DECISION ANALYTIC APPROACH TO CUSTOMER EXPERIENCE DESIGN

A DISSERTATION

SUBMITTED TO THE DEPARTMENT OF MANAGEMENT SCIENCE AND ENGINEERING

AND THE COMMITTEE ON GRADUATE STUDIES

OF STANFORD UNIVERSITY

IN PARTIAL FULFILLMENT OF THE REQUIREMENTS

FOR THE DEGREE OF

DOCTOR OF PHILOSOPHY

Byungwook Christopher Han

August 2011

http://creativecommons.org/licenses/by-nc/3.0/us/

This dissertation is online at: http://purl.stanford.edu/tt701hw4347

© 2011 by Byungwook Christopher Han. All Rights Reserved.

Re-distributed by Stanford University under license with the author.

This work is licensed under a Creative Commons Attribution-Noncommercial 3.0 United States License.

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I certify that I have read this dissertation and that, in my opinion, it is fully adequatein scope and quality as a dissertation for the degree of Doctor of Philosophy.

Ronald Howard, Primary Adviser

I certify that I have read this dissertation and that, in my opinion, it is fully adequatein scope and quality as a dissertation for the degree of Doctor of Philosophy.

Larry Leifer

I certify that I have read this dissertation and that, in my opinion, it is fully adequatein scope and quality as a dissertation for the degree of Doctor of Philosophy.

Janine Giese-Davis

Approved for the Stanford University Committee on Graduate Studies.

Patricia J. Gumport, Vice Provost Graduate Education

This signature page was generated electronically upon submission of this dissertation in electronic format. An original signed hard copy of the signature page is on file inUniversity Archives.

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ABSTRACT

Customer experience influences purchasing behavior. How do we measure this

subjective phenomenon called customer experience? What are the best approaches to

designing customer experience? In my research I used emotion cues to develop a new

method of assessing customer experience, which is a departure from commonly used self-

reporting methods like surveys. I also developed a new modeling approach to designing

customer experience using decision analysis, ethnographic methods, and emotion coding.

I applied what I developed to a real situation in the field - to improve the customer’s car

buying experience – and demonstrated that my approach generates more actionable

insights than relying on best practices and basic principles. I call this the Decision

Analytic Approach to Customer Experience Design.

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ACKNOWLEDGMENTS

My PhD journey was long in years and in distance. The path was not a straight line. I see the

footprints on the ground I trod and beside my own are many others. A few even carried me at

times along my journey. With their guidance I crawled, walked, and ran to the finish line.

Professor Ron Howard

Reason why I applied to Stanford, got in and got out. My turning point was realizing on day

one of class that I didn’t know how to make hard decisions. Learned DA from him; taught DA

with him. Caught clear thinking from him. It truly was like, in his words, installing a new

operating system for the mind.

Professor Larry Leifer

Designer to the core. Knack for taking something and making it better, especially PhD students.

Welcomed me to wayfare with the design community. I discovered how design and decision

analysis are fraternal twins. Caught the design spirit from him. Researching design is studying

life; it resonates with the soul.

Professor Janine Giese-Davis

Opened up the world of emotion research to me. Better than Ekman and Gottman and all the

other men in the field. Epiphany for inventing CXQP came while swimming in emotions,

literally. Learned awareness of my emotions, and those of others. Valuable life skill, since

emotions are the language of the heart.

Friends

Too many to list, really. By reading this, you’re likely one of them. Stanford friends; alums

before me with their sage advice; and cohorts beside me, especially at the DA, CDR, and KGC

communities. Bridgeway Church friends who helped me through prayer.

Family

Mom, my sisters, my brother and extended family members. Being born into this family was

the best decision of my life. And to Dad, who taught me through his life. During his last days,

he experienced shortness of breath, yet he spent them praising God.

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My dissertation is dedicated to:

Dad

whom I long to see again in Heaven

and

Jesus Christ

my Lord and Savior (and thesis adviser)

Jesus replied: “Love the Lord your God with all your heart

and with all your soul and with all your mind.”

Matthew 22:37 (NIV)

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TABLE OF CONTENTS

Abstract iv

Acknowledgments v

1. Introduction 1

1.1 Background 1

1.2 Research Contributions 2

1.3 Relevant Bodies of Knowledge and Research Methods 3

1.4 Understanding Customer Experience 5

1.5 Decision Analytic Perspective of Customer Experience 7

1.6 Research Questions 8

1.7 Outline of Chapters 9

1.8 Collaborations 10

2. Basis Development Challenges 12

2.1 Chapter Overview 12

2.2 Description of Basis Development 12

2.3 Importance of Basis Development on Decision Quality 13

2.4 Extant Research on Basis Development 14

2.5 Role of Distinctions and the Clairvoyant 15

2.6 General Manager’s Decision Diagram (&1) 16

3. Customer Experience in Purchase 19

3.1 Chapter Overview 19

3.2 Clarification of Customer Experience 19

3.3 Customer Experience and Its Effects 20

3.4 Research on Customer Experience 20

3.5 Ethnography of Dealerships 21

3.6 General Manager’s Decision Diagram (&2) 24

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4. Emotions and Customer Experience 26

4.1 Chapter Overview 26

4.2 A Gap in the State of the Art 26

4.3 Emotions and Human Interactions 27

4.4 Emotion Codes 28

4.5 Sales Conversation on the Phone 29

4.6 Procedure for Analyzing Phone Conversations 29

4.6.1 Enrollment in the Study 29

4.6.2 Data Collection 29

4.6.3 Data Filtering Criteria 29

4.6.4 Data Selection Criteria 30

4.6.5 Emotion and Topic Coding 30

4.6.6 Analysis 30

4.7 Key Findings 31

4.8 General Manager’s Decision Diagram (&3) 32

5. Customer Experience Quality Partition (CXQP) 33

5.1 Chapter Overview 33

5.2 Defining Customer Experience Using Salesperson Emotion Cues 33

5.3 CXQP Explained 35

5.4 Plotting the Phone Conversation Data from the Field 36

5.5 Value Model Explained 37

5.6 General Manager’s Decision Diagram (&4) 40

6. Future Work 42

6.1 Step Towards Transforming Customer Experience 42

6.2 Customer Experience as a Direct Value 42

6.3 Computerized Coding of Emotion 42

6.4 Customer Experience in Sales Interactions with a Robot 43

Bibliography 44

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LIST OF FIGURES

1.1 Basic Aspect of Modeling 4

1.2 Generalized Decision Diagram for Customer Experience Design 7

2.1 Decision Analysis Cycle 13

2.2 Decision Analysis Concept Map 16

2.3 General Manager’s Initial Decision Diagram (&1) 18

3.1 Gallup Poll (November 2010) 23

3.2 General Manager’s Revised Decision Diagram After Ethnography (&2) 25

4.1 Emotion Codes (Giese-Davis, 2005) and Topic Codes (Han, 2009) 28

4.2 General Manager’s Revised Decision Diagram After Sales Conversations (&3) 32

5.1 Basis for Creating the Customer Experience Quality Partition 34

5.2 Customer Experience Quality Partition (CXQP) 35

5.3 CXQP with Phone Calls Plotted 37

5.4 Value Model – Tree 38

5.5 Distribution of Monthly Profit Based on Probability of Good

Customer Experience on Phone 39

5.6 General Manager’s Final Decision Diagram (&4) 41

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Chapter 1 Introduction

1.1 Background

When we buy something, an experience surrounds the act of purchasing. Let us call this

customer experience. When that something is an inexpensive accessory, say a $20

bracelet at a discount store, our customer experience may involve little human interaction

(think Walmart). When that something is a $200 bracelet at a department store (like

Nordstrom), more interaction with a salesperson is likely and we certainly expect a better

experience than at a discount store. Our expectation of good customer experience is still

higher when that something is a $2,000 bracelet at a high-end jewelry store (such as

Tiffany & Co). Customer experience would definitely be even better if that something

being purchased is a $20,000 item, right? Not always, as it appears, when that thing is a

car.

Car buying at a typical dealership in the U.S. is riddled with bad customer experience.

