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- Herausgeber: John Wiley & Sons
- Kategorie: Fachliteratur
- Sprache: Englisch
- Veröffentlichungsjahr: 2020
Master the hottest technology around to drive marketing success Marketers are faced with a stark and challenging dilemma: customers demand deep personalization, but they are increasingly leery of offering the type of personal data required to make it happen. As a solution to this problem, Customer Data Platforms have come to the fore, offering companies a way to capture, unify, activate, and analyze customer data. CDPs are the hottest marketing technology around today, but are they worthy of the hype? Customer Data Platforms takes a deep dive into everything CDP so you can learn how to steer your firm toward the future of personalization. Over the years, many of us have built byzantine "stacks" of various marketing and advertising technology in an attempt to deliver the fabled "right person, right message, right time" experience. This can lead to siloed systems, disconnected processes, and legacy technical debt. CDPs offer a way to simplify the stack and deliver a balanced and engaging customer experience. Customer Data Platforms breaks down the fundamentals, including how to: * Understand the problems of managing customer data * Understand what CDPs are and what they do (and don't do) * Organize and harmonize customer data for use in marketing * Build a safe, compliant first-party data asset that your brand can use as fuel * Create a data-driven culture that puts customers at the center of everything you do * Understand how to use AI and machine learning to drive the future of personalization * Orchestrate modern customer journeys that react to customers in real-time * Power analytics with customer data to get closer to true attribution In this book, you'll discover how to build 1:1 engagement that scales at the speed of today's customers.
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Table of Contents
Cover
Title Page
Copyright
Introduction
THE PIZZA CHALLENGE
THE PERILS OF PERSONALIZATION
RISE OF THE AVOIDANT CUSTOMER
THE DISCONNECTED DATA DILEMMA
CROSSING THE CUSTOMER DATA CHASM
CUSTOMER DATA PLATFORM (CDP)
CHAPTER 1: The Customer Data Conundrum
DATA SILOS
KNOWN DATA
CUSTOMER RELATIONSHIP MANAGEMENT (CRM)
CUSTOMER RESOLUTION
DATA PORTABILITY
UNKNOWN DATA
CROSS-DEVICE IDENTITY MANAGEMENT (CDIM)
CONNECTING THE KNOWN AND UNKNOWN
DATA ONBOARDING
PEOPLE SILOS
CUSTOMER-DRIVEN THINKER: KEVIN MANNION
SUMMARY: THE CUSTOMER DATA PROBLEM
NOTE
CHAPTER 2: The Brief, Wondrous Life of Customer Data Management
CUSTOMER DATA ON CARDS AND TAPE?
DIRECT MAIL AND EMAIL: THE PROTOTYPES OF MODERN MARKETING
A BRIEF HISTORY OF CUSTOMER DATA MANAGEMENT
THE RISE OF CRM AND MARKETING AUTOMATION
THE MULTICHANNEL MULTIVERSE OF THE THOROUGHLY MODERN MARKETER
CUSTOMER-DRIVEN THINKER: SCOTT BRINKER
SUMMARY: THE BRIEF, WONDROUS LIFE OF CUSTOMER DATA MANAGEMENT
CHAPTER 3: What Is a CDP, Anyway?
RISE OF THE CUSTOMER DATA PLATFORM
THE GREAT RFP ADVENTURE
CDP CAPABILITIES
A SYSTEM OF INSIGHTS
SYSTEM OF ENGAGEMENT
THE THIRD TYPE: ENTERPRISE HOLISTIC CDP
THE FUTURE IS HERE
CUSTOMER-DRIVEN THINKER: DAVID RAAB
SUMMARY: WHAT IS A CDP?
