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CHAPTER 3

CUSTOMER EXPERIENCE: THE FRONT LINE OF THE BATTLE

What is customer experience? What makes it good . . . or terrible? Let’s take a look at three experiences I recently had with different companies. In each case, ask yourself, How did the information systems or data within these companies create—or destroy—the opportunity to deliver an experience that would make me a loyal customer?

Experience the first: I needed to clean my granite countertops. The local store stocked only the brand I didn’t want. I found an online retailer that stocked the brand I wanted and I purchased a bottle. Unfortunately, the online retailer delivered the same undesirable brand stocked at my local store. When I called to complain and say that the return was not even worth my time, the service rep assured me that it would not take any of my time: UPS would come to pick it up with a label and all I had to do was give them the package.

When I put the package out, my local mail carrier (USPS, not UPS) tried to help by taking it. When UPS showed up, the package was gone, so they did not leave the label. Then the post office called to tell me I had to pick it up or they would throw it away. I drove to the post office, picked up the package, and then called UPS—spending 30 minutes on hold and leaving a voice mail—only to receive a callback from a rep who told me to call the retailer and promptly hung up the phone! Somebody at the retailer emailed a label, and I then had to find a drop-off location for the package after I printed the label. It took four hours of my time to process a return for a $12 purchase.

In this instance, the breakdown in customer experience happened at many points in the process. There were data issues at the point of purchase, a lack of clear explanation of the process, a breakdown at the local post office, and a disconnect at UPS. Do you think I will go back to that online retailer?

Experience the second: I received a mortgage refinancing offer from my bank. It was a great deal. But I was already in the process of refinancing with the same institution, a process I’d started a month before I received that particular offer. The bank’s marketing organization was trying to sell me something that I had already purchased.

Experience the third, which restored my faith in companies’ ability to do customer experience properly: My wife is an avid cook, as well as a fan of any and all quality cooking tools. I had heard good things about Kamikoto chef’s knives, but I assumed they were very costly.

I clicked on an offer in my Instagram feed and found a deal for a Kamikoto knife set at a savings of several hundred dollars. This engaged me enough to take the next step and continue exploration. I researched the knives a bit more by reading web reviews and comparisons and decided to buy them for my wife (okay, they were really for me). The purchase process allowed me to use my Amazon account, which made it simple since Amazon already knows my credit card number and shipping address. After the purchase, the retailer enabled me to track my shipment (text or email) and a mechanism for returning it for any reason to reinforce the wise choice that I had made to purchase these fine knives.

The knives arrived in a beautiful wooden display case, along with certificates of authenticity and information about the history of the company, its manufacturing process, the high grade steel it used, the precisely balanced ratio between the handle and blade to allow for effortless cutting and chopping, and instructions on how to best care for the knives (wipe with a clean damp cloth and never put them in a dishwasher or toss them into the sink with dishes and silverware).

At each stage of the process of buying the knives, my customer experience was seamless and easy. Very little got in the way of my becoming a satisfied customer. I like these knives. My wife likes them, too. And I like the company that sold them to me—unlike the retailer who drove me nuts returning $12 worth of granite cleaner, or the bank that tried to sell me on a financing offer I’d already taken them up on.

CUSTOMER EXPERIENCE SUCCESS DOESN’T HAPPEN BY ACCIDENT

Customer experience (CX) is hard to get right and easy to get wrong. It’s not a question of will: everyone wants to be good to the customer (except maybe Ryanair). It’s not even a question of investment. It’s a question of the proper integration of technology. A well-designed customer experience accounts for the customer’s needs and all possible events the customer may experience (as the Kamikoto knife ads and delivery did). It allows for the possibility that things can go wrong and delivers an acceptable experience accounting for those cases (as the granite cleaner retailer didn’t). And it leverages all the knowledge that the company has about an individual, even if it’s held in different information systems (as the bank’s system obviously didn’t).