This is an odd phenomenon considering the magnitude in dollars of buying a car – the

biggest purchase for most Americans, next to buying a home. Odder still is that bad

customer experience in car buying appears to have persisted for years, even decades, and

there is no clear indication that radical improvement is imminent. Here lies an

opportunity to change the current situation into a preferred one. The question is, how?

The fact that bad customer experience has lingered for years shows this is a difficult

problem to solve. What can help us design better customer experience? While I raise

this question in the specific context of car buying, it extends to the broader topic of

conceptual tools and methods used in customer experience design or, more generally, in

service design.

Many of the prevailing approaches to designing customer experience can be categorized

as either “best practices” or “basic principles”. Utilizing best practices involves

identifying a successful case, figuring out how that company attained success, and

attempting to adopt a similar strategy to replicate its success. During the 1990s,

Starbucks’ phenomenal rise from a single café in Seattle to a popular global franchise

made the company a model to emulate (Michelli, 2007), and during the most recent

decade commanding similar attention is the iconic Apple Store, a lynchpin behind

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Apple’s retail success. The other category of customer experience design, basic

principles, is a set of assertions that is believed to be fundamental and widely accepted.

These assertions typically come in form of “dos” and “don’ts” and represent the view of

an expert or the consensus of a professional community.

While both best practices and basic principles are helpful, each has an essential flaw.

What if the current situation is just different enough from prior situations as to render the

available knowledge in the field insufficient? Turning to existing best practices or

relying on basic principles is an inadequate compass when your situation is unique and

full of uncertainty. Building a model of your situation is a better approach. I envision

this model as having the ability to handle uncertainty using probabilities, help designer(s)

learn what matters most and discover the mechanisms of influence for generating

alternatives, and provide a logical way of evaluating alternatives to achieve clarity of

action. I present here a new way of designing customer experience through modeling. I

call it the Decision Analytic Approach to Customer Experience Design.

1.2 Research Contributions

I will begin by sharing my contributions to decision analysis (DA) and design practice.

Before I do, allow me briefly to share a conversation with my advisor, Professor Ron

Howard, which set the tone for my research.

Early on in my doctoral studies Prof. Howard asked me whether I was a scientist or an

engineer. He explained that a scientist wants to better understand and describe the world

as it is; an engineer wants to change the world and strives to figure out how to do it. I

replied that I am an engineer.

With the perspective of an engineer, I set out to contribute to the body of knowledge in

DA in a way that is both useful and usable. In my view, this meant demonstrating that

my research helps to solve a real-life problem. So that is what I did. I investigated

whether a modeling approach could be applied to understanding and improving customer

experience within the real context of car buying. In the process, I discovered a new

know-that and demonstrated a know-how underlying my approach to customer experience

design.

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Know-that and know-how represent different categories of knowledge (Ryle, 1949).

Know-that involves facts or propositions supported by empirical evidence. Know-how

represents methods and procedural knowledge. My research makes a contribution to both

categories.

The know-that contribution is a new distinction for customer experience based on

emotions. I call this distinction, Customer Experience Quality Partition (CXQP), and I

develop it using emotion coding of recorded sales interactions and on actual outcomes of

interest in the field. This way of assessing customer experience is a major

methodological departure from commonly used self-reporting methods like surveys.

Also important to underscore is that CXQP passes the clarity test used in DA (more on

this in Chapter 2).

The know-how contribution is the demonstration of the modeling approach to customer

experience design to a real decision situation in the field – a first of its kind. Within the

DA literature, no published work could be found at the time of this writing that described

the application of DA to situations involving customer experience design. Within design

practice, this DA approach to customer experience design is new and it is a departure

from the current approaches relying on best practices and basic principles

My research contributes in part to decision analysis practice and in part to customer

experience/service design. The development of CXQP extends the application of DA to

decision situations involving customer experience. The demonstration of the DA

approach to a real-life problem in the field shows design practitioners a new know-how to

design better customer experience and services.

1.3 Relevant Bodies of Knowledge and Research Methods

Three bodies of knowledge integrated within this dissertation are decision analysis,

design research, and emotion coding. I combine key aspects from these three disciplines

to develop the Decision Analytic Approach to Customer Experience Design.

Decision analysis is a term coined by Howard in 1964, and it is a discipline comprising

the philosophy, theory, tools, and methods to make decisions in a normative manner. I

have come to love DA, because it has transformed the way I think about decision making.

In fact, it has transformed the way I think, period. My training in DA has equipped me to

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step into a complex and confusing situation and systematically conduct the process of

achieving clarity. Part of what enables this is the modeling approach – creating a useful

representation of the real situation to understand it better. See Figure 1.1 below that

shows the basic aspects of modeling.

Figure 1.1: Basic Aspects of Modeling

The left-hand side is the real world as it is and the right-hand side is the model (an

abstraction and simplification of the real world). The modeling activity begins by

observing the current situation, such as the present state of customer experience at

dealerships. I employ primarily ethnographic methods and semi-structured interviews

with domain experts. The next step involves abstracting the situation to represent it in a

model. This is no simple task, especially when attempting to model a complex situation

involving customer experience that has not been modeled before. Going from the current

situation in the real world to a model of the situation, shown by Arrow 1, is the focus of

my research.

Arrow 2 represents the activities involved in generating the solution to the model. This

often involves mathematical methods and probabilities to handle uncertainty. Once the

solution to the model is determined, it needs to be translated into a course of action that

can be implemented in the real world, shown by Arrow 3. When we implement the

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course of action, shown by Arrow 4, we arrive at a new situation, which we would expect

is a preferred one, but may not necessarily be. The new situation may not always turn out

to be better than the original even though our process of choosing the course of action

was good. This highlights the distinction between decisions and outcomes – one of the

fundamental distinctions in DA.

The second discipline I integrated into my research is design – “design” in the sense

described by Herbert Simon in The Sciences of the Artificial. “Everyone designs who

devises courses of action aimed at changing the current situation into preferred ones”

(Simon, 1969). Simon considered an engineer to be a kind of designer.

Changing the current situation into a preferred one sounds a lot like the intention of an

engineer. In order to accomplish this, the ability to devise courses of action is essential,

as is the ability to choose the best alternative among devised courses of action. The

disciplines of design and decision analysis are quite complementary, I realized.

Designers commonly use ethnographic methods to understand the people they want to

serve with their design. As mentioned earlier, I used ethnographic methods extensively

in my research, and I have learned that it is an indispensible tool to modeling situations

involving complex social phenomenon such as customer experience.

Finally, the discipline of emotion coding is the third essential component of my research.

Learning emotion codes is behind my eureka moment when I discovered that emotion

cues can be utilized to create a distinction for customer experience that passes the clarity

test. Emotion codes represent the wide ranging set of emotions people express and I

adopted for my research the emotion coding scheme developed by Giese-Davis (2005),

which is a refinement of the SPAFF scheme developed by Gottman (1995).

1.4 Understanding Customer Experience

One of the key conceptual challenges is creating a clear distinction for customer

experience that can be used in a DA model. I have yet to provide even a definition for

customer experience, so I will do so here with the caveat that this definition does not pass

the clarity test. Here I will use the definition adapted from Pine and Gilmore (1998)

which defines it as that which a customer perceives or encounters while interacting with

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a supplier of goods or services. I will provide a more sophisticated version that passes

the clarity test in Chapter 5.

The literature on customer experience has grown rapidly in recent decades (Gentile et al,

2007). Some have conceptualized it in terms of value components such as product

quality, price, shopping environment, service channel, and brand image (Wilburn, 2006).

Others discuss the interrelatedness of the factors to create the total customer experience

(Mascarenhas et al, 2006; Berry et al, 2002). This underscores the fact that customer

experience occurs at multiple touch points between a customer and a supplier of goods or

services (Meyer and Schwager, 2007). A touch point, according to Meyer and

Schwager’s definition, is an instance of direct contact by the customer either with the

product or service itself or with representatives of it by the company or some third party.