CHAPTER 4: Organizing Customer Data
MUNGING DATA IN THE MIDWEST
ELEMENTS OF A DATA PIPELINE
DATA MANAGEMENT STEPS
GETTING IT DONE
DIFFERENT SPHERES OF INFLUENCE
CUSTOMER-DRIVEN THINKER: BRAD FEINBERG
SUMMARY: ORGANIZING CUSTOMER DATA
CHAPTER 5: Build a First-Party Data Asset with Consent
PRIVACY-FIRST IS CUSTOMER-DRIVEN
PRIVACY POLICE: BROWSERS AND REGULATORS
WEB BROWSERS AND STANDARDS BODIES
GOVERNMENT REGULATORS
THE MISTRUSTFUL CONSUMER
ATTITUDES AROUND THE WORLD
THE PRIVACY PARADOX
FOUR PRIVACY TACTICS TO TRY
CUSTOMER-DRIVEN THINKER: SEBASTIAN BALTRUSZEWICZ
SUMMARY: BUILD A FIRST-PARTY DATA ASSET WITH CONSENT
CHAPTER 6: Building a Customer-Driven Marketing Machine
KNOW, PERSONALIZE, ENGAGE, AND MEASURE
ORGANIZATIONAL TRANSFORMATION
THE CDP WORKING MODEL
THE PEOPLE AT THE CENTER (THE CENTER OF EXCELLENCE MODEL)
HOW THE COE WORKS
HOW TO GET THERE FROM HERE: A WORKING MATURITY MODEL
SUMMARY: BUILD A CUSTOMER-DRIVEN MARKETING MACHINE
CHAPTER 7: Adtech and the Data Management Platform
THE MAGIC COFFEE MAKER
BACKGROUND/EVOLUTION OF THE DMP
FIVE SOURCES OF VALUE IN DMP
ADVERTISING AS PART OF THE MARKETING MIX
ROLE OF PSEUDONYMOUS IDS IN THE ENTERPRISE
ADVERTISING IN “WALLED GARDENS” WITH FIRST-PARTY DATA
END-TO-END JOURNEY MANAGEMENT: THE CDMP
CUSTOMER-DRIVEN THINKER: RON AMRAM
SUMMARY: ADTECH AND THE DATA MANAGEMENT PLATFORM
CHAPTER 8: Beyond Marketing
THE EXPANDING ROLE OF CUSTOMER DATA ACROSS THE ENTERPRISE
COMMERCE: THE STOREFRONT AND THE NEXUS OF RESPONSE
SALES: THE B2B CONTEXT, AND WHAT THAT MEANS FOR CUSTOMER DATA
MARKETING: THE BRAND STEWARDS, REVENUE, AND THE ENGAGEMENT ENGINE
CUSTOMER-DRIVEN THINKER: KUMAR SUBRAMANYAM
SUMMARY: BEYOND MARKETING: PUTTING SALES, SERVICE, AND COMMERCE DATA TO WORK
CHAPTER 9: Machine Learning and Artificial Intelligence
ONCE UPON A TIME … IN
SILICON VALLEY
DEEP LEARNING AND AI
CUSTOMER-DRIVEN MACHINE LEARNING AND AI
DATA SCIENCE IN MARKETING
CUSTOMER DATA AND EXPERIMENTAL DESIGN
CUSTOMER DATA, MACHINE LEARNING, AND AI
APPLYING MACHINE LEARNING AND AI IN MARKETING
IMPORTANCE OF CUSTOMER DATA FOR AI
AI/ML IN THE ORGANIZATION: DATA SCIENCE TEAMS
CUSTOMER-DRIVEN THINKER: ALYSIA BORSA
SUMMARY: MACHINE LEARNING AND ARTIFICIAL INTELLIGENCE
CHAPTER 10: Orchestrating a Personalized Customer Journey
THE RISE OF CONTEXT MARKETING
PRESCRIPTIVE JOURNEYS
PREDICTIVE JOURNEYS
REAL-TIME INTERACTION MANAGEMENT (RTIM) JOURNEYS
CUSTOMER-DRIVEN THINKER: LAURA LISOWSKI COX
SUMMARY: ORCHESTRATING A PERSONALIZED CUSTOMER JOURNEY
CHAPTER 11: Connected Data for Analytics
CUSTOMER DATA FOR MARKETING ANALYTICS
ANALYTICAL CAPABILITIES
ANALYTICS DATA SOURCES
BEYOND THE BASICS
KEY TYPES OF ANALYTICS
CUSTOMER-DRIVEN THINKER: VINNY RINALDI
SUMMARY: CONNECTED DATA FOR ANALYTICS
CHAPTER 12: Summary and Looking Ahead
SUMMARY
LOOKING AHEAD
CATEGORY SHAKE-OUT!
AGGREGATE-LEVEL DATA AND “FLOCTIMIZATION”
A FRESH START FOR MULTITOUCH ATTRIBUTION
AI FINALLY TAKES OVER
THE FUTURE
Further Reading
Acknowledgments
About the Authors
Index
End User License Agreement
List of Tables
Chapter 4
TABLE 4.1 Examples of common data attributes.
List of Illustrations
Chapter 1
FIGURE 1.1 Marketers' top challenges.
FIGURE 1.2 Data silos, organized by marketing function, across known and unk...
FIGURE 1.3 Statistics on identifying customers.
FIGURE 1.4 This representation of Salesforce's Audience Studio shows a segme...
FIGURE 1.5 Connecting known and unknown user data.
FIGURE 1.6 Business silos.
Chapter 2
FIGURE 2.1 The marketecture of the Society of the Divine Savior.
FIGURE 2.2 An example of a punch card for storing data.
FIGURE 2.3 Marketing automation in its first iteration.
FIGURE 2.4 Marketing automation featuring a campaign database.
FIGURE 2.5 Growth in the “Marketing Technology Landscape.”
FIGURE 2.6 The ideal marketecture.
Chapter 3
FIGURE 3.1 Who are marketers using as a CDP?
FIGURE 3.2 Depiction of the idealized CDP feature set.
FIGURE 3.3 The history of customer relationship management.
FIGURE 3.4 Five categories of CDP capability.
FIGURE 3.5 The three types of CDPs.
FIGURE 3.6 Results of the “great RFP adventure.”
FIGURE 3.7 Platform ecosystem.
Chapter 4
FIGURE 4.1 High-level data management model.
