Analytics at The Wharton School of the University of Pennsylvania.
   Asked how brands can become better at using AI for mass-personalization at scale, Hoyne says, “When we talk about personalization, we have to broaden our lens a little bit to say, what do we know about these customers and what are their actual needs? How do we deliver them? That also removes that pressure from us where we don’t need to get them to convert every time.”
   Hoyne offers two examples from the travel industry to make his point. He often books hotels using an online travel aggregator or an OTA, which generally buys its inventory of hotel rooms from other sites. “If they’re focused only on short-term transactions, their goal is, every time I go to the site, to get me to book a new hotel room. That’s where their investments go, their marketing messages, and their call to action. So they’ll underinvest in personalization to improve the customer experience, because in their mind it doesn’t immediately lead to any more incremental reservations,” he notes. “Instead, if they took a step back to look at the lifetime value of customers and provided them with ancillary benefits, they would see that those customers stick around longer and spend more money.”
   Another example is that of a mobile app developer that was managing an airline app. The company tracked the number of people who used the mobile app to immediately book a new reservation. “That’s what they were focused on, ROI (return on investment),” Hoyne notes. The company discovered that the lifetime value of people who used the app compared to that of those who did not was roughly the same, so the mobile app was not adding anything incremental to the experience.
   They started adding more features to help customers with their reward points, navigate their way around the airport, and manage their travel reservations. This did not lead to immediate bookings, but they found that customers were happier and made more bookings, thus increasing their lifetime value.
3. Create more immersive experiences with AI and the metaverse: Rajesh Jain, founder of Netcore, one of India’s most innovative digital marketing companies, recommends The Metaverse: And How It Will Revolutionize Everything by Matthew Ball.⁶ Jain notes that “The book gives a glimpse about how brands and customers will engage in the future – in a world of infinite computing power, storage, bandwidth, consumer data, and AI. The future is about how brands can build better hotlines with their customers, and the metaverse will be one of the key places to engage with present and future customers. Every brand will need a metaverse strategy and Matthew Ball’s book serves as an excellent introduction to get started.”
   Ball is the managing partner of the early stage investment fund Epyllion and the former global head of strategy for Amazon Studios.⁷ He adapted his book into an essay that appeared as a cover story in Time magazine in July 2022.⁸ In January 2022, Ball notes, “Microsoft paid $75 billion for the gaming giant Activision Blizzard, which will provide building blocks for the metaverse.” Ball quotes an estimate by McKinsey and Company that “corporations, private equity companies, and venture capitalists made $120 billion in metaverse-related investments during the first five months of this year.”
   What does this have to do with customer engagement? As a case in point, consider Roblox, an online game platform and game creation system that lets users develop games and play games that other users have created. In January 2022, Roblox was averaging more than 4 billion hours of use a month. “Part of Roblox’s surging engagement is driven by its growing user base,” Ball writes. “From Q4 (fourth quarter) 2018 to January 2022, average monthly players increased from an estimated 76 million to more than 226 million (or 200%), while average daily players grew from around 13.7 to 54.7 million (or 300%).”
   Though it might seem that the games that Roblox users play have little to do with AI or customer engagement, the metaverse is likely to usher in the next generation of the Internet. Companies will be able to use similar technologies such as virtual or augmented reality to allow customers to try on virtual garments before ordering them, visit a digital replica of a home before making an offer, or test drive a virtual car before buying the real one, and much more. Some companies such as Nike, Samsung, and Hyundai have already begun using AI in conjunction with these technologies to deepen customer engagement.⁹
4. Make precise, cost-efficient predictions: In many aspects of business, AI makes excellent predictions cost-efficiently. This is the principal argument of the book Prediction Machines: The Simple Economics of Artificial Intelligence,¹⁰ recommended by Kartik Hosanagar, a professor of operations, information, and decisions at The Wharton School of the University of Pennsylvania and faculty co-lead of the AI for Business initiative.¹¹
   Authored by Ajay Agrawal,¹² Joshua Gans,¹³ and Avi Goldfarb,¹⁴ economists at the University of Toronto’s Rotman School of Management, this book posits that the rise of AI has reduced the cost of making predictions, just as the coming of the World Wide Web in the mid-1990s reduced the costs of search, communication, and so on.
   “The central point of the book is that from a business/economic standpoint, AI increases the accuracy and reduces the cost of making data-driven predictions. The rest of it is focused on managerial implications of that,” Hosanagar says. “For example, if predictions are cheaper and accurate, the premium might then go towards how one uses those predictions for judgment and managerial decisions. While this isn’t specifically on customer engagement, arguably all managers should care about this.”