The touch points relevant for my research are a prospective customer’s direct, human-to-

human interaction with the supplier of a good, typically a salesperson. Interaction with

the product or service itself is not within the scope of this research. This scoping down of

the definition of customer experience is useful because my primary interest is in

understanding the customer’s experience leading up to his purchase decision – one of the

two outcomes of interest addressed in this research. Hence, the scope of my

investigation involves solely customer experience in purchase and does not involve

customer experience in use, in after-sale service, or in disposal. Bringing the discussion

into the context of car buying, it is what the customer experiences when he has a sales

conversation over the phone or an in-person interaction with a salesperson at the

dealership.

The general manager at the dealership where I conducted my field study believes that

customer experience during sales interactions affects the customer’s purchase decision.

He draws this belief from nearly two decades of being in the car dealership business. As

the decision maker in charge of the business, the general manager wants to know what to

do to improve customer experience and increase his sales. This is the real-life situation

that I address in my research, and by framing the customer experience design problem as

a decision situation, I use tools and methods from DA to begin modeling the situation.

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1.5 Decision Analytic Perspective on Customer Experience

I describe the key conceptual issues in my research by creating the most basic decision

diagram representing the general manager’s decision situation. I develop a fuller

decision diagram in each subsequent chapter to highlight my research progression and the

contribution made by each step of the research. More extensive coverage of decision

diagrams as a tool for representing the decision situation can be found in Chapter 2.

The basic decision diagram shown in Figure 2.2 captures the essence of the general

manager’s decision situation. He wants to decide on a course of action to influence

customer experience believing that it will affect the customer’s purchase decision. There

are four nodes and the shape of the node represents different elements of the situation.

There are four arrows and what each one means depends on the two nodes that it

connects. I elaborate on each of the nodes and arrows one by one.

Figure 1.2: Generalized Decision Diagram for Customer Experience Design

The hexagonal node is the value node, which contains what the general manager (that is,

decision maker) wants and the method to measure it. To help clarify what he wants, it is

useful to introduce Howard’s distinction of direct value and indirect value (Howard,

2007). A direct value is an end in itself and an indirect value is a means to an end. For

this research, profit is the direct value and customer experience, while viewed as

important by the decision maker, is an indirect value that contributes to profit.

The two oval nodes represent uncertainties to the decision maker. The customer’s

purchase decision is an observable event that, in its most basic form, has two degrees:

purchase and no purchase. Customer experience is the challenging uncertainty, since we

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currently do not have a definition that passes the clarity test. I provide extensive

treatment of this issue in the subsequent chapters.

The rectangular node is the decision node that contains the alternatives. The decision

maker has yet to generate alternatives, so it is a place holder for possible actions that can

be taken to influence customer experience. The belief that there can be influence exerted

on customer experience is important, because without that belief there is no point in

doing anything about it. The arrows connecting the nodes indicate what the decision

maker believes is the relationship between two nodes.

Arrow 1 going from the decision node to an uncertainty is an influence arrow. Influence

means that the course of action taken through the decision may affect the outcome of the

uncertainty – in this case, customer experience. As will be shown in Chapters 3 and 4,

influencing customer experience is not done directly, but through other factors.

Arrow 2 that goes between the two uncertainties is a relevance arrow. The underlying

belief of the general manager is that customer experience affects the customer’s purchase

decision. If there is no relevance between the two, then it would be advisable for him to

focus his attention on other factors relevant to the customer’s purchase decision. On this

note, the absence in this decision diagram of other factors relevant to the customer’s

purchase decision does not mean that there are no other factors. Clearly, price is a

relevant factor. This basic decision diagram simply draws attention to the role of

customer experience, which is the centerpiece of this research.

Arrow 3 is a functional arrow that shows the effect of the customer’s purchase decision

on the value function, which in this case is the calculation of profit. Likewise, Arrow 4 is

also a functional arrow that shows the cost associated with the alternative taken and its

effect on the value function.

1.6 Research Questions

The generalized decision diagram described above reveals at least four key questions that

I address in my research. One: what is a useful distinction for customer experience that

passes the clarity test? Without a clarity test definition, we lack the means to

appropriately assess customer experience and to measure its effect on outcomes of

interest.

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Two: how does customer experience affect the customer’s purchase decision (or

subsequent actions)? This is an empirical question that requires the collection and

analysis of field data.

Three: what is the value of control (value of influence) of customer experience? This

means, if you could control (deterministically) or influence (probabilistically) customer

experience, what is the additional value created as compared to the status quo? This

value is measured in dollars and it helps to understand the maximum price the decision

maker should be willing to pay for some intervention (course of action).

Four: what should be the focus in generating creative alternatives? The decision analytic

approach I present here helps designers to learn and focus attention on what matters most.

Guiding attention is essential in the midst of the ambiguity that often confronts designers.

1.7 Outline of Chapters

Chapter 2 presents a brief background of the basis development phase of the DA cycle

where this research is situated. It introduces the central role of distinctions and covers

Howard’s map of DA concepts. This chapter also explains decision diagrams as a tool to

capture the decision situation and the notion of the state of knowledge represented by an

ampersand (&). It ends with the first decision diagram elicited from the general manager

at the dealership as part of the field study.

Chapter 3 presents the ethnographic study of car sales that helped to understand the

phenomenon of customer experience. Specifically, it helped to describe where and how

the experience was taking place – namely, in the customer-salesperson interactions over

the phone and in-person at the dealership. The ethnography also brought to light that

salespersons view customers as liars as much as customers view salespersons as liars.

This perception is an undercurrent in sales interactions, according to the salespersons.

During my visits to the dealership over 18 months, I also observed that salesperson

experience can suffer from accusations of lying and verbal hostility from customers. This

chapter ends with the second decision diagram informed by insights from the

ethnography.

Chapter 4 presents a detailed analysis of phone sales interactions using the methods of

emotion coding and topic coding. If the ethnography discussed in the previous chapter

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was a broad horizontal study of dealership culture and the customer experience

phenomenon, then the coding of phone conversations is a deep vertical dive into what

happens between a customer and a salesperson. The work discussed in this chapter

generated the key insights towards a distinction for customer experience using emotion

cues that passes the clarity test. This chapter ends with the third decision diagram

informed by the results from emotion coding.

Chapter 5 presents the Customer Experience Quality Partition (CXQP) for creating a

useful distinction for customer experience. It also presents the value model to evaluate

the effect of customer experience on profit and to calculate the value of control (influence)

for prospective interventions. This helps to show how much the decision maker should

be willing to pay for an alternative with varying degrees of success in raising the quality

of customer experience. Lastly, the decision model serves to inform the alternative

generation activity (or design activity) by providing an insightful design requirement. In

other words, the model helps us to know where to focus our attention to influence the

situation.

Chapter 6 presents the research summary and possible future work.

1.8 Collaborations

This research benefited from the collaboration of others described below.

The first nine months of the ethnographic study of car dealerships involved the

collaboration of other Stanford students who took Professor Steve Barley’s doctoral

seminar on ethnographic methods, Kathy Lee and Alison Wong. The next three months

involved two new students, Rosanne Siino and Eli Blatt. The last six months involved

only me.

I received valuable support from Toyota Sunnyvale where I conducted my ethnography

and subsequent field work – Mike Shum (general manager) and Adam Simms (owner).

They provided me with access (both to people and to data) that was essential to carry out

my research.

The emotion coding aspect of this research relied on the help of two sources. Expert

emotion coders from the firm, iEMPATH, coded the sales conversations that I collected

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from the dealership. Dr. Janine Giese-Davis, previously the director of the Emotion

Coding Lab at Stanford and now at the University of Calgary, showed me how to work

with the field data step by step (an extremely laborious task) and demonstrated how to do

the analysis. I gratefully acknowledge her help.

I presented aspects of this research while in progress primarily to two academic

communities at Stanford: the decision analysis community through the weekly DA

seminar forum, and the design research community through the weekly designX meetings.

Both communities are hotbeds of intellectual energy and sources of both scholarly and

social support. I received their many helpful comments too numerous to list here. I am

grateful to the two professors, Ron Howard and Larry Leifer, around whom the two

respective constellations of bright DA and design PhD students revolve.

