Identifying Customers' Unmet Needs Using
a Virtual Advisor and Engineer
by
Hoi Wai Thomas Cheng
Submitted to the Department of Electrical Engineering and Computer Science
in Partial fulfillment of the Requirements for the Degrees of
Bachelor of Science in Computer Science and Engineering
and Master of Engineering in Electrical Engineering and Computer Science
at the
MASSACH
SEJ INSTITUE
OF TCOLOGY
Massachusetts Institute of Technology
May 11, 2001
JUL 1 2001
LIBRARIES
Copyright 2001 Hoi Wai Thomas Cheng.
All rights reserved.
BARKER
The author hereby grants to M.I.T. permission to reproduce and distribute publicly
paper and electronic copies of this thesis and to grant others the right to do so.
Author
Hoi Wai Thomas Cheng
Department of Electric1 Engineering and Computer Science
May 11, 2001
Certified by
Glen L. Urban
Thesis Supervisor
d Austin Professor of Management
-d
Accepted by
AutnPo
K
Arthur C. Smith
Chairman, Department Committee on Graduate Theses
Identifying Customers' Unmet Needs Using
a Virtual Advisor and Engineer
by
Hoi Wai Thomas Cheng
Submitted to the Department of Electrical Engineering and Computer Science
May 11, 2001
In Partial fulfillment of the Requirements for the Degrees of
Bachelor of Science in Computer Science and Engineering
and Master of Engineering in Electrical Engineering and Computer Science
The interface of most online shopping sites let their customers perform only
end-product searches and selections. This conventional interface assumes customers
know their exact needs and are aware of all the latest products available in the market.
Unfortunately, the assumption is often unrealistic; thus, the current interface often
leads to customers' frustration and confusion. This research elaborates and extends a
previously proposed trusted virtual advisor as a much more effective e-commerce
interface to deal with general customers. A virtual advisor is a piece of software on a
web server that is accessible by web browsers. It is programmed to behave like an
experienced human advisor. It asks simple questions, records responses and
proposes personalized recommendations using utility and Bayes' probability analysis.
Customers can rely on the virtual advisor to recommend up-to-date products tailored
to their special needs, and narrow down their considerations to a handful of products
in a much shorter time than the conventional interface. When the virtual advisor
finds nothing in the market that completely satisfies the customer's particular needs, a
newly conceptualized virtual engineer and a design palette is proposed to explore the
reasons behind the customers' unmet needs. Therefore, companies can better
understand customers' needs and are able to identify any unmet needs. After
collecting a large number of customers' unmet needs, market share and revenue can
be estimated for new products that satisfy the unmet needs. This novel system
represents a new opportunity for manufacturers in both market research and the
traditional product development process. It also gives customers significantly better
online buying experiences.
This thesis describes the analytical system
considerations in designing such a selection and need identification method.
Thesis Supervisor: Glen L. Urban, Professor of Management
2
Acknowledgement
First of all, I would like to thank Professor Glen Urban for giving me the inspirations,
insights and the chance to work on this fascinating research project during the past
two and a half years. His trust and patience in me is greatly appreciated.
I would like to extend my gratitude to all the team members - Brian Chan, Hunter
Chen, Stanley Cheung, Chris Mann, Chaki Ng, and Jim Ryan for being wonderful
team members. Thank you for your help and making my life at Sloan more
enjoyable.
I am grateful to the generous support of General Motors and the staff members during
the team's visit to the GM headquarter in Nov 2000.
My very special thanks to my fiancee Ching Ching and our "daughter" Momo (our
toy poodle). They have endured my long working hours and frustrations with
understanding, care and love.
Finally, this thesis is dedicated to God.
Thank you Lord for everything He has done.
3
Content
Introduction
1.1 Background
1.2 Objective of the research
1.3 Organization
6
6
7
8
2
Previous works
2.1 By the team
2.1.1
Trusted Advisor
2.1.2
Apriori probability
Bayesian Attribute Effects
2.1.3
2.1.4
Bayesian Information Value
2.1.5
Market Segment Analysis
Recommendations
2.1.6
Advantages of the virtual advisor
2.1.7
Example: Trucktown
2.1.8
2.2 By other websites
2.2.1
ActiveBuyersGuide.com
Personalogic.com
2.2.2
Dealtime.com
2.2.3
2.3 Market research for new product development
9
9
9
10
11
12
13
14
14
15
15
16
16
16
17
3
Methodology
3.1 Contribution of team members
3.2 Updating Trucktown
Updating truck database
3.2.1
Identifying unmet needs & two-level Bayesian look ahead
3.2.2
3.3 Virtual Engineer
Motivation
3.3.1
Differences between identifying & explaining unmet needs
3.3.2
Correlation Matrix
3.3.3
Listening In - Identifying the unmet needs
3.3.4
Determining the Virtual Engineer questions
3.3.5
Introducing the virtual engineer to customers
3.3.6
Dialog examples
3.3.7
3.4 Design Palette
3.4.1
Motivation
3.4.2
Design Attributes
19
22
23
23
24
26
26
27
29
30
35
36
39
42
42
43
4
Attributes extension
3.4.3
Cost model
3.4.4
Default truck
3.4.5
Evaluation of the dream truck
3.4.6
3.5 Clustering
Motivation
3.5.1
Clustering method
3.5.2
Simulations procedure
3.5.3
Results of simulations
3.5.4
Discussion of clustering results
3.5.5
3.6 Potential Market Report
44
44
45
46
48
48
48
50
51
56
57
4
Technical Design Issues
4.1 Design issues of Trucktown 3.0
4.2 Overall Architecture
4.3 Database and drivers
4.4 3-D images for Design Palette
59
59
63
64
64
5
Discussion
5.1 Field Test
5.2 Comparison to traditional market research methods
5.3 Limitation of the system
5.4 Suggestion of future work
Easier to change previous answers during the
5.4.1
virtual advisor dialog
5.4.2
Real time visual feedback during the
virtual engineer dialog
Collaborative design for the design palette
5.4.3
Extensions of options for the design palette
5.4.4
Data Mining of customer profile
5.4.5
67
67
67
68
70
Conclusion
Reference
72
73
6
7
Appendix A:
Appendix B:
Appendix C:
Appendix D:
Attributes used for Trucktown 3.0
Virtual Advisor's questions for Trucktown 3.0
Customer Segmentation Tree
Trucktown Survey for Field Test
5
70
70
70
71
71
1.
1.1
Introduction
Background
With many new products entering the market daily, most people do not have updated
information on the latest products and features available when making their online
purchase decisions.
Unfortunately, not many people can afford the time to research
every related product offered in the market. People try to solve this problem by
asking friends or reading magazine reviews.
However, friends might not have
experience with the latest products, and magazine reviews usually test only a limited
range of products, and these tested products may not be up to date.
Researching online is not much better.
Most online shopping sites let their
customers perform only end-product searches and selections (these sites are
commonly known as the "self-service" sites).
This interface assumes that the
customers know their needs and understand most of the technical jargon in the
product descriptions.
Unfortunately, this assumption is unrealistic; hence, the
current interface often leads to customers' frustration and confusion.
This
dissatisfaction is confirmed by the Gartner group's survey study done in August 2000
[1].
In the study, online shoppers are asked to rate the top 50 e-tail sites. Not one
was rated excellent or good. Three-quarters were rated fair. From this study and
6
the previous discussion, it is clear that a better web customer interface is needed.
Previous research done by Professor Glen Urban, Dean Emeritus of M.I.T. Sloan
School of Management, has shown that websites can make personalized
recommendations using a trusted virtual advisor [2]. A trusted virtual advisor is an
intelligent and personalized online advisor that can be incorporated into any
database-backed e-commerce websites.
First, it asks a series of questions that
reveals the consumers' real preferences.
Next, it uses artificial intelligence to make
recommendations based on these derived consumer preferences.
A prototype of the
system has already been built [4][5][6].
1.2 Objective of the research
The research team's objective is to extend the earlier work of Professor Glen Urban.
Specifically, the extension concentrates in identifying any special customers' unmet
needs that no products in the current market can satisfy. The proposed final system
will consist of three parts: a virtual advisor recommends products to the customers
by asking simple questions and identifies any unmet needs; a virtual engineer
explores the reasons behind these unmet needs; a design palette identifies customers'
feature needs.
This system will benefit both the customers and the companies.
7
Customers will have much better web experiences, and let the manufacturers know
what they really desire; companies can identify the unmet needs in the market, build
better products, and establish potential sales projections of new products that satisfy
the identified unmet needs.
1.3 Organization
This document is organized into six sections: Introduction, Previous work,
Methodology, Technical Design Issues, Discussion and Conclusion.
The Previous
work section covers the original research done on the Trusted Advisors, the current
solutions available on the Internet, and traditional methods of conducting market
research for new product development. The Methodology section explains how the
trusted advisor, engineer and the design palette can solve the current problems, with
examples from the prototype site developed called Trucktown.
The Technical
Design Issues section covers the implementation issues of Trucktown.
The
Discussion describes the field test that will be conducted with a sample of one
thousand people who have bought a pickup truck in the past three years.
covers any possible extension to the current research.
section summarizes the implication of this research.
8
It also
Finally, the Conclusion
2
Previous works
2.1 By the team
The project was started in 1996, when Urban et al. [3] [4] have suggested a novel
approach of using a virtual advisor to assist online customers to make purchasing
decisions.
The theories were implemented in a software prototype called
"Trucktown" [4] [5] [6]. Trucktown assists potential pickup truck buyers to identify
the most suitable truck for them.
Since General Motors (GM) is the main sponsor of
the research project, pickup trucks are chosen as the example, but the theories are
The following describes the way that the
general and applicable to other products.
virtual advisor recommends products.
2.1.1
Trusted Advisor
The recommendations of the virtual advisor are useful only if the customers trust the
advisor as a dependable and knowledgeable expert.
Hence, the first task for the
virtual advisor is to gain the customers' trust, which can be accomplished by
providing many "trust cues" [3][4].
One of the examples of trust cues is to declare
that the virtual advisor does not receive any money from manufacturers, so that it
gives unbiased recommendations.
9
2.1.2
Apriori probability
The apriori probability of a product is the initial probability of recommending the
product to the customer over others in the database.
The calculation of the apriori
probabilities is based on knowledge of the truck utility rankings from previous third
party marketing research, plus customer's preferences of a set of pre-defined
Customers indicate the preferences by
perceptual dimensions of the product.
answering a constant-sum preference question, in which they assign the relative
importance of the perceptual dimensions.
