Customer 360 E-Book

Martin Kihn

• Herausgeber: John Wiley & Sons
• Kategorie: Fachliteratur
• Sprache: Englisch
• Veröffentlichungsjahr: 2024

Seitenzahl: 319

Beschreibung

Become more competitive by developing a superior customer experience through data, AI, and trust - and get your organization ready for AI agents like Agentforce 

Customer 360: How Data, AI, and Trust Changes Everything delivers key insight and vision on using emerging technologies to delight customers and become more competitive by providing a superior customer experience. Find out why AI agents like Agentforce need a strong foundation of customer data. This book helps readers attract and engage their customers across channels and throughout their journey, from acquisition and onboarding, through service, upsell, retention, and win-back.

To demonstrate the influence and importance of these ideas, this book contains a multitude of real-world case studies from companies in a range of industries, with business models, and at various stages of digital maturity. Readers will learn about:

• Using exciting technologies like AI and GPT while building a commitment to ethical use, safety, and privacy through secure guardrails
• Getting ready to use exciting emerging technologies like AI agents and autonomous AI
• Organizing data around customers, prospects, and accounts—even if that data comes from many different sources in different formats
• Making new technologies an extension of your existing data investments so that both work better
• Choosing a strategy and implementation plan to minimize time-to-value and ensure success weighing build, buy, or partner
• Handling internal stakeholders and dealing with change in a way that benefits the business

For business leaders, executives, managers, and entrepreneurs, Customer 360: How Data, AI, and Trust Changes Everything is an essential read to understand and connect technology, people, processes, and strategy—truly the future of customer engagement—and leave competitors wondering what just happened.

Leseprobe

Table of Contents

Cover

Table of Contents

Title Page

Copyright

Dedication

Preface

Introduction

NOTE

SECTION One: THE FIVE FORCES OF CUSTOMER EXPERIENCE

CHAPTER 1: Formula 1’s Race Toward Personalization

BUILDING ON ITS GREATEST ASSET

WITHOUT CONNECTED DATA, PERSONALIZATION ISN’T EASY

A NEW ERA OF DATA-BASED FAN ENGAGEMENT

GETTING TO THE FINISH LINE

CHAPTER 2: How the Customer 360 Approach Provides Value

NOTE

CHAPTER 3: Customer 360 in Action: Some Common Tactics

CHAPTER 4: The Five Forces of Customer 360

THE FIVE FORCES OF COMPETITIVE STRATEGY

PUTTING TOGETHER A STRATEGY

NOTES

CHAPTER 5: What Do Customers Want Right Now?

WHERE ARE ALL THE CUSTOMERS?

ATTITUDES TOWARD PERSONALIZED EXPERIENCES

FRUSTRATIONS AND EXPECTATIONS ABOUT PERSONALIZATION

EXPECTATIONS AROUND PRIVACY AND CONTROL

NOTES

CHAPTER 6: What Do Companies Need Right Now?

THE PERCEPTION-REALITY CHASM

DATA VARIETY AND MANAGEMENT

THE CUSTOMER EXPERIENCE TECH STACK

PRIORITIES AND INVESTMENTS

FIRST-PARTY DATA AND PRIVACY

LET’S REVISIT THESE FORCES FOR A MOMENT

NOTES

SECTION Two: DATA + AI + TRUST IN ACTION

CHAPTER 7: The Evolution of Customer Data and Platforms—A Case Study

THE SALESFORCE STORY

THE EMERGENCE OF DATA CLOUD

DATA CLOUD ARCHITECTURE

KEY ELEMENTS OF A CUSTOMER 360 PLATFORM

CHAPTER 8: Data Types and Sources

EVOLUTION OF DATA TYPES

DATA STORAGE

TYPICAL CUSTOMER DATA TYPES

THE DATA PARTY

DATA AND BUSINESS GRAVITY

EXAMPLES OF UNTRAPPED DATA IN ACTION

SALESFORCE DATA CLOUD ADAPTS TO UNSTRUCTURED DATA

NOTES

CHAPTER 9: Customer Data in the Enterprise Today

CUSTOMER RELATIONSHIP MANAGEMENT

MASTER DATA MANAGEMENT

ENTERPRISE DATA WAREHOUSES

MODERN DATA WAREHOUSE (SNOWFLAKE)

FINAL CONSIDERATIONS

NOTES

CHAPTER 10: Composable Versus Packaged and Build Versus Buy

DO-IT-YOURSELF OR DO-IT-FOR-ME?