Customer experience is the sum total of all of the experiences related to your brand. It starts with the first marketing impression; expands with the customer’s increasingly detailed understanding of the features, benefits, price points, style, and other selection considerations; and continues through the actual purchase, acquisition, usage, maintenance, and post-purchase relationship.

During this lifecycle, in which prospects become first-time customers, then repeat customers, and then loyal brand advocates, they will have a series of interactions online and off-line. They may talk to friends or colleagues, conduct research, look for recommendations and online reviews, talk to customer service reps and salespeople, visit stores, test products, and investigate competitive products. In this process they also experience a range of emotions: they may become confused on a website or by instructions, read conflicting reviews, become excited by the prospect of purchase, become disappointed, get frustrated, feel happy about getting a good deal, or become angry about not getting a better deal. They may experience regret at missing an opportunity, or buyer’s remorse after making a purchase. They may be delighted by an unexpected level of customer service or attention to their needs, or they might be freaked out by overly personal service that feels intrusive. All of these emotions, interactions, perceptions, and events are part of the customer experience.

For CX design to work right, the brand must avert countless opportunities to fumble the sale by using comprehensive planning and immaculately consistent messaging that eliminates all potential friction. AI can help, but if the supporting processes and systems are not solidly aligned and integrated, no AI can enable a friction-free experience.

There are now many more dimensions to a successful engagement strategy than there were before people went online, including social media, content marketing, cross-channel customer experience, big data, and analytics. We’ll touch on digital marketing as well, because much of the data used to power these experiences comes from new marketing technologies. (We’ll explain the role of the digital marketer in more detail in the next chapter.)

In this chapter, we’ll explore how problems can get in the way of a systematic approach to customer experience and describe the eventual solution: the high-fidelity journey map and the plan for applying it to your technology. The result is a technique for applying the ontology to customer experience in your marketing technology stack and making more thoughtful decisions about technology. We will show you how to use the customer journey to make marketing technology plans, blueprints, and investments.

Let’s get started by looking at the fundamental challenge: how can you move quickly and personalize experience at the vast scale that’s necessary in the enterprise?

Ontology Enables Agility and Scalability to Coexist

Enterprises interacting with customers pursue two conflicting goals: agility and scalability. The newly integrated worlds of customer experience and marketing demand the ability to react and respond quickly and efficiently across departments, tools, platforms, and business functions. They also require organizations to globally manage a significantly larger volume of personalized and localized content than ever before. The customer experience emerges from this, since it is the sum total of all interactions: with marketing, with sales, with customer service, and throughout the organization.

The first goal is agility—or more specifically, agile execution. Agility is crucial as the pace of business and markets accelerate. Organizations must keep up with changing priorities, strategies, and execution challenges. What is working and what isn’t? Where are resources focused? What do market research and customer data tell us, and how should organizations respond, adapt, and evolve? What tools are most appropriate to solve engagement problems?

The second goal is scalability. Enterprises must be able to scale sophisticated and nuanced marketing, product, and service initiatives across platforms and channels, and operate them efficiently to support customers at every stage. This goal conflicts with agility in that it typically entails coordination of many tasks and processes that cannot be completed quickly.

How can you harmonize these two goals? The correct governance structure combined with consistent organizing structures provided by the ontology is the key. A fully integrated, holistic, cross-department execution perspective is most effective with digital technologies based on a consistent architecture—and managed with reference to the ontology. Agility is based on reacting and adapting. Scaling requires consistency and standardization. It is only through the use of an ontology that the two goals are no longer at odds with each other.

Complexity and the Customer Experience

As more customers transact most or all of their business online, organizations are forced to stitch together a seamless customer experience from what in many cases is a series of disparate systems.

For example, we once conducted a customer experience project for a financial firm using more than 50 platforms for managing customer investments. Customers could conduct some transactions predominantly online. For more complex investment redemptions, changes in beneficiary, or transactions with tax implications, the customer needed to call and speak with a customer service representative. Of these tasks, some required transfers to specialists for callbacks, and others required the call center agent to interact with a back-office transfer agent. In extreme cases, the transfer agent required support from a special “hotline” of top-tier specialists who had even greater levels of training, expertise, and system access.