FIGURE 4.2 High-level model, mapped to CDP requirements.
FIGURE 4.3 The “profile mapping” screen within Customer 360 Audiences, the S...
FIGURE 4.4 Mapping fields into a common data information model.
FIGURE 4.5 The spectrum of “Identity.”
FIGURE 4.6 How different types of “identity” work within the spectrum, such ...
FIGURE 4.7 Screenshot of the Identity Resolution setup screen in Salesforce ...
FIGURE 4.8 Screenshot showing the segment building tool inside of Customer 3...
Chapter 5
FIGURE 5.1 Google's Chrome dominates desktop and mobile browser share global...
FIGURE 5.2 Dentsu Aegis Network Global CMO Survey, 2018 (n = 1,000).
Chapter 6
FIGURE 6.1 An early version of the “know, personalize, and engage” framework...
FIGURE 6.2 CDP working model.
FIGURE 6.3 CDP Center of Excellence model.
FIGURE 6.4 Center of excellence core responsibilities.
FIGURE 6.5 Where marketers get stuck on the engagement maturity curve.
FIGURE 6.6 The three major phases of engagement maturity.
Chapter 8
FIGURE 8.1 Different people ID types across clouds.
FIGURE 8.2 Connecting a multichannel customer journey.
FIGURE 8.3 Using inference to link common data attributes to insights.
Chapter 9
FIGURE 9.1 Tweet depicting results from the “Not Hotdog” app.
FIGURE 9.2 Depiction of how deep learning works.
FIGURE 9.3 Lab setup to create the app.
Chapter 11
FIGURE 11.1 An example of an early “brand health” analytics dashboard for a ...
FIGURE 11.2 Value chain of data in business intelligence.
Guide
Cover
Table of Contents
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“At Procter & Gamble, Georgia-Pacific, and Piedmont Healthcare I experienced how Marketing can become the human-centric ‘machine' that drives growth, with data as its main ‘fuel.' Customer Data Platforms is a must-read guide to the modern approach of optimally building and leveraging a human-centric and data-powered Marketing Machine.”
—Douwe Bergsma, Chief Marketing Officer, Piedmont Healthcare
“Every company needs to deliver experiences that turn consumers into loyal customers – and data is at the heart of it. Executives interested in leveraging technology to drive digital transformation will find a wealth of actionable insights in Customer Data Platforms to start their journey putting customers at the center of everything they do.”
—Doug Hammond, Chairman and CEO, NFP Corp.
“Having personal experience running large software companies like Computer Associates, I can personally attest that there is no bigger challenge than getting customer data right. Every time you introduce a new capability, there is a good chance you create another data silo that makes it harder to provide seamless experiences across sales, marketing, and service. Unifying 'people data' such that you can deliver the type of personalization today's customers expect is still one of the biggest challenges in business today, something that I am working on with Ringlead, a data quality and orchestration company. Kihn and OHara make this highly technical problem easily understood by business people, and offer clear and compelling solutions couched in customer stories and anecdotes which bring the challenge and potential solutions to life. Must-reading for any business executive looking to take on the challenge of digital transformation.”
—Russell Artzt, Co-Founder CA Technologies, Executive Chairman and Head of R&D at RingLead, Inc.
“Customers don't care how hard it is. In the best case they merely push through when it's not a connected experience, and in the more likely case they move on to something else. In today's climate, this capability is expected and it is obvious when it fails. Digital transformation has been a hot topic over the past few years, but today this is no longer about ‘if.' We are now at the point where it is about execution. Kihn and O'Hara bring years of experience and learning to catch up to the current state of play and lay out a practical guide through these challenges.”
—Mike Cunningham, President, CrowdVision (former CIO, Keurig Green Mountain)
MARTIN KIHN
CHRIS O'HARA
CUSTOMER DATA PLATFORMS
USE PEOPLE DATA TO TRANSFORM THE FUTURE OF MARKETING ENGAGEMENT
Copyright © 2021 by Martin Kihn and Christopher B. O'Hara. All rights reserved.
Published by John Wiley & Sons, Inc., Hoboken, New Jersey.
Published simultaneously in Canada.
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Introduction
THE PIZZA CHALLENGE
Art Sebastian had a problem. The affable, Chicago-born VP of Digital Experience at Casey's convenience stores joined a team on a journey to transform the company into “a more modern, contemporary and digital brand” – one that recognized each guest as an individual with their own preferences and needs.
“With retailers in the digital era, what consumers want is more relevance,” he said. “They don't want marketing that goes to everyone but rather relevant promotions, messages that speak to them as individuals.”
The problem? In a word: data. Not a lack of data, but a lack of connection, organization, and accessibility. A frustrating inability to create a customer profile that can be used to deliver a seamless digital experience.
“We had made some good progress,” said Sebastian, “but with the progress, we accumulated more data. We have a website and mobile app that collect purchase behavior. We collected pizza orders in one system and loyalty in another. We spent a lot of time thinking about how to unify these multiple data sets.”