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Chapter 2 Basis Development Challenges

2.1 Chapter Overview

This research is situated in the basis development phase of decision analysis (DA). This

all important starting point of the analysis is about getting the decision right in order to

get to the right decision. Getting the decision right is tantamount to appropriately

framing and representing the decision situation, which avoids what Kimball called “error

of the third kind”, or coming up with the right answer to the wrong problem (Kimball,

1957). Establishing the decision basis is particularly challenging in the case of decision

situations involving customer experience, because an experience is a wholly subjective

phenomenon occurring within a person having the experience and there is not a reliable

instrument for measuring it. In DA jargon, the clairvoyant does not understand the

common term, customer experience.

Developing a useful distinction for customer experience that passes the clarity test

becomes a fundamental problem to solve while creating a decision diagram of the

situation. This chapter begins the journey to such a distinction, but does not complete it

here. I show how framing the customer experience design problem as a decision

situation is useful, and demonstrate the application of the decision analytic tools and

methods I use. I also show how decision analysis alone is insufficient to tackle this

customer experience problem in the field. This gap will be evident in the decision

diagram representing the general manager’s initial state of knowledge (&1) that I generate

with him through a decision conversation.

2.2 Description of Basis Development

Basis development refers to creating the decision basis – the first step in decision analysis.

The decision basis comprises preference (what you want), information (what you know),

and alternatives (what you can do). Basis development and three subsequent phases

make up the decision analysis cycle which is, along with the tools and methods utilized

throughout each phase, the conceptual foundation for decision analysis practice. Figure

2.1 below shows the four phases and the iterative nature of the decision analysis cycle as

indicated by the arrow from Basis Appraisal to Basis Development.

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Figure 2.1: Decision Analysis Cycle (Howard, 1983)

An expert in decision analysis that helps the decision maker is known as the decision

analyst. He serves as an elicitor of the decision basis and an evaluator of the logic

throughout the analysis. The decision analyst need not be an expert in the domain of the

decision. He elicits the decision basis from the decision maker (or proxy) and from

relevant domain experts as necessary. This occurs through a facilitated conversation

aided by various tools such as the force field diagram, back-casting, decision hierarchy,

strategy table, and decision diagram to name a few. The decision diagram is particularly

central among the tools employed during basis development, since it is what captures the

decision situation in graphical form. A decision diagram uses words and symbols that

show relevant distinctions and relationships between them. In fact, the output of basis

development and likewise the input to deterministic structuring is the completed decision

diagram that most appropriately represents the decision situation.

2.3 Importance of Basis Development on Decision Quality

The importance of generating the most appropriate decision diagram during the basis

development phase cannot be overstated. It directly affects decision quality. While there

is not a generally accepted quantitative measure for decision quality, Howard offers a

useful method of appraising decision quality using six dimensions: framing, preference,

information, alternatives, logic, and commitment to action. Following the first pass

through the decision analysis cycle, one can appraise the robustness of each dimension

and decide whether to continue (iterate the cycle) or complete the analysis. For example,

along the logic dimension, one may appraise whether the mathematical model is free of

calculation errors. Including decision framing, four of the six dimensions are addressed

first and foremost in the basis development phase, and thus the trajectory for decision

quality may be said to begin here. Given the importance of the basis development phase,

let us consider the extant research that addresses this topic.

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2.4 Extant Research on Basis Development

Many early works relating to decision analysis have all recognized the importance of

framing the decision appropriately (Howard, 1966; Howard & Matheson, 1968; Raiffa,

1968; Pratt, 1965; Keeney, 1976). Emphasizing the importance of basis development,

Howard remarked: “Often the introduction of a new alternative eliminates the need for

further formal analysis” (Howard, 1966). This remark underscores the importance of

thinking clearly about the decision basis to the overall decision.

Despite its importance, extant research directly on the topic of basis development is

sparse, and there is currently no generally accepted normative theory about how to create

the decision basis. From the perspective of consulting practice, some research offers

techniques on decision framing and generating alternatives (Russo & Shoemaker, 1991;

Keeney, 1992; McNamee & Celona, 2001). Deficiencies in the decision basis at the start

of the analysis, such as a failure to include a relevant uncertainty, is viewed as correctable

during the iterative cycle of the analysis as learning occurs in the process (Howard, 1988).

Matheson (1990) addressed the topic of frame shifts during analysis, and Belkora (1997)

researched the effect of different decision frames of patients, doctors, and other

stakeholders to decision quality in the context of breast cancer treatment.

Two recent works relating to basis development are the research by Bergner (2006) on

dialogue processes for generating decision alternatives and by Raha (2010) on achieving

clarity on value. Bergner investigated how the content of decision-related conversation

influences the generation of alternatives. His worked encompassed an empirical

dimension involving the analysis of conversations from design teams. In this way,

Bergner’s work shares similarities to my own, but my research differs significantly in that

I studied emotions in interactions (in addition to content) in a sales context. Raha studied

conversations about decision maker values (preferences) using distinctions borrowed

from formal axiology. He created a tool which he calls “value diagrams” to facilitate the

value conversation.

All of the prior work helps to inform decision analysis practice. In the same spirit, my

research aims to advance the practice of decision analysis with new distinctions and

insights, so that it may be applied to decisions involving customer experience.

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2.5 Role of Distinctions and the Clairvoyant

The role of distinctions is pivotal to decision analysis. In his map of DA concepts, shown

in Figure 2.2, Howard has the notion of a distinction at the very top (Howard, 2004).

This placement at the top signifies the role of distinctions as elemental cognitive acts

(Varela, 1979). The oval nodes represent the concepts and the arrows indicate parent-

child relationships where understanding the parent is necessary to understanding the child.

Along with the concept of a distinction, the concept of a clairvoyant is also at the top.

The clairvoyant is an imaginary figure that can tell you the answer to any clearly phrased

question that is measurable and not a matter of human judgment. What will the price of

an ounce of gold be on November 11th, 2011 at the close of trading on the Chicago

Mercantile Exchange? The clairvoyant can tell you. Does Jane love John? The

clairvoyant does not understand matters of judgment. Nor can the clairvoyant know if a

customer has had a good or bad experience. The clarity test serves to reveal inexactness

and promote clarity in the way we think about a distinction. This problem of creating a

distinction for customer experience that passes the clarity test is a central topic of my

research.

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Figure 2.2: Decision Analysis Concept Map (Howard, 2004)

The numerous concepts shown on the map ends with the concept of a decision diagram

(see lower right-hand corner of the map). This indicates that all of the concepts in

thinking about decisions go into the construction of the decision diagram – a powerful

tool for understanding, communication, and analysis of decision problems (Howard,

2007).

Let us now consider the phenomenon of bad customer experience in car buying and the

general manager’s problem of figuring out the best course of action. Framing his

situation as a decision problem, I use the tools and methods of decision analysis to begin

the process of attaining a clearer grasp of the situation for both the decision maker and for

me; I am taking on the role of a decision analyst while being a researcher.

2.6 General Manager’s Decision Diagram (&1)

Taking on the role of a decision analyst, I carried out a decision conversation with the

general manager and elicited from him a decision diagram of his situation. Every decision

17

diagram represents the state of knowledge of those involved when it was produced. The

ampersand symbol (&) is used to indicate the state of knowledge and a numeric subset is

a time stamp. There are four phases in this research, so the subset indicates the phase

number. Each phase adds new and relevant knowledge to the previous one.

The conversation began with the topic of preferences. The decision maker said he valued

customer experience and profit. Using Howard’s distinction of direct and indirect values,

I was able to elicit that profit was his direct value and customer experience was an

indirect value that mattered because of its relevance to profit. Profit is measured in

dollars and, in simple terms, is calculated as total revenue minus total cost. The

conversation then turned to the uncertainties that affect total revenue and, in particular,

sales.

Speaking in terms of the classic marketing funnel paradigm, the general manager shared

what he believed matters for sales. First it is the number of leads per month (uncertainty),

then the number of those who decide to visit the dealership (uncertainty), and finally the

number who decide to purchase (uncertainty). When asked what affects the number of

leads, he replied the amount of advertising and other lead generation activities he chooses

to conduct (decision).

When asked what influences the number of visits, the general manager said the two most

important factors were inventory and price – whether the dealership has the car the

customer wants and the price of the car that his salesperson quotes. Let us call whether

the dealership has what the customer wants as inventory match (uncertainty). The

general manager shared that the cars he chooses to order from the manufacturer called

inventory mix (decision) influences inventory match.