The a-priori probabilities are calculated using the following formula [2]:
exp(I adDd,)
P(Aa)=
a
P(Aa)
=
Eq. 1
d
=exp(
adDda
d
Probability of purchase of product alternative a,
i.e., the a-priori probability
=
ad
Dd,a
=
constant sum importance of dimension d given by customer, and
standardized database value of dimension d for alternative a
i.e., the values stored in the utility knowledge database.
It has been acknowledged that questions about constant-sum preferences are difficult
to answer [7].
Default values for the dimensions can be used until the answers to the
10
questions about constant-sum preferences are collected.
be asked at any time during the conversation.
Hence, these questions can
The trusted advisor could establish a
rapport with the customers before asking these difficult questions.
However, in
Trucktown, the question about constant-sum preferences is always asked first, so that
Bayesian ordering of questions is more effective (see section 2.1.4 and 3.3.4 for more
information).
There are five dimensions in Trucktown: price, performance, fuel
economy, reliability, and safety.
2.1.3
Bayesian Attribute Effects
After the customer has answered a question, the Bayesian probabilities of every
product in the database will be updated using the following equation [2]:
P'(Aa) = P(Aa I Rq )
P(AaIR,.)
=
P(A,)P(R,, IA,)
P(Rr,)
Eq.2
the conditional probability that the customer will purchase the
product alternative a given that he answered with response r to
question q;
P(Rr,.q A,)
= the conditional probability of answering question q with
response r given that the purchased alternative is a (these data are
stored in the knowledge database of Bayesian Probabilities);
P(Aa)
= the prior probability of purchase of product alternative a before
11
the question q is answered;
= the marginal probability that a customer answers question q
P(R,)
with the response r,
P(Rrq) = I P(Aa)P(Rrq IAa)
Eq. 2a; and
= the new probability of purchasing the product alternative a.
P'(A4)
The probability can be iteratively updated across all responses to questions.
probabilities are calculated whenever the customer answers a question.
products is sorted according to the updated probabilities.
The
The list of
Products with the highest
probabilities will be recommended to the customers.
2.1.4 Bayesian Information Value
Equation 3 calculates the Bayesian Information Value of a question [6]:
I(q) -
X1 P(Aa IRr,q)- P(Aa)\
r
Eq. (3)
q = the question number of the yet-to-be-asked question;
n = the total number of possible responses for question q;
r = a possible response for question q;
P(Aa) = Bayesian probability of product Aa after all the answered questions, and
IP(AalRrq)
- P(Aa) = Bayesian attribute effect influence of each response.
12
The information value of every yet-to-be-asked question is calculated during the
dialog with the virtual advisor. The virtual advisor selects the question with the
highest information value as the next question to be asked.
2.1.5
Market Segment Analysis
Customers of any products can be divided into different market segments.
People in
the same segment share similarity in their purchasing needs and patterns.
From
previous market research on the products that the virtual advisor is recommending
(pickup trucks in the case of Trucktown), it is possible to identify the segment that
customers belong to based on their answers to the virtual advisor's questions.
Since
the market research reports the top few most suitable products for each segment, the
virtual advisor can recommend some of those trucks to the customers once the
customers' market segments are identified.
This serves as the second method for
making recommendations.
In some cases, the customer might be classified into multiple segments.
In this case,
the Bayesian probabilities of all the suitable products are calculated, and the top few
products are chosen as the recommendations.
13
2.1.6
Recommendations
The virtual advisor decides the recommendations in two ways.
with the top two Bayesian probabilities are recommended.
First, two products
If the customer's market
segment is identified, two trucks based on the segment analysis are recommended.
Otherwise, the virtual advisor would recommend the next two products with the
highest Bayesian probabilities.
2.1.7
Advantages of the virtual advisor
There are many advantages of using a virtual advisor over other interfaces:
1) Personal touch: it has a real person image and engages customers in a dialog,
hence it simulates a real knowledgeable salesperson.
This adds trust and comfort,
especially to novice Internet users [3].
2) Control: unlike a real salesperson who sometimes is annoying, the virtual advisor
gives customers total control.
Customers can choose the advisor they want to
talk to and stop the conversation at any time to get product recommendations.
3) Trust: customers are guaranteed that the product data are accurate, complete and
up to date. The privacy policy states clearly up-front that no personal records are
sold or disclosed to third parties.
4) Unbiased recommendation: Four unbiased recommendations are made at a time
14
with
justifications
in plain English.
The virtual advisor is intelligent enough to
recommend some good products even if certain criteria are not met; again this will
be explained to the customers in plain English.
5) High satisfaction: with a virtual advisor, customers can get personalized and
unbiased recommendations in a short time.
This value-added service has high
customer satisfaction [3].
2.1.8
Example: Trucktown
Lynch [4], Tian [5] and Wang [6] developed Trucktown 2.0 as the research prototype
for the implementation of the above theories.
In this version, the virtual advisor
would ask the customers fifteen questions in a fixed order. After collecting the
answers, the virtual advisor computes the utilities of every truck and returns four
trucks from the database as recommendations.
The database contained all 1997
pickup truck models available in the market.
2.2 By other websites
Several websites have interfaces that assist customers in making choices.
following are the more popular websites.
The
All these websites provide some
recommendations of products to customers, but none of them let customers voice their
15
needs, nor provide design feedbacks to the manufacturers.
2.2.1
ActiveBuyersGuide.com
ActiveBuyersGuide.com lets customers fill in a six-page form to determine the best
products for them. It uses conjoint analysis to make product recommendations.
The major problem of this interface is that customers must complete every part of the
lengthy survey before recommendations are provided.
2.2.2
Personalogic.com
AOL's personalogic.com has another approach: it presents a list of questions on
product features to the customers.
As customers indicate what features they like, any
product that does not contain those features is immediately eliminated and will not be
considered again.
Customers who use this website need to know the exact product
features they desire and not change their minds later (this is often not the case.)
2.2.3
Dealtime.com
Dealtime.com has virtual advisors to recommend products like digital cameras.
They ask some simple questions and lead the customers to three recommendations.
In many ways, they are similar to the virtual advisor in Trucktown.
16
One major
weakness of Dealtime's advisors is the absence of any justification for the
recommendations.
recommended.
The website fail to explain how and why the products are
Although the website has options for customers to view "more
expensive" and "less expensive" recommendations, clicking "more expensive"
followed by "less expensive" does not return the original recommendations.
Instead,
three intermediately priced recommendations will appear. Therefore, customers may
be confused and skeptical about the reliability of the recommendation process.
2.3 Market research for new product development
Several traditional methods are employed for market research for new product
development:
1) Survey
Surveys are sent to a targeted sample population.
incentives to complete the surveys.
Some surveys offer respondents
Although this method has a low cost and is able
to reach a large targeted population, many of the responses collected may not be
accurate, as the respondents are doing the survey for the incentives.
The response
rate is usually low, about 3 to 10 percent on average.
2) Interview
The sample populations are invited to have interviews. These interviews are usually
17
conducted by the sales and marketing department or professional market researchers.
Although the information yield is very high and usually accurate, the cost is
exceptionally high and impractical for a large sample size.
3) Focus Group
A small group of targeted samples is invited for a group interview, with their answers
recorded and a panel of observers watching the discussion process. This method
supports a slightly larger sample size than interviews, but the cost is still too high to
be practical for a large sample size.
Again, the members of the focus group may do
the interviews for the incentives.
All in all, the current methods in market research for new product development are
clearly not optimal for the cost involved.
18
3
Methodology
Building on the previous works of Trucktown, several extensions have been designed
and implemented to provide more functionalities and powers for the Trucktown model.
Specifically, there are four new additions to the model: a virtual engineer for detail
needs analysis, a design palette for identifying feature needs, a clustering process for
market segmentation, and a potential market report for estimating potential market
capitalization.
After the addition of the virtual engineer and design palette, the flow of the entire
process can be depicted in Figure 1.
After collecting a large number of customers'
responses, the process of data mining, the clustering and the generation of a potential
market report can be depicted in Figure 2.
19
Virtual Advisor
Greetng, Purpose
Virtual Advisor
Constant-Sum
Virtual Engineer
-
Virtual Engineer
Greeting, Purpose
Detect drop in
highest utility
NO
Virtual Engineer
.
op Mihighes
G=MenXse di.Qgu=S
uility?
preference question
based on conflict pairs
recorded before
YES
Calculate initial
utility value
(EQ 1)
Vtual Engineer
Virtual Engineer
Identify and Record
Comment and
conflict pairs
Open-end question
Virtual Advisor
Ask for permission to
Ask highest
+
information value
attribute question.
introduce Design
Pallet System
Calculate Bayesian
attribute effect
(EQ 2)
Agree to use Design
NO
Paellsystem?
Calculate attribute
information value
(EQ 3)
YES
Populate
recommendation with
DesigaPalUt System
highest utility
products
Launch design pallet
system
Are there any
conflict pairs?
NO
NO
Desipa PaWe
Al atriue
YES
qustions asked?
Slsam
Allocate preferences
between
recommended and
self-dsigne truck1
YES
Agree to talk to
Virtual Engineer?
Figure 1: The Flowchart of the et ire
recommendation and design proc oss
NO
20
YES
Send Customer to
showroom for
recommendations and
record data for
analysis
-
Collect a large number
of sample responses
Perform clustering to get
the major groups of
customer profiles that
have uniet needs
4
Perform other data
mining operations
(optional)
-I
Generate Potential
Market Report
Figure 2: Generation of Potential Market Report
21
3.1 Contributions of team members
Team member Professor Urban first proposed the methodology of identifying the
unmet needs in 1999.
team's weekly meetings.
Since then, the methodology had been evolving from our
In addition to the weekly meetings of discussions and
brainstorming, each member contributed in different ways. Table 1 is the summary
of the members' main contributions.
Main contributions
Team member
Glen Urban
Started and supervised the project
Participation period
Since 96
Thomas Cheng Programmed and integrated the virtual engineer Feb 99
and the design palette. Designed the system
architecture and the clustering process.
