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Are There Groups of Advertising Creatives with Unique Perceptions of the Usefulness of Marketing Information?

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ARE THERE GROUPS OF ADVERTISING CREATIVES WITH UNIQUE PERCEPTIONS OF THE USEFULNESS OF MARKETING INFORMATION? By PHIL WILLET A THESIS PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLOR IDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF ADVERTISING UNIVERSITY OF FLORIDA 2005

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Copyright 2005 by Phil Willet

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iii ACKNOWLEDGMENTS I would like to give special thanks to Dr. John Sutherland for his guidance and support not only in the writing of this thesis but throughout my entire enrollment at the University of Florida. I would also like to thank my supervisor y committee chair (Dr. Lisa Duke Cornell) and member (Professor Elaine Wagner) for their assistance. Special thanks go to Brian Parker for his contributions.

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iv TABLE OF CONTENTS page ACKNOWLEDGMENTS.................................................................................................iii LIST OF TABLES.............................................................................................................vi ABSTRACT......................................................................................................................v ii CHAPTER 1 INTRODUCTION........................................................................................................1 Overview....................................................................................................................... 1 Research Purpose..........................................................................................................1 Study Outline................................................................................................................2 2 LITERATURE REVIEW.............................................................................................3 Creative Process and Problem Solving.........................................................................3 Creative Process in Advertising....................................................................................8 Creative Brief..............................................................................................................11 Sutherland, Duke, and Abernethy Study....................................................................14 Causal versus Descriptive Method......................................................................15 Research Questions.............................................................................................16 3 METHOD...................................................................................................................17 Cluster Analysis..........................................................................................................17 Profile of Sample........................................................................................................19 Respondent Profile..............................................................................................20 Study Design...............................................................................................................20 Stage 1: Cluster Analysis Objectives..................................................................20 Stage 2: Cluster Analysis Design........................................................................20 Stage 3: Assumptions..........................................................................................21 Stage 4: Driving Clusters.....................................................................................21 Step 1: Hierarchical Cluster Analysis..................................................................21 Step 2: Nonhierarchi cal Cluster Analysis............................................................24

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v 4 RESULTS...................................................................................................................26 Cluster Membership and Usefulness Ratings.............................................................26 Cluster Membership and Primary Duty......................................................................27 Cluster Membership and Current Title.......................................................................28 Cluster Membership and Gender................................................................................29 Cluster Membership and Product Experience............................................................29 Cluster Membership and Years in the Industry..........................................................30 Cluster Membership and Number of Employees........................................................30 5 CONCLUSIONS AND IMPLICATIONS.................................................................32 Summary.....................................................................................................................32 Conclusions.................................................................................................................33 Practical Implications.................................................................................................36 Limitations..................................................................................................................37 Future Research..........................................................................................................37 REFERENCES..................................................................................................................39 BIOGRAPHICAL SKETCH.............................................................................................42

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vi LIST OF TABLES Table page 3-1 Hierarchical cluster analysis: Analysis of Wards agglomeration coefficient.........23 3-2 Difference between cluster centers..........................................................................23 3-3 Two-cluster solution.................................................................................................24 3-4 Twoand threeclustering variable profile: Wards hierarchical cluster analysis....................................................................................................................24 4-1 Cluster by informational usefulness.........................................................................27 4-2 Cluster by primary duty............................................................................................28 4-3 Cluster by current title..............................................................................................28 4-4 Cluster by gender.....................................................................................................29 4-5 Cluster by product experience..................................................................................30 4-6 Cluster by years in the industry................................................................................30 4-7 Cluster by number of employees..............................................................................31

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vii Abstract of Thesis Presen ted to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Master of Advertising ARE THERE GROUPS OF ADVERTISING CREATIVES WITH UNIQUE PERCEPTIONS OF THE USEFULNESS OF MARKETING INFORMATION? By Phil Willet August 2005 Chair: Lisa Duke Cornell Major Department: Advertising Our study was a secondary analysis of data collected by Sutherland, Duke, and Abernethy on market information flow. Their study focused on the usefulness of types of information that art directors, copywriters and creative directors wanted and obtained from clients. Out study aimed to answer 2 questions their study left unanswered: Are there groups of creatives, with uni que perceptions of the usefulness of marketing information? What characteristics differentiate the groups? Units of analysis were of 583 completed questionnaires of employed creatives. The participants included art directors, co pywriters, and creative directors. We did a cluster analysis of perceive d usefulness ratings of 6 marketinginformation items to explore for unique groups. Two unique groups emerged with different perceptions of the usefulness of the marketing information. One group valued all six information items significantly mo re than the other group. A demographic

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viii comparison of the groups revealed that creat ives with more experience perceived the information as more useful than di d the younger, less-experienced creatives.