Since this dealership exists in a competitive market, what is relevant to the customer’s

decision to visit is the price delta, the difference between the dealership’s quoted price

and the minimum competitive price. Price delta (uncertainty) is influenced by the

dealership’s offering price.

Our conversation yielded the decision diagram shown below in Figure 2.3. This

particular decision diagram is not the only possible one, but it is the diagram that the

general manager found to be satisfactory to begin the work to achieving clarity of action.

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Figure 2.3: General Manager’s Initial Decision Diagram (&1)

What is missing in this initial decision diagram is customer experience. In order to

inform how we might treat customer experience in the decision diagram, we required

additional knowledge about the phenomenon of customer experience. For this I turned to

my ethnographic study of dealerships, which I present in the next chapter.

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Chapter 3 Customer Experience in Purchase

3.1 Chapter Overview

Customer experience was missing in the initial decision diagram (&1) that I generated

through a decision conversation with the general manager. We did not know at the time

how to properly treat it. In this chapter I present a more extensive coverage of customer

experience as a topic. I also present the findings from an ethnographic study of the

dealership that proved insightful on how to think about customer experience in the

context of car buying. This chapter ends with a revised decision diagram based on new

knowledge of the customer experience phenomenon (&2).

3.2 Clarification of Customer Experience

The term “customer experience” was first introduced by Pine and Gilmore (1998) and

actively studied by management researchers and practitioners alike (Schmitt, 1999; Smith

& Wheeler, 2002; Shaw & Ivens, 2002; Gilmore & Pine, 2002). Customer experience

occurs in at least four possible phases. Those phases are customer experience: 1) in

purchase, 2) in use, 3) in after-sale service, and 4) in disposal. Not all goods and services

will involve all four phases.

Customer experience in purchase refers to the experience while in the shopping process

leading up to the decision to purchase or not. Customer experience in use refers to the

experience while using the good or consuming the service. Customer experience in after-

sale service refers to the experience while having the good fixed or maintained. Lastly,

customer experience in disposal refers to the experience while disposing the good,

whether at the end of its product life or when the consumer no longer wishes to possess it.

This research is concerned solely with customer experience in purchase. The definition I

use for the time being is: that which a customer perceives or encounters while interacting

with a supplier of goods or services during the buying process. Bringing the discussion

into the context of car buying, it is what the customer experiences when he has a sales

conversation over the phone and an in-person interaction with a salesperson at the

dealership.

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3.3 Customer Experience and Its Effects

Anyone can recall an instance of good customer experience, and a case of bad customer

experience as well. We can vividly recall how we felt at the time and how the experience

influenced our subsequent behavior. Research into the phenomenon of customer

experience and the recognition of its growing importance in the overall economy has

given rise to the term “experience economy” (Pine & Gilmore, 1999; Boswijk et al.,

2007).

In industrialized economies, such as our own in the United States, there exists intense

competition in many product and service categories. Producers must continuously vie

with one another to win over customers and to keep them. One of the important sources

of differentiation is customer experience. How much does customer experience matter to

the success and failure of companies? An illustrative example from the past decade is the

contrasting performance of two electronics retailers Best Buy and Circuit City. The

former experienced stellar growth and the latter faltered and eventually declared

bankruptcy. A netnography (Kozinets, 2001) of on-line community sites reveals myriad

testimonies of customers dissatisfied with their experience at Circuit City in the years

leading up to its demise.

3.4 Research on Customer Experience

There is much in the marketing and strategy literature about the presumed influence of

customer experience on the success and failure of companies (Michelli, 2007; Mckain,

2005; Smith & Wheeler, 2002; Shaw, 2007; DiJulius, 2008). These come in the form of

case studies that describe the strategies taken by successful firms and their stated

emphasis on customer experience. Some have even distilled what they believe to be the

absolute essentials for success, going as far as to call them laws. One author titled his

work The Six Laws of Customer Experience (Temkin, 2008).

The customer experience literature contains mostly research that can be categorized as

either best practices or basic principles, as discussed before. While useful to consider in

general terms, it is unclear how to connect the current literature in customer experience to

the real situation confronting the general manager of the dealership. In order to

understand the customer experience phenomenon directly in the context of car buying,

especially the human-to-human interaction between a customer and a salesperson, I

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turned to ethnographic methods to discover just what is going on in terms of customer

experience.

3.5 Ethnography of Dealerships

My ethnographic study comprised two phases. The first phase began in 2007 when I

conducted an ethnographic study of two dealerships in the San Francisco Bay Area – one

GM and one Toyota. The aim of this ethnography was to describe dealership culture

from the perspective of the people within it, namely the salespersons. During the second

phase, in 2008, I conducted an ethnographic study to investigate the phenomenon of

customer experience. This was done only at the Toyota dealership where I was given

complete access by its management. Though the first phase was necessary to understand

dealership culture in general, it is the findings from this second phase relating specifically

to customer experience that I present here.

A typical ethnographic write-up would be too lengthy, so I present only the summary of

the findings relevant to understanding how to treat customer experience in the decision

diagram. Recall that the aim is to learn what matters in order to change the current

situation into a preferred one. That is, devising and selecting the course of action that

will improve customer experience and increase profit.

While observing in-person sales interactions on the dealership floor, I began to notice a

pattern. Some customers came into the dealership and asked for a specific salesperson

while others just started to interact with the salesperson that first approached them.

Those who asked for a specific salesperson turned out to have called prior to coming in

and already had a rapport with that salesperson. Their interactions were typically more to

the point (let’s go to the lot and see that car you want) and spent less time spent on

probing questions about the customer’s background or preferences. Naturally, the

opportunity to have spoken over the phone prior to coming in explains much of this

pattern.

If the outcome of a prospective customer’s visit to the dealership is viewed as either

purchase or no purchase, then the likelihood of purchase for those who come in after a

phone call is significantly higher than for those who do not. This observation was

corroborated by both the feedback of salespersons and by the data recorded in the CRM

(customer relationship management) database. According to the general manager,

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roughly 2 out of 3 customers who came into the dealership (this particular dealership, I

should add) after a phone call ended up making a purchase. Compare this to the

approximately 10% of those who come in off the street buying a car during that

interaction (an estimate also from the general manager). This figure roughly corresponds

to the popular “8-4-2-1 Rule” believed by veteran salespersons. This rule says that for

every eight customers that come into the dealership, only four are seriously looking to

buy a car (the rest are tire-kickers). Of the four, only two are willing to buy from you

(for whatever reason). And of the two, only one will actually buy today. One of eight is

12.5%. By the numbers, selling cars is not easy. Part of the reason may be found in the

customers’ perception of salespersons as liars.

The fact that customers perceive car salespersons as willing to lie to make a sale is hardly

surprising. In fact, a recent Gallup Poll (shown in Figure 3.1) asked respondents to rate

the honesty and ethical standards of people in different fields. The poll results reveal that

car salespersons are viewed with suspicion. They are placed near the very bottom, above

only lobbyists, and just below members of Congress. When I conducted exit interviews

with customers leaving the dealership, many expressed that they believed a salesperson

would lie at some point in the conversation (especially about price) and others politely

stated that customers need to be vigilant and to do their homework (caveat emptor).

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Figure 3.1: Gallup Poll (November 2010)

What was surprising to me was the revelation that salespersons believe that customers are

also liars. The majority of the salespersons that I encountered expressed, either on their

own or when asked, that customers routinely lie about competitor pricing. True or not,

this is the prevailing perception among salespersons. The undercurrent of sales

interactions, then, is one of mutual suspicion by the customer and the salesperson.

Customer experience is broken, and salesperson experience is broken, too. Conducting

the ethnography and spending nearly 250 hours at the dealership shadowing salespersons

gave me an insider’s perspective on the work of car sales. Imagine a work environment

where the majority of the people you interact with believe that you would lie to them. It

can take its toll, as it did to one salesperson who I encountered at the dealership

immediately after his phone conversation with a customer who hung up on him after

calling him a liar. His face was flush red and was visibly shaken. Upon seeing me, he

uttered a polite hello and when I asked him how he was doing, he replied, “I’m

shocked… this middle-aged woman, a school teacher, just called me a liar. She wouldn’t

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believe my pricing on the Camry. I tried to explain maybe there’s a difference in trim,

but she just shouted at me and hung up.” (TS-080714-RH).

