DRAPERMETER: UNDERSTANDING ADVERTISING CREATIVITY
THROUGH CONSUMER EYES
By
SANDEEP BEDADALA
SUPERVISORY COMMITTEE:
Angelos Barmpoutis, Chair Seung Hyuk Jang, Member
A PROJECT IN LIEU OF THESIS PRESENTED TO THE COLLEGE OF THE ARTS OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT
OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF ARTS
UNIVERSITY OF FLORIDA
2017
© 2017 SANDEEP BEDADALA
ACKNOWLEDGEMENTS
I would like to thank my mother, father and sister for encouraging me to pursue what I truly love.
I’m forever grateful to the Digital Worlds Institute and the University of Florida for all the
opportunities I have received.
I would like to thank Prof. Angelos Barmpoutis and Prof. Seung Hyuk Jang for their feedback on
this project and mentorship throughout my time at DW.
I’m thankful to my friends Nikhil Tiwari, Mayank Shekhar and Aditya Shirvalkar for helping with
this project.
Finally, this project is dedicated to the fantastic open source community across the world that
selflessly contributes to the development of fellow programmers.
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TABLE OF CONTENTS LIST OF FIGURES & TABLES .................................................................................................... 06
ABSTRACT .................................................................................................................................... 09
CHAPTERS
1. INTRODUCTION ...................................................................................................................... 11
2. REVIEW OF LITERATURE
2.1 What is Creativity ................................................................................................................ 14
2.2 What is Advertising Creativity ............................................................................................ 14
2.3 Effects of Creativity on Advertising ................................................................................... 16
2.4 Need for Research in Advertising Creativity ...................................................................... 17
2.5 Creative Product Semantic Scale ........................................................................................ 18
2.6 Consensual Assessment Technique ..................................................................................... 20
2.7 Current Trends in Survey Design ........................................................................................ 20
3. METHODOLOGY
3.1 Research Design .................................................................................................................. 23
3.2 Product Selection ................................................................................................................ 24
3.3 Survey Type ........................................................................................................................ 26
4. BUILDING THE INSTRUMENT
4.1 UX in Survey Design
4.1.1 UX Design Process .................................................................................................... 28
4.1.2 User Flows ................................................................................................................ 32
5
4.1.3 Branding and UI Design ............................................................................................ 36
4.2 Development
4.2.1 Technology Stack ...................................................................................................... 39
4.2.2 Project Setup ............................................................................................................. 42
4.2.3 Usability Tests ........................................................................................................... 51
5. FINDINGS
5.1 Research Protocol ............................................................................................................... 55
5.2 Data Analysis ...................................................................................................................... 55
5.3 Visualizing the Results ....................................................................................................... 62
6. CONCLUSION
6.1 Learnings ............................................................................................................................. 65
6.2 Future improvements .......................................................................................................... 66
7. LIST OF REFERENCES AND RESOURCES .......................................................................... 67
8. BIOGRAPHICAL SKETCH ...................................................................................................... 72
6
LIST OF FIGURES AND TABLES
Figure Page
1-1 Three-dimensional model of Creativity .................................................................................. 11
3-1 Logos for Cannes Lions and The One Show .......................................................................... 21
3-2 Ad1: “Millions of Images. Endless Possibilities” .................................................................. 21
3-3 Ad2: “Equal Pay Billionaires: Marcia Zuckerberg” ............................................................... 21
3-4 Ad3: “Perfect Traction” ............................................................................................................. 21
3-5 Five point Likert scale ............................................................................................................ 21
4-1 Don Draper from Mad Men .................................................................................................... 21
4-2 Interactive survey ‘How many slaves work for you?’ ................................................................
4-3 How many households are like yours? .......................................................................................
4-4 How Y’all, Youse and You Guys Talk .......................................................................................
4-5 User flow diagram .................................................................................................................. 26
4-6 Home page wireframe ............................................................................................................ 26
4-7 About page wireframe ............................................................................................................ 26
4-8 Survey page wireframe ........................................................................................................... 27
4-9 Drapermeter logo .................................................................................................................... 27
4-10 Radio button responses illustration ....................................................................................... 28
4-11 Cityscape illustration ............................................................................................................ 29
4-12 Home page mockup .............................................................................................................. 31
4-13 About page mockup .............................................................................................................. 32
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4-14 Survey page mockup ............................................................................................................ 38
4-15 MEAN stack diagram ........................................................................................................... 41
4-16 MVC web architecture .......................................................................................................... 41
4-17 Views in Drapermeter ........................................................................................................... 41
4-18 Rotating slider UI ................................................................................................................. 41
4-19 Layout A ............................................................................................................................... 41
4-20 Layout B ............................................................................................................................... 41
5-1 Age Demographics ................................................................................................................. 44
5-2 Gender Demographics ............................................................................................................ 45
5-3 Correlation between Novelty and composite Ad score .......................................................... 49
5-4 Correlation between Elaboration and composite Ad score .................................................... 49
5-5 Correlation between Style and composite Ad score ............................................................... 50
5-6 Visualization patterns ............................................................................................................. 51
5-7 Visualization pattern for each advertisement ......................................................................... 52
Table Page
2-1 Three-dimensional model of Creativity .................................................................................. 18
4-1 User Experience in Survey Design ......................................................................................... 42
4-2 A/B testing between layouts ................................................................................................... 42
4-3 A/B test results for layout A ................................................................................................... 43
4-4 A/B test results for layout B ................................................................................................... 43
5-1 Mean values for each question in the survey .......................................................................... 46
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5-2 Mean values and Standard deviation for each ad in the survey .............................................. 47
5-3 Mean of individual scales vs total mean score ....................................................................... 48
9
Summary of Project in Lieu of Thesis
Presented to the College of the Arts of the University of Florida
in Partial Fulfillment of the Requirements for the
Degree of Master of Arts
DRAPERMETER: SURVEY WEB APPLICATION TO VALIDATE THE
CREATIVE PRODUCT SEMANTIC SCALE (CPSS)
By
Sandeep Bedadala
August 2017
Chair: Angelos Barmpoutis
Major: Digital Arts and Sciences
Drapermeter is a web survey application built to validate the Creative Product Semantic Scale
(CPSS) – a theory proposed to measure creativity in products and services by Dr. Susan P Besemer.
Although creativity research isn’t new, the application of this research to a particular industry
is still emerging and in the existing research, only a few are empirical in nature. One such research
states “in all, research on advertising creativity is limited, abstract and fairly recent in suggesting
that this advertising dimension deserved additional investigation” [1]
Creativity further affects purchasing intentions [2]. Advertisers and marketers understand
the role of creativity in advertising and to get the attention of consumers, you should do something
that creates a positive effect and increases purchasing intentions.