WHAT IS A COMPOSABLE CDP, AND HOW DOES IT WORK?

WHAT IS A FULL-FEATURED CDP, AND HOW DOES IT WORK?

FULL-FEATURED OR COMPOSABLE CDP: WHICH IS BETTER FOR CUSTOMER EXPERIENCE?

CAN A FULL-FEATURED CDP BE COMPOSABLE?

HOW CAN A CDP COMPLEMENT YOUR DATA WAREHOUSE?

ZERO-ETL IS NOT YOUR UNCLE’S COPY AND PASTE

HOW IT WORKS: FROM CDP TO DATA WAREHOUSE

HOW IT WORKS: FROM DATA WAREHOUSE TO CDP

ZERO-ETL/ZERO-COPY IN REAL LIFE

NOTES

CHAPTER 11: What Does “Real Time” Really Mean?

WHAT DO WE MEAN BY “REAL TIME”?

AN EXAMPLE: A WELL-KNOWN AIRLINE

DIFFERENT SPEEDS FOR DIFFERENT NEEDS

CUSTOMER DATA PLATFORMS

REAL-TIME DATA MANAGEMENT

CHAPTER 12: AI in Action Today!

A BRIEF HISTORY OF AI

AN OVERVIEW OF AI

PREDICTIVE AND GENERATIVE AI

AI NEEDS HIGH OCTANE DATA AS FUEL

WILL GENERATIVE AI HAVE AN IMPACT ON BUSINESSES RIGHT AWAY?

HOW PREVALENT IS GenAI USE TODAY?

GUCCI: CHANGING THE PARADIGM OF CLIENT SERVICES WITH GUCCI 9

L’ORÉAL: LEARNING AND PRIORITIZING INNOVATION USING AI

TURTLE BAY RESORT—CUSTOMER 360 DATA FUELING AI TO REINVIGORATE TOURISM

NOW IS THE TIME TO PUT AI INTO ACTION

NOTES

CHAPTER 13: Having Faith in the System: How Can We Trust the AI?

HOW EXTENSIVE ARE THE RISKS?

THE BIGGEST RISK IS IGNORING AI

HUMANS, WE STILL NEED YOU!

HUMAN AT THE HELM (HATH) VERSUS HUMAN IN THE LOOP

TYPES OF LLMs

WHERE IS THE LLM BEING TRAINED?

THE GenAI SUPPLY CHAIN

HOW A “TRUST LAYER” IS BUILT: THE STORY OF SALESFORCE (CONTINUED)

NOTES

CHAPTER 14: Data Collaboration—A Rising Imperative

GOODBYE TO BROWSER-BASED THIRD-PARTY COOKIES

COLLECT FIRST-PARTY DATA AS A STRATEGIC DIFFERENTIATOR

PURSUE DATA SHARING AND COLLABORATION

CIRCLING BACK TO THE CDP

DATA AS A CORE DISCIPLINE

NOTES

CHAPTER 15: Privacy, Compliance, and Consent

WEB BROWSERS AND STANDARDS BODIES

GOVERNMENT REGULATORS

BUILDING TRUST

FOUR PRIVACY TACTICS TO TRY

NOTES

CHAPTER 16: Next-generation Analytics for the Enterprise

THE STATE OF TODAY’S ANALYTICS LANDSCAPE

THE RISE OF “CONSUMER-BASED” ANALYTICS

EVOLVING FROM

REACTIVE

TO

PROACTIVE

ANALYTICS

ANALYTICS IN THE FLOW OF WORK

SO WHAT ARE THE ORGANIZATIONAL BENEFITS?

NOTES

SECTION Three: DATA + AI + TRUST IN THE WORKPLACE

CHAPTER 17: AI Hype Versus Reality? What Does This Mean for Humans?