A wide range of emotions are present in sales interactions. This is not to suggest that all

sales interactions contain a wide range of emotions; some are distinctly tilted towards

some kind of emotions, like hostility and contempt. When observing emotions expressed

during the sales interaction and attempting to capture it in field notes, I realized one of

the shortcomings of the ethnographic method. I, as the instrument of observation, have

distinct advantages and disadvantages. Collecting complete and precise emotion data

without the aid of a recording device is simply not humanly possible. It was for this

reason that I sought access to the phone conversations recorded by the dealership. This

will be discussed further in Chapter 4.

Despite the human limitation of making precise and complete documentation of all

emotion data in field notes, I was able to observe with my eyes and ears the incidence and

frequency of emotions expressed during sales interactions. I perceived the relevance of

emotions on customer experience; I just did not know yet how to measure it. This

motivated me to pursue this topic more deeply. Furthermore, I learned from the

ethnography that the strongest impact on customer experience occurred during the two

touch-points involving human-to-human interactions – on the phone and in-person.

3.6 General Manager’s Revised Decision Diagram (&2)

The revised decision diagram following my ethnographic study places two customer

experience nodes (both uncertainties nodes) into the diagram. The first one is customer

experience on phone and the other is customer experience in-person. The customer’s

experience on the phone is relevant to his decision to visit the dealership and is also

relevant to his experience interacting with the salesperson in-person if he visits the

dealership. The customer’s experience in-person is relevant to his purchase decision.

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Figure 3.2: General Manager’s Revised Decision Diagram After Ethnography (&2)

These two customer experience distinctions still do not pass the clarity test. One might

be tempted to define those two distinctions as the clairvoyant’s observation of survey

results. This is the most common method employed in practice by companies and

researchers. However, assessing customer experience using surveys or other self-

reporting methods is often not reliable (Staw, 1975). Furthermore, in the case of the

distinction customer experience on phone, a survey is not even feasible to conduct.

Hence, it is not an option available to the general manager.

What distinction for customer experience that does not rely on self-reporting methods

would pass the clarity test? I begin to answer this question in the next chapter.

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Chapter 4 Emotions and Customer Experience

4.1 Chapter Overview

Following the ethnographic study of sales interactions, I added two nodes for customer

experience to the revised decision diagram (&2). Those additional nodes are customer

experience on phone and customer experience in-person, and the general manger believes

that they are relevant to the two outcomes of interest – customer’s decision to visit the

dealership and his decision to purchase. In this chapter, I present the link between

emotions and customer experience that forms the basis for creating a clarity tested

distinction for customer experience.

At the end of chapter 3, I highlighted the need for a distinction for customer experience

that does not rely on self-reporting methods. I present a brief description of existing

emotion coding research that sparked a eureka moment for creating my new distinction

for customer experience based on emotions.

4.2 A Gap in the State of the Art

Self-reporting methods, like surveys, are most commonly used to assess customer

experience. I recently took a flight on a major airline and within days of completing my

trip, I received an email request to fill out an on-line survey to report my experience. I

believe I received around ten such requests during the past year. My response rate is

under 50%, and when the survey has too many questions, I lose motivation to answer

thoughtfully right around the middle of it.

Literature on what makes for a good survey exists (Moser & Kalton, 1971; Brenner et al,

1985; Oppenheim, 1992; Patton, 2002), but it is not relevant to the phenomenon of

customer experience I investigated. I highlight here some of the problems with self-

reporting methods to explain why I chose to exclude it in my research.

First of all, there is a lag time between the experience and the reporting. This lag time

could cause inaccuracies stemming from problems with recollection. Secondly, the

survey design may not be able to eliminate biases. This is a particularly difficult task

when the target audience is diverse, with some segment of the audience interpreting

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questions differently than the rest. Thirdly, there is often a lack of motivation to respond

as my own anecdote illustrates. In some cases, there is the opposite problem of being too

motivated to respond when incentives are provided. This may lead to respondents

outside of the target group participating in the survey. Fourthly, there is the potential

problem of lying. Some people may misrepresent their own true feelings or views.

Lastly, self-reporting may not be logistically feasible in some cases. This last point

applies to assessing customer experience on the phone. Once the customer hangs up after

a sales call, there is no reliable way to contact the customer for a survey. Even if

technically possible in some cases, it may not be legally acceptable.

4.3 Emotions and Human Interactions

One of the seminal works that attracted my interest to emotion research is Gottman’s

longitudinal study of marital couples, in which he claimed to predict the incidence of

divorce with over 90% accuracy (Gottman & Levenson, 1992). While his claims were

subsequently challenged and he himself modified the description of his research,

Gottman’s work nonetheless presents valuable insight into the link between emotions and

interaction quality. Gottman video recorded couples interacting during sessions in his lab

and analyzed those interactions for emotions. He found four observable emotional

expressions to be particularly destructive in the context of marriage: criticism,

defensiveness, stonewalling, and contempt. Reading Gottman’s work made me realize

that the outer expression of emotions that are observable provides clues to the

phenomenon of experience that is not directly observable.

As I investigated the field of emotion research, I found work on facial expressions

(Ekman, 1969, 1999), on voice tone (Scherer, 1982, 1988), and on vocal communication

of emotion (Johnstone & Scherer, 2000) to name a few. The work that influenced me and

my research the most, however, was done close to home right here at Stanford University

by Dr. Janine Giese-Davis. At the time I began this research, Dr. Giese-Davis was

directing the Emotion Coding Lab at Stanford. When I met her for the first time at her

lab, she shared with me the research she had conducted involving emotion coding of

interactions during breast cancer therapy (Giese-Davis et al, 2005). She was

investigating the possibility that certain emotions may be correlated with certain

outcomes of interest, like longevity. Her methods seemed like a perfect match for my

sales interaction data. Would certain emotions found in sales interactions be correlated

28

with sales outcomes? This sparked the investigation of the role of emotions in sales

interactions using actual field data from the dealership – the first of its kind.

4.4 Emotion Codes

My research utilized Giese-Davis’ emotion coding scheme, which is a refinement of the

SPAFF coding scheme (Gottman, 1995). The emotion codes are shown in Figure 4.1

along with the topic codes. The scheme contains 23 distinct emotion codes, including

neutral. Giese-Davis describes some of these emotions as having the effect of drawing

people together and other emotions as pushing people away. I prefer this description to

the use of labels like negative and positive. Negative and positive are value-laden

descriptors that may lead us to judge the emotions in and of themselves as either good or

bad when what we really care about is the effect the emotions have on the outcome of

interest. For instance, if expressing a certain “bad” emotion raises the likelihood of a

preferred outcome, then would it not be considered “good”? To avoid this confusion, it is

best not to use the words good, bad, positive, and negative. Rather, I refer to the

emotions by their name as defined in the coding scheme, such as contempt, anger, and

genuine interest, to name a few.

Figure 4.1: Emotion Codes (Giese-Davis, 2005) and Topic Codes (Han, 2009)

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4.5 Sales Conversation on the Phone

Sales conversation on the phone is often the first human-to-human interaction with the

dealership. Based on the ethnographic findings, an increasing number of prospective

customers prefer to call the dealership before deciding to visit. Furthermore, from

listening to the recorded calls and tracking their outcome, it appears that the phone

conversation is a make-or-break interaction for the salesperson. If the customer

experience on the phone is good, there is a fair chance that the customer will choose to

visit the dealership. If the customer experience is bad, then it is often a deal-breaker.

In addition to the importance of the phone conversation to the dealership’s success, the

fact that the dealership recorded each call made it a highly valuable data set to collect and

analyze. Each recording contains high fidelity data without the influence of an observer.

In the case of in-person observations during my ethnographic study, it is not possible to

claim that I had no influence on the sales interaction, despite my use of techniques to

minimize the awareness of my presence. In summary, the recorded phone conversations

are perfectly suited for emotion coding (and topic coding). I now describe the procedure

used for analyzing the phone conversations.