Casey's logo is well known across the Midwest and the South, boasting 2,200 locations in 16 states and counting. It has a customer base devoted to its heart-of-the-community convenience stores, which are famous for comfort foods, like the tasty Taco Pizza, and friendly staff that treat you like their neighbor.
“Half of our stores are in small towns with a population of 5,000 or fewer,” said Sebastian. “We really play a central role in those towns. We're the grocer, the restaurant, and the place where people meet. We support the local high schools and nonprofits some of the area's bigger companies don't focus on.”
Like many retailers, Casey's aspired to deliver a better digital experience to its guests, one that paired relevant promotions with the soul of the brand. “When you look at all the things that people love about Casey's,” said Sebastian, “it's authentic to be friendly and relatable.”
Pulling insights from his customer research team, Sebastian knew that most customers actually bought the same or similar items every time they dropped in or ordered online. This was particularly true for pizza buyers, who nearly always bought the same pie.
What would happen if Casey's could personalize the emails to customers based on their pizza preference? Specifically, what if they could put an image of the customers' usual choice in the email, rather than the generic pepperoni they'd been using?
The challenge: Casey's email system was not integrated with its point-of-sale system. This mattered because it was not possible to know what a person's preference was without the point-of-sale data, showing what they bought. Lacking a connection, the email system could not personalize the message or offer.
“There was a lot of data stitching,” said Sebastian, “but we did manage to do it.”
His team ran a test in email using different pizza images, all featuring $3 off a large pie with a promo code. The image selected was based on the most recent pizza type each guest had purchased.
The results? There was a very significant 16% lift in conversion rate compared to the previous generic image.
“It feels almost silly to be talking about this in a world where people are doing so many sophisticated things,” admitted Sebastian. “But we've been able to build on it for other initiatives, like better default settings on the website. Everything improves as we're able to hone in on the guest more and get more personalized.”
Personalization works. Casey's story is repeated dozens of times by many customers we've met, and doubtless thousands of others across the globe. The value of relevant, personalized communication cannot be overstated and companies who don't provide it risk seeing their response rates decline and more organized competitors – or nimble startups – eat their pizza.
Although results vary based on factors such as the industry, product, and starting point, studies show that using key customer data – such as product purchase history, in Casey's case – to build more personalized messages yields results. For example, conversion rates on e-commerce websites rise an average 15–20% and engagement rates rise 30% after implementing better personalization. Personalized emails have at least a 6% higher open rate. Targeting ads on social networks based on website visits can increase click-through rates by a factor of two. Multiple studies estimate going from large segments to more one-to-one targets – even in a simple, incremental kind of way – quickly raises average customer lifetime value 20% and engagement rates 30% and more.
How do consumers feel? Our research shows that they not only tolerate but actually expect some level of personalization from brands. Salesforce runs a number of global surveys each year, enlisting thousands of consumers and marketing leaders around the globe and across industries, including business-to-consumer (B2C) and business-to-business (B2B) buyers and practitioners. These surveys, compiled into detailed annual reports, tell a compelling story of empowered consumers and marketers battling to keep up with their demands.
As should be obvious by now, customers are increasingly willing and able to move their business when a brand lets them down; they are in control. Across the globe, we see customers exerting their power in reasonable ways, demanding better products and services, and above all better experiences, built on data. Starting in 2018, 80% of customers told our researchers that the experience a company provided was as important as its products and services. And even more (84%) said that some level of personal treatment was key to winning their business.
Defining concepts like “personalization” and “1:1” is not as easy as it seems. But it is clear from the data that at a basic level, personalization requires different brand channels to behave in concert. According to our “State of the Connected Consumer” report, 69% of customers say they expect “connected experiences.” They believe that behaviors and preferences expressed in one channel should be reflected in others; that they won't have to enter the same data twice, and so on. The same report showed that 57% of consumers said they had already stopped buying from a brand because a competitor had a better experience. Loyalty is an increasingly fragile asset.
Loyalty to consumer brands continues to decline, as the importance of in-the-moment experiences outweighs brand equity (or the psychological-emotional value of a logo). The consultancy Interbrand, which tracks changes in brand equity over time, showed the average equity of consumer product brands (e.g. Coca-Cola, Pepsi, Kellogg's) declined by 4% in recent years, and retail brand equity declined further. (The study was done prior to the impact of COVID-19.) Meanwhile, equity in the big four platform brands – Google, Apple, Facebook, and Amazon – went up almost 10% in the same period.
Why do proprietary ecosystems such as Facebook and Amazon see their perceived value climb while traditional brands suffer the opposite? In part, because these platforms have a personalization advantage. They treat a universe of mostly logged-in, repeat users, about whom they have amassed a deep data file. For example, Facebook and Instagram see about three billion monthly active users globally; and, astonishingly, more than half of those users access the platforms every day. About 62% of US households are members of Amazon Prime, and they spend an average $1,400 per year.
Having more data, these platforms can provide more personalized marketing and experience on channels such as websites and mobile apps. Greater personal depth yields a beneficial flywheel effect, as experiences yield data and loyalty, which increases over time – even as competitive brands, who generally lack the customer data and the ability to personalize, fall behind. Research shows, for example, that Amazon's product recommendations, based on greater customer-level information, perform about 2x better than those of other large retailers.