VALUE WILL BE GENERATED BY GenAI

THE OTHER 99% OF HUMANS AND BUSINESSES

NOTES

CHAPTER 18: Organizational Structures and Centers of Excellence

FIRST, DREAM BIG …

BUILD AN ADAPTIVE CULTURE

LEFT-BRAINED AND RIGHT-BRAINED LEADERSHIP

NEW PARADIGM FOR HUMAN RESOURCES

FOSTER A CHANGE MANAGEMENT CULTURE AND AI IQ

EVOLVING OLD ROLES AND NEW ROLES

HIRE CURIOSITY AND A CONTINUOUS-LEARNING MINDSET

NOTES

CHAPTER 19: Leading Through Transformation—What’s Next?

WHAT’S NEXT? KEEP THE HORIZON IN SIGHT

WATCH FOR NEW REGULATIONS

GAUGE THE PACE OF THE FOURTH INDUSTRIAL REVOLUTION

LIFE AFTER LLMs? … PERSONAL AGENTS

LARGE ACTION MODELS

MULTIMODEL, MULTIMODAL, AND MORE!

STATE SPACE MODELS

CONCLUSION

NOTES

CHAPTER 20: Summing Up

TO RECAP

Acknowledgments

About the Authors

Index

End User License Agreement

List of Illustrations

Introduction

FIGURE 0.1 Time to 100 Million Users

FIGURE 0.2 The Three Layers of AI Experience

FIGURE 0.3 The Four Waves of AI

FIGURE 0.4 Customer 360 Changes How We Work

FIGURE 0.5 Trust Layer for AI

Chapter 2

FIGURE 2.1 Customer 360 Value Sources

Chapter 4

FIGURE 4.1 The Five Forces of Customer Experience

FIGURE 4.2 Architecture for a Customer 360

Chapter 5

FIGURE 5.1 Daily Time Spent on Media (mins.)

FIGURE 5.2 Preferred Contact Channels (%)

FIGURE 5.3 Privacy and Trust Preferences (%)

Chapter 6

FIGURE 6.1 Data Interactions per Person per Day (#)

FIGURE 6.2 Change in Channel Strategy (%)

FIGURE 6.3 The Five Forces of Customer 360

Chapter 7

FIGURE 7.1 Early Salesforce.com Homepage

FIGURE 7.2 Example Enterprise Customer Data Stack

FIGURE 7.3 Data Cloud High-level Architecture

Chapter 8

FIGURE 8.1 The Evolution of Data Management

FIGURE 8.2 Data Types by Source

FIGURE 8.3 Three Types of Enterprise Gravity

FIGURE 8.4 Example of a Keyword Search Versus a Semantic Search

Chapter 9

FIGURE 9.1 Customer Data Domains

FIGURE 9.2 Approaches to Customer Data Stack (%)

Chapter 11

FIGURE 11.1 Key Phases of Customer Data Management

Chapter 12

FIGURE 12.1 Time for AI Models to Reach Human Accuracy (for illustrative pur...

FIGURE 12.2 The Four Industrial Revolutions

FIGURE 12.3 Recent GenAI Announcements

FIGURE 12.4 Domains of AI

FIGURE 12.5 Data Maturity Curve

FIGURE 12.6 Improving Prompts with First-Party Data

FIGURE 12.7 Use Case–Driven AI Approaches

Chapter 13

FIGURE 13.1 Generating Creative Content with AI

FIGURE 13.2 Salesforce Data Cloud and the Customer Experience Life Cycle

Chapter 14

FIGURE 14.1 Schematic of a Data Clean Room

Chapter 16

FIGURE 16.1 State of Data-Driven Organizations

FIGURE 16.2 The Conventional Analytics Life Cycle

FIGURE 16.3 Tableau Pulse Enhanced by Tableau AI

Chapter 17

FIGURE 17.1 The Generative AI Value Chain

FIGURE 17.2 Creating and Capturing Value with AI

Chapter 18

FIGURE 18.1

FIGURE 18.2 AI Planning Framework

FIGURE 18.3 Create Layers of AI-Driven Experiences

FIGURE 18.4 Human and Machine: Share of Tasks

FIGURE 18.5 AI Policies in the Workplace (%)

Chapter 19

FIGURE 19.1 GPT Performance on Various Tests

FIGURE 19.2 Training Compute of AI Models

Guide

Cover

Table of Contents

Title Page

Copyright

Dedication

Preface

Introduction

Begin Reading

Acknowledgments

About the Authors

Index

End User License Agreement

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MARTIN KIHN

ANDREA CHEN LIN

CUSTOMER 360

HOW DATA, AI, AND TRUST CHANGE EVERYTHING

Copyright © 2025 by John Wiley & Sons, Inc. All rights, including for text and data mining, AI training, and similar technologies, are reserved.