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To facilitate a practical framework for understanding how creativity is perceived by
consumers, an instrument has to be built that will record the consumer’s attitude towards a particular
advertisement. Simplest form of data collection involves a form given to the user to make his choice
but this traditional way of data collection through forms, either physical or digital results in negative
respondent behavior such as speeding, random responding, premature termination, and lack of
attention [28] [29] [30]. This is the other dimension of this research i.e., achieving high quality in
the data collected by providing an innovative interface to the user that almost gamifies the survey
experience. This way of data collection by providing a novel user experience is proven to achieve
accurate survey results [31][32]. This high level of user experience is achieved by replacing the
typical text based questionnaires with images [38]. This app integrates UI elements from award
winning web interactives like the ‘Slavery Foot Print survey’ [40] and some of the gamification
methods of the ‘SciencOmat’ application [38].
Based on the Creative Product Semantic Scale (CPSS), a three-dimensional model of
creativity in Advertising is developed with Novelty, Elaboration and Style being the three
dimensions. Advertisements from different industry festivals like the Cannes Lions and One Show
Awards are collected and are rated by the survey participants on the aforementioned scales i.e.,
Novelty, Elaboration and Style. This application was deployed on a web server and data is collected
from participants across the University of Florida. Finally, the collected data is analyzed to see
patterns in the attitudes of different groups of participants based on their gender, age etc., and the
results are compared with how the respective advertisement performed at industry award shows.
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1. INTRODUCTION
A brand’s attitude towards advertising is reflected in its advertising budget (Ad spending). Large
budgets are often associated with larger market share but as it is proven time and again, even a small
brand with little advertising budgets make strong and long-lasting connections with consumers
through creative campaigns. While there have been good number of studies on measurement of this
effectiveness, the number of studies on what makes up the creativity that influences this effectiveness
is very limited.
Creativity is often used synonymously with the advertising industry and advertising award
ceremonies are called creativity festivals. The way advertisements are judged at these festivals is a
matter of debate and this method of creativity testing is given the name of ‘Consensual Assessment
Technique (CAT)’ [33]. Essentially this test comprises of experienced judges assessing creative
work individually and in isolation. But CAT is often criticized for not considering the impact of
creativity on the consumer attitude and that the judgement is prone to bias. This bias may be due to
many factors including cultural and educational backgrounds and expectations.
This lack of agreement on existing methods led to further research trying to understand
Creativity at a lower, specific level rather than as an abstract entity. Questions like ‘Can creativity
be broken down into multiple dimensions?’, ‘What are psychological effects of creativity?’, ‘Does
creativity promote positive attitude and increase purchase intentions, ‘Is there a measurable relation
between an Advertisement’s creativity and the product’s sales performance?’.
Even though many researchers tried to answer these questions, one research that really stood
out and tried to answer the important question of what makes up Creativity was carried by Bessemer
and Triffenfer (1981). They developed a model called Creative Product Analysis Matrix (CPAM)
which was later modified into the Creative Product Semantic Scale (CPSS) [3] in 1989. This model
evaluates creativity in a product or service based on three scales namely Novelty, Resolution (
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Elaboration & Synthesis) and Style. Measured on these three scales, it is decided how creative a
product is or if a product is creative or not.
Fig 1-1. Three-dimensional model of Creativity
Considering an advertisement as a product, this model of measuring creativity is employed in the
current research.
Novelty:
The first of three dimensions is Novelty. Any product or service or action or person is said to be
creative if the intended action of the product or the person or the service holds a novel or original
idea, an idea that has never been used before [3]. For example, when mobile phones first came out,
a phone that can do more than just calling and texting like playing games is deemed to be creative
in nature. This kind of creativity is often associated with innovation since innovation is solving
problems through creative thinking and a creative or innovative product is more profitable than a
regular product and attracts customers.
The traits of a novel product are Surprising, which means the product presents unexpected
and unanticipated experience to the user, Original which means the product, whether in terms of the
Novelty
Resolution Elaboration & Synthesis
CREATIVITY
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solution or in terms of the approach to the solution stands out from products in the similar category.
Resolution (Elaboration & Synthesis):
Resolution is the second dimension in the CPSS model of creativity. Resolution is the degree to
which a product meets the needs of a customer or fits the needs of a situation. A product or service
that is novel and original but doesn’t fit into the user needs is not creative. A product is deemed to
be creative if and only if it effectively solves the problem it is designed for in the first place.
Being Logical, Useful, and Valuable are the three main traits of a product with high degree
of Resolution. In the context of advertising, an advertisement is logical if the story or thought that is
conveyed in promoting a product is clear and easily understandable. The advertisement is said to be
Useful if the concept is relevant to the product it is promoting and motivates the user to buy the
product [3]. Finally, the advertisement is Valuable if it makes that emotional connection with the
consumer and put the product in a stronger position among its peers.
Style:
Style is the last of three dimensions in the CPSS model of measuring creativity. Style is often the
most subjective among all the three creative scales since style is very subjective and dependent on
different factors. Style measures the craft in a product. A product which is highly creative on the
Style scale combines the above two scales i.e., Novelty and Resolution to produce a refined coherent
product.
Measuring units of Style is different for different creative products. In this research, the
product is an Advertisement hence different measuring units of Style can be Typography, Color
palette, efficient usage of CGI etc.
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2. REVIEW OF LITERATURE
2.1 What is Creativity?
The word Creativity comes from the Latin term creō which means “to create or make” and its
derivational suffix also come from Latin [4] but the modern definition of creativity has undergone
several changes before it evolved into something as Michael Mumford’s defines “creativity involves
the production of novel, useful products” [5]. There are hundreds of different definitions for
creativity found in literature with each definition holding true across different disciplines like
Psychology, Cognitive sciences, Education, Philosophy, Technology, Theology, Sociology,
Linguistics, Business, Economics, Arts etc.,
All the different theories on Creativity can be categorized based on their focus. This focus is
identified through what is commonly known as “Four Ps” – Process, Product, Person and Place”.
Theories that are focused on the cognitive process behind producing new ideas fall under Creative
Process category. JP Guilford’s early studies on Divergent Thinking are foundation for the study of
Creativity Process. Theories that focus on the product attempt to measure creativity in people or
products. This research falls under Creative Product category. Creative Person studies focus on the
nature and habits of the creative person like intellectual habits, behavior, expertise etc., and finally
Creative Place focuses on understanding the best circumstances under which creativity flourishes
including the degrees of autonomy, access to resources and the nature of gatekeepers [6].
2.2 What is Advertising Creativity?
Creativity in the context of Advertising as approached by the legendary advertising executive Leo
Burnette is “the art of establishing new and meaningful relationships between previously unrelated
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things in a manner that is relevant, believable, and in good taste, but which somehow presents the
product in a fresh and new light”. [7]
Applying the four frameworks to understand Creativity, i.e., Product, Process, Person and
Place to the study of Advertising Creativity, we can break the different studies into the following
categories [34].
a) Place – studies on where Creative Advertisements are produced:
There is an inherent reason why even today the Advertisement industry works in agency-client
model. Organizations are more systematic and run on long term strategies unlike Advertising
Agencies which run on spontaneous and creative approaches. Organizations undertake agency
reviews to hire an agency to produce campaigns that meet their goals and fit in their budgets. This
agency review is an example of Place oriented creativity.
b) Person – studies on who produces Creative Advertisements:
This framework especially holds true for creative industries like Design, Fashion, Films and Music.