-May
01
Chris Mann
Modeled the virtual engineer and dialog
Sep 99 - Jan 00
Hunter Chen
Made cost models of the design palette
Feb 00 - May 01
James Ryan
Modeled the Bayesian probabilities of 2001
trcsmdlSep
Brian Chan
Made 3D truck models for the design palette
Oct 00 - May 01
Stanley Cheung Programmed and updated the Design Palette
Mar 01 onwards
trucks model
00 -May 01
Table 1: Team members and summary of their main contributions
22
3.2 Updating Trucktown
3.2.1
Updating truck database
To prepare for the field test in May 2001, the trucks in the database have to be
updated to the 2001 models.
Trucktown limits its database and recommendations to
pickup trucks only. However, the definition of pickup trucks varies across the auto
industry. We decided to follow the definitions of autosite.com, an authoritative
third-party website for autos.
Most of the trucks' specifications are from the
manufacturers' websites, with some customers' ratings from edmunds.com and the
Consumer Reports, consumerReports.org.
Some new attributes are added to make better recommendations among the 2001
models (see Appendix A).
The virtual advisor's questions are revised to reduce the
number of questions asked from sixteen to fourteen (see Appendix B).
Furthermore,
the most updated segmentation data provided by General Motors were incorporated
into Trucktown 3.0 (see Appendix C).
With the 2001 models, the conditional probabilities (see 2.1.3) need to be updated.
The system is very sensitive to the magnitude of the conditional probabilities.
Hence,
it is very important to fine-tune these probabilities through simulations before the
23
system goes to production.
3.2.2
Identifying unmet needs and two-level Bayesian Look-ahead
The previous Trucktown presents the virtual advisors' questions in a fixed sequence.
This is not the optimal solution, as the customers have an option to terminate the
dialog at any time.
Therefore, there is a possibility for the customers to miss some
pivotal questions that are set to be asked towards the end of the dialog.
Another reason to ask the highest information value questions first is the identification
of unmet needs (details about unmet needs can be found in section 3.3).
Using the
identical set of answers but a fixed sequence question order, it was observed that
many of the unmet needs could not be discovered.
In addition, two-level Bayesian
look-ahead sometimes can identify new unmet needs that would not be discovered if
one-level look-ahead is used instead (table 2, after question 13 in two-level look
ahead).
This is the empirical evidence that question order plays an important role in
discovering the unmet needs. Using two-level Bayesian look-ahead ensures that the
question order is always optimal (or close to optimal) for discovering unmet needs.
24
One-level look ahead
Two-level look ahead
Question Utility
Top Recommendation
Question Utility
Top Recommendation
7
0.022 Dodge Ram 2500 (2WD)
7
0.022 Dodge Ram 2500 (2WD)
15
0.02 Dodge Ram 2500 (4WD)
15
0.029 Dodge Ram 2500 (4WD)
12
0.039 GMC Sonoma (4WD)
12
0.039GMC Sonoma (4WD)
1
0.039 GMC Sonoma (4WD)
1
0.039GMC Sonoma (4WD)
5
0.042 GMC Sonoma (4WD)
5
0.042 GMC Sonoma (4WD)
13
0.046 Ford F150 (4WD)
2
0.042GMC Sonoma Extended (4WD)
2
0.042 Ford F1 50 Extended (4WD)
13
0.042 Ford F1 50 Extended (4WD)
9
0.051 Ford F150 Extended (4WD)
9
0.051 Ford F150 Extended (4WD)
3
0.051 Ford F150 Extended (4WD)
3
0.051 Ford F150 Extended (4WD)
14
0.056 Ford F150 Extended (4WD)
14
0.056 Ford F150 Extended (4WD)
Table 2: Partial simulation results using one-level and two-level look ahead
The "importance" of the question can be determined in real time using the Bayesian
information value (see 2.1.4).
During the dialog with the virtual advisor, the
information value of every yet-to-be-asked question is calculated using two-level
Bayesian look-ahead in the following way.
For each question that has not yet been
asked, the Bayesian attribute effect of every possible answer to the question is
calculated.
This calculation is completed as a single question look-ahead [6][8].
Then, for every possible answer of every yet-to-be-asked question, another question
look-ahead is performed.
The new Trucktown uses this two-level look-ahead to
determine the Bayesian information value of all the yet-to-be-asked questions.
25
3.3 Virtual Engineer
3.3.1
Motivation
During the design phase of the new product development life cycle, the design
engineers receive some rough estimates of "what the market wants" from the sales
department.
needs.
The engineers need to design new products that satisfy the market's
Unfortunately engineers do not know if they have really satisfied the
customers' needs until the prototype or the actual product is released to the market.
By that time it will be very expensive to make changes to the product.
Ideally the design engineers should first talk to thousands of potential customers to
understand their real needs. Products made in this way will naturally have a much
higher chance of high market acceptance because the engineers know exactly the
customers' problems and needs.
However, this assessment is practically not feasible,
as engineers usually do not interact with customers directly or have little time and
interest to do so.
A virtual engineer can solve this problem.
The virtual engineer listens to the
conversation between the customer and the virtual advisor - a process called
"listening in" [8].
If there are products currently available in the market that satisfy
26
all the customers' needs, then the virtual advisor would recommend those products
and the virtual engineer does not come into the picture.
However, if one or more unmet needs (defined in section 3.3.4) are identified, then the
virtual engineer would ask for permission for a short conversation with the customer,
after the customer has completed the dialog with the virtual advisor.
Once the customer's permission is granted, the virtual engineer looks at the unmet
needs of the customer and states an example of why there is no product in the market
that satisfy the customer's needs. Next, the virtual engineer asks specific questions
targeted to this customer's unmet needs and tries to find out the reasons behind those
unmet needs.
The customer's answers are saved and sent to the real design
engineers for considerations.
Finally, any new designs from the engineers will go
through the manufacturer's conventional concept test and auto clinic studies.
3.3.2
Differences between identifying and explaining unmet needs
It is very important to distinguish the differences between identifying the unmet needs
and explaining the reasons behind them.
The unmet need might be a conflict
between two attributes that is constrained by the law of physics.
27
However, after
understanding the customers' reasons of requesting these conflicting attributes, real
design engineers can do some brainstorming that may lead to new solutions that
satisfy the needs of the customers.
For example, a customer wants a truck with a long bed that is easy to park. This is
an unmet need that seems to be impossible to solve, because generally trucks with
long beds are harder to park than short-bed trucks.
However, during the dialog with
the virtual engineer, the customer explains that he needs the long bed because he is
going to relocate.
The virtual engineer collects this extra information and sends it to
After some brain-storming, these design engineers may
real design engineers.
discover that a truck with a foldable (a 3 feet extension on slider track) bed can
perfectly satisfy this customer, considering that the customer probably will not
relocate frequently.
The customer can unfold the bed to increase the hauling
capacity when necessary.
Although the virtual advisor can identify the unmet needs, it would be harder to
formulate solutions to the unmet needs without knowing the reasons behind them,
which is collected by the virtual engineer.
28
3.3.3
Correlation Matrix
A correlation analysis is done on some selected attributes in the conditional
probability table in the knowledge database (figure 3) [8].
compact
compact
full
Manual
Auto
2WD yes
4WD yes
Off road_yes
-9 Towing yes
Hauling yes
Constructionyes
12 Plowing Snow ye
.1 Z
Manuel
ful
Auto
2WD yes 4W0 yes C
1.00
-1.00
0.01
-0.01
1.00
0 01
0.05
-0.05
0 05
-0.05
-0.05
-0.64
-0.71
-0.72,
n V7
1.00
-1 00
0.66
-0.01
0 54
-0.14
0.
0.772
fl2
7
1.00
0 14
-0.14
-0.14
-0.10
n om
0.14
0.14
0.01
-0.14
0.14
0.10
n 4*
-1.00
-1 00
09
0.05
-0.07
-0.73
n no
1 00
1.00
0.09
-005
0.07
0,73
n no
Figure 3: A partial correlation table of selected attributes
Not all the attributes are in this correlation matrix. In general, attribute that does not
form meaningful conflicts is excluded from the correlation matrix.
In the case of
Trucktown, the following attributes are excluded:
a) Price: it is not meaningful to identify a conflict between price and any other
attributes like truck size, as we know that customers always prefer lower prices if
possible. Instead, it is more important to know if the entire truck, not specific
attributes, is within the customer's budget.
In Trucktown, after the virtual
advisor has completed a dialog with the customer, the MSRP (Manufacturer
Suggested Retail Price) of the virtual advisor's top recommendation truck is
compared with the customer's budget.
29
Both over and under budget are
considered as unmet needs, and trigger a dialog with the virtual engineer.
b) Median values: some attributes like "big & comfortable" has a scale of one to five.
A "three" means neutral, which does not form a meaningful conflict with other
attributes.
c) Negative answers: some attributes do not form meaningful conflict with any other
attributes.
For example, "no hauling" and "long bed" has a negative correlation,
but it does not mean that the customer needs a long bed truck that must have a low
hauling capacity.
d) No negative correlations: some attributes do not have any negative correlations
with any other attributes. Hence, they will not generate any unmet needs.
3.3.4
Listening In - Identifying the unmet needs
After every question answered by the customer, utilities of all products in the database
are calculated.
The value of the products with the highest utility should keep
increasing if all needs can be satisfied by the product (figure 4).
Using Trucktown as
an example, a customer has the following responses during the dialog with the virtual
advisor:
Qi) Price 20, Fuel Economy 20, Performance 20, Reliability 20, Safety 20;
Q2) Not sure about engine size;
30
Q3) Prefers a compact truck;
Q4) No preference for transmission type, but wants a 2 wheel drive;
Q5) The driver is between six and six and a half feet tall;
Q6) No special needs for off-road driving, hauling and towing;
Q7) The truck is mostly for personal use;
Q8) Prefers a short bed;
Q9) Like all brands except Chevy and Toyota;
Q10) Prefers a sporty style;
QIl) Carry 2 passengers, easy rear entry relatively not important;
Q12) Big, quiet and comfortable averagely important;
Q13) Maneuverability averagely important; and
Q14) Budget: $20,000 to $22,000.
Although trucks in the database overtake each other as the dialog progresses, the
overall top utility is constantly increasing after each question. Hence, there is no
unmet needs from a macro perspective.
However, looking at the utility path of final
top recommendation (Nissan Frontier 4x2 Regular cab), we observe that there is
actually a slight drop in utility after question 5.
This reflects that this truck does not
perfectly satisfy every need of the customer, but it is good enough to be the top
recommended truck.
The manufacturer of this top truck can review this truck
31
individually, but drop in individual product does not translate to a market-wide unmet
Therefore, the virtual engineer does not come into the picture.
need.