Published by John Wiley & Sons, Inc., Hoboken, New Jersey.Published simultaneously in Canada.

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“There is only one boss. The customer.”

—Walt Disney

Preface

Every company wants to deliver a great experience to its customers, but very few can do it.

Why not? The challenge isn’t really fair: customers’ expectations are set by their very best memories—at an Apple Store, with Tesla customer care, Amazon returns, Disney’s Magic Kingdom. How can the rest of us compete at that level, with our own (likely limited) budgets and our own (probably understaffed) teams?

The good news is that it’s possible to deliver a great end-to-end experience even if you don’t have supernatural resources or other special advantages. A new generation of technology is making it possible for almost any enterprise to deliver excellent experiences that truly make sense from end to end—from the moment of awareness through loyalty, service, in-store, on websites, apps, and kiosks, and targeted advertising.

New platforms for data management combined with increasingly powerful artificial intelligence (AI) tools, delivered with respect, create a once-in-a-generation opportunity to break through the competitive pack and win, no matter what your size, industry, or competitive reality.

These platforms are open, heterogeneous, and flexible, and they are even getting easier to use. They are not about signing on to a single vendor for life or buying into a small cabal of power players, like locking yourself into a timeshare you don’t really need. Rather, the new winning approach centers on a disciplined delivery of a set of core capabilities.

What are these core capabilities? At the most basic level, there are three:

Data:

Organizations have an average of 450 different applications and dozens of sources of customer data. Combining and organizing all this data is a critical first step to delivering a coherent customer experience—and, crucially, to delivering on the growing promise of (you saw this one coming) …

AI:

AI’s potential is virtually unlimited to unlock personalization and insights at the speed of thought, but real risks abound: toxic and unhelpful content, data leakage and insecurity, hallucinations and half-truths, and a lack of grounding to make the output actually useful to your business. Successful, enterprise-ready AI requires a careful approach and the right internal processes.

Trust:

Customers and governments alike—not to mention your own, increasingly nervous legal team—require the security, availability, and privacy of data to be locked down. More and more, companies will be differentiated based on their ability to deliver consistently on the promise of trust.

So that’s it: Data + AI + Trust. Easy, right? Well, not exactly. But we’re all on a journey, and nobody—not even the gold standard enterprises in your industry—do everything right, all the time. As Martin Luther King, Jr., said, “Setting goals is the first step in turning the invisible into the visible, … taking the first step even when you don’t see the whole staircase.”

The goal we’ve set for this book is to help you take those first steps toward your own Customer 360. We’ll show you how the combination of Data + AI + Trust can catapult any company into the next dimension of growth.

***

Introduction

Known for its meticulous Italian craftsmanship, imaginative design and attention to detail, Gucci is one of the most influential luxury brands in the world. Founded in 1921, it is currently redefining luxury for a new generation of customers, building experiences that extend from its retail outlets to its websites and apps and its global client service network, called Gucci 9—a reference to the historic Gucci headquarters campus in Florence.

One of Gucci’s goals was to enable its 600 client advisors across seven global hubs to communicate in a clear brand voice, elevating the service experience beyond the realm of daily conversation.

It wasn’t easy. Customers engage on multiple channels—inbound calls coming from the website and from stores, WhatsApp, live chat, etc.—and on a wide range of topics, from learning about upcoming collections to making reservations at a Michelin-star Gucci Osteria. To meet the challenge and maintain consistency, Gucci was able to use organized internal data about products as well as previous examples of Gucci communications in its authentic brand voice.

The latter was applied to AI models, which were trained to recommend replies provided to customer care reps. The magic of these AI-generated replies, grounded in product and brand data, was that they were in a “Guccified” tone of voice. The advisors could adapt them for the human touch, but they provided a conversation framework that amplified the interaction—elevating advisors above traditional templates and providing customers with an experience entirely consistent with what they’d expect from the brand.