4.6 Procedure for Analyzing Phone Conversations

4.6.1 Enrollment in the Study

At the time of the study, there were 20 salespersons in Direct Sales at the Toyota

dealership. This is the group within the sales department that receives phone calls as the

starting point of a sales engagement. Each of the 20 salespersons agreed to participate in

my study.

4.6.2 Data Collection

At the dealership, every phone conversation was automatically recorded and stored on a

server. The general manager gave me access to the database that stored the recorded

clips. During a period from September to November 2008, I accessed over 2,800 audio

clips. Not all of those clips were useful for my study, so I devised a way to filter them.

4.6.3 Data Filtering Criteria

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In order to separate the usable clips from the unusable ones, the filtering criteria that I

used were: 1) only first-time customers to the dealership, 2) only first call to the

salesperson, 3) new cars and trucks only, and 4) exclude the Prius (since demand

exceeded supply at the time and might alter the sales interaction). These criteria filtered

out most of the clips, leaving only 140 clips to be deemed usable.

4.6.4 Data Selection Criteria

In order to select the clips for coding among the usable clips, the criteria that I used were:

1) ensure each of the 20 salespersons was represented, and 2) within a set of clips for a

specific salesperson, use a random selection process (used MS Excel random number

generator). At the end, 38 clips were selected in total – two clips each for 18 of the

salespersons, and one clip each for two of the salespersons (because those two

salespersons only had one usable clip each).

4.6.5 Emotion and Topic Coding

I adopted the emotion coding scheme developed by Giese-Davis (2005) and the topic

coding developed by Han (2009), which contained 20 topic distinctions. Each clip was

uploaded into the iEMPATH website where a coding application enabled coders to code

each of the clips. Expert emotion coders conducted the emotion coding, and I conducted

the topic coding. For any given clip, every fraction of a second of the conversation was

coded for the emotion and the topic it contains. The result is a continuous stream of code

that can be viewed, for instance, according to emotion type, frequency, duration, order,

accumulative proportions, and speaker.

4.6.6 Analysis

Once the coding was completed, the data was formatted and imported into SPSS software

for statistical analysis. Also imported into the SPSS was the data set representing the

outcomes of interest for the corresponding 38 phone conversations – namely, customer

visit (yes/no) and customer purchase (yes/no). I collected this data separately by

accessing the customer relationship management (CRM) and finance databases at the

dealership. The types of analysis performed on the data set were regression and

correlation, and we also generated descriptive statistics to find relevant patterns.

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4.7 Key Findings

I present here some of the findings that contribute insights to updating the decision

diagram. First and foremost, the salesperson’s expression of genuine interest and

validation as a percentage of his total talk time was highly correlated with the customer’s

decision to visit the dealership. In other words, the more the salesperson expresses

genuine interest and validation, the higher the likelihood of customer visit.

When the salesperson expresses verbal contempt, constrained anger, and domineering as

a percentage of his talk time, the likelihood of customer visit goes down, but it was not

found to be significant. This finding is a bit surprising to me in view of Gottman’s

finding on marital couples where emotions like contempt showed strong relevance to

outcomes (divorces). I had contemplated the possibility that the same phenomenon might

exist in the two contexts. This goes to show that results from one context, no matter how

similar it may appear, may not transfer to another. Let us now look at the customers’

expression of emotions.

When the customer expresses a lot of genuine interest and validation as a percentage of

his talk time, it does not mean that he will visit. There appears to be little to no

correlation. Likewise, when the customer expresses verbal contempt, constrained anger,

and domineering as a percentage of his talk time, it does not tell us much about how

likely he will visit the dealership.

What is informative is the customer’s talk time as a proportion of total time of the phone

call. The more the customer talks, the higher the likelihood that he will visit the

dealership. It is worth noting here that this relationship shows relevance and not

causality. Artificially inducing the customer to talk a lot is unlikely to be constructive.

Regarding topic codes, price inquiry was highly prevalent comprising nearly 19% of the

total talk time (customer and salesperson combined). Price negotiation, on the other hand,

was not common at all, comprising just over 2% of the total talk time. It appears that

price negotiation is often reserved for in-person sales interactions.

Emotions appear to be broadly distributed across topics, meaning that the emotions

expressed are less affected by what is discussed than by who is talking. Taking these

insights gained from analyzing the phone conversations, I update the decision diagram.

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4.8 General Manager’s Revised Decision Diagram (&3)

I revised the decision diagram following the emotion coding and topic coding of phone

conversations by placing two salesperson emotion nodes in the diagram. The first is

salesperson emotion cues on phone and the other is salesperson emotion cues in-person.

The salesperson emotion cues on phone is relevant to the customer experience on phone

and to salesperson emotion cues in-person. This latter relevance arrow highlights the

observation that emotional expressions have more to do with the person than the topic of

conversation. Salesperson emotion cues in-person is relevant to customer experience in-

person.

I did not include customer emotion cues on the phone, because the analysis showed that

customer emotion cues were not as informative as salesperson emotion cues when

considering their relevance to the outcomes of interest.

Figure 4.2: General Manager’s Revised Decision Diagram After Analyzing Sales Conversations (&3)

In the next chapter, I take the data analyzed up to this point and show how investigating

emotions in interactions led to my developing the Customer Experience Quality Partition

(CXQP).

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Chapter 5 Customer Experience Quality Partition

5.1 Chapter Overview

At the end of Chapter 4, I revised the decision diagram with new nodes for the

salesperson’s emotion cues with relevance arrows to the customer’s experience on the

phone and in-person. The findings from the emotion coding and topic coding of phone

conversation data served to update the state of knowledge (&3) and direct my focus to the

relevance of the salesperson’s emotion cues on customer experience. Building on the

evidence from the phone recordings, I present in this chapter the concept behind

Customer Experience Quality Partition (CXQP) – a distinction for customer experience

that passes the clarity test and does not rely on self-reporting methods to assess.

With the CXQP as the distinction for customer experience, I then proceed to develop the

value model for the decision situation. The value model shows the distribution of

monthly profit, sensitivity analysis on the probability of good customer experience on

phone, and value of control (value of influence) on customer experience. These outputs

from the value model provide valuable insights for pursuing interventions. The final

decision diagram includes a decision node with influence arrows going into salesperson

emotion cues to inform the designer of the interventions where to focus his attention. By

revealing the mechanism of influence on customer experience and, ultimately, on the

customer’s purchase decision, I present a systematic approach to deconstructing and

modeling a real-life problem that has eluded a solution.

5.2 Defining Customer Experience Using Salesperson Emotion Cues

Up to now, I have yet to present a distinction for customer experience that passes the

clarity test. The pieces are now ready to put together. For our discussion, I focus on a

specific part of the latest decision diagram as shown in Figure 5.1. As the diagram shows,

salesperson emotion cues on phone is relevant to customer experience on phone, which in

turn is relevant to customer visit.

Customer visit is an observable distinction with two degrees: visit or no visit.

Salesperson’s emotion cues are also observable and the question here is what emotion

34

cues and measuring method should be used for this distinction. The uncertainty called

customer experience on phone is still not directly observable and requires a method of

assessment. I address both of these issues at once.

Figure 5.1: Basis for Creating the Customer Experience Quality Partition

Based on the insights from analyzing phone conversations in Chapter 4, I developed the

following two-dimensional graph shown in Figure 5.2. I begin by explaining the two

axes and then explain how I constructed the blue line that I call CXQP, which partitions

the two dimensional space into two regions - good customer experience and bad customer

experience.

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Figure 5.2: Customer Experience Quality Partition (CXQP)

5.3 CXQP Explained

The x-axis is the percentage of the total conversation time the salesperson expressed

interest and validation (let us call this X). The y-axis is the percentage of the total

conversation time the salesperson expressed verbal contempt, constrained anger,

domineering, belligerence, and tension (call this Y). The rest of the emotions expressed

by the salesperson during the call, including neutral, are then 100% - (X+Y).

The blue line on the graph is a smoothened version of a line (connecting a series of points)

that I assessed from the general manager to represent his (the decision maker’s) view on

what constitutes good customer experience. For simplicity, I attribute only two degrees

for customer experience – good and bad. The assessment procedure involved the

following steps.