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1 CHAPTER 1 INTRODUCTION Overview Advertising, as we know it, cannot exist without creativity. Creativity plays a role at many stages of the advertising process, including the planning stage (Zinkhan, 1993). The planning stage is where the informati on gathering begins. Ma ny advertising-agency staff members are involved in this process, but executing the stra tegy and producing the actual campaign it is the responsibility of the copywriters and art directors (Percy, Rossiter, & Elliot, 2001). Those working to create advertisements, must know as much as possible about the brand and its targeted consumers. Creative people are always looking for some spark that will ignite their creative jui ces. The more unique and inte resting the facts about the product, brand, and consumer, the more likely the creatives are to find that big idea (Percy et al., 2001). What information do creative ad agency personnel find useful in creating the advertising that corporations spend billions of dollars on annually? Do these perceptions of the usefulness of the information vary by job title, experience, or agency size? Research Purpose The purpose of our study was to determine if there are groups of creatives with different perceptions of the usefulness of marketing information in the creation of advertising. We took the findings of Suth erland et al. (2004) classified them by demographics, and did a more in-dep th analysis of the results.

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2 To address the question of information usefulness, Sutherland et al. (2004) surveyed art directors, copywr iters, and creative directors to determine if persons from these areas differed in their perceived usef ulness of marketing information. While an important study in the area of creativity, the Suther land et al. (2004) study classified individuals on the basis of j ob title and function. These are demographic or descriptive variables with the assumption that persons in th ese positions (copywriter, art director, creative director) were similar to each other yet different from the other positions. From an analysis of variance pers pective, the assumption was th at each group (copywriters, art directors, copywriters) had less within-g roup variance and more among-group variance. In other words, it assumed that differen ces among copywriters were fewer than differences between copywriters and art directors. According to Haley (1968), the proble m with classifying individuals by demographics is that they are descriptive va riables, not causal vari ables. Attitudes (need and perceived usefulness) cause behavior and are more appropriate for creating homogeneous groups. Some copywriters may sh are perceived usefulness of information with art directors and creative directors, and vice versa. Our study aimed to reanalyze the Sutherland, et al. (2004) data to determine if unique groups of creatives with similar perceived usefulness ratings were not re presented or accounted for by comparing individuals by title or job function (e.g., copywriters, art di rectors, and creative directors). Study Outline Our study addressed 2 research questions: Are there groups of creatives, with uni que perceptions of the usefulness of marketing information? What characteristics differentiate the groups?

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3 CHAPTER 2 LITERATURE REVIEW Creative Process and Problem Solving The process of creating ads has two basi c functions: 1) writing the copy and 2) laying out a print ad or developing a storyboa rd for a commercial. A team of creatives (consisting of a copywriter and art director) usually carries out these creative functions. In smaller agencies, however, the process can rest with a single indi vidual (Percy et al., 2001). Before the 1960s, art directors and copyw riters worked separately. William Bernbach made one of the most significan t changes in the proc ess of creating an advertisement (Young, 2000). In the 1960s, Ber nbach instituted creative teams, each consisting of a copywriter and art director. This revolutionized th e advertising business and is believed to have greatly improved th e quality of creative work. Combining the talents of an art director and a copywriter enhanced the ability to create great and powerful advertising. These teams could work as a sounding board for each others ideas and provide consensual validation (Young, 2000). When we try to define great advertising, we start with adve rtising that sells; but the definition needs to go well beyond this. Great advertising has equ ity because it adds something to the brand or company that is gr eater than immediate sa les. Bill Backer of the Philip Morris Company suggested that thei r annual report should list under assets a line that reads, Marlboro Man estimated valu e--incalculable. Great advertising should have legs, able to work over a long period of time (Morgan, 1984 p.34).

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4 Advertising campaigns, particularly t hose brought about by outstanding creative executions, are usually the result of what is termed a big ide a (Schultz & Barnes, 1994). A winning creative idea (one that sta nds out from the crowd and is memorable), can have an enormous impact on sales. Winni ng creative ideas can greatly increase sales (Buzzell, 1964). Exceptionally creative imagin ative ideas also greatly affect hiring and firing of advertising agencies, and help agencies win creative awards. These awards, in turn, enable agencies to generate more business and hire and retain better staff (Wackman, Salmon, & Salmon, 1986). A small but growing trend among clients is to remunerate agencies based on creative effectiveness. The process of conceiving effective advert ising is idiosyncratic and unpredictable. Creatives use their abilities in somethi ng comparable to an artistic production: copywriters and art directors engage in an intensely personal process (Kover, 1970). Advertising that gets results be ars the imprint of the copywrite r and/or art director and his or her life, needs, and perceptions. Ghiselin (1985) described th e creative mindset as: A certain amount of eccentricity, some exce ss, taint, or tykeishness is often prized by creative minds as a guarantee of ability to move apart or aside, outside. Drugs or alcohol are sometimes used to pr oduce abnormal states to the same end of disrupting the habitual set of the mind, but they are of dubious value, apart from the dangers of addiction, since th eir action reduces judgment, a nd all the activities they provoke are hallucinatory rather than illumi nating. What is needed is control and direction (p. 52). But to think of advertisin g writers and art directors as simply one group may be misleading. Important differences exist in the attitudes and perceptions of these creatives. The ability to easily process between verbal and visual informa tion, distinguishes art directors and copywriters. Art directors and copywriters process various kinds of information in different ways and theref ore see the world differently. A better understanding of the process of producing an effective advertisement is more likely to be