Few brands today would deny the benefits of deeper customer data, better insights, and the challenge of rising consumer demands for personalized experiences. Given these realities, and the competitive heft of large platforms such as Facebook and Amazon, we want to pose a simple question: Why doesn't everyone just start doing highly effective, one-to-one marketing now?
What's the problem, exactly?
THE PERILS OF PERSONALIZATION
Around 2010, Coca-Cola execs realized they faced a make-or-break moment. Sales of the #1 consumer brand were falling each year, around the globe, and there was no obvious solution. Aware of the experiential imperative outlined above, Coca-Cola wasn't sure how to act. It had the burden of being a mass consumer product that appealed to many people but lacked basic individual-level data about its customers. Its MyCokeRewards loyalty program was discontinued in 2017, after a decade of disappointment. If there was one brand that would never really be customer-driven, it seems, it was Coke.
But then somebody had an idea. The enterprising field team at Coca-Cola's Australian subsidiary had a brainwave. Sure, they couldn't hope to personalize at a one-to-one level, since they lacked that kind of relationship with customers. But they could approximate one-to-one marketing, couldn't they? They could get more personalized than they were; after all, anything was better than nothing.
So, they launched a campaign in 2011 called “Share a Coke.” Aimed at reaching Australian millennials, it remade the iconic Coke cans by putting the 200 most common millennial first names on them. The hypothesis was that younger shoppers would be more likely to purchase a can that had their name, or the names of their friends, written on it in bold white-on-red. They were right. Extended to the US and other markets in 2014, the “Share a Coke” campaign drove Coke sales up for the first time in years.
When it comes to delivering a truly customer-driven experience, many companies find themselves facing a version of the Coca-Cola conundrum: not enough customer data, and not enough time. Roadblocks to the customer-driven future are fierce. Some of these are external and others, self-imposed. To provide a partial list, companies are:
confronted by consumers' own changing attitudes toward data collection, storage, and access
shadowed by a systemic decline in trust, inspiring a rash of ad-avoidant and marketing-hostile behavior
racked by rising regimes such as Europe's General Data Protection Regulation (GDPR) and the California Consumer Protection Act (CCPA)
And those are just the market-driven challenges; there are plenty within the walls of companies, too.
We face a moment of ironic tension, one that forces companies to think in creative, empathetic ways about customer data. What do we mean? We know that customers require unprecedented levels of personalized experience from all brands and are voting with their wallets if they don't get it. At the same time, these same customers are increasingly wary of providing access to the very information that is required to provide that experience – namely, behavioral, attitudinal, and demographic data at the individual level. It's a tension that researchers increasingly and aptly refer to as “The Privacy Paradox.”
RISE OF THE AVOIDANT CUSTOMER
In recent years, marketers are counseled to shore up their customer data. Blue-chip consultancies such as McKinsey and Accenture wax on about the “customer data imperative” and the “first-party revolution.” Just to compete with personalization powerhouses like Amazon and direct-to-consumer upstarts like Harry's and Casper, brands try to lure more customers to their channels, to sign up for newsletters and promotions, download mobile apps, and join social network communities. And consumers are more aware than ever of what these brands are trying to do.
It's not that consumers eschew data-sharing. It just has to be the right data, and the right recipient. Research shows that within certain parameters, we are okay with many forms of tracking. Salesforce's most recent “State of Marketing Report” revealed that 58% of customers are comfortable with their data being used “transparently.” Yet only 63% of companies comply with this basic requirement, even as they become more attuned to consumers' needs. The percentage of marketers who admit to being “more mindful of balancing personalization with customer comfort” soared from 51% in 2018 to 81% in 2020 – meanwhile, the percentage of marketers who feel “completely satisfied” with their ability to do so fell from 30% to 28% from 2018 to 2020.
As we've seen, it isn't personalization per se that bothers people. Nor is “transparency,” however defined, the only requirement for comfort. The dimensions are both context and openness. One study published in Harvard Business Review indicated that data collected based on behavior on a brand's own channels (what we call first-party data) was often acceptable for use. However, data inferred (via statistical methods), collected by unknown third parties, or collected without advance announcement, was not. In fact, if a consumer found out that one of these less-obvious methods of tracking were used, they were much less likely to buy.
Meanwhile, all brands wrestle with a secular decline in trust. Each year, the bellwether Edelman Trust Barometer plots a depressing death spiral. Trust in business (52%), media (43%), and government (41%) continues to drop. Established brands often share in the general malaise, suffering by proxy. For example, a recent Censuswide survey revealed that one-third of social network users have “little or no trust” in brand information they see on networks.