Gucci is known for infusing beauty into everything it does, including its unique customer experiences. New technologies such as AI help the fashion house practice its mantra, “the human touch, powered by technology,” by scaling the capabilities of its advisors with brand-ready messages. As we’ve said, all companies want to deliver superior experiences from end-to-end to their customers. We all want the Gucci 360, tailored to our own situation. We know as consumers that the brands we feel good about are more likely to get more of our business. It’s simple logic.

In one comprehensive survey of 14,300 consumers around the globe, 80% of respondents said that the experience a company provides is as important as their products or services. Take a moment to think about that. Most companies might reasonably assume their customer satisfaction is tied to the quality and price (or the value) of the items and services they sell. But consumers themselves see things differently: they value their interactions with companies just as much.

Moreover, consumers increasingly expect a level of relevance or personalization from companies they trust with their data and consent. The same global survey revealed that 65% of consumers expect companies to adapt the experiences they provide to customers’ changing needs. In other words, we expect companies to know when we’re complaining or just want to get a price quote or the hours the store is open, or when we’re in the market for a deal.

Yet the evidence shows most companies fall far short of the ideal. In fact, 61% of consumers said that they felt like the average company treated them “like a number.” Forget Guccified or Customer 360, these consumers are saying, just treat me like I’m a human being.

Among business-to-business (B2B) buyers, the situation isn’t any better. According to a recent study, 63% of B2B buyers said their customer experience was worse than it could be, and almost as many said that their sales reps didn’t really try to understand their needs. These customers, often making big-ticket decisions that can have career-affecting consequences, too often feel like their reps treat them transactionally when what they want is an advisor they can trust.

And there’s evidence that as consumer expectations continue to rise, we’re getting more demanding—and that companies are falling even further behind. Every few years, dating back to the early 2000s, the W. P. Carey School of Business at the University of Arizona has collaborated on a survey of 1,000 Americans on the topic of customer service. It’s a fascinating study that puts a dollar estimate on revenue at risk due to poor complaint handling, among other things.

The study paints an alarming picture of the state of consumer-company interactions. According to the researchers, nearly three-quarters of respondents reported a product or service problem in the past year—more than double the level in earlier years. More than half said the problem wasted their time and one-third said they suffered “emotional distress.”1

And they weren’t keeping it to themselves, either: one-third posted negative information about their problems on social media, more than double the rate in 2020. In the end, the researchers estimated that about $887 billion of future revenue was put at risk due to poor service, also almost double the amount estimated in 2020. Put simply: people are getting angrier, are sharing their feelings, and are taking revenge where it hurts the most, with their wallets.

Granted that service is just one part of the Customer 360, the study is directionally concerning: either service levels are going down, people are expecting more, or both. Either way, only one of those levers can be pulled by the companies themselves: they need to improve customer experience. Real revenue is at risk in disappointing the people who keep us in business.

“It’s not what happens to you but how you react that matters.”

—Epictetus

***

So consumer attitudes and behaviors are changing, as they always have and will. (We’ll cover more about consumer trends in Chapter 5.) But they’re only one part of the story. There’s also the incredibly important role of technology—how it’s changing, transforming businesses and work, and upending our assumptions about what it takes to deliver a Customer 360.

“Customer 360”: What is Customer 360? We’ll be using this term often, and so we want to describe what it means. It’s not a particular technology, vendor, or established term of art. Customer 360 describes a constellation of technologies, processes, and people that are all directed at building a coherent, end-to-end customer experience. It encompasses Data + AI + Trust. The purpose of Customer 360 is to serve the total customer journey from the customers’ point of view, regardless of internal departments, siloes, structures, or habits. So building a Customer 360 just means building a customer experience that makes sense.

We are all adapting to a changing technosphere. Most obviously, the generative AI revolution took most of us by surprise in November 2022, when OpenAI’s ChatGPT began to talk to us in ways that seemed quite human, and image-generation tools like MidJourney and DALL-E blew our collective visual synapses.

As we slowly emerged from the economic shock of the pandemic, among other challenges, we faced a technology environment of careful budgeting, some vendor consolidation, reprioritization, and the need to improve our skills. Meanwhile, the pace at which new technologies infiltrate our lives is breathtaking. (See Figure 0.1.)