Imagine the customer experience this generated: beginning a process online, running into a roadblock, searching for help, sending an email request, calling in, being transferred, getting placed on hold, leaving messages, calling back, having to provide basic identifying information repeatedly, and so on. Rather than a smooth paved highway, that journey was filled with bumps, detours, and side ramps that slowed the process, increased support and transaction costs, and damaged the customer relationship. Each step and task could require a different department, system, process, technology, cost center, or procedure. So much for a smooth, seamless journey. Disconnected internal systems and processes destroyed it.

This type of poor experience will likely result in customer ill will, higher support costs, missed opportunities, and lost business.

Reducing the Customer’s Cognitive Load

We have all been transferred from department to department and have had to repeat account information and security questions. We have all dealt with confusing and maddeningly unhelpful call trees (press 1 to be annoyed, press 2 to be really annoyed, press 3 to be really, really, annoyed . . .) while yelling “Operator!” and frantically pressing “0”. Some of us have even tried to return granite cleaner and wasted fruitless hours on the task.

In theory, the customer experience can be drastically improved in almost any organization. However, the groups that are typically tasked with improving customer experience (for example, call center operations) do not always have the resources to get to the root of the problem and fix issues upstream. Call centers are incented to maximize productivity, not to actually solve the problems that are causing customers to call in the first place.

To solve the problem, eventually the root cause needs to be addressed. But all too often, the organization lacks the discipline to make the investment in finding the root causes of experience problems. Or the organization has tried solutions before but has been burned due to the complexity of the problems, the number of systems involved, interdepartmental dependencies, a lack of overarching governance, incorrect metrics, outdated or poorly selected technology, or an insufficient application of resources to the problem.

Many customer experience improvements are rooted in reducing the cognitive load on the customer—by simplifying menus, understanding customers’ intent, and finding a way to cut directly to the chase. If I had been presented with hundreds of choices for chef’s knives rather than one or two options, I likely would not have purchased any of them. The “paradox of choice” states that providing too many options increases anxiety and leads to second-guessing decisions and unrealistic expectations.

This is why simple interfaces presenting just the information needed in a particular situation reduce the mental work and allow for faster, easier decisions with higher levels of satisfaction. But it’s not at all easy to figure out how to best simplify the experience. The key lies with exercises that map the customer’s journey with the company and attempt to understand the customer, including their decision criteria, values, and considerations, along with the features, functions, and characteristics that are important to them. These components become a subset of the data attributes that represent the customer; those attributes must be included in the ontology so that systems can align the correct content with the signals gathered during their journey.

Just as a great salesperson reads verbal and nonverbal cues and body language to gauge what a customer needs, our digital customer engagement technologies read the customer’s digital body language and present what they need—no more, no less. We understand those needs and digital signals through journey mapping.

TOWARD A HIGH-FIDELITY MAP OF THE CUSTOMER JOURNEY

Executives believe they understand their customers and their customers’ needs. They most likely have groups of employees tasked with understanding and researching this exact topic. They may run focus groups, conduct user studies, analyze data in voice-of-the-customer surveys, and organize research teams.

One goal of all this activity is to develop a journey map: a high-level description of the steps the customer takes on the way to achieving their objectives. The customer journey:

•traverses multiple channels and touch points;

•interacts with every part of the business (throughout the product or service lifecycle);

•is supported by multiple departments;

•includes transactions across many systems and applications;

•is governed or managed through various processes and organizational structures;

•leverages models of the customer to varying degrees, including attributes, characteristics, and preferences from the ontology; and

•extends well beyond marketing, sales, and support and depends on all the other parts of the organization.

Maps of customer journeys are typically developed at a high level; the details of moment-to-moment needs are difficult to analyze, understand, and serve. Many customer journey maps are based on what the organization thinks its customers experience rather than what they actually experience. The reason for this is that primary research (talking to, interviewing, and observing customers in their actual environment) is expensive. Even when organizations undertake costly customer research, there can be biases in what the researchers are looking for or interpreting.