UuJIty Anlysis of selected trucks
oCe
0,07,
0,06
0,05.
004
003
402
0,01
0
I
2
3
4
5
7
6
8
9
10
11
12
13
1A
Questions answered
1--- Toyota Tundra4x2 (RegularCab) --- Mazda Bseries RegutarCab 4x2 (2600) -*- Nissan Frontier 4x2 RegularCab
Figure 4: The value of the highest utility product is constantly increasing
On the other hand, a drop in the value of the highest utility indicates that the customer
has some needs that no products in the market can satisfy (figure 5).
In another
example, a customer gives the following responses (notice that the question order is
different than the previous example, due to the dynamic ordering of questions using
two-level Bayesian look ahead):
QI) Fuel Economy: 40, Performance 40, Reliability 10, Safety 10;
Q2) Not sure about engine size;
Q3) Compact Trucks only;
32
Q4) No preference for 2WD/4WD and transmission;
Q5) Need a truck for off-road driving, some hauling and tow light loads;
After the customer has indicated that she needs to have a compact truck that is
designed for hauling and off-road driving, the utility of the top truck, Nissan Frontier
4x2, drops.
This is because Nissan Frontier 4x2 is a compact truck with a short bed,
hence not suitable for hauling large and heavy loads.
In fact, no compact truck in the
market is designed for hauling large and heavy loads. Hence, the utility of the top
truck drops leads to the discovery of this unmet need.
The customer, however, will
continue the dialog with the virtual advisor at this moment.
Q6) Need a truck for construction, mainly for personal use (Toyota Tacoma
overtakes Nissan Frontier to become the top truck);
Q7) Driver is less than 6 feet tall;
Q8) Like all brands except Nissan;
Q9) Prefers an extra short bed;
Q10) Like Sport Utility style;
Another drop occurs at this time.
The top truck, Toyota Tacoma, has a
conventional-sporty-mix style, which is not what customer wants.
On the other hand,
none of the sport-utility-style trucks (and all other trucks) in the market can satisfy
most of the previous requirements to overtake Toyota Tacoma at this point. As a
33
-
result, another drop in the overall utility occurs, which indicates another unmet need.
Customer continues the dialog:
Q11) Carry 2 passengers, easy rear entry very important; (Mazda Bseries
becomes the top truck, but there is no drop in the highest overall utility)
Q12) Big, Quiet and comfortable not important at all;
Q13) Prefers a truck that can do tight turning; and
Q14) Budget is $18,000 to $20,000.
The customer has completed the dialog with the virtual advisor. The utility analysis
of this dialog can be found in figure 5.
Utility analysis of selected trucks
0 05
004
-003
0.02
0.01
0
1
2
3
4
5
6
7
8
9
10
I1
12
13
14
Questions answered
Nissan Frontier 4x2 KngCab -- Mazda Bseries 4x4 (RegulwCab) (2DR)
-+-Toyota
Tacoma 4x4 (DoubleCab)
Figure 5: Drop in the value of the highest utility product (at the arrows)
34
r-
4W
When a drop occurs, all the attributes related to the question that the customers just
answered are examined.
For each of these attributes, if the correlation between it
and all the previously answered attributes is lower than a predefmed threshold
(e.g. -0.3), then the conflict between these two attributes is considered as an unmet
need.
Using the previous example again, the customer first indicates a need of a compact
truck. Later she answers that high hauling capacity is required.
At this point, the
overall utility of the top truck drops. The correlation between high hauling capacity
and all other previous answered attributes are examined.
Figure 3 indicates that
there is a strong negative correlation (-0.54) between "compact" and "high hauling
capacity".
Hence, a need of a compact truck with high hauling capacity is
considered as an unmet need.
3.3.5
Determining the Virtual Engineer questions
The team visited the GM headquarter and interviewed Harvey Bell, the engineering
director of GM, and Zhihong Zhang, the Technology manager of the Vehicle Design
Center [10].
We presented a list of attributes and potential unmet needs to them, and
asked them what questions they would ask the customers who have those unmet needs.
35
Their responses were recorded and compiled to be the questions of the virtual
engineer.
3.3.6
Introducing the virtual engineer to customers
If there is an overall utility drop during the dialog with the virtual advisor, the virtual
advisor will explain to the customer that no trucks in the market meet all the
customer's needs, and invite the customer to meet the virtual engineer (figure 6).
The customer has the freedom to choose not to talk to the virtual engineer, to ensure
that only customers who are willing to help designing better products will talk to the
virtual engineer.
This ensures the quality of the customers' feedback.
36
Figure 6: Inviting customer to talk to the virtual engineer
Once the customer's permission is granted, the virtual engineer introduces itself
(figure 7).
Next the virtual engineer gives an example of the customer's unmet
needs, and explains why most trucks in the market cannot fulfill the specific needs
(figure 8). Then the virtual engineer starts asking questions. The virtual engineer
only investigates a maximum of three conflicts so that customers would not be too
tired answering too many questions.
If there were more than three conflicts, the top
three conflicts (ranked by the absolute magnitude of the correlation) of different
attribute pairs would be chosen.
37
Figure 7: Virtual Engineer's introduction
38
Figure 8: Virtual Engineer explains one of the conflicts
3.3.7
Dialog examples
The following is several examples of the type of questions the virtual engineer would
ask. Figure 9 shows a question on the needs of a compact truck, figure 10 is a
question on an eight-cylinder engine, and figure 11 is the open-ended question.
The
open-ended question is always asked as the last question, which allows customer to
give any extra information or suggestions.
39
Figure 9: A question on the need of a compact truck
Figure 10: A question on the need of an eight-cylinder engine
40
Figure 11: The open-ended question
41
3.4 Design Palette
3.4.1
Motivation
The design palette let customers create their own dream products. With the real-time
feedback of estimated data and 3D images, there is a higher probability for customers
to arrive at a reasonably good design [9].
Figure 12 shows a sample screen of
Trucktown's Design Palette.
Figure 12: The Design Palette
The design palette is an excellent compliment to the virtual advisor. While the
virtual advisor concentrates in identifying the functional needs, the design palette
explores the feature needs.
Manufacturers can therefore understand more
specifically what the customers have in mind, and their own solutions to their unmet
42
needs (if any).
3.4.2
Design Attributes
For Trucktown, a list of attributes that is common to all pickup trucks and not too
technical to novice customers is chosen.
These attributes can be categorized into
four main groups:
a) Power:
1)
engine size: 4, 6, 8 or 10 cylinders;
2) wheel drive: 2 WD(front), 2WD (back), 4WD;
3) transmission: 4 speed auto, 5 speed auto, manual;
4) hauling capacity: 1500lbs, 3000Ibs, 45001bs;
5) towing capacity. 1000lbs, 45001bs, 6000lbs, 85001bs;
6) steering: 2 wheel steering, 4 wheel steering;
b) Size:
1) bed length: 4 to 10 feet;
2) cab size (by number of doors):2-door, 2-door extended, 3-door, 4-door;
3) cab size (by dimension): height: 5-7 feet; width: 5-7 feet;
4) wheel size:
c) Style:
43
1) color: red, blue, green, black, white, pink;
2) style: standard, rugged, sporty, hummer;
d) Features:
1) internal features: air condition, CD player, GPS system, keyless entry;
2) external features: power sunroof, convertible;
Some attributes are specifically requested by General Motor's platform engineers as a
market acceptance test of some of the new features (e.g. four wheel steering).
3.4.3 Attributes extension
It is possible to extend the options of some of the attributes to "fictional" - options
that are not available in any existing products (e.g. a six-doors cab for pickup trucks).
The design palette provides an excellent opportunity for customers to stretch their
imagination to design trucks with options nothing similar to any existing products.
3.4.4
Cost model
To provide real-time technical specifications of custom-built trucks, a series of
regression was run to build models for various attributes like price and fuel economy
[11].
The following are some examples of the model used in the Trucktown's design
44
palette, based on the 2001 truck models:
Price (MSRP)= 6166.922 + 0.196(dim) - 1914.605(cab0) + 1411.466(cab2) +
3050.159(wd4) + 2034.661(trans) - 2718.822(V4) - 1328.307(V6) +
428.668(V1O) - 405.970(bodyl) - 418.094(body3) + 1257.791(body4) +
(Eq. 4),
optional features
City Fuel Economy = 19.206 - 0.625(wd4) + 0.142(trans) + 4.619(V4) (Eq. 5),
0.716(V8) - 2.292(V1O) - 0.0000403(dim)
Towing capacity = 4415.606 - 36.267(wd4) - 371.878(trans) +
319.940(bodyl) + 2415.443(body2) + 985.574(body4) - 309.182(V4) +
(Eq. 6),
4223.086(V8) + 4673.086(V1O)
where
dim = dimension, surface area of the truck in inch2 ;
cabO = 1 for standard cab, 0 otherwise;
cab2 = 1 for extended cab, 0 otherwise;
wd4 = 1 for Four wheel drive, 0 otherwise;
trans = 1 for Automatic transmission, 0 otherwise;
V4 = 1 for 4 cylinder engine, 0 otherwise;
V6 = 1 for 6 cylinder engine, 0 otherwise;
V8 = 1 for 8 cylinder engine, 0 otherwise;
V10 = 1 for 10 cylinder engine, 0 otherwise;
bodyl = 1 for conventional body style, 0 otherwise;
body2 = rugged body style, 0 otherwise;
body3 = sporty body style, 0 otherwise; and
body4 = SUV-hybrid body style, 0 otherwise.
3.4.5 Default truck
When the design palette is launched, the default truck is a close mapping of the top
truck recommended by the virtual advisor during the dialog.
Since it is impossible
to perform an exact map of many attributes like body style and color, the closest value
45
is used as the default truck.
Specification attributes like fuel economy are not
mapped from the original truck. Instead, they are calculated using the models
described in the above section.
3.4.6 Evaluation of the dream truck
After the customers completed their designs, the final design is being compared
side-by-side with the original truck (figure 13):
Figure 13: Evaluation of the new design
A slide bar let the customers indicate their preferences between the top recommended
truck and the truck that designed by the customers themselves.
46
This is to estimate
the extra utility gained with the new design.
The click streams and the final design by the customers are logged for the
manufacturers to review.
47
3.5 Clustering
3.5.1
Motivation
It is inevitable that some of the customers are not serious when giving answers and
designing products (they are considered as the "noise" of the sample populations).