Given a general rise in paranoia and distrust in institutions, customers are unsurprisingly less and less receptive to marketing and advertising messages. A study sponsored by the Advertising Research Foundation (ARF) into the phenomenon of “ad receptivity” divides consumers into cohorts determined to have high, medium, low, or no receptivity to ads. In recent years, the high and medium-receptivity cohorts were down 3–8% in size, while the “Low Receptivity” group grew from 25% to 32% of the adult population. The group with “No Receptivity” grew to about 10%. In other words, about two in five consumers either don't respond at all or barely respond to marketing – and that group is getting bigger.
THE DISCONNECTED DATA DILEMMA
So much for the customer side of the customer-driven equation. Companies themselves tell us again and again that they face a host of challenges that can perhaps best be summed up in the phrase: disconnected data. As the customer experience mandate grows and companies fall over their picks mining customer data, they look at their own internal systems and see, in the words of one colorful customer we know, “a hot mess.”
There's no doubt companies continue to grapple with more internal systems that contain data about channels, customers, prospects, and accounts (among other things). Our recent research showed that the number of significant data sources used by marketers alone grew 50% from eight in 2019 to twelve (projected) in 2021. Keep in mind these are major sources, and companies naturally host and/or manage many times more both inside and outside IT. In fact, other research showed that the average enterprise has about 900 different applications, an average of only 28% of which are integrated with a system of record.
So there's more places where data can sit, more complexity, more demands. It would be encouraging to report that this data was itself in good shape, properly formatted, harmonized, cleansed, and deduplicated. But companies tell us that is very much not the case. Across the board, in virtually every industry and region surveyed, we found that companies' satisfaction with the state of their customer data was low. Our research showed that the percentage who declare themselves “satisfied” with their data quality and hygiene (37%), timeliness (34%), integration (34%), consent management (34%), and identity reconciliation (33%) – all fall below thresholds for customer-driven success.
Even more disruptive, disconnected data both causes and is a symptom of disconnected organizations and teams. As channels appeared over the past two decades, teams were “spun up” to manage them, and these teams are often still operating as semiautonomous fiefdoms, compiling data and executing tactics with as little attention to hygiene and integration as the data substrate they're using. It's the disconnected team and disconnected data double threat that is inspiring so many so-called “Digital Transformation” initiatives among our customers.
CROSSING THE CUSTOMER DATA CHASM
What is a customer-driven company to do? That's the question at the heart of Customer Data Platforms, one we will try hard to answer. Various answers have been given in the past, as marketers and other departments applied technology solutions to their evolving business needs. We don't believe in rip-and-replace as a panacea, nor in the existence of a silver bullet; nor do we believe technology alone can solve people, process, and (especially) strategic issues. We believe the new paradigm of Customer Data Platforms is an evolution of what you've done before and is compatible with existing solutions. Our approach is one of compatibility and growth, not replacement and self-imposed crisis.
Since the 1990s, customer relationship management (CRM) has provided a great answer to many customer engagement needs. It has improved response rates, satisfaction, throughput, sales, market share, closing rates – in short, it's upped the game for many customer-facing disciplines well beyond marketing. At the same time, the category of CRM has expanded so far and wide that the leading analyst firm Gartner no longer covers it as a single market, instead dividing it into 190 distinct subcategories.
CUSTOMER DATA PLATFORM (CDP)
In the last few years, we've seen the rise of a category called the “customer data platform” or CDP. According to the Salesforce 2020 State of Marketing Report, 86% of marketers who use them are increasing or maintaining their use of CDPs, a sign of strong category adoption. Intended to help marketers and other customer-facing departments solve some of the dilemmas described above, the CDP has caused both excitement and confusion in C-suites around the world. Companies wonder if they need one, if they have one, and more importantly, what is this thing called a CDP, anyway? As we explain below, we don't believe the category is new; nor do we believe most vendors with the name “CDP” really sell one. In fact, one wide-ranging survey of marketing tech professionals found that 62% of them said they were using the “Salesforce CDP” before we even had one on the market.
It makes sense that a company known for pioneering CRM would be considered a CDP company. (After all, the “C” in both three-letter-acronyms stands for “Customer.”) We will argue that CDP is just the latest evolution of the CRM category, with an emphasis on marketing use cases to begin, quickly expanding to other areas such as service, sales, and commerce. We will define the key components of the CDP, from data ingestion, processing, and identity management; to segmentation, machine learning, and artificial intelligence (AI); to cross-channel activation, reporting, and optimization.
Moreover, unlike other frameworks for this emerging and still undefined category, we'll argue for an expansive view, one that goes well beyond any single vendor's current feature set. One that encompasses the holistic customer journey, from pseudonymous (explained in Chapter 1) to known, and the holistic marketers' requirements, from real-time contextual personalization to traditional rule-driven campaigns.
In particular, we argue that a true enterprise-grade CDP must provide:
Anonymous to known
. Since customer journeys usually start with an anonymous ad viewer or visitor to a website, CDPs must start to capture data (with proper consent) in the anonymous or pseudonymous state. For this reason, we include capabilities associated with a data management platform (DMP) within the CDP.