   According to Jerry Wind,¹⁵ professor emeritus of marketing at The Wharton School, and editor of this special issue, AI can be particularly valuable when companies want “to focus on the need to predict levels of customer engagement and the determinants of engagement.”
   At a talk about the book at Google in 2018, Goldfarb pointed out that advances in AI have largely been the result of advances in prediction technology, which enables better, faster, and cheaper predictions.¹⁶ Why does that matter? When computing reduced the cost of arithmetic, new applications for computing proliferated. A similar process is at work with the falling cost of prediction driving an upsurge in AI. Prediction means using information you have to fill in information you do not have. The first predictive AI apps addressed loan defaults, helping banks to figure out whether their loans would be repaid.
   The insurance industry also loves AI tools because it is steeped in making predictions. Insurers want to know if a customer is likely to make a claim or not, and for how much. New ways of thinking about prediction have arisen because the cost of prediction has fallen.
   For example, medical diagnosis is, at heart, a prediction problem in which a doctor looks at apparent symptoms and fills in the missing information about the cause. Autonomous driving also solves a prediction problem. Automakers try to predict what a good human driver would do under the prevailing circumstances on the road. Once they can do that, they can build AI-powered cars to do what a human driver would. The key element in understanding and identifying new opportunities for cheap predictions is filling in missing information.
   According to Goldfarb, when coffee becomes cheap, consumers will drink more of it. That is Economics 101. As customers consume more coffee, though, the demand for complementary products such as cream and sugar also increases. So next, companies should strive to find those complementary products.
   As the cost of prediction falls, what will become more valuable? By answering that question, we should be able to discover where AI could make the most difference. For example, one complementary product is data. Data is often described as ‘the new oil’ because the falling cost of making predictions through AI has vastly increased the demand for data. Among myriad other things, data is used to fuel insights that will help all top executives to understand the impact of AI on customer engagement.
5. Understand the limits of customer choice: In his own book, A Human’s Guide to Machine Intelligence: How Algorithms Are Shaping Our Lives and How We Can Stay in Control, Hosanagar devotes a chapter to AI and customer engagement.¹⁷ “My book covers how AI is driving consumer choice,” he says. “But it also warns that when personalization systems overly rely on past data and don’t explore enough, it can lock customers in filter bubbles where they see more and more of what they have already liked and consumed at the expense of discovering new items and media and expanding their tastes.” According to Wind, this represents a tremendous “opportunity for those using AI to design the consumer engagement, to move beyond historical data and augment it with new experiences.”
   Hosanagar explains, “all of us realize how much of our lives are shaped by decisions we make online.” This may be through searching for products or services on Google, connecting with friends on Facebook, or shopping for books or other products on Amazon. “Many of us are aware that the companies running these sites are guiding our choices by customizing our experiences. Personalization algorithms help us choose the optimal products to buy on Amazon, the best movies to watch on Netflix, the ideal person to date on Tinder or Match.com, or the most useful contacts on LinkedIn.”
   Hosanagar notes that though we might imagine that we are making these choices, the fact is that the websites exclude a lot of the choices they offer to customers and present a relatively narrow set of options from which to make choices. As a result, some 80 percent of viewing hours streamed on Netflix originate from automated recommendations presented by the site’s AI-powered personalization algorithms. “Products are often designed in ways that make us act impulsively and against our better judgment,” he writes. “Popular design approaches such as the use of notifications and gamification to user engagement exploit and amplify human vulnerabilities.”
6. Reinvent customer engagement through data: If data has indeed become the new oil it may be wise to learn how to drill for it. That is what the book, Data Driven: Harnessing Data and AI to Reinvent Customer Engagement, claims to do.¹⁸ The authors are Tom Chavez,19 the CEO of super{set}, who built a data management platform called Krux that Salesforce acquired in 2016; Chris O’Hara, a former columnist; and Vivek Vaidya, a serial tech entrepreneur and former co-founder and CTO of Krux. This book recommendation comes from Greg Shea,20 an adjunct professor of management at The Wharton School.
   According to the authors, “Ubiquitous connectivity only partially explains the rise of Internet giants and the surprising success of digital upstarts, such as Spotify, Tinder, and Twitch. A powerful steel thread runs through all these companies. What they have in common is data: the ability to capture it − increasingly every scrap − and put it to work to generate insights, recommendations, and offers that dazzle their customers.”
   The authors write that anyone who is in the business of engaging with existing customers or trying to find new ones can “run but you can’t hide from data. It is the fuel that enables any company to know its customers intimately, improve its products, deliver better customer service, optimize any business process, and predict the future direction of markets.”