Hence, after collecting the responses and unmet needs of a large number of sample
customers, it is necessary to identify the more "popular" unmet needs for the
manufacturers to concentrate in. This also acts as a noise filter.
Counting the absolute frequency of the unmet needs only provide a partial picture of
the customer profiles.
For example, customers that have a common unmet need of
"high power compact truck" may have very different special needs in other areas.
Clustering is a better approach to identify the more popular unmet needs.
3.5.2
Clustering methods
The unmet needs of all the customers are stored in a database (figure 14):
48
XY
W
V
U
T
S
QR
0
0
0
01
0
0 -0.62;
0
0
-i1 -I
-034
-062
0
0:
0'
0
0
0
0
0.
0
0
0
0
0
O
0
-062.
0i
0i
0
0'
0
0'
0
0
0
0
-1
-1
0
0
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0"
0
0
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0'
0 -062
-1
-1
-0.341
0
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-1
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n:
ZP
-u. b.;
0
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-045
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01 -046 -052
01 -045
0" 0
0
0 -0.45 -0.52
0 -0. 46 -0.52
U
-0.45 -062
0
-0.45
n; n ACr
n rn'
Figure 14: Partial view of the unmet need database
Each column in the database represents a conflict pair (an unmet need), while each
row represents a particular customer's unmet needs.
The value is the negative
correlation found in the correlation matrix (see section 3.3.3).
K-mean clustering
with iterative means is used to cluster this database into a pre-defined number of
clusters. Every customer in the cluster shares a similar set of unmet needs.
Cluster
with a very small sample size is considered as noise.
To increase the accuracy of clustering, an additional rule for attributes that use a scale
of one to five is employed. If a conflict between a scale-one (or scale-five) and
another attribute is found, then a conflict between the scale-two (scale-four in the case
of scale-five) and the other attribute is added to the conflict database automatically.
For example, an unmet need to carry five passengers in a compact truck should not be
completely distinguished from a need to carry four passengers in a compact truck.
49
Hence, the conflict database should have an additional entry under the "Compact
truck and 4 passengers" for this customer (the colored cell in figure 15 for record 2).
The rule does not apply to a customer who has the need of a compact truck to carry
four passengers (record 3 in figure 15).
Compact:Towing Compact:4 passengers Compact: 5 passengers
Record 1
Record 2
Record 3
Record 4
o
0
0
-1
0
0
0
0
0
-1
o
-0.66
Record 6
Record 6
1
0
-1
Figure 15: Addition of conflict for Record 2
3.5.3
Simulations procedure
To test the effectiveness of clustering, simulation tests are conducted.
Six customer
profiles with their specific needs are designed, with 10 samples for each profile:
1) A compact truck that can tow and plow,
2) A low-budget large truck that can tow and plow,
3) A two wheel drive truck that is designed for plowing and off-road driving,
4) A compact truck that is suitable for tall drivers
5) A low budget truck that is easy to maneuver and designed for construction,
and
6) An easy to maneuver truck with a long bed.
50
For each profile, four sets of responses are generated:
Set A: identical (no variation in both constant sums and responses within a
profile);
Set B: no variation in constant sums, 10% variations in other responses;
Set C: magnitude of 5 points as standard deviations in constant sums, 10%
variations in responses of all attributes; and
Set D: magnitude of 10 points as standard deviations in constant sums, 20%
variations in responses of all attributes.
In addition, two methods of recording conflicts are used:
Method 1: Record conflict pairs only when there is a drop in the overall utility
Method 2: Record any conflict pair in the customers' responses, regardless of the
change in the utility
3.5.4 Results of simulations
The results of the simulations are tabulated in Table 3 to 6.
In a perfect clustering,
each profile is clustered into one cluster only. Some of the simulation inputs do not
generate any unmet needs, and we will discard them in calculating the success rate.
51
Set A: identical (no variation in both constant sums and responses within a profile);
Clustering method 1
Rate: 60/60 = 100%
Cluster
2
Cluster
1
Profile 1
Cluster
3
Cluster
4
Cluster
5
Cluster
6
No
unmet
needs
10
10
Profile 2
Profile 3
_
_10
10
Profile 4
10
Profile 5
10
Profile 6
Table 3a: Clustering Results of set A, method 1
Clustering method 2
Rate: 60/60 = 100%
Cluster
1
Profile
Profile
Profile
Profile
1
2
3
4
Cluster
2
Cluster
4
Cluster
3
Cluster
5
Cluster
6
10
10
10
10
10
Profile 5
10
Profile 6
Table 3b: Clustering results of set A, method 2
52
No
unmet
needs
Set B: no change in constant sums, 10% variations in other variables
Clustering method I
Rate: 52/56 = 93%
Cluster
2
Cluster
1
Profile
Profile
Profile
Profile
Profile
1 8
2
3
4
5
Cluster
3
Cluster
4
1
1
8
Cluster
5
Cluster
6
No
unmet
needs
1
1
9
1
10
10
7
1
Profile 6
2
Table 4a: Clustering Results of set B, method 1
Clustering method 2
Rate: 55/60 = 92%
Cluster
1
Profile
Profile
Profile
Profile
Profile
11
2
3
4
5
Profile 6
Cluster
2
Cluster
3
Cluster
4
9
1
10
Cluster
5
Cluster
6
10
9
1
8
2
Table 4b: Clustering Results of set B, method 2
53
No
unmet
needs
Set C: 5 point Standard Deviation for constant sums, 10% variations in other variables
Clustering method 1
Rate: 44/51 = 86%
Profile 1
Cluster
1
Cluster
2
6
1
Cluster
3
Cluster
4
Cluster
5
No
unmet
needs
1
2
1
3
1
1
1
6
Table 5a: Clustering Results of set C, method 1
4
6
Profile 2
8
Profile 3
9
1
Profile 4
Profile 5
Profile 6
Cluster
6
.9
Clustering method 2
Rate: 54/59 = 92%
Profile 1
Cluster
1
Cluster
2
7
2
Profile 2
10
Profile 3
1
Cluster
3
Cluster
4
Cluster
6
1
9
10
Profile 4
Profile 5
Cluster
5
9
1
9
Profile 6
Table 5b: Clustering Results of set C, method 2
54
No
unmet
needs
Set D: 10 points Standard Deviation for constant sums, 20% variations in other
answers
Clustering method 1
Rate: 33/45 = 73%
Profile 1
Cluster
1
Cluster
2
6
2
Cluster
4
Cluster
3
Cluster
5
Cluster
6
No
unmet
needs
1
1
Profile 2
7
1
2
Profile 3
1
7
2
16
Profile 6
1
1
7
1
Profile 4
Profile 5
1
2
1
3
6
Table 6a: Clustering Results of set D, method 1
Clustering method 2
Rate: 35/58 = 60%
Cluster
1
Cluster
2
Cluster
4
Cluster
3
Cluster
5
Cluster
6
No
unmet
needs
2
Profile 1
Profile 2
1
Profile 3
2
8
Profile 4
1
1
Profile 5
3
Profile 6
6
9
4
4
6
1
1
1
Table 6b: Clustering Results of set D, method 2
55
2
3.5.5
Discussion of clustering results
Not surprisingly, set A with identical entries within every profile achieves a perfect
clustering.
In fact, set A is used as verification of the clustering methodology.
set B, either method achieves a very high (>90%) profile recovery rate.
In
After the
introduction of variations in constant sums preferences in set C, the profile recovery
rate for both methods drop.
The rates drop further with the increase of noise in set D,
however method 1 can still achieve a 73% recovery rate.
At a 20% noise level, some
of the inputs are varied too much that they no longer have any resemblance of the
original profile.
Hence, it is unusual to have a high recovery rate at a 20% noise
level.
Although method 2 performs better than method 1 in set C, method 1 is a better
approach for clustering in general.
It also reflects more closely to the nature of the
"listening-in" methodology. Hen(ce future clustering should be performed using
method 1.
56
3.6 Potential Market Report
After collecting a large sample of responses and clustering out the major groups of
unmet needs, a potential market report is generated.
The potential increase in market
shares by introduction of a product that meets the need requirements identified is
estimated.
This estimation is calculated based on the assumption that the customers
are completely rational and base their purchase decisions upon the maximization of
personal utility.
The methodology of estimation was first suggested by Mann [8].
First, pick the top
recommended product by the virtual advisor. Next, change the specific attributes of
this product such that it can satisfy the identified unmet needs, and note the
corresponding Bayesian probabilities in the knowledge database.
Using these new
probabilities, the expected utility of the new product can be calculated.
Contrasting
the new utility with the original utility, along with the data of estimate market size of
the unmet need population, the increase of the market shares can be calculated.
Using this methodology, the final potential market report for Trucktown may look like
table 7.
57
Name of Profile
Small, high
towing capacity
4 Doors, Short
Size (share of
customers)
10%
Our share
now (Profile)
20%
Our new share
(Profile)
30%
5%
25%
37.5%
Total share
gain
1%
($50 millions)
0.6%
($30 millions)
bed
Table 7: Potential market shares report
Further research needs to be done to determine the best way to estimate market share
and sales projection of the new product that satisfies the unmet needs.
58
4
Technical Design Issues
4.1 Design issues of Trucktown 3.0
The key of Trucktown is interactivity. The previous version of Trucktown (version
2.0) was built in 1997 using Java JDK (Java Development Kit) version 1.1.4 as a Java
applet.
It communicates with the server through RMI (Remote Method Invocation),
and only support Netscape version 4.05 or above. It was built as a Java applet for
the high interactivity that an applet provides (minimal network communications after
the initial download of the applet).
Unfortunately the high-speed Internet connection is still not very popular in 2001, and
web browsers' support for Java technology has not advanced much.
In this situation,
some designs decisions were made for the field test in May 2001:
1)
Thin client vs Thick client
We had an option to convert the Trucktown to a Java servlet-based program (thin
client approach).
Using a servlet-based system means all the calculation would be
done at the server side.
Considering that the Trucktown is a very computational
intensive program, centralizing all computations is only good if the server can handle
large hits.
Hence, distributing the calculations to the clients' computers is a better
59
design.
Furthermore, interactivity is the key to the success of Trucktown.
Since Internet
broadband access is not yet very popular, we need to consider the modem users'
experiences.
We estimate that modem users need to wait for 7 to 10 seconds for the
next question to appear in the virtual advisor, which is not as interactive as using
applet.