Insights and engagement
. Researching marketers' real requirements – rather than vendors' press releases – we discovered that they actually encompassed two major systems:
System of engagement:
Providing real-time engagement, such as channel optimization, next-best-offer management, and dynamic creative optimization
System of insight:
Providing a more persistent “single view of the customer,” or Customer 360, for the purposes of in-depth analysis, modeling, and measurement
Ultimately, our discussions with customers, our reading of the research, and our own broad experience tell us that marketers aren't looking for yet another application to build yet another customer-data silo. What they want is a long-term solution that lets them provide more personalized, trusted, one-to-one messaging and marketing, yielding a better customer experience.
In short, they want a trusted platform for marketing: one that is reliable, future-proof, and customer driven.
CHAPTER 1The Customer Data Conundrum
Nobody but a farmer wakes up one morning and decides to build a silo – yet that's exactly what has happened, naturally, over the past decades. Starting with the best intentions, marketers and other divisions acquired applications and built customer data stores, then built patches and organized data lakes and marts, signed up with exciting start-ups and … ended up with over a dozen (on average) separate databases storing data, often about the same customer. Meanwhile, organizations have built up around these silos, making the problem much worse. In this chapter, we discuss the current state of (disconnected) marketing and why it needs to be solved.
DATA SILOS
What's keeping marketers from achieving the “right customer, right message, right time” nirvana they've been chasing for decades? Put simply, it's the nature of customer data itself – it's siloed in different databases, stored in different formats, and used by different parts of an organization in different ways. It's constantly growing, pervasively available, and getting accessible in real time, but continues to defy brands' efforts to unify it and make it easily actionable.
Salesforce's sixth State of Marketing report surveys over 7,000 senior marketers across a wide swath of industries to find out what their top priorities and challenges are from year to year (the survey is conducted by a third party, and respondents are not aware that Salesforce is the sponsor of the research). In the most recent edition, two of the top five challenges were “unifying customer data sources” and “sharing a unified view of customer data across business units” (Figure 1.1). In this section, we will talk about why data unification continues to defy marketers' efforts – and discuss the challenges and opportunities for sharing customer data across your organization.
FIGURE 1.1 Marketers' top challenges.
Source: Courtesy of Salesforce State of Marketing 2020.
Does this experience sound familiar? You start to carefully research new cars online, you “build” a few models on the website, research pricing and financing – even go to several dealerships and take test drives. After a few weeks or months narrowing down your choices, you pick the right vehicle and drive it home. Then for the next two years you get consistently blasted with emails, social media ads, and mobile ads for the car you already bought. What a tremendous waste of money! Despite all of the technological innovation driving marketing and advertising over the last 20 years, it seems like every brand who smells even the smallest whiff of purchase intent immediately starts a barrage of full-funnel marketing meant to overwhelm consumers into buying something.
The problem is not a lack of data. In fact, we have seen that marketers report using a median of 8 distinct data sources in 2019, and will expand that to 12 next year, a 50% increase. Think about the amount of data generated by a consumer in the car shopping process: online cookies generated through website visits that reveal the type and model of the car; web form data with name, phone number, and email address gathered from test drive request and finance forms; a user's mobile advertising ID from in-app experiences; and lead form data input at the dealership.
Put together, these data would reveal intent across the entire lifecycle of a car purchase – with attributes ranging from vehicle type, price range, color, location, financing type (buy or lease), and even past purchase history. With this type of data, a smart automotive marketer could move consumers from consideration to purchase in stages, close the deal – and continue the campaign post-sale with service deals and offers.
However, this rarely happens today. The problem is not the amount of data being generated, but where that data is stored and who is using it. The data from the website is not connected to the customer relationship management (CRM) system at the dealership. The “take a test drive” form never makes its way to the web marketing team to improve their targeting. The purchase data from the dealership and manufacturer's warranty data stay in silos and never get used to update the marketing team to prevent the company from trying to sell you a car you have owned for six months.
Over the years, we've built up a number of data silos, where critical customer information never gets the opportunity to enrich each other and lead to insights. How much money could a marketer save by simply turning off marketing for products a customer has already purchased? By using the carefully tuned buying propensity models from known customer data and applying them to unknown prospects coming to the website? By connecting what's happening on their e-commerce site to their email marketing? It seems obvious, but the problem of disparate, siloed customer data has only expanded and hardened over time.
Defining the problem space is simple: too many types of customer data, stored in different systems. But to start to solve it, we must look at the problem in two distinct ways, defined by fundamentally different data types: “known” and “unknown” customer data (Figure 1.2).
FIGURE 1.2 Data silos, organized by marketing function, across known and unknown data types.
KNOWN DATA
For simplicity, “known” data is any type of customer data that is personally identifiable, called “PII” or “personally identifiable information.” This is where you fill out a web form, purchase something from a website, give the cashier your information at a retail store, subscribe to a site like the New York Times. This is the virtual gold of marketing – real information about real people that has been given with consent to a company you trust. It is increasingly rare thanks to privacy legislation (more on that in Chapter 5), and very expensive to obtain. In 2019, Mary Meeker's Internet Trends Report called out the increasingly high cost of customer acquisition as traditional brands start to compete with AI-driven direct-to-consumer startups, such that a customer's lifetime value has started to become less on average than the cost of acquiring them.