If the goal of an Advertisement is to sell then the best work comes from someone who is considered
the best in their trade. Studying the work habits of these people can produce results that can in turn
explain the reason behind why an advertisement is well received or campaign became successful.
c) Process – studies on how Creative Advertisements are produced:
Producing an ad that is creative doesn’t involve any magic sauce. These studies try to look closely
at successful advertisement and what went into their making. A process presents a clear picture of
the lows and highs that are involved in producing a creative product.
d) Product – studies on what a Creative Advertisement is:
These studies are aimed at understanding what a Creative Advertisement is rather than who, how or
where the advertisement is produced. This paper falls under this category of Advertising Creativity
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research where already produced advertisements are examined to qualify or disqualify them from
being called Creative.
2.3 Effects of Creativity on Advertising
The New Oxford American Dictionary describes defines Advertising as “the activity or profession
of producing advertisements for commercial products or services” in order to “describe or draw
attention (to a product, service, or event) in a public medium in order to promote sales or attendance”.
Total Ad spending worldwide is projected to jump 5.7% from $513.07B in 2015 to $573.36B in
2017. Research over the years has found that advertising has a direct effect on firm performance,
such as sales [8], profit [9], brand equity [10] and firm value [11]. Indirectly, by increased brand
equity, price premiums and lower price sensitivity [12], contribute to greater product differentiation
[13], and work as a protection against substitute products. [14]
In today’s media landscape, advertisers face many challenges in gaining consumers attention.
Advertising in general holds a negative connotation among consumers because of the excess of
advertisements they are exposed to. Digital advertising unlike TV or radio or print, being a user
centric programming experience allows for either skipping an Ad or disabling Ads completely in a
website by using ad-blocking technology. This is a huge challenge as the digital advertising
dominates other platforms in ad spending. Creativity is the most viable solution for this problem.
Advertising executives during its golden age like David Oglivy and Bill Bernbach supported and
promoted Creativity in Advertising and over the years Advertising has become synonymous with
creativity. Ad industry shows are dubbed creativity festivals and Advertising is suggested as a career
path for people with the creative flair.
The Gunn Report, an annual publication on award winning work in the Ad industry and IPA’s
17
(Institute of Practitioners in Advertising) Advertising Effectiveness Report – estimate that winning
campaigns at industry shows are on an average eleven times more efficient (higher impact on market
share for additional advertising spending) compared to non-awarded campaigns. Academic research
also indicated that an Advertisement that is deemed to be highly creative outperforms other
Advertisements that lack the creative aspect. [15]
2.4 Need for research in Advertising Creativity:
Majority of the research on Advertising Creativity is focused on Production framework defined
above. Getting insights into the materials and processes that will produce creative content even
though may help in refining and advancing the production qualities in advertising but this research
doesn’t take into consideration all the different groups that are involved in the advertising ecosystem
including consumers and professionals. This thesis focuses on advertising creativity research that
refines the understanding of what creativity is with respect to an advertisement, how different groups
qualify an advertisement as creative.
The psychometric approach of analyzing a creative product or service is said to be “the
starting point, indeed the bedrock of all studies of creativity” [16]. MacKinnon’s theory argues that
irrespective of which framework of Advertising Creativity is under research i.e., the place, person
or process, one must still assess in terms of the product [16].
Several methodologies and instruments have been devised based on theoretical models to
assess creative products. Taylor’s 1975 Creative Product Inventory model evaluates creativity in
products using seven criteria: “generation, the extent to which it generates or produces new ideas;
reformulation, the extent to which the product introduces significant change or modification in
oneself or others; originality, the degree of the product’s usefulness, uncommonness, or statistical
18
infrequency; relevancy, the extent to which the product satisfactorily provides a solution to a
problem; hedonics, the valence or degree of attraction the product commands; complexity, the degree
of range, depth, scope, or intricacy of the information contained in the product; and condensation,
the degree to which the product simplifies, unifies, and integrates” [17]
2.5 Creative Product Semantic Scale
Besemer & Treffinger (1981) attempted to synthesize the characteristics of previous studies and
proposed the Creative Product Analysis Matrix (CPAM). This model attributes creativity in any
product, tangible or intangible into three independent but related scales. These scales are (1) Novelty
(2) Resolution and (3) Elaboration and Synthesis. Novelty refers to the newness of the product in
terms of concepts, techniques, methods, and materials used to make the product. The resolution of a
product indicated the appropriateness of a solution to the given problem and elaboration and
synthesis measure the craft or style of the product [18] [19] [20]. The attributes of each scale in this
model are discussed in the table below:
Table 2-1. Creative Product Analysis Matrix (Besemer & Treffinger, 1981, p. 164; Besemer, 2003)
Novelty Resolution Style
The extent of newness in a
product; in terms of the
number and extent of new
processes, new techniques,
new measures, new concepts
including; in terms of the
newness of the product both in
and out of the field.
Surprise:
How well the product works,
functions, and does what it is
supposed to do. The degree to
which the product fits or
meets the needs of the
problematic situation.
Logical:
The product or solution
The degree to which the
product combines unlike
elements into a refined,
developed, coherent whole,
statement or unit.
Organic:
The product has a sense of
wholeness or completeness
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The product presents
unexpected or unanticipated
information to the user,
listener, or viewer.
Original:
The product is unusual or
infrequently seen in a
universe of products made by
people with similar
experience and training.
follows the acceptable and
understood rules for the
discipline.
Useful :
The product has clear,
practical applications.
Valuable:
The product is judged worthy
because it fills a financial,
physical, social, or
psychological need.
Understandable
The product is presented in a
communicative, self-
disclosing way, which is
“user-friendly.”
about it. All the parts “work
well” together.
Well-Crafted:
The product has been worked
and reworked with care to
develop it to its highest
possible level for this
point in time.
Elegant:
The product shows a solution
that is expressed in a refined,
understated way.
The CPAM model was later modified into the Creative Product Semantic Scale (CPSS) in 1987.
CPSS measured creativity on these three scales. Over the years many studies including this one have
used CPSS to assess the creativity in a product but each of these studies modified the scales according
to the type of product and the requirements. Cropley and Cropley (2000) applied CPSS to test the
work produced by a class of Engineering students. In addition to the three scales, one more scale
Effectiveness was added. These scales were “Effectiveness (Distance traveled), Novelty (Originality
and surprisingness), Elegance (Understandability and workmanlike finish), and Germinality
(usefulness, ability to open up new perspectives)”.