Believe it or not, it took mobile technologies 16 years to reach 100 million users. (That may be why “The Year of Mobile” kept happening again and again.) It took social networks like Facebook and Instagram only 2.5 years to reach the same milestone, and TikTok just 9 months. Yet ChatGPT reached 100 million users in two months, ushering in the era of widespread generative AI (GenAI).

The AI opportunity itself is clear, even if we’re not always sure how to proceed. One survey showed that 84% of business leaders agreed that GenAI would improve customer service (good news for the researchers at Arizona State), and two-thirds are hiring people to work in this area. The consulting firm McKinsey is sanguine about AI, forecasting that it will free up 30% of employee time by 2030, generate $4.4 trillion in annual GDP impact, and that three-quarters of companies will be using GenAI in some form by 2027.

FIGURE 0.1 Time to 100 Million Users

FIGURE 0.2 The Three Layers of AI Experience

Most of the companies we talk to seem to believe that their businesses can grow by becoming more connected to customers through the medium of AI and GenAI. At the same time, they believe (hope?) they can use AI to reduce costs, increase employee and process productivity, improve efficiency, and exceed customer expectations. Suddenly, every business transformation is an AI transformation, and companies know they need an AI strategy to be competitive in the near future.

None of the fundamental trends treated by AI and GenAI is new. What the new generation of AI tools and techniques has done is accelerate all timelines, teleport all transformations. We’ve been on the productivity journey for some time, enabled by technology. Workflow automation has improved, and infrastructure like storage, compute, and bandwidth only gets better. AI now means we can make almost every function more productive, in ways we haven’t seen before.

When employees are more productive, businesses can grow faster, with better margins. It’s an attractive equation and explains why AI is now the number one priority for business leaders. But in building our Customer 360, how are we to think about AI? What is its role in our formula of Data + AI + Trust?

Let’s think about the consumer experience of AI first. We’d argue that this experience has three discrete layers; see Figure 0.2.

User interface (UI):

This is the place where the consumer directly interacts with the technology, the thing they log into or the website they visit. In the case of ChatGPT, it was an app that accepted prompts in natural language and would deliver a desired output, like an email, answer, summary, or computer code.

Model:

This is the trained model itself, which transforms the input (such as a prompt) into the output (the response). These can be open source and freely available, like Meta’s Llama, or more closed, like OpenAI’s models.

Data:

The foundational level, this is all the data that were used to train the model. In the case of ChatGPT, we assume it was trained on pretty much the entire open web, including Wikipedia, Reddit, podcasts, news reports, etc.

You can see how these levels map to our overall Customer 360 framework of Data + AI + Trust—except where is the Trust? That’s not a trivial question.

When translating consumer-facing AI like ChatGPT into a corporate setting, the rules change. As fun and exciting as ChatGPT and its relatives are to us as people at leisure, when we become part of an enterprise—delivering a product or service, perhaps regulated, at least constrained by customer expectations and serious business requirements—we have to be much more careful. The fact is, consumer-facing AI apps like ChatGPT can’t safely and reliably be plugged into the enterprise context without a lot of care.

That’s why 88% of IT leaders say they feel they can’t meet their company’s demands for AI safely—with an emphasis on safely. To use AI and GenAI safely, the enterprise needs guardrails around the output; it needs to be sure that what it’s automating and putting in front of customers is free of toxicity, bias, hallucinations, and text and images that aren’t in the brand’s voice.

Specifically, in bringing advanced AI into the enterprise, companies must see to the following:

Ensure trust and safety: Avoid hallucinations, bias, and misinformation.

Access the full set of customer data: Free data trapped in different apps, warehouses, lakes, and more.

Fine-tune AI models using their own data: Make the model relevant.

Integrate AI outputs into employees’ workflows.

All these challenges are real, but when facing AI as a business imperative, the most difficult to solve is the requirement for trust and safety. The fact is, what we could call a fundamental trust and results gap remains with AI. More than half of consumers don’t believe AI is secure. Meanwhile, 60% of customer experience leaders say they don’t know how to get value out of AI. On both sides—consumers and companies—there’s some understandable hesitation in the face of these new powers.