Depending on the industry, acquiring a new customer can cost anywhere from 5 to 25 times more than retaining current ones – and the cost will continue to rise as marketers attempt to stay afloat in a noisy digital marketplace. Two years ago, Comcast paid over $1,200 for every net-new wireless subscriber. The cost to purchase email can range anywhere from $200 to $600 per thousand addresses (CPM, or cost per mille), depending on the accuracy and granularity of the list. Real “people data” is expensive, and most companies have put a lot of energy into organizing and optimizing it, starting with its use in traditional CRM systems by sales, email systems by marketing, and service applications by call center employees.
The problem, however, is that even when the brand has a customer record, it lives in those silos (sales, service, commerce, marketing) and rarely gets unified in such a way that can power a customer journey. A typical company may use one CRM application to power its sales operation, another to keep track of customer service details, and yet another system to store data used for marketing. In addition, they maintain a data warehouse or data lake to create a “golden record” of customer data, and use it for things like propensity scoring and lifetime value (LTV) modeling. But a customer record may be replicated four times across those four systems – and the company may have multiple instances of data across different regions, brands, or operating companies.
Imagine how great it would be if Joe Smith in the service system (Joe returned the shoes he bought) could be unified with the Joe Smith inside the marketing system (don't email Joe about these shoes)? Or the Joe inside the data warehouse (Joe has a high propensity to buy) could be connected to the sales system (call Joe now)! This seems like a fairly straightforward problem to solve – why not just create a single system with one record? As above, most “stacks” are like old rambling houses, featuring additions built over many years – they're almost impossible to renovate.
CUSTOMER RELATIONSHIP MANAGEMENT (CRM)
The CRM system is the operating system for customer data that the sales organization plugs into, and is in many ways the organization's true “source of truth,” going well beyond sales records. The service system in the call center is where tickets are opened and logged, and call center reps own a relationship with customers and prospects that may start on Twitter, go to the phone, and end up on email or text message. That system is the operational heart of the call center, and the “source of truth” for how customers move through specific parts of the funnel. Marketing and commerce systems are their own closed ecosystems, complete with different ways of identifying customers, storing their data, and analyzing it. Customer data is the lifeblood pumping through those systems and powering their operations and, over time, those systems have evolved to leverage customer data in the service of different outcomes: revenue (sales), customer satisfaction (service), direct purchase (commerce), and engagement (marketing).
Breaking down those silos requires companies to resolve customer data, and create data portability across applications. Let's talk about the first, and most important requirement: known customer resolution. How do you take data from a number of different sources (records) and create a single, unified profile for every customer? It's a daunting challenge. Joe Smith might have three different email addresses, several different phone numbers, and even multiple mailing addresses. What is the “source of truth” for Joe among that data, and how can Joe be resolved into one profile that serves as the “golden record”?
Moreover, when doing the actual “data munging” required to unify records, it quickly becomes apparent that each system stores data from fields in different ways (e.g. one system may identity Joe as belonging in the “FirstName” field, and another may call it “First_Name”). It's always a little bit different, and those small differences are what keeps the quest for a “golden record” a multiple billion dollar opportunity over the next 10 years. We will dig into this topic in depth in Chapter 4, but summarize it below.
CUSTOMER RESOLUTION
Customer resolution is the method by which companies create a data model to standardize those fields, and also build the ability to make those systems smarter by applying logic to map fields together accurately. For example, if Joe has a gmail address and an AOL address, which one takes priority? Or, if Joe has three postal addresses, which one is the most recent? These many individual conflicts need to be solved continually, at scale, across many databases. It's not a trivial problem, but the reward for solving it unlocks incredible business value in terms of the ability to know and understand customers from an analytics perspective – and activate customers across many known customer touchpoints including sales, service, commerce, and marketing.
DATA PORTABILITY
Once a model is established for resolving customer data, the next step is to make different attributes available to other systems, or “data portability.” Data portability is an incredibly broad area, but from a marketing perspective it's concerned with making sure customer data flows accurately from system to system in the service of a better understanding of a customer's status and intent. For example, if Joe bought the shoes (commerce data), his next email would be about socks or something else (marketing data). Each system is concerned with different attributes around the customer, but not necessarily relevant to the day-to-day operation of those systems.
It might be great to know that a customer has ten closed tickets over the last twelve months in the call center, but that's not mission critical data for the email marketer. However, if that fact was translated into a “satisfied recent customer” marketing attribute that could be segmented upon, then it turns into marketing gold. Deciding what data to port between applications (and ultimately store and persist in a single “golden record” system like a CDP) is one of the battlegrounds of marketing that will decide the winners and losers for years to come.
The CDP seems to be the technology de jour for solving the problem of unifying known customer data and provisioning a golden record that marketers can rely on to power the majority of their PII-based marketing efforts, largely centered around messaging. But what about the massive amounts of data created when customers are now known, but interacting anonymously with a brand's digital advertising, website, or mobile application?