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2.6 Consensual Assessment Technique
Amabile (1983) developed a theoretical framework for the assessment of creativity called the
Consensual Assessment Technique (CAT). According to this framework: “A product or response
will be judged as creative to the extent that:
1. it is both a novel and appropriate, useful, correct or valuable response to the task
at hand, and
2. the task is heuristic rather than algorithmic” [21]
The Consensual Assessment Technique relies heavily on the subjective judgments of experts within
the domain of the product under evaluation. There are several requirements for this method that
should be mentioned: the judges involved in the assessment process should have some experience
with the domain at hand; judges should make their assessment independently; judges should assess
the product for other dimensions in addition to creativity; judges should rate products relative to one
another on the specific dimensions in question; and each judge should examine the products
randomly and in a different order (Amabile, 1983). use
2.7 Current Trends in Survey Design
Surveys have been in use since the middle ages when clergymen and nobles reported the numbers
and living conditions of the people living in city at the order of their emperors. But the purpose of
employing a survey has changed a lot over the centuries. Today surveys are employed wherever
there is a need for understanding the behavior or attitude of a sample of people. Even though the
nature of the survey changes from industry to industry e.g., surveying in a medical industry involves
careful design of an experiment to ensure that only the intervention (e.g., chemical agent of drug)
influences the test subject. In social science, such control is not possible. Many uncontrollable factors
influence market research studies [37].
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Most of the marketing surveys are intended to gain feedback on a product or service from the
consumer, to understand whether the product meets their expectations or if the customer is satisfied
at the service but this kind of psychometric measurement requires the complete attention of the
respondent or participant. A response that is answered wrongly does greater harm to the purpose of
the survey than having no response. Getting feedback from the participants can be done in many
ways i.e., through questionnaires, one-on-one interviews, observation in a controlled experiment etc.,
but choosing the type of survey depends upon the purpose of survey. In a marketing research to
understand the user’s attitude towards different aspects of the product, a questionnaire is the apt
choice.
Because of the high number of survey requests faced everyday, customers respond to these
requests with a lack of interest and are forced to complete the survey only for the incentives they
gain at the end of the survey. This attitude leads to several challenges like speeding, drop-off, bad
data quality etc. These challenges can be overcome by clever design of surveys and that is the prime
goal for UI design of this project. Providing an interface that is intuitive and novel but not obtrusive
to the goals of the survey, results in high quality data [30]. Surveys that try to measure user’s attitude
through text based questions are proven to be less effective at engaging the participant compared to
a survey with visual aid [38]. To understand what makes for an intuitive UI, existing practices in the
design of surveys by various enterprise and cloud survey applications are studied. Following are the
current trends and differences between the biggest survey generation applications.
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Qualtrics Zoho Surveys Typeform Google Forms
Question Navigation
Multiple pages
Single
page
Single page
Single page
Data Entry Radio Buttons + Text Space
Radio Button + Text Box
Option Button + Text space
Text Box + Radio Button
Style options Yes No No Yes
Multiple Languages
Yes No Yes Yes
Support for Multimedia
Yes No Yes Yes
Studying these different applications gave a broad idea about the state of survey design. One
common trait among all these survey tools is the ability to build large scale surveys. You can scale
your survey to any number of questions and by embedding multimedia but only a few does the job
of creating interest in the user about using the survey. A good example for this is Typeform.
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Typeform provides a layout that is very intuitive and aesthetic by providing custom buttons for data
input and seamlessly embedding multimedia but each of these platforms are not templates. Which
means, we do not get to decide how the interaction between the multimedia and the questions are
laid out and considering that in the current research the requirement is to let participant answer the
same question for each image and not allow for repetition of questions. None of the above reviewed
survey platforms provide that flexibility and going forward with the provided layout, it makes the
questions repeat for each Advertisement which results in undesirable confusion in the layout. Hence,
to best meet the interests of the research, a custom survey interface will be built for this project.
3. METHODOLOGY
3.1 Research Design
Besemer’s CPSS model (1987) will be used to measure creativity on three different scales in a pool
of advertisements. The original three scales are modified to be called as (1) Novelty, (2) Elaboration
and (3) Style. An instrument will be built and participants will be asked to answer 9 questions for
each advertisement using this instrument. Each of these 9 questions belong to one of the three scales
i.e., Novelty, Elaboration and Style. Data collected from this instrument is later followed by some
statistical proceedings including the Alpha coefficient of Cronbach and some statistics regarding the
relation between them. Finally, results from the data analysis of the CPSS model are compared with
the award show results which are determined by experienced professional judges which is based on
the Consensual Assessment Technique. The comparison gives the discrepancy, any between these
two models and the differences between the perception of creativity between consumers and
professionals.
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3.2 Product Selection
A group of Advertisements from various advertising award archive websites including Cannes Lions
and the One Show Awards are selected. Selections include both winners and nominees of these
festivals. Although there are no criteria in the selection of these advertisements, all the selected
advertisements are from the same year and same category i.e., print advertisements. This study can
be applied to any category but for the sake of the instrument that will be built to conduct this test,
the subjects are selected print advertisements from the year 2016 in the print ad category.
Fig 3-1. Logos for Cannes Lions and The One Show
The following are the three advertisements chosen for the final test:
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Fig 3-2. Ad1: “Millions of Images. Endless Possibilities” (Source: Cannes Lions Archives)
Fig 3-3. Ad2: “Equal Pay Billionaires: Marcia Zuckerberg” (Source: Cannes Lions Archives)
26
Fig 3-4. Ad3: “Perfect Traction” (Image courtesy: Digiday)
3.3 Survey Type
There are 9 questions in the survey with 3 questions representing each of the three scales. The
response to these questions is ordinal type data. In ordinal type of data, order of the values is
important but the difference between each one is not definite. For such type of data likert scales are
the right type of survey method and a likert scale of 5 points is employed in the current survey.
Following are the five points on the survey scale
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Fig 3-5. Five point Likert scale
4. BUILDING THE INSTRUMENT
About the name ‘Drapermeter’:
Fig 4-1. Don Draper from Mad Men (Image courtesy AMC Network)
Drapermeter is a portmanteau of the words Draper and Meter. Draper is derived from the name of
the fictional character Don Draper of the popular TV show Mad Men. Don Draper is a creative
director who goes on to become a partner at Sterling Cooper Agency, a fictional advertising agency
in Manhattan, New York. This character in show played by the actor Jon Hamm is known for being
brilliant ad man who brings new clients for the agencies and gains respect from his peers and a
Strongly Agree Agree Neutral Disagree Strongly Disagree
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maverick creative who proposes brilliant and original solutions to creative problems at the agency.
This application is named after this character for the users to associate well with the research problem
i.e., perceptions of advertising creativity.
Although the setting of the TV show is much different from today’s advertising landscape
including radical changes in the advertising content but the importance of producing creative content
and a person who is creative is still held high in this industry and in a way the name of this application
is a tribute to the workers in creative departments in the advertising industry.