Going back to our consumer AI framework, in the context of the enterprise, the following need to be solved:

UI:

A conversational interface for employees such as call-center agents, marketers, chatbots, and more

Model:

A way to use powerful open-source and third-party models but also retrain or adapt them using first-party data, to make them your own

Data:

Speaking of first-party data, making sure it’s secure, gathered with consent, compliant, and used with trust

What’s the right approach? It makes sense to start at the foundational level, with data. It’s been reported that about 71% of the average company’s applications are disconnected. Many hard-working enterprise IT departments and others are dealing with hundreds of independent databases, each of which is an island of trapped data. These data sit in mainframes, in apps on the internet, cloud databases, personal computers, and so on.

Moreover, these data are in many different formats: customer relationship management, transactional, unstructured text and images, email, social posts, etc. The volume is undeniable and growing. Trapped data about customers lead directly to a disconnected experience, as each channel works off its own partial view.

Starting 5 or so years ago, a new class of enterprise technology, called the customer data platform (CDP), emerged to solve the trapped data problem. We won’t dwell on it here, since it was the topic of a previous book, Customer Data Platforms (Wiley, 2020), by Martin Kihn and Chris O’Hara. CDPs continue to mature and provide a more and more flexible solution to the trapped data challenge.

The purpose of the CDP is to build unified profiles of customers and accounts and to make them available to line-of-business users and data specialists. It does this by providing a suite of tools to ingest (or access, in some manner) customer data; harmonize it so it’s interoperable; perform identity management; provide or enable analytics; and allow audiences to be delivered to business systems such as marketing, service, and sales.

Now let’s take a moment to imagine what a broader solution would look like—one that incorporates a CDP for data management, but also integrates a method to train trusted models on the data in the CDP (and elsewhere), as well as a user interface that fits into the flow of work.

The design principles for a system for Customer 360 in the enterprise might look something like this:

Integration across all the apps you use for different customer-facing functions—sales, service, marketing, commerce, etc.

Use of a metadata framework. (Metadata is data about data, or a data taxonomy, that helps different applications work together by ensuring they all speak the same language.)

CDP as the single source of truth for customer data, with unified profiles for customers and accounts.

Real-time data collected from the web, apps, and other customer touchpoints, streaming into the unified profiles in the CDP.

Intelligence in the flow of work—meaning predictive and generative AI and machine learning (ML) are available within the tools used in each department.

Automation for common tasks and flows, again across departments and apps.

No-code and low-code model-building tools, so users don’t necessarily need to be power players or code adepts to be able to get real benefits.

Ecosystem of partners, custom apps, learning resources, and talent to make the platform work with other vendors’ products and within a sphere of influence with network effects.

Easy ways to activate decisions and segments on the most common external platforms such as Amazon, Meta, Alphabet; and to integrate the data layer with leading cloud data platforms like Snowflake and Databricks.

These principles adhere no matter what vendors you’re using or your industry. A number of leading technology companies are rolling out a platform along the previous lines—one of them, Salesforce, employs both your authors—and a solution can be implemented in numerous ways, including homegrown or hybrid models. What’s important is to keep the end in mind.

“Begin with the end in mind.”

— Stephen Covey, The 7 Habits of Highly Effective People

The current AI revolution will improve productivity. As we’ve said, McKinsey estimates that 30% of employee time would be freed up by 2030, and 66% of that would be felt in the front office. It will lead to better margins, with a significant impact on global GDP.

And—most important from our Customer 360 perspective—it will lead to better customer relationships. In fact, 84% of enterprise leaders polled by Salesforce recently agreed that GenAI would allow them to serve their customers better. (See Figure 0.3.)

Remember that we’re hardly in the end zone with AI; we’re closer to the kickoff. As a class of technologies, AI is coming at us in waves:

Wave 1:

Predictive—ML models, for the most part, used for years to perform tasks like recommend products or make credit decisions.

Wave 2:

Generative—Models that can provide structured insights, summaries, briefings, and imagery.

Wave 3:

Autonomous and agents—Relatively self-contained agents that can talk to one another and perform tasks in the digital realm.

Wave 4:

Artificial general intelligence (AGI)—A future state when AI can do many of the value-added tasks that people do, just faster.