With the above considerations, Java Applet (hence a thick client design) is chosen to
be the main architecture of the Trucktown.
However, if Internet broadband access
becomes very popular, a thin-client approach should be considered.
2) Logging
In the previous version of Trucktown, no logging of any users responses is done.
However, logging of customers' responses is clearly necessary especially for the field
test.
Logging at the client side is not possible due to the famous "sandbox
restriction" of unsigned applets.
The sandbox restriction refers to the security design
that the only 1/0 operation allowed for an unsigned applet is communication with the
originating server.
60
Logging can be done at the server side using RMI, in which the applet sends the
customers' responses back to the server through RMI, and the server executes the
actual logging.
Unfortunately, RMI is not supported by any version of Microsoft
Internet Explorer [12] (Microsoft uses proprietary COM as a substitute to RMI, which
is not supported by Netscape).
Hence, another communication method is needed to
perform the logging.
Java serviet has several advantages to be the solution to the logging problem.
First,
it is an extension of Java officially endorsed by Sun Microsystems, and it has received
a wide acceptance since its launch in 1997.
It can blend with the existing RMI and
applet architecture easily with a small modification of existing codes [12].
Second,
it uses standard HTTP protocols and port 80 to communicate, which means the
communications will not be blocked by most firewalls.
Lastly, using Java servlet
allows future extensions to thin client architecture that can support advisor on wireless
devices.
As the result, Java servlet was chosen and it was implemented in Trucktown 3.0.
The details of the new architecture can be found at section 4.2.
61
3) Swing vs AWT
Since 1997, Java SDK has evolved from 1.1.7 to 1.2 and now 1.3.
Java 1.2 and 1.3
provide a huge improvement in speed, ideal for Trucktown's computational intensive
nature.
Another major advantage of Java 1.2 and 1.3 is the introduction of the Swing
(or JFC, Java Foundation Classes) extension.
The Swing extension enables
professional graphical components with high details and variations, which is a giant
leap from AWT (Abstract Window Toolkit) found in variations of Java 1.1.
At the first glance, it seems that migration to Swing is a natural move for Trucktown.
Unfortunately, only Microsoft Internet Explorer 5 (5.0 and 5.5) Java-enabled editions
and Netscape 6 offers built-in support for Swing. All other versions of web browsers
requires users to perform a one-time download and installation of a 5MB free
browsers plug-in called JRE (Java Runtime Environment), and restarting their
computer after the installation for Windows version.
Alternatively, users need to
download the entire Swing package (1.7MB) every time they use the virtual advisor.
Either way is clearly not user-friendly. Furthermore, the performance of Swing is
only average.
Java JDK 1.4 has promised a faster version of Swing by using a new
pipeline architecture, but at this time it has not been released yet.
62
We have decided to use AWT with a free GUI (Graphical User Interface) toolkit by
Symantec Corporation.
We made a special package that only consists of the specific
components used in Trucktown, instead of requiring users to download the entire
toolkit. The original package has a size of 2,083KB, while the new customized
package has a size of only 208K, a 10-fold reduction in size that significantly reduces
the downloading time required by the users.
4.2 Overall Architecture
The overall architecture of Trucktown 3.0 is depicted in figure 16.
Besides the
addition of the Java Servlet communications, there is no major change in the main
architecture of the original Trucktown, which uses a well-accepted 3-tier architecture
[6].
Web Server: Java &,Wy
(based on JSDK 2.0)
HTTP
Applet client (compiled using
JDKL 1.8_007) on Netscape 4 or
Internet Explorer 4 or 3
RMI
RMI Server (JDKl 1.8_007)
JDBC Type 4
Driver
Database that supports JDBC
(g.g. Oracle 8)
Internet
Figure 16: Architecture for Trucktown 3.0
4.3 Database and drivers
In Trucktown 2.0, Microsoft Access 97 was chosen as the backend database, along
with the ODBC-JDBC bridge (ODBC: Open Database Connectivity, JDBC: Java
Database Connectivity) for communication between Java and Access.
Although
both Microsoft Access and ODBC-JDBC are notorious for their slow performance,
they are used in Trucktown 2.0 for ease of deployment and low cost [6].
For
Trucktown 3.0, since logging of customers' responses is introduced, performance of
the database becomes an important factor.
Hence, Oracle 8.0.5 database is used to
replace Microsoft Access 97 as the backend database, and a type 4 JDBC (pure Java)
driver replaces the ODBC-JDBC driver for enhanced performance.
4.4 3-D images for Design Palette
The real-time pictorial feedback is the most attractive feature for the Design Palette.
It is also the most difficult implementation part of it.
Since no real-time rendering
technology in a web application is available today, all truck images must be
pre-defined and pre-generated before deployment.
For Trucktown 3.0, we need to
make permutations of all the look-changing attributes.
The total number of
permutations equals to 7 (bedlength) * 3 (width) * 3 (height) * 4 (styles) * 3 (wheels)
* 6 (color) * 4 (cab) = 18,144 images.
64
Streaming-video of pickup trucks with 360-degree rotations is clearly not an option,
because trucks in the design palette are imaginary. ActiveX or Macromedia Flash
format requires users to download and install a plug-in, which decreases the user
friendliness of the system. The large file size for each truck using ActiveX or Flash
also makes them infeasible for most Internet users.
Using plain Java, a 3D truck model could be built, with operations like rotation and
stretching done using linear algebra. However a mathematical 3D truck model with
high detail and texture is very difficult to build.
Finally, we have decided to use static jpeg images.
Although no graphical operations
can be performed on them, their relative small file sizes (average 9KB for each truck)
are excellent for web deployment.
All image files are stored in the server, and are
downloaded in real time during the design process.
The small file size allows high
interactive without the need to download all files (>300MB) in advance.
All 3D images are done using Rhinoceros 3D version 1.1, a popular CAD (Computer
Aided Design) tool. First, different components of a truck are mad. Using the
scripting language in Rhinoceros, different components are assembled automatically,
65
like an assembly line in the manufacturing plant. Finally the final image is exported
as a jpeg file.
The modular design enables us to generate all 18,144 pictures
automatically in about 40 hours using an Intel Pentium III 600MHz machine with 256
MB of RAM.
66
5
Discussion
5.1 Field Test
Harris Interactive (www.harris.com) is hosting and organizing the field test with us.
The Trucktown system is hosted in the company's server, and the URL is sent to one
thousand targeted sample population as the early users of the Trucktown system.
These one thousand people have purchased a pickup truck in the past three years, and
have signed up to be the early users of the system.
After they have used the
Trucktown, they fill a survey online as a feedback of what they feel about the entire
experience of using Trucktown (see appendix D for the survey questions).
The field test for one thousand users is scheduled to start and complete in May 2001.
5.2 Comparison to traditional market research methods
As discussed in section 2.3 and 3.3.1, traditionally customers have limited channels to
voice their real needs and concerns to the manufacturers.
On the other hand, the
manufacturers' design engineers do not interact with customers directly.
Since
market research initiatives are very expensive to conduct, many manufacturers mainly
identify unmet needs and potential markets by trail-on-error.
67
Using the system proposed by this research, both the customers and manufacturers
benefit.
Customers can quickly narrow down their choices to save time, and enjoy
easy access of unbiased and complete information.
Because customers are using the
virtual advisor system to help themselves, there is a high probability and incentives to
give honest and real answers, as opposed to randomly clicking the system to complete
a market research study and get the incentives.
For the virtual engineer dialog and
the design palette, since explicit permissions are granted, and no incentives are
offered to the customers, those customers who chose to use them are truly
volunteering to give their responses. The unmet needs identified by this system is
much more reliable compared to needs identified using traditional market research
methods.
Therefore, we propose that the system is superior than traditional methods of market
research in identifying customers' real and unmet needs.
But we need the formal
results of the field test to validate our claim.
5.3 Limitations of the system
Virtual advisor is not a cure-all interface for shopping sites. Rather, it is targeted
specifically to novice customers and busy people who needs trustworthy and updated
68
information, and personalized recommendation fast.
For experienced customers,
they prefer to view complete specifications of every product available in the market
before making their final decisions [3]. For this group of users, Trucktown also
provides an alternative interface called "automile" [6].
For the design palette, the main weakness is the limited range of options available.
For example, customers might want to slightly modify a conventional looking truck,
but not a complete change to a sporty style.
We recognized this limit.
However,
for each additional option that affects the look of the truck, a large number (a
permutation of all possible combinations) of pictures need to be generated.
Therefore, we limit the range for this research prototype. The modular nature of the
system that generates the pictures makes future extension become relatively easy (as
opposed to making the picture one by one manually). Furthermore, the designs
recorded by the design palette are not final - instead, they are sent to manufacturer's
design engineers for review, and need to go through the conventional concept tests
and auto clinic studies.
Hence, the designs are fine tuned during these conventional
procedures.
69
5.4 Suggestion of future work
5.4.1
Easier to change previous answers during the virtual advisor dialog
For the current system, customers must keep clicking the "back" button until they
reach the question to change their answers, then they would need to answer all the
remaining questions again. A visible answer history panel can be deployed at the
side of the screen to enable easy access to previously answered questions, and
facilitate changing of answers.
5.4.2 Real time visual feedback during the virtual engineer dialog
During the virtual engineer dialog, pictures of the imaginary truck that the virtual
engineer has in mind can be displayed as a real time visual feedback to the customers,
and let customers know that the direction that the virtual engineer is thinking in.
This way, the customers might give more suggestions in the open-ended question
asked at the end of the dialog.
5.4.3 Collaborative design for the design palette
Similar customers can view other customers' designs, and therefore enable
collaboration of design and exchange of ideas, as opposed to every individual
customer design the truck from scratch.
70
To achieve this, customers should be able to save their designs on the server. They
can retrieve other designs done by other customers who belong to the same cluster.
In addition, real time chats can be added to the design palette for customers from the
same cluster to exchange ideas while designing their trucks, if they are logged in at
the same time.
Manufacturers can observe the customer-to-customer interactions to
gain more insight into the customers' needs.
5.4.4
Extensions of options for the design palette
Imaginary options (like 6-door cab) should be available in the design palette.
However, more attention must be put in the organization of the categories, so that the
wide range of options will not overwhelm the customers.
5.4.5
Data Mining of customer profile
Further research is needed to determine a better data mining method to identify
customers' profiles of unmet needs.
Currently the clustering method only considers
customers' answers in the virtual advisor dialog.