4.1 UX in Survey Design
Other than the research hypothesis, the goal of this project is to build a survey platform that has a
novel and innovative User Interface . Self-reported survey data is often the basis for the measure of
effectiveness of a marketing campaign but the drop-off rate, which is the rate at which participants
quit the survey before completion has been steadily increasing because organizations creating these
surveys fail in keeping the users engaged. Studies say that participant engagement is directly
proportional to the quality of the data, since bored or inattentive respondents produce subpar data
quality [22]. Questionnaire length, fatigue effects and response quality revisited. Hence, the goals
for the instrument apart from its main purpose is to possess an interface that will improve the
motivation of the survey participant, subjective preference, and data quality.
4.1.1 UX Design Process:
Forms have been a tool for surveying information since 16th century when Spanish provinces used
questionnaires to standardize interviews and modern day digital surveys also serve the same purpose
of data processing [25]. Even though much have changed regarding the medium through which
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these forms are supplied, the outlook of forms as a bureaucratic and dull is still present. To build an
interactive survey application that holds true different UX design principles and at the same time be
effective in data processing, we will follow some of the Gamification principles proposed by Harms,
Winner, Kappel and Grechenig (2014) [26]. Gamification is “the use of game elements in non-
gaming contexts” [23]. Before jumping into the UI, a thorough understanding of existing trends in
survey design have been studied and researched how to build an UI around the data.
Table 4-1. UX in Survey Design
Good User Experience Design Bad User Experience Design
Empathetic Bureaucratic
Better data quality Bad data quality
Better engagement Eschewing
Reduced speeding Motivates speeding
Reduced drop-off rates High drop-off rates
The UX design for this application involves the following five steps based on the Harms, Winner,
Kappel and Greechenig (2014) theory) [26].
1. Inspiration
2. Aesthetics and Relationships
3. Dynamics and Conversations
4. Mechanics and Appearance
5. Prototyping, Evaluation and Iteration.
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1. Inspiration:
Prior to building the application, existing similar applications are reviewed to collect elements and
understand design choices that went into building such application. These elements act as
“ingredients of great games” [27].
Fig 4-2. Interactive survey ‘How many slaves work for you?’
Fig 4-3. ‘How many households are like yours?’ Fig 4-4. ‘How Y’all, Youse and You Guys Talk’
(Image Courtesy: New York Times, www.nytimes.com)
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The above three surveys engage users by using design elements that generate feedback to responses
and at the same time enhancing the typical form filling experience by adding graphical elements,
animation and color.
2. Aesthetics and Relationships:
At this step, the intended user is studied (i.e., the survey’s target population) and tasks, context should
should be described. Based on this description, goals are set regarding the intended aesthetics i.e.,
the intended emotional responses and user experiences that shall be elicited by the survey [26].
3. Dynamics and Conversations:
Dynamics are the intended parameters under which we want the user to complete the survey. For
example, a timed survey is a parameter that is recommended to not let users provide lengthy answers
in a test and the navigation preferences i.e., whether the questions should be randomized or if the
participant should retain the choice of going back to questions etc., are decided in this layer.
4. Mechanics and Appearance:
This level is where several architecture and design decisions are made for the application. For
example, the choice of the libraries used in the front-end and the color-palette etc. Also at this level,
important design decisions like whether the survey should employ radio buttons or sliders etc., are
also studied.
5. Prototyping, Evaluation and Iteration
In this final stage, a basic application with no business logic that provides a clear understanding of
how the final application works will be built and will be tested with several users to understand
his/her pain points and ease of using the application. Results from this level are later used to build
the final survey.
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4.1.2 User flows:
Target user:
The average participant of this survey is a millennial aged between 18-34. As this experiment is
conducted within the University of Florida and participants are recruited by invitation through email
and social media, it is assumed that the average participant has a good understanding of the web and
using it. This stage of the UX design process falls under the Aesthetics and Relationships layer
discussed previously. Goals of the project and the effort needed from the participant are also
reviewed at this stage. Following the user research, following user persona is developed:
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Goals and frustrations of the user developed in the research phase will be translated into the
application dynamics, this stage is the Dynamics and Conversations layer of the UX design process.
As our user research states, the average participant won’t be interested in participating in a survey
that is too lengthy and non-intuitive. Making the application a regular website means the user can
navigate across the website according to his/her wish but since this is a survey, the application should
decide how and where the participant starts his journey and ends his journey. The following
dynamics are considered for the application:
1. One way navigation
2. Randomize the question but should answer with no preference of order
3. Provide visual feedback for every answer to the question
Considering the above three dynamics, the user’s journey through the application is described in the
following user flow:
Fig 4-5. User flow diagram (View high resolution here)
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There will be three pages in total in the application.
1) Home / Landing Page
2) About Page
3) Survey Page
1) Home/Landing Page:
This is the page the user lands on when the app is launched. From here she/he can navigate to the
About page or the survey page. To access the survey page, the user has needs to agree to participate
by clicking on the corresponding button which launches the survey page and the user can know more
about the project or research but clicking on the ‘About’ button.
Fig 4-6. Home page wireframe
2) About Page
About page has the information regarding the current research and a little background on the Creative
Product Semantic Scale model and its three scales. This page will also have the results of the survey
visualized.
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Fig 4-7. About page wireframe
3) Survey Page
Survey page is where the actual data processing happens. The design of this page has a two-column
layout with the Advertisements being displayed on the left side and the survey quiz on the right side.
The default view of this page has the first of three Ads and the corresponding questions in the layout.
The UI of the quiz after multiple design iterations, came up with the idea of an interactive circle with
nine sliders places along its circumference. Each of these nine sliders is associated with a specific
question in the question box on top of it. The active state of the slider has a dark background and has
the sliding action enabled whereas the rest of the sliders are inactive. When the user clicks next
question, the corresponding slider gets activated and its background changes to dark. Once the user
completes a full cycle, she/he should will click on the next Ad or browse through the different ads
through the buttons on top of the image. Users choice will be submitted only after the submit button
is clicked.
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Fig 4-8. Survey page wireframe
4.1.3 Branding and UI Design
This is the Mechanics and Appearance stage of our application design. At this stage various decisions
regarding the visual style and branding of the application are made and prototyped. The branding
and UI design of this project is also inspired by the Mad Men TV show and the iconography of
advertising’s golden age. All the illustrations embedded in the application are original illustrations.
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Fig 4-9. Drapermeter logo
Fig 4-10. Radio Button Response Faces illustration
Fig 4-11. Cityscape illustration
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Fig 4-12. Home Page Mockup
Fig 4-13. About Page Mockup
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Fig 4-14. Survey Page Mockup
4.2 Development
4.2.1 Technology Stack:
This Web Application is built using MEAN stack which is as a stack of JavaScript frameworks for
both the front-end and back-end. M stands for MongoDB which is a NoSQL database for Node.Js
server, E is Express.Js which is built atop Node.Js to simplify the process of setting up the server
and routes, A stands for Angular which is a front-end framework that offers powerful functionalities
to create re-usable components and thereby allowing for building single page applications. Finally,
Node.Js is the JavaScript based server that that takes JavaScript, which was known as a client side
language to build server side applications. It runs on the local machine and provides access for files,
listen to HTTP requests and sends responses.