First, I reviewed the emotion cues represented in the two axes with the general manager,

who was already familiar with emotion coding (from prior presentations). The next step

involved a series of questions to assess the general manager’s belief, starting with the

question: “During the phone conversation, if the salesperson expressed only neutral

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emotion, do you consider this to be good customer experience?” He replied, “No, you

need at least 2% or 3% interest and validation in the total conversation.” I plot the point

at 2.5%.

The next question addresses the other extreme: “What is the amount of verbal contempt,

constrained anger, domineering, belligerence, and tension that would create a bad

customer experience if you assume the remaining time is all interest and validation?” He

replied, “20%. Anything beyond 20% is irreparably a bad experience.”

The next question: “If those emotions expressed were just under 20%, what amount of

interest and validation would there need to be in order for the customer experience to be

good?” He replied: “A minimum of 25%.” Having plotted some key points on the graph,

the remaining questions addressed points between zero and 20% along the y-axis and

2.5% and 25% along the x-axis. Some questions were repeated after rephrasing in order

to test consistency.

The result of these questions is the assessed blue line shown on the graph that I call

CXQP. Any point above CXQP is bad customer experience and below it is good

customer experience. We now have a partition in the two dimensional space formed by

the combination of emotions comprising the x-axis and the y-axis that represents the

belief of the decision maker. A clairvoyant can now listen to any phone conversation, or

any interaction for that matter, and determine where to plot the conversation in the graph

and tell you whether it was a good or bad customer experience.

5.4 Plotting the Phone Conversation Data from the Field

Plotting the 38 phone conversations on to the two-dimensional space reveals some

insightful findings. Majority of the points are clustered around the left hand side, which

means that those emotions represented by the y-axis are expressed more often by the

salespersons than emotions like interest and validation. For many conversations, it is

more than twice as much.

Looking at the plotted conversations with the CXQP overlaid, as shown in Figure 5.3, we

can also see that all but eight of the conversations are considered to be bad customer

experiences by the general manager.

37

The solid dots represent phone conversations that lead to customer visit. There are nine

total and, of those, seven are within the space considered to be good customer experience.

One conversation linked to a customer visit is noticeably distant from the CXQP line

(X=5%, Y=36%). This example goes to show that bad customer experience and

customer visit is not a deterministic relationship. Likewise, there is an example of good

customer experience that did not result in a customer visit (X=31%, Y=3%).

Figure 5.3: Customer Experience Quality Partition (CXQP) with Calls Plotted

With a clarity-tested distinction for customer experience based on salesperson emotions, I

next developed the value model.

5.5 Value Model Explained

The value model provides useful insights into the current situation. The tree portion of

the model is shown in Figure 5.4. Reading from left to right, the tree describes a series of

38

events for a prospective customer. All of the probabilities shown were assessed with the

general manager.

5.2

Figure 5.4: Value Model - Tree

The customer calls the dealership and has a phone conversation with a salesperson. The

customer experience is either good or bad. The customer then decides to either visit the

dealership or not. Knowing that customer experience on phone is relevant to customer

visit, I assess from the general manager the conditional probability of customer visit given

a good customer experience and given a bad one.

If the customer visits the dealership, then the customer experience in-person is either

good or bad. This probability is conditioned on whether the customer came having had a

39

good customer experience on the phone or not. Lastly, the customer makes the purchase

decision either yes or no, and this probability is likewise conditioned on what happened

prior to the purchase decision.

There are ten possible outcomes in this situation, as shown by the branches at the right-

hand side of the tree. If there is a purchase, then some amount of money is made from

the sale (the $625 is shown for illustration). Because the general manager (decision

maker) is risk neutral over the range of dollars involved in this situation, I use the dollar

measure for calculations rather than converting the outcomes in terms of u-values (a

measure used in decision analysis when the decision maker is not risk neutral over the

range of dollars).

Assuming independence of each new customer, I calculated the distribution of monthly

profit using a binomial distribution. The result is shown in Figure 5.5. The three

different distributions represent three different probabilities of good customer experience

on the phone.

Figure 5.5: Distribution of Monthly Profit Based on Probability of Good Customer Experience on Phone

The general manager assesses the current probability of good customer experience on the

phone as 0.6 (blue). If the probability increases to 0.7 (green), it would shift the profit

distribution to the right and increase its mean from $211K to $225K. Conversely, if the

probability decreased to 0.5 (red), the profit distribution shifts to the left and decrease its

mean to $150K. The downward shift is greater due to the nature of the events modeled in

the tree.

40

If the general manager can find a way to influence customer experience on the phone

(raising it from 0.6 to 0.7, for instance), then the difference in the monthly profit

distribution would represent the value of that influence. Calculating this value of

influence (or value of control in the idealized case) informs the decision maker about how

much it is worth to intervene and the maximum he should pay for any intervention.

5.6 General Manager’s Final Decision Diagram (&4)

The general manager’s final decision diagram, shown in Figure 5.6, represents the

updated knowledge gained from the preceding steps in the research described in the

previous chapters. This final decision diagram includes a decision node for customer

experience design with influence arrows going into salesperson emotion cues on phone

and salesperson emotion cues in-person. The other arrow is a functional arrow

representing the cost of implementing some intervention and its effect on the profit.

The decision node, for now, is a place-holder for the alternatives that have yet to be

devised. Devising those alternatives is the design problem. Based on my research, what

the decision maker now knows is that he wants to influence the salesperson’s emotional

expressions during his interactions with customers, because it is through this mechanism

that he can influence customer experience. Knowing where to focus attention in

generating alternatives is one of the valuable benefits of using the Decision Analytic

Approach to Customer Experience Design.

41

Figure 5.6: General Manager’s Final Decision Diagram (&4)

42

Chapter 6 Future Work

6.1 Step Towards Transforming Customer Experience

My research introduced the Decision Analytic Approach to Customer Experience Design

that is a departure from using best practices and basic principles. I demonstrated the

application of this modeling approach to a real-life situation in the field, providing a

general manager a method to understand and improve customer experience at his

dealership. One of the key contributions of my work is the new distinction for customer

experience using the Customer Experience Quality Partition (CXQP), which makes it

possible to implement my modeling approach.

While conducting my research, I have uncovered some fascinating new topics for

investigation and I choose just three of them to briefly describe here.

6.2 Customer Experience at a Direct Value

In my research, I modeled the situation for a decision maker who considered profit to be

a direct value and customer experience to be an indirect value. The model of the decision

situation would change considerably if customer experience is considered a direct value

in addition to or in lieu of profit. I would like to model such a situation, where customer

experience is the direct value. Since there is no decision situation without a decision

maker, I will need to first encounter a decision maker holding such values.

6.3 Computerized Coding of Emotion

Emotion coding is currently done by specialized experts trained in emotion cues. If this

coding process could one day be automated by a computer, then the application of this

technology could be far reaching. As of today, there is sparse evidence of success in

conducting computer recognition of emotions in large part due to current limitations of

computer and software technology. Petrushin (2000) investigated computer agents for

emotion recognition in a laboratory study, but no subsequent work has been published.

As advancements occur in both software and hardware technology, we may one day see

this possibility come true. And, if it happens, then this will open up the possibility for

43

humans to have real-time (or near real-time) feedback of emotions expressed by both

oneself and others. If we could receive accurate and real-time feedback about our

emotion cues, then we would become far more aware of our own affective qualities. This

awareness may alter behavior and affect human interactions in ways that would be

fascinating to investigate. Computerized emotion coding technology would also usher in

creative applications for customer experience design.

6.4 Customer Experience in Sales Interactions with a Robot

This last topic is also dependent on technology. How do we understand customer

experience when we take the salesperson out of the sales interaction and replace him with

a robot? Humans are already interacting with robots in various settings such as an

automobile assembly plant where workers rely on robots for heavy lifting. However,

there are currently few opportunities to interact with a robot where its role is to help a

person make a purchase decision. If a robot were to interact with a customer at a

dealership, what would that sales interaction be like? Would the customer perceive the

robot as willing to (or programmed to) lie in order to make a sale? There is growing

research interest in human-robot interactions, and I believe there are some interesting

implications for the topic of customer experience design.

44

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