7. Transform the way products and services are delivered: Saikat Chaudhuri,²¹ faculty director of the Management, Entrepreneurship & Technology Program and the Berkeley Haas Entrepreneurship Hub at the Haas School of Business of the University of California, Berkeley, suggests Competing in the Age of AI: Strategy and Leadership When Algorithms and Networks Run the World,²² by Marco Iansiti²³ and Karim Lakhani,²⁴ who are on the faculty of Harvard Business School.
   “I recommend this book because many manuscripts on AI these days focus heavily on the technology and possibilities that AI offers,” says Chaudhuri. “However, the deployment of AI can fundamentally alter not just what products and services are offered to customers, but how organizations should create and deliver them. This requires product, process, and business model innovation. Iansiti and Lakhani tackle how firms can reinvent themselves to take advantage of the opportunities on the horizon and navigate the challenges as well as the risks, suggesting new architectures that can help us enhance scale, scope, and learn. They bring together technology, strategy, and organization in provocative ways that offer profound food for thought.”
   In this book, business leaders will find several instances of how AI-driven companies are challenging traditional industry leaders in customer engagement. Consider retail. The authors show how Amazon, with its AI-powered processes, is taking on Walmart. They write: “No industry is feeling the impact of Amazon more keenly than retail. Amazon’s convenience, low prices, personalization, and recommendation capabilities and software-enabled logistics infrastructure was a formidable challenge to traditional firms. In 2017, we saw more than twenty long-standing retailers file for bankruptcy, and in 2018, even the 105-year-old giant Sears joined this list. Walmart – the world’s largest company by revenue – is doing all it can to avoid that fate.”
   The authors write that Walmart has hardly been shy about investing in technology. “For decades, it set the standard in retail supply chain technology and network infrastructure, with its constantly evolving retail link system and its early commitment to EDI and RFID technologies. A data rich supply chain has consistently been an important part of Walmart’s operating model, and the key to its impressive scale,” they say. Still, for all its technological prowess, Walmart has had to transform itself to deal with Amazon’s challenge. “To put up a credible fight with Amazon, Walmart is re-architecting its operating model on a digital and AI-enabled foundation,” they say.
   For leaders who want to understand how AI will shape the future of customer engagement, there does not appear to be a single blueprint showing them what kind of organization to build or what kind of AI system to develop to best engage customers and other stakeholders. The wide range of perspectives that these books and their authors provide will nonetheless help to prepare them for whatever the future may hold.

Author Bio

Mukul Pandya is the founding editor in chief and executive director of Knowledge@Wharton, the online research and business journal of the Wharton School. After retiring from K@W, Mr. Pandya was a senior fellow with Wharton Customer Analytics and AI for Business. A four-time award winner for investigative journalism, Mr. Pandya has published articles in The New York Times, The Wall Street Journal, The Economist, Time, The Philadelphia Inquirer, and more. He has written or coauthored four books.

Endnotes

1. https://executiveeducation.wharton.upenn. edu/faculty/neil-hoyne/
2. https://www.sup.org/books/title/?id=32597
3. https://www.darden.virginia.edu/faculty-research/directory/rajkumar-venkatesan
4. https://www.kellogg.northwestern.edu/faculty/directory/lecinski_jim.aspx
5. https://www.penguinrandomhouse.com/books/688629/converted-by-neil-hoyne/
6. https://wwnorton.com/books/978132 4092032
7. https://www.matthewball.vc/about
8. https://time.com/6197849/metaverse-future-matthew-ball/
9. https://influencermarketinghub.com/metaverse-brands/
10. https://store.hbr.org/product/prediction-machines-updated-and-expanded-the-simple-economics-of-artificial-intelligence/10598
11. https://oid.wharton.upenn.edu/profile/kartikh/
12. https://www.rotman.utoronto.ca/Faculty AndResearch/Faculty/FacultyBios/Agrawal
13. https://www.joshuagans.com/
14. https://www.rotman.utoronto.ca/Faculty AndResearch/Faculty/FacultyBios/Goldfarb.aspx
15. https://marketing.wharton.upenn.edu/profile/windj/
16. https://www.youtube.com/watch?v =ByvPp5xGL1I
17. https://wsp.wharton.upenn.edu/book/a-humans-guide-to-machine-intelligence/
18. https://www.mhprofessional.com/data-driven-harnessing-data-and-ai-to-reinvent-customer-engagement-9781260441536-usa
19. https://tomchavez.ceo/
20. https://www.gregoryshea.com/
21. https://haas.berkeley.edu/faculty/saikat-chaudhuri/
22. https://hbsp.harvard.edu/product/R2001C-PDF-ENG
23. https://www.hbs.edu/faculty/Pages/profile.aspx?facId=6482
24. https://www.hbs.edu/faculty/Pages/profile.aspx?facId=240491