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Fig 4-15. MEAN stack diagram
Core components of this web application can be broken down as follows:
1) Back-end (Server and Database)
2) Front-end (Views)
The Server:
As mentioned above this application is built on the MEAN stack which is a JavaScript based Web
technology stack. Node.Js which is the server component in this stack is a web server that can be
built using JavaScript and provides facilitates event based and non-blocking response to client
requests. The main difference between Node Js and other servers like Apache Web Server is, the
former requires language like PHP, Perl etc. whereas Node.Js can be interfaced completely through
JavaScript. It essentially brings the power of JavaScript which was primarily considered as a client
side (browser) language to server side operations.
Setting up the server and building a template for an application for a Node.Js project can be
achieved through various frameworks, modules and utilities. Frameworks create a skeletal shell for
us to write the code or logic whereas modules are similar libraries that we can implement in our
application.
The Database
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MongoDB is a NoSQL database which means we do not use a query language like SQL for data
operations. MongoDB works very well for applications that do not have heavy business logic like
ours. Data in a NoSQL database is stored in JSON objects which stand for JavaScrtipt Object
Notation unlike in a relational database like MySQL in which data stored in tables.
MongoDB and Node.Js are often used together because they use JavaScript for their
operation. JSON is quickly becoming the standard data format for web APIs. We use Mongoose as
the ODM (Object Data Modeling) library for MongoDB. Mongoose defines the structure of the data
we store in the database by defining the schema.
The Views:
Angular is a JavaScript framework with powerful features to make the front-end development
streamlined. Angular framework is built on the MVC architecture. MVC which means Model-View-
Controller is a way to structure our application.
An MVC model essentially defines the flow of our website. Model is the database like
MongoDB in this project but it can be any type of database i.e., relational or non-relational. View is
the client or the browser which renders the information it receives from the Server which is the
Controller. The operation of a typical MVC model application can be divided into the following five
steps:
1. The client (browser) makes a request.
2. The Server receives the request and processes the request and send it to the database either
for storage or retrieval depending on the type of request. Here, the server is not storing any
kind of information but only the request.
3. The database responds with the necessary operation either by fetching the data or storing
the data and sends it to the server
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4. At this stage the server uses this information to create web pages as using HTML and CSS.
5. Finally, the pages are sent to the client i.e., which renders them in the browser and these
pages are called the Views.
Fig 4-16. MVC Web Architecture
4.2.2 Project Setup:
Front-end setup:
In Angular Js the MVC architecture is implemented in JavaScript and HTML. The View is defined
in HTML whereas the Model and Controller are implemented in JavaScript.
As previously stated Angular Js provides for building Single Page Applications (SPA). In a
single page application, we inject specific content into a core file depending on the route whereas
considering the case of a non-single page application, multiple AJAX calls ae made through the
XMLHttpRequest object. We achieve this SPA functionality through Angular JS views. Drapermeter
has multiple views and these views are injected according to the state. Below is the structure of views
in our application
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Fig 4-17. Views in Drapermeter
The app has an index.html file that will act as the core view into which we will inject each of the
above views. We launch the app as an angular app by referencing it in our HTML using an angular
directive called ng-app. Angular directives are extensions of HTML with the prefix ng, by specifying
a directive, we control the behavior of the specific element.
The controller does the job of connecting the views to our model or the database. In our application,
the core functionality is to collect data from the survey questions and push to them to a database.
The logic for this is built inside the controller which is written in JavaScript. Any number of
controllers can be used in the application but for the requirements of this application, we just one
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controller i.e., pollController.js. To use this controller in our application, we use another angular
directive called ng-controller. This is specified on the body tag of our index.html file.
We create an app.js files that will hold all the JavaScript related to our front-end which is controlled
by angular by creating a new file called app.js.
We register our controller using the controller function with a name and constructor function
In the above code $http, $scope etc. are called dependencies and this is a software design pattern
known as dependency injection. In Angular dependency injection works across the framework and
it works for functions defined for controller, directive, service, factory etc. In the above code $http
is a core AngularJS service that facilitates communication with remote HTTP servers via the
browser’s XMLHttpRequest object.
For client side routing in our application, we use a framework called UI-Router. In a Single
Page Application (SPA), the browser’s URL is updated as the user navigates through the app.
Conversely, with UI-Router changes in the browser URL drives the navigation through state based
routing. Each component in our view is defined as a state and one state is active at any given time
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and the UI-Router provides for transitions between states.
We have our CSS in a single file named as main.css and it holds the style for all the views in the
page. We use bootstrap framework for the styling of elements like buttons and dropdowns across
the applications.
This completes the basic setup of front-end in our application.
Back-end setup:
Installing packages:
To handle our back-end, which is accessing the data from the front-end and pushing it to a database
we will create a RESTful API.
REST stands for Representational State Transfer (REST). In the simplest form, a RESTful API
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allows us to create a web service providing the following features
• Handle the CRUD (Create, Read, Update, Delete) operations for our data (a poll by user)
• Have a standard URL
• Return JSON data
The following is the structure of our API
- api/
- models/
- pollSchema.js
- node_modules/
- package. json
- index.js
In a Node.Js application, we install various packages (dependencies) requires by the application
using a packet manager like NPM (node packet manager). When we install these packages, their
information is stored in the package. json file
As seen in the image below, the package. json file has the information regarding the various
dependencies needed by our API. Some of the packages we are using here are Express.Js, which is
a framework that configures various server related tasks like setting up middleware to respond to
HTTP requests, defining routing tables to perform various actions based on HTTP method and URL.
We install these modules through NPM as below
$ npm install express –save
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Al the modules installed in our application can be seen in our package.json file below:
Setting up the server:
We setup our server in the index.js file. To establish we need to setup a port address and write a
constructor function that responds to the server requests. This is done as below:
Defining our Routes:
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In this application, we will need the server only to post the data from the survey in our front-end and
get the results. We won’t need the update route or delete route. Below are the POST and GET routes
defined in our application.
Setting up the Database:
After we have all the routes setup and server running, we will setup the database that will store the
data sent from our front-end but before setting up our database we need to write a function that makes
a HTTP POST request from our front-end.
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Fig 4-18. Rotating slider UI
In our front-end we have nine sliders each corresponding to the nine questions that should be
answered for an advertisement. We have three advertisements, so each user generates 27 responses
that we need to store in the data. The data should be stored at the end of third advertisement, where
the user will see the submit button. We use Angular’s ng-click directive and define a function named
submitAns() to determine what happens when the button is clicked.