A better data mining technique
should also consider customers' answers in the virtual engineer dialog and the designs
in the design palette.
71
6
Conclusion
Trucktown 3.0 is a major improvement from the previous version. I have designed,
implemented and integrated four additions to the previous version of Trucktown: a
virtual engineer, a design palette, a clustering process and a potential market report.
The proposed methodology of unmet need identification is internally validated
through simulations.
external validation.
A field test of 1000 users is scheduled in May 2001 for
The design palette demonstrated the possibility of a highly
interactive and graphics-intensive thin-client application using Java AWT applet.
With the additional features, Trucktown is able to identify, explore and cluster the
unmet needs, and it is still very user-friendly and easy to use.
This 5-year research
proposes a methodology that represents a revolutionary and low cost method for new
product development on the Internet.
72
7.
Reference
1) Gartner Group commentary,
"http://www.gartner.com/public/static/hotc/00093369.html," Aug 2000.
2) Glen L. Urban, Farreena Sultan, William Qualls, "Design and evaluation of a trust
based advisor on the Internet," July 1999, MIT-SSM working paper, MIT 1999.
3) Glen L. Urban, Farreena Sultan, William Qualls, "Placing Trust at the center of
your Internet strategy," MIT Sloan Management Review Fall 2000 Vol 42 Number 1.
4) Ken Lynch, "Al Needs-based configurator implemented in Java," MIT M.Eng
Thesis, MIT 1998.
5) Andy Tian, "Nonlinear navigation and context-sensitive help in the Sloan electronic
commerce project," MIT M.Eng Thesis, MIT 1998.
6) Xingheng Wang, "Extending the Sloan E-commerce project with intelligent
customer interactions," MIT M.Eng Thesis, MIT 1999.
7) Glen L. Urban, John R. Hauser, "Design and Marketing of New Products,"
Prentice-Hall 1993.
8) Christopher Mann, "Listening In - developing a virtual engineer for the online
identification of unmet customer needs," MIT Master Thesis, MIT 2000.
9) Eric von Hippel, "Toolkits for customer innovation," MIT-SSM Working paper,
MIT 1999.
10) Personal communication with GM engineers, Warren, MI, Nov 2000.
11) Hunter Chen, "Web based market research methodology for unmet customer
needs: estimating cost function for Design Palette," MIT Master Thesis, MIT 2001.
12) Sun's Java developer connection,
"http://developer.java.sun.com/developer/technicalArticles/RMI/rmi/," Oct 1999.
73
Appendix A: Attributes used for Trucktown 3.0
Size (Compact, Full)
Transmission (Manual, Automatic)
2 Wheel Drive, 4 Wheel Drive
Budget (10-12k, 12-14k,..., 30-32k, over 32k)
Off-road Driving (Yes/No)
Towing (Yes/No)
Hauling (Yes/No)
Construction (Yes/No)
Plowing (Yes/No)
*
Easy Rear Entry (h=Not Important, 5=Important)
* Body Style (Conventional, Convertiona/Sporty, Sporty, Rugged/Sporty, Rugged, Sport
Utility)
Number of passengers (1 to 5)
Manufacturer (Chevy, Dodge, Ford, GMC, Lincoln, Mazda, Nissan, Toyota)
Bed Length (extra short, short, long)
Height of the driver (<6 feet, 6-6.5 feet, >6.5 feet)
Engine (4 cylinder, 6 cylinder, 8 cylinder, 10 cylinder, diesel)
Big Quiet Comfortable (1=Not Important, 5=Important)
*
Maneuverability (l=Not Important, 5=Important)
*
= new attribute for Trucktown 3.0
74
Appendix B: Virtual Advisor's questions for Trucktown 3.0
B = This question is used for Bayesian Update
N = This question is used for Segmentation
B: CHIPS: How do you feel about each of these trucks qualities? Please slide up whichever sliders that you
feel are important until the left slider falls to the bottom
*
*
*
*
"
Price
Performance
Fuel Economy
Reliability
Safety
1. B: Trucks basically come in two sizes - full size and compact. Do you have a certain size truck in mind?
(Probabilities Done)
Only full size trucks
Only compact trucks
Either size is OK
2a. B&N: How many people do you need to carry in the ENTIRE truck, including the driver? (Need
Segment: PCAP2)
1 person
2 people
5 or more
3 people
4 people
2b. N: How many people, including the driver, do you need to carry in the FRONT SEAT of the truck?
(Need Segment: FSCAP)
1 person
2 people
3 or more
2c. B&N: How important is it that the truck is designed for easy entry/exit of rear occupants? (Need
Segment: GPV46; Probabilities done)
1:not important
5:important
3. B&N: Let's talk a little about how you will be using the truck. Can you tell me the kind of things you
will be doing with the truck? You can check more than one use. (Need Segment: GDC 18; Probabilities
Done)
Driving on icy or snowy roads
Other hauling
Hauling house supplies
Fishing or Hunting
Driving on rough roads
Off-road driving
Trailering heavy loads (GDC18)
Trailering light loads (GDC 18)
75
4a. N: Will you use the truck for business or personal uses? (Need segment: GDC5)
5: Personal
1: Business
4b. B&N: If 1,2,3,or 4, which commercial uses? (Probabilities done)
Plowing snow
Hauling
Construction
Towing
5. B: Let's talk a little bit about how much money you want to spend. Did you have a budget in mind?
(Probabilities Done)
$22,000 - $24,000
$24,000 - $26,000
$26,000 - $28,000
$28,000 - $30,000
$30,000 - $32,000
over $32,000
$10,000 - $12,000
$12,000 - $16,000
$14,000 - $16,000
$16,000 - $18,000
$18,000 - $20,000
$20,000 - $22,000
6. B: Please check all the car manufacturers that you are interested in: (Probabilities Done)
Chevrolet
Dodge
Lincoln
Mazda
Ford
GMC
Nissan
Toyota
(no question 7)
8. B: What transmission and drive type do you prefer? (Probabilities Done)
Two Wheel Drive
Four Wheel Drive
Either
Standard
Automatic
Either
9. B: What type of body look do you prefer? (Probabilities Done)
3: Sporty
4: Extra sporty
5. Don't know
1: Conventional
2: Rugged
10. B: How tall is the tallest person who will ride in the truck? (Probabilities Done)
under 6 feet tall
/2 feet
between 6 and 6
over 6
tall
feet tall
11. B: Trucks are manufactured with three basic bed sizes - extra short, short and long. Do you have a
specific bed size in mind? (Probabilities Done)
Extra short beds (less than 6 feet)
Short beds (6 to 6
'/2
feet)
Long beds (greater than 6 %/feet)
Any size is OK
76
12. B: What engine size(s) do you prefer? (Probabilities Done)
Gas Engines
4 cylinders
6 cylinders
8 cylinders
10 cylinders
Diesel Engines
6 or 8 cylinders
Not sure
13. B&N: Over the years, I've also learned that some trucks are more comfortable than others. How
important is it that you have a big, quiet, comfortable vehicle? Can you position the slider to give me an
idea of the how important this is to you? (Need Segment: GPV70; Probabilities Done)
5:important
I:not important
14. B: Depending on where you drive, you may require a truck that can be easily maneuvered or parked in
tight situations. Can you position the slider to give me an idea of the how important this is to you?
(Probabilities Done)
5:Not important
1:Tight turning important
Last
name: Pure N Ouestions:
Please rate the extent to which you disagree or agree with the following statements:
I like a vehicle that combines the comfort of a car
with the capabilities of a truck (GPV73)
1:Disagree
5:Agree
I want to buy a prestigious vehicle (GPV1 1)
1:Disagree
5:Agree
I prefer a youthful looking truck: (GPV27)
1:Disagree
5:Agree
I prefer a cute and friendly looking truck: (GPV37)
1:Disagree
5:Agree
Exterior design preference: (GDC 1)
5: Round
1: Square
Do you prefer the biggest available or the smallest available vehicle? (Need Segment: GD4)
5: biggest
1: smallest
77
GPV73
4,5
1,,,
GDC18
00
20P
3,4,5
1,
1,2/1,2,
lop
32CPU
14P
GDC18
7P
1OP
19CP
GPV27
28UP
GDCs
GPVI
,
36UP
4,5
45
1
36UP
1,2,45
1,2,45
1, ,/
1,2
29PV
24P
GDCI
1,2/
45
1,2,/
23TP
GDC1
18P
35P
GDC4
35P
GDC4
GPV73
GPV70
PCAP2
1,/
3,4
1
3
L2,45
4CP
FSCAP
FSCAP
FSC
26CPV
45
16UP
37U
Questions key:
Question
number of passengers at the front seat
FSCAP
exterior design preference (1=square, 5=round)
GDC1
truck size (1=smallest, 5=biggest)
GD4
business or personal use (1=business, 5=personal)
GDC5
importance of towing heavy loads (l=not important, 5=important)
GDC18
GPV11 importance of prestige (1=not important, 5=important)
importance of youthful looking truck (1=not important, 5=important)
GPV27
importance of cute and friendly looking truck (I=not important, 5=important)
GPV37
importance of easy rear entry (I=not important, 5=important)
GPV46
importance of big, quiet and comfortable (l=not important, 5=important)
GPV70
importance of combining the comfort of a car with the capabilities of a truck (1=not
GPV73
important, 5=important)
PCAP2
number of passengers in the truck
Segments key:
Segment
4CP
20P
32CPU
26CPV
34C
loP
19CP
29PV
24P
18P
Segment discriptions
are value-conscious, and want a truck that they can drive in any weather
condition without skidding or getting stuck.
want a practical and affordable truck, which can trailer heavy loads and
withstand hard use without being damaged. They want to be able to
maintain the truck themselves, and prefer a styling that is traditional with
a rounded exterior design.
want a reliable and durable truck. They also place a high importance on
the ability to trailer heavy loads and drive safely in any weather
conditions.
want a big, quiet and comfortable truck that allows rear passengers to
enter and exit easily. They need a proven, practical and affordable truck
that can trailer heavy loads. Also, their truck must be durable, safe and
conservatively styled.
want an elegant looking truck with "living-room" comforts that can carry
six or more passengers.
prefer a practical and affordable truck with the comforts of a car. They
want a sporty looking truck that is able to withstand hard use.
need a big, quiet and comfortable truck that is of high quality. They
prefer a prestigious and elegant, yet sporty looking truck with a luxurious
interior.
need a big, quiet and comfortable truck for business purposes. They want
a truck with rounded exterior design that can carry cargo and trailer heavy
loads.
want a big, quiet and comfortable truck that can trailer heavy loads and is
of high quality. They prefer an aggressive and sporty looking truck that
has good road handling.
require a big, quiet and comfortable truck that must be able to trailer
heavy loads. They want a sporty, durable and powerful truck that looks
and feels solid.