One revealing exercise is to compare a hypothetical understanding (getting executives in a room to chart out how they believe their customers interact with their organization) with one that is validated by actual users in as close to a real-life circumstance as possible. Many organizations employ usability testing labs, and although those environments attempt to simulate real-world conditions, they are, by nature, a simulation.

AI-powered customer experience takes this approach to an entirely new level. It does so by considering customers’ needs in their specific contexts as they go about their tasks and building from this information data models that represent moment-to-moment needs of the customer. We all play different roles throughout the day—parent, employee, boss, colleague, and customer of a range of businesses. Our needs change as our role and our immediate objective changes.

The High-Fidelity Journey Map Is the Keystone of a Better Customer Experience

Preparing for AI-powered customer experience demands a new kind of journey map: a high-fidelity journey map. While you may think you know your customer, you’ll know them a lot better with a high-fidelity journey map informed by AI.

No doubt your organization has developed customer journey maps at some level of detail from your research activities, testing, user focus groups, and internal working sessions. To keep things manageable, people who create these maps tell me things like this: “We are keeping it simple. We don’t want to overcomplicate things.” That is not a bad idea. “Don’t make it the Mardi Gras,” as a colleague of mine was fond of saying.

However, in practice, many customer journey maps lack enough specificity—they are oversimplified. Not only do they fail to include enough detail in the tasks that the customer needs to execute, but they lack a way of representing the customer stages and objectives in ways that computers can understand and act upon.

The solution is to create what I call high-fidelity customer journeys. Why “high-fidelity”? Well, beyond sounding cool and different and buzzwordy, the term actually means something. High-fidelity customer journeys are representations of the customer’s needs in data terms. The map of the high-fidelity customer journey models customers’ “attributes”—the descriptors and identifying features indicate their role, buying stage, interests, demographics, goals, and even state of mind.

Developing customer journeys that can convert what the customer is trying to achieve into things that the technology can present back to the customer presents a problem. We have to think through the “So what?” question. What does it mean when we say that the customer is from a particular industry or that they are trying to select one product or another? How do we represent (and how does it matter) when our customer is at the “choose” stage in trying to decide what to purchase? The process is logical and objective, but there is art along with the science.

Oftentimes, internal research groups and external agencies can miss the critical linkages of the customer experience to attributes that your systems can interpret and act upon. These linkages are mechanisms that allow the journey map to come alive within the systems and technologies within your organization. They inform the machine learning and AI tools and are defined as elements within the enterprise ontology.

To complete the high-fidelity journey map, you must validate the journeys through primary research—that is, through actual observations of customers, interviews, and simulations that prove out your assumptions and insights about what the customer wants and how they think about the world.

High-fidelity journey maps are validated models. They are distinct from other customer journey representations because they include a detailed, nuanced, and multidimensional understanding of various aspects of the customer. The high-fidelity journey map requires new and evolved ways of thinking. It also takes into consideration variations in use cases and makes those part of the ontology. This data allows AI and machine learning programs to assemble and optimize offers by recognizing signals that indicate the customer’s intent and context.

When high-fidelity journey maps are combined with customer attribute models (descriptors that represent the customer’s interests, needs, tasks, objectives, role, history, propensity to buy, and so on), these representations tell our AI technologies how to enhance weak signals like a simple keyword search with more contextual clues that piece together what the customer really needs. (We will explore how to model customer attributes in chapter 5 as we explore ecommerce and personalization.)

SIX STEPS TO CREATING AND APPLYING A HIGH-FIDELITY JOURNEY MAP

With this mental model in place, the ultimate solution to customer experience challenges is to create a high-fidelity customer journey map and implementation blueprint that goes along with it. This is an exercise that addresses how customers interact with all the company’s systems throughout their journey—the relationships between the customer journey and your company’s stack of technologies. The purpose of this six-step exercise is not just to identify how those technologies are affecting the customer experience, but to build a roadmap for making technology improvements that will optimize that experience.