To collect and store the data in a structure that we want, we need to define a schema for our data
model and for this purpose we use Mongoose. Mongoose models data in key-value pairs. We model
the data, define an instance and export to use it anywhere in our application. A new folder is created
in the app directory titled ‘Models and it has the file ‘pollSchema.js’ that holds the mongoose schema
for our polls. The structure of an instance of our data object should have the following properties:
1. Name of the participant
2. Gender of the participant
3. Participant response on Slider1, Slider2, Slider3.
For the above conditions, we model our schema as below:
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And the final step in setting up the database is to connect the server to a local database or a remote
database (cloud). mLab is used in this project to store the data from the application. mLab is a free
cloud database service that hosts MongoDB databases. Connection to the database is made by
creating a new folder in our directory called database that will have the file ‘db.js’ which is
referenced in our server (index.js) code.
Now, we have a fully functional MEAN application that allows users to submit their choices and
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stores the data for us.
4.2.3 Usability Tests:
This is the final stage of the UX Design process where the mockups developed are undergone a test
with real participants and observed how they are navigating through each layout and what their pain
points are. As previously mentioned, one of the goals of this project is to have an interface that will
improve the drop-off rate from participants. Although the selected participants might complete the
survey they’re recruited for the task it does not confirm a positive experience for the user. After the
initial layout was developed, the front-end was tweaked and two different layouts were developed.
Each layout has a completely different information architecture and navigation but the elements
remained same. Below are the two final layouts:
Fig 4-19. Layout A
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Fig 4-20. Layout B
The following are the prime differences between these two layouts:
Table 4-2. A/B testing between layouts
Property Layout A Layout B
Layout Left - Right Top - Bottom
Accessing Questions Navigation Menu Randomized
Response Layout Radio Buttons Slider Scales
Response Feedback Animated Illustration Animating Text (Response)
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A simple A/B testing was conducted with two participants with the two layouts and the following
parameters were measured:
1. Time taken to complete the survey
2. Number of clicks to complete the survey
3. Noticing visual feedback
A chrome browser plugin was used to record each session of the participant and these are the A/B
test results:
Results for Layout A
Table 4-3. A/B test result for layout A
Property Participant 1 Participant 2
Time taken to complete survey
(HH:MM:SS)
00:03:25 00:3:50
Number of clicks before completion 33 34
Noticed the visual feedback No No
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Results for Layout B
Table 4-4. A/B test result for layout B
Property Participant 1 Participant 2
Time taken to complete survey
(HH:MM:SS)
00:04:47 00:05:11
Number of clicks before completion 47 51
Noticed the visual feedback No No
From the above results, the average time taken to complete the survey using layout A is 3.37 whereas
for layout B it is 4.79 and the average number of clicks to complete for layout A is 33 (rounded to
one decimal) and for layout B it is 49. As the numbers clearly suggest, the average time taken to
complete and the number of clicks to complete the survey for Layout A is less than Layout B and
the former is chosen for the final survey.
Although many of the UI elements used in the final layout are similar elements used in the
different products reviewed previously i.e, Qualtrics, Typeform etc., but the current layout offers
greater flexibility of navigating through each Advertisement and by not having to load questions
each time, less number of requests will be made to the server and this reflects in slower page load
times.
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5. FINDINGS
5.1 Research Protocol
This study is approved by the Institutional Review Board (IRB) at the University of Florida under
the ‘Exempt’ review category. An Exempt review states that there is less than ‘minimal risk’
involved for participants in the study. Under this type of study, records are de-identified and the
survey participation is anonymous. Following are some of the details of the IRB approval:
Principal Investigator: Sai Bedadala
Co-Inverstigator: Angelos Barmpoutis
Approval Date: 4/27/2017
IRB#: IRB201700496
Participant Recruitment:
Participants are recruited by sending an invitation through UF email and the contacts are accessed
through UF listserv. Participants are also contacted through Facebook and Gmail. No prior
knowledge of the research theme is required to participate in this survey. Participants have the right
to withdraw from the study anytime and the participation is voluntary.
De-identification:
Participants only provide the details of their gender and age group. Every participant is identified
through a unique id, an alphanumeric code generated by the database that stores the data. This data
is stored in a secure database and will be erased after the study is completed.
5.2 Data Analysis
The app is deployed through Heroku, a free cloud hosting platform that provides easy deploying by
connecting to Github. By 5PM July 17 2017, a total of 25 people participated in the survey. Majority
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of the participants are contacted through authorized University of Florida networks like UF email
and listserv. Following are the demographics of the survey participants:
Fig 5-1. Age Demographics
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Fig 5-2. Gender Demographics
Data collected through this application is a mix of nominal and ordinal type. Nominal, ordinal,
interval and ratio are the four different types of data according to Steve’s Scale of Measurement
[24]. In the nominal scale we have labels for categories, for example gender, eye color, and race.
Ordinal scale observations are ranked in some measure of magnitude. Numbers assigned to each
level are arranged in an order of magnitude. Examples of ordinal data include letter grades,
rankings etc. Interval scale data also use numbers to indicate order and reflect a meaningful relative
distance between points on the scale. Interval scales do not have an absolute zero. A ratio scale is
similar to an interval scale except that this type of data also has an absolute zero.
Given the type of data collected through this app, the following data analysis procedures are
conducted
1. Mean
2. Standard Deviation
3. Pearson’s r test for Correlation
Mean:
Mean value across each scale for all the participants as well as the mean of all scales for each Ad is
calculated to be as:
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Table 5-1. Mean values for each question in the survey
Question Ad 1 Ad 2 Ad 3
This Ad concept is fresh and unique
3.9
3.7
3.3
This Ad concept is unpredictable
2.7
3.2
3.1
This Ad concept is original (never seen before)
3.7
3.4
3.5
This Ad makes sense (logical)
3.7
3.6
3.9
This Ad is relevant to the brand
3.5
3.0
3.6
This Ad is appropriate to me
2.9
3.2
3.3
This Ad is very well crafted
3.7
3.1
3.3
This Ad has great copy
3.4
3.1
3.1
This Ad was easy to understand/interpret
3.7
3.4
3.7
2) Standard deviation:
Standard deviation shows how spread out the user votes on a particular Ad. In our data we
calculate the standard deviation for the mean scores of the advertisement by each participant. This
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gives us the distribution of how each participant rated each ad. A high value of standard deviation
denotes a big difference between the mean and each value in the dataset whereas a low standard
deviation means, every value in the dataset aligns close to the mean value.
Standard deviation is represented by the letter sigma (σ).
Table 5-2. Mean values and Standard deviation for each ad in the survey
Overall Mean score Standard deviation
Advertisement 1 3.3 0.8
Advertisement 2 3.1 0.5
Advertisement 3 3.2 0.6
As the σ represent, the second advertisement has very low which standard deviation, which means
there is a similar tone among the participants on the creativity of the advertisement whereas the
first advertisement has a high standard deviation score which means there is a varied opinion
among the participants.
3) Correlation
Correlation in statistics is used to test relationships between quantitative variables. It’s a measure
of how two variables are related and the corresponding degree of relation. The study of correlation
between different variables in a dataset is known as correlation analysis.