14P
prefer a practical and affordable truck with the comforts of a car. They
79
7P
28UP
want a durable truck that is able to trailer heavy loads and is easy to
repair.
prefer a practical and affordable truck with the comforts of a car. They
want a sporty and stylish truck that they can repair themselves. Their
truck must withstand hard use and poor road conditions, and provide good
fuel economy.
prefer a sporty and durable truck with the comforts of a car. They want a
truck that can trailer heavy loads, is durable for off-road driving, has
maximum cargo space and can accommodate four passengers.
23UP
prefer a stylish, sporty and durable truck with the comforts of a car. They
want a truck that is fun to drive, yet can trailer heavy loads, handle poor
road conditions and store cargo out-of-view.
16UP
prefer a stylish, sporty and durable truck with the comforts of a car. They
want a powerful and performing truck with out-of-view storage that is fun
to drive, big enough to carry four adults and trailer heavy loads.
36UP
35P
41UP
prefer the biggest, top-of-the-line truck, which has the comforts of a car.
They like a quiet and durable truck with a powerful engine, luxurious
interior and maximum cargo space, and is designed to handle poor road
conditions.
want a sporty and aggressive-looking truck that feels solid, handles poor
road conditions, and can carry and trailer heavy loads. They like a
powerful truck that is durable with maximum interior space.
want the biggest available truck that is still economical. They prefer a
sporty, powerful and durable truck with a luxurious interior, which can
trailer heavy loads and handle poor road conditions.
37U
prefer an economical truck that is comfortable, can handle poor road
conditions and is big enough to accommodate six passengers.
80
Appendix D: Trucktown Survey for Field Test
Trucktown Survey
Thank you for visiting TruckTown! We hope you enjoyed your stay. In order to improve
the experience for future visitors, we ask that you take a few moments to complete our
survey. We will ask you questions related to three main features of the site: the Shopping
Advisor, the Design Engineer and the Design Pallet.
Before we begin, we would like to know a little more about you and your experience with
pick-up trucks. Please indicate your agreement with the following statements.
With respect to pickup trucks, I consider myself an expert.
1
Very
Strongly
Disagree
2
Strongly
Disagree
3
4
5
6
7
Disagree
Neutral
Agree
Strongly
Agree
Very
Strongly
Agree
When I purchase a new truck, I want to buy the latest model available.
1
Very
Strongly
Disagree
2
Strongly
Disagree
3
4
5
6
7
Disagree
Neutral
Agree
Strongly
Agree
Very
Strongly
Agree
I am a pick-up truck enthusiast, and keep up-to-date on the latest models and trends in the
industry.
1
Very
Strongly
Disagree
2
Strongly
Disagree
3
4
5
6
7
Disagree
Neutral
Agree
Strongly
Agree
Very
Strongly
Agree
Please check the products you now own:
Mobile Phone
Flat Screen Monitor (for your computer)
High-Definition Television (HDTV)
Palm Pilot (or any other PDA)
Check the features you have in any vehicle you own:
After-market parts
GPS mapping
Voice-activated controls
"On-Star" type service
81
In the first (of three) section of the survey, we will ask you some questions about the
Shopping Advisor. In case you forgot, this is the person whom you selected to make truck
recommendations - either Irene Wilson, Craig Lynch or Michael Delaney. Think about
your experience with the Shopping Advisor when asking the questions in this section.
For the following questions, you have 100 chips to allocate between two buying processes:
through a dealer (think of the dealer from whom you last purchased a truck) and using the
Shopping Advisor from TruckTown.
For example in the first question, if you prefer the purchasing process at TruckTown, let's
say, 3 times more than at a dealership, you would allocate 25 points to the Dealer and 75
points to TruckTown:
# of points
25
75
Total= 100
Dealer
TruckTown
It is important to have all the allocated points add up to 100. In this case 25+ 75= 100.
If you had 100 points, how would you allocate them to indicate your overall preference for the
purchasing process. More points indicate greater preference.
# of points
Dealer
TruckTown
Total = 100
If you had 100 points, how would you allocate them to indicate your understanding of how a
recommendation was made. More points indicate greater understanding.
# of points
Dealer
TruckTown
Total = 100
If you had 100 points, how would you allocate to them to indicate the amount of freedom you had
in choosing the type of information you required. More points indicate greater freedom.
# of points
Dealer
TruckTown
Total = 100
If you had 100 points, how would you allocate them to indicate the amount of control over the
information you provided during the process. More points indicate greater control.
# of points
Dealer
TruckTown
Total = 100
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If you had 100 points, how would you allocate them to indicate the objectivity of the information
you received. More points indicate greater objectivity.
# of points
Dealer
TruckTown
Total = 100
If you had 100 points, how would you allocate them to indicate the independence of the source of
information. More points indicate greater independence.
# of points
Dealer
TruckTown
Total = 100
If you had 100 points, how would you allocate them to indicate the trustworthiness of the process.
More points indicate greater trustworthiness.
# of points
Dealer
TruckTown
Total= 100
If you had 100 points, how would you allocate them to indicate the likelihood that you would
consider a purchase based on the information received through this process. More points indicate
greater likelihood.
# of points
Dealer
TruckTown
Total= 100
What did you like about the Shopping Advisor?
What did you dislike about the Shopping Advisor?
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In the second section of the survey, we will ask you some questions about your experience with Ray, the
Design Engineer. Remember, Ray wanted to know more about your requirements so he could design
future trucks that better fit your needs.
Please indicate your agreement with the following statements.
I found the Design Engineer's (Ray's) questions easy to answer.
1
Very
Strongly
Disagree
2
Strongly
Disagree
3
4
5
6
7
Disagree
Neutral
Agree
Strongly
Agree
Very
Strongly
Agree
The Design Engineer's questions were related well to my needs.
1
Very
Strongly
Disagree
2
Strongly
Disagree
3
4
5
6
7
Disagree
Neutral
Agree
Strongly
Agree
Very
Strongly
Agree
I was glad to help the design effort by answering the Design Engineer's questions.
1
Very
Strongly
Disagree
2
Strongly
Disagree
3
4
5
6
7
Disagree
Neutral
Agree
Strongly
Agree
Very
Strongly
Agree
The Design Engineer asked all the necessary questions about my needs and uses.
1
Very
Strongly
Disagree
2
Strongly
Disagree
3
4
5
6
7
Disagree
Neutral
Agree
Strongly
Agree
Very
Strongly
Agree
I feel confident that my input to the Design Engineer will help design better vehicles.
1
Very
Strongly
Disagree
2
Strongly
Disagree
3
4
5
6
7
Disagree
Neutral
Agree
Strongly
Agree
Very
Strongly
Agree
What did you like about the Design Engineer?
What did you dislike about the Design Engineer?
84
In the final section of the survey, we will ask you some questions about the Design Pallet.
Remember, this was the application that allowed you to design your dream truck.
Please indicate your agreement with the following statements.
I found the Design Pallet to be an enjoyable experience.
1
Very
Strongly
Disagree
3
4
5
6
7
Disagree
Neutral
Agree
Strongly
Agree
Very
Strongly
Agree
2
Strongly
Disagree
Using the Design Pallet was similar to playing a game.
1
Very
Strongly
Disagree
3
4
5
6
7
Disagree
Neutral
Agree
Strongly
Agree
Very
Strongly
Agree
2
Strongly
Disagree
I prefer the truck I designed to existing trucks.
1
Very
Strongly
Disagree
3
4
5
6
7
Disagree
Neutral
Agree
Strongly
Agree
Very
Strongly
Agree
2
Strongly
Disagree
The designs were easy to modify.
1
Very
Strongly
Disagree
3
4
5
6
7
Disagree
Neutral
Agree
Strongly
Agree
Very
Strongly
Agree
2
Strongly
Disagree
I approached the Design Pallet as a serious exercise.
1
Very
Strongly
Disagree
3
4
5
6
7
Disagree
Neutral
Agree
Strongly
Agree
Very
Strongly
Agree
2
Strongly
Disagree
I would buy the truck that I designed.
1
Very
Strongly
Disagree
2
Strongly
Disagree
3
4
5
6
7
Disagree
Neutral
Agree
Strongly
Agree
Very
Strongly
Agree
85
I feel confident that my input in the Design Pallet will help design better vehicles.
1
Very
Strongly
Disagree
3
4
5
6
7
Disagree
Neutral
Agree
Strongly
Agree
Very
Strongly
Agree
2
Strongly
Disagree
Which design features are important to keep in the Design Pallet: (check those that you
think are important to be able to modify)
Color
-
Engine size
-
(e.g., 6 cylinder)
Cab style
(e.g., extended)
Number of doors
___
__
(2WD/4WD)
Transmission
type
Hauling capacity
Bed Length
Interior features
(e.g., A/C)
Wheel drive
Steering type
(2 or 4 wheel)
Bed length
___
Number of Cab doors
__
Towing capacity
Body look
(e.g., sport)
Exterior features
(e.g., sunroof)
Is there another feature of truck that you would like to modify?
For which design features would you want more options: (for example, if you check
"colors" then you would want to see more colors available for the truck you designed)
___
Color
__
Steering type
(2 or 4 wheel)
Bed length
Engine size
(e.g., 6 cylinder)
___
Cab style
(e.g., extended)
Number of doors
Bed Length
Interior features
___
Wheel drive
(2WD/4WD)
___
___
Number of Cab doors
Transmission
type
Hauling capacity
-
Body look
Towing capacity
-
(e.g., sport)
Exterior features
(e.g., sunroof)
(e.g., A/C)
What did you like about the Design Pallet?
What did you dislike about the Design Pallet?
86
You're almost done, just one last question. Think of TruckTown in its entirety. If there
were no direct charge for TruckTown, but rather you had to pay more for your truck to
access the service, what is the maximum additional price you would be willing to pay?
More than $200
$175 - 199
$150- 175
_$125 - 149
$100- 124
$75
-
99
___$50-74
___$25-49
$1 -24
__$0
Don't Know
You are all done! Thanks again for visiting Trucktown.
87