As we’re enjoying Wave 2, we can see that AI in the enterprise is already being incorporated into the flow of work in many different departments, making them more efficient and effective and (we hope) less stressful. The ultimate goal is to put AI to work where it has the most positive impact.

AI is already having an impact in many areas:

Sales:

Automating prospecting emails, call summaries, sales summaries, call exploration

Service:

Delivering proactive service with automated replies, summaries, knowledge articles, search answers, mobile work briefings

Marketing:

Personalizing engagement with bespoke email creation, segment generation

Commerce:

Increasing conversion rates with product descriptions, smart promotions, commerce concierge

IT:

Developing faster with natural language to code, auto-completion, chat-based coding assistants

FIGURE 0.3 The Four Waves of AI

Ultimately, the nature and tenor of work will change; it is already changing. We will rely on our AI sidekicks more and realize it less. In a way, tools are becoming easier to use even as we ourselves use less of them, relegating much of our more tedious, routinized work to the software, which is able to take on more intelligent tasks, even those requiring a decision. (See Figure 0.4.)

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Let’s return to our original formula: Data + AI + Trust.

We’ve hinted that our answer to the Data term is basically a CDP. This is true, but with some qualifications. Since the release of Customer Data Platforms, the CDP market has matured. Originally a tool for marketers—particularly retailers, with complex online and offline data sources and prolific customer communications—the CDP is now used by every customer-focused department, including customer service, sales, IT, finance, and even research and development.

Take London’s Heathrow Airport. This major international hub serves 200+ destinations, operates 1,000+ flights a day, and welcomes 79 million+ passengers a year. Operating with the support of around 75,000 people, Heathrow is now Europe’s busiest and the world’s most frequently connected airport.

“How customers engage with airport services has completely changed,” said Meenal Varsani, head of Marketing and Customer Engagement. “Around 90% of passengers now use websites and apps as part of their overall journey.” With 14 websites and 45 backend systems, Heathrow Airport was struggling to keep pace with this shift in behavior. “Our digital services were very disjointed,” said Bob Stickland, head of Technology for Commercial and Digital Platforms at Heathrow. “We needed to focus more on the customer experience and less on our internal processes.”

FIGURE 0.4 Customer 360 Changes How We Work

With the help of Salesforce, shopping, parking, support services, and customer communications now all run on the same platform, which means passengers get the seamless experience they expect. Now, a daughter can contact customer service to check airport security rules for her mom’s favorite Penderyn Welsh Cream Liqueur and place an advance order online to collect later at the terminal.

By connecting customer service, marketing, and e-commerce interactions in Data Cloud, Heathrow will be able to anticipate what passengers need before their next visit to the airport. For example, it could see that a business traveler always buys the same products in duty free and reminds that traveler to place a click and collect order.

Heathrow also transformed how it processes online quotes and purchases for its parking services, which has helped to increase conversion rates. “Within a month of migrating our parking services to [a unified platform], we achieved our highest-ever revenue and online Net Promoter Score,” said Peter Burns, marketing and digital director.

As our Heathrow example indicates, in addition to expanding beyond marketing, the CDP has become more interoperable with external systems. This expansion includes added pre-built connectors to common sources and stronger APIs, but it also includes an ability to share and federate data to and from cloud data warehouses in a highly efficient “zero-copy” way. In effect, modern CDPs like the Salesforce Data Cloud and others can use data resident in popular cloud data stores such as Snowflake and Google BigQuery without the complexity and expense of lifting-and-shifting the data, greatly streamlining data operations. (We have more to say on the zero-copy concept in Chapter 10.)

At the AI level, there are a number of ways to take advantage of the power of the new generation of large-language models (LLMs) like Claude and Anthropic, in the enterprise, without sacrificing security and trust:

Shared trust:

Providing secure API access to an external LLM with a provision of zero data retention on the part of the LLM

Vendor-hosted:

A per-tenant model provisioned and hosted on the platform of a highly-trusted vendor

Bring-your-own-model (BYOM):

Building and training your own AI model using a data science tool such as Amazon Sagemaker or Google Vertex, and importing parameters into the CDP or data layer

Bring-your-own-LLM (BYOLLM):

Building your own LLMs and running it yourself using platforms such as OpenAI, Amazon Bedrock, or Google Vertex AI