A correlation coefficient represents the relation between two variables. Any correlation
coefficient is between -1 and 1. When the coefficient is zero, it means there is no relation between
two variables and +1 or -1 means there is a perfect positive or negative correlation between the
variables.
Pearson’s correlation coefficient:
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Pearson’s correlation coefficient also called as Pearson’s r or bivariate correlation, is a measure of
linear correlation between any two variables X and Y. It is represented by ‘r’. +1 value for r mean
a perfect positive linear correlation and -1 means perfect negative linear correlation. It is defined as
the covariance of the two variables divided by the product of their standard deviations. For two
variables X and Y the pearson’s r coefficient can be calculated through the following formula:
In our case, we look for the correlation between each scale and the final mean calculated for each
advertisement in the test. We use Microsoft Excel’s built in data analysis tool to calculate this
correlation.
Table 5-3. Mean of individual scales vs total mean score
Novelty Mean Elaboration Mean Style Mean Mean of All Scales
3.3 3.3 3.1 3.3
3.2 3.1 3.5 3.1
3.5 3 3.2 3.2
From the data analysis it is found that there is zero correlation between the novelty scale and the
final score of the advertisement whereas the elaboration scale and style scale have positive
correlations with the final advertisement score and the style scale correlation coefficient is a near
perfect 0.99. Results from the above analysis are visualized on a scatter plot below:
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Fig 5-3. Correlation between Novelty and composite Ad score
Fig 5-4. Correlation between Elaboration and composite Ad score
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Fig 5-5. Correlation between Style and composite Ad score
5.2 Visualizing the results:
Data is collected for multiple quantitative variables in the survey. These variables are the different
questions in each of the three scales i.e., novelty, elaboration and style in the three - dimensional
model of measuring creativity.
Radar charts also known as Spider charts are a way of comparing multiple quantitative
variables. This type of chart allows us to see the similarity between each variable. This type of chart
is particularly useful for understanding which variables are scoring high or low within a dataset.
Each variable in our dataset is provided an axis that starts from the center and each of the
nine variables in our dataset are arranged radially with equal distance between each other while
maintaining the same scale between all axes. Each of this axis is further divided into 5 points which
represent values from Strongly Agree (1) to Strongly Disagree (5).
To test our hypothesis i.e. validating the results of award winning advertisements by taking
user response, we have several plot areas in the chart that represent different categories. For example,
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a ‘1’ score (Strongly Agree) on every scale from the user is equal to a Gold Winner and
advertisements that score on ‘3’ and less i.e., Neutral to Strongly Agree make up the set of nominated
advertisements.
Fig 5-6. Visualization patterns
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Below are the radar charts for each advertisement based on the survey data.
Fig 5-7. Visualization pattern for each advertisement
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6. CONCLUSION
6.1 Learnings:
Drapermeter attests some existing research with some new findings. The results obtained through
the survey based on the Creative Product Semantic Scale (CPSS) validates theories like the
Creativity Assessment Technique (CAT) and at the same time reveals interesting patterns in terms
of what professionals find as Creative and what regular consumers find as Creative. Advertising is
an industry that depends on the client business hence judging creativity only through the eyes of
experienced professionals and neglecting what consumers think puts the performance of a brand in
the market at stake.
In our data analysis it is found that the third advertisement i.e., Volkswagen’s, which is
labelled as one of the worst advertisements of 2016 in editorial reviews [35] was received positively
by the survey participants and the Forbe’s ad on equal pay received significantly low rating from the
participants but since the majority of the participants in the survey are male, there is a chance of bias
here nevertheless this ad received the lowest score in terms of craft. Here, presenting Mark
Zuckerberg’s face in a coming way might not have resonated well with the participants, majority of
whom are millennials and whom in many survey voted Mark Zuckerberg as an idol [36].
The other side of this research is revealing the importance of User Experience Design in
designing marketing surveys. If consumer survey is at the core of gaining actionable insights into a
customer’s satisfaction with the product, employing a badly designed and typical surveys will
produce bad data which cannot reveal true insights.
In building Drapermeter many of the standard practices in software development and User
Experience Design are followed. Starting with the right information architecture to building user
flows to design a better product and iterating on a product by user testing provided great feedback.
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By building this project I brought together different disciplines that I’m interested in like
Advertising, User Experience Design, User Research, Web Development and Data Visualization. I
learnt a lot in the process, especially in UX Design and Back-end technologies. I hope to apply
learnings from this project to my future projects.
6.2 Future Improvements:
The relevance of this project in the current research in Advertising Creativity is varied. As the entire
media landscape is moving from ‘mobile first’ to ‘AI first’, there is a lot of talk around Creativity
becoming the center for the success of AI. The question is whether the traditional approach to
Creativity in Advertising will prevail in this landscape? Hayao Miyazaki, the legendary Japanese
animator and filmmaker called the animation work produced by an AI system as “an insult to life
itself”. Retaining the power to create works of creativity with us and employing machines to predict
for the performance will save time and money. As the effectiveness of AI is dependent on how well
it is trained to predict certain outcome, say for example the level of novelty in an Ad, the response
of the AI system is nothing but Drapermeter at a very high magnitude of scale where the system
responds based on the collective knowledge it received.
Data also informs better decisions that go into producing a creative product. In a study
conducted by Razorfish analyzing 15 years of entries and awards from Cannes Lions it was found
that:
1. High creative performance is not budget-dependent
2. Award winning creative is not location-dependent
3. High creative performance is a result of multidisciplinary teams
4. Long-term client-agency relationships have double the win rate than average
5. A distinct code of conduct is consistently revealed
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All the above findings, especially reveal that creativity doesn’t involve any magic or magicians but
a thorough study of the practices and habits of successful creative organizations and people will
result in positive performance across all indices i.e., Novelty, Elaboration and Style but what is yet
to be understood and learned if the AI world results in newer dimensions in addition to the above
three that make up Creativity in products and services.
7. LIST OF REFERENCES:
[1] Stone, Gerald, Donna Besser, and Loran E. Lewis (2000). Recall, Liking, and Creativity in TV
Commercials: A New Approach. Journal of Advertising Research, 40 (3), 7–18.
[2] Smith, Robert E., and Xiaojing Yang (2004). Toward a General Theory of Creativity in
Advertising: Examining the Role of Divergence. Marketing Theory, 4 (1–2), 31–58.
[3] Bessemer, S.P., & O'Quin, K (1987). Creative product analysis: Testing a model by developing
a judging instrument. In S. G. lsaksen (Ed.), Frontiers of creativity research: Beyond the basics, 341-
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BIOGRAPHICAL SKETCH
Sandeep Bedadala is originally from Anantapur, India. He graduated with a Bachelors in Electrical
Engineering from Sri Krishnadevaraya University in 2013. He moved to the United States in 2015
to attend Graduate School at the University of Florida. During his undergrad, he co-founded the
official college magazine and served as the President of the student body. Prior to graduate school
he worked with creative agencies and non-profits in India. His academic interests include Visual
Communication, Data Visualization and User Interface Design.