These are the six steps in that process:

1.Understand and map the customer lifecycle.

2.Define customer engagement strategy at each step of that lifecycle.

3.Survey and assess existing tools and approaches.

4.Assess the maturity of your supporting processes.

5.Assess tools, technologies, and internal processes with regard to engagement strategy and technology landscape.

6.Develop the implementation roadmap based on enterprise maturity and high-value areas of opportunity.

The process begins with understanding the customer lifecycle.

Start at the Highest Level by Mapping the Customer Lifecycle

This is the first step in creating a high-fidelity journey map. Ask yourself, where do customers come from? How do they go from not knowing anything about your product or service to becoming a loyal, repeat customer and market advocate? Answering these questions leads to mapping your customer lifecycle.

What are the stages your target customers experience, such as learning about your organization and value proposition, determining which products and offerings they need to evaluate, making a choice and purchasing your product, using the product, getting support, maintaining the product, and becoming an advocate? Each industry has prototypical customer journeys, but the precise journey will vary from organization to organization (see Figure 3-1). The nuances of messaging, engagement, experience, and interactions are what differentiate your organization and comprise the character of your relationship with your customers.

Figure 3-1: Industries’ Unique Stages of Customer Engagement

All these lifecycles have similar elements. If people don’t know anything about you, they need to be informed that you exist and that there is a reason for them to continue the exploration. Some industries call this stage “research,” others call it “learn,” still others call it “discovery.”

In the pre-digital era, the learn or discovery stages were mostly the purview of marketing organizations, whose job ended once the customer walked through the door or picked up the phone, responded to a mailing, and so on. Each subsequent stage was handled by a different department, and therefore issue resolution could require multiple handoffs.

Organizations are still structured in discrete processes, but with end-to-end digital experiences available, customers do not see the organization in this way. They care about solving their problem and don’t want to be bounced around from one group to another.

Define Customer Engagement Strategy at Each Step of the Lifecycle

This is the second step in the application of the high-fidelity journey map. At each stage in the customer lifecycle, you can differentiate your company from the competition with the positioning of your message, the approach to reaching your audience, the way you describe your value, the nuances of your message, and the character of the relationship.

Aligning the right content with an effective customer engagement strategy allows you to engage the customer at each stage of their lifecycle with content and information to move them down the path toward purchase or solving their problem. That’s what happened in my story about the Kamikoto knives—the marketers understood something about me through understanding my behaviors and they optimized their offers based on large numbers of experiments with people who exhibited similar behaviors to mine.

The right customer engagement strategy helps customers make a decision by providing exactly the information they need at that point in time, aligned with their mental model and vocabulary. Their mental model is how they think about their problem and how they think about solving it. Engagement happens on multiple levels and has various dimensions. Engagement is about meeting the user’s needs, which may be emotional or logical. It is dependent on their context and perspective. It can change from moment to moment and from step to step.

How can an organization anticipate and meet the needs of the prospect or customer with content data, knowledge, products, functions, or services? The first step is to describe the customer in terms of everything we know about them. This “customer model” is represented according to the ontology, and it changes as the user goes through their day. We can think about metadata swirling around us describing what we need and when we need it. If I am hungry and want pizza, that can be represented by elements in an ontology. If I am in the role of parent, boss, husband, or colleague, each of these can be represented by data and the ontology. We can use explicit descriptors: facts and history such as the products I own or where I live, or we can infer and derive data about who I am and my behaviors. These are the implicit descriptors that can either be predicted by other indicators or can be based on the knowledge or judgment of an expert (see Figure 3-2).

Figure 3-2: Customer Descriptors at Different Stages in the Customer Lifecycle

These descriptors tell our systems and technologies who we are at any given point in our journey and role, and what we may need at that point in time. Just like that great customer service rep who knows your needs and helps you solve your problem, the marketing technology engagement stack, informed by the ontology, can assemble messaging components and content aligned with your digital body language to anticipate what you need at each step of the journey.