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5 achieved only as a result of a better understanding of the diff erences in how art directors and copywriters operate, rather than through a generic definition of the term creativity (Young, 2000). To understand why some advertis ements are more creative than others, we first need to understand why perceptions of creativity differ from person to person (Koslow, Sasser, & Riordan, 2003). According to Young (2000), two-thirds of cr eatives realiz e that major differences exist in how writers and art directors con ceptualize advertising. Roughly half of the creatives agree that you can tell the differe nce between a commercial created by an art director and a commercial created by a writer. Art directors, more than copywriters, cite originality, uniqueness, and the visual and graphic look of the a dvertising, followed by the attention-getting power and memorability of their favorite commercials. In contrast, copywriters appear to value more highly the persuasiveness of the commercial, particularly in terms of its credibility, its intelligence value, and how well people can relate to it. Creatives with less time in the business are mo re likely to be driven by the desire to be fresh and original (Young, 2000). With experience, however, creatives are more likely to understand the limitati ons of their own professional perceptions in terms of how they make a connection with the consumer. In particular, more experienced creatives appear to become increasingly sensitive to the importance of visual effectiveness in television advertising. As a pos itive note for advertising resear chers, it also appears that with experience some creatives may become mo re accepting of the role that research can play in the creative process (Young, 2000).

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6 It is no surprise that creatives are misunderstood, given the widely dispersed perspective in creativity research in both th e academic and practitioner realms. Creatives often resist testing and will often rebel wh en given too many rules and mandatories. However creatives may be different because they have been insulated more than any other members of the advertising agency. By being buffered from the daily account, budget, and clientele, they may be able to co ntribute to the strategic thinking with an open mind other agency personnel cant. An or iginal creative strate gy, in the form of a creative brief is a stimulus for the creative team in its efforts to create a successful and memorable campaign (Koslow et al., 2003). Agencies, however, that do nothing more th an execute a dull and overused strategy are often left with boring, unconvincing commerc ials that do not cut th rough the clutter. They essentially waste millions in media dollars. To communicate effectively, the creative st aff working on an account needs to have adequate knowledge about the target audience, product, and market. Little research has examined differences in information needs am ong creative directors, art directors, and copywriters (Sutherland et al., 2004). Creating effec tive advertising cannot be accomplished without proper res earch, market, and consumer information--the basis for any type of advertising campaign (Schultz & Barnes, 1994). One of the major inputs received by real-world adve rtising creatives is a clie nt background document which typically contains information about the company, the product and the competitive environment (Johar, Holbrook, & Stern, 2001, p. 4) This information is used to develop the creative brief. Once art directors and copyw riters receive the brief, they begin their brainstorming process. According to Davies (2000), Poor briefing has been attributed to

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7 poor creative solutions because the creatives are less likely to be on the same wavelength of their clients, or alternatively clients ma y be indecisive, causing much unnecessary rework (p. 101). When it comes to the creative process, cr eative ideas often appear mysteriously, even to the person responsible. Nevertheless, in most businesse s, these ideas are the result of organizational processes (Kover & Goldbe rg, 1995). The key orga nizational issue in the creative process is how to balance and op timize the degree of freedom that creatives need to produce the big idea. Studies have s uggested in the management of creativity that supervisors should shift between involvemen t and detachment throughout the process. Control and freedom should be alternated st rategically throughout the process. In the early idea-generation stage, there should be complete freedom, but during the execution process, control is necessary. One of the best-known, proven methods of conceiving creative ideas was developed by Young (1975). Young suggested th at a new idea is nothing more than a new combination of old elements. Young, in his book, A Technique for Producing Ideas developed a five-step appro ach to creating new ideas: First, the gathering of raw materials--both the materials of the immediate problem and the materials which come from the cons tant enrichment of your store of general knowledge Second, the working over of th ese materials in the mind Third, the incubation stage, where someth ing besides the conscious mind does the work of synthesis Fourth, the actual birth of the idea--Eureka! I have it! stage Fifth, the final shaping and developmen t of the idea to practical usefulness

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8 This simple formula Young developed in 1940, while at J. Walter Thompson, has worked for many successful creatives and is one of the best-known and widely accepted methods of developing creative ideas. Creative Process in Advertising The battle between agency creatives and researchers is a ne ver-ending struggle in the advertising business, which represents the conflict of art versus sc ience (Vaughn, 1983). Creative people have an innate rebellio usness, which inevitably leads them to dislike taking orders. It is imperative, how ever, for managers to show understanding of creative people and their ideas, but they must retain overall control and make few concessions (West, 1993). Some research has indicated professionals in creative industries (e.g., advertising, architecture, a nd film) tend to be insecure, egotistical, stubborn, rebellious, poor timekeep ers, perfectionists, fame seekers, and not possessing out-of-the-ordinary intelligence (Fletcher, 1988). The difficulty that researchers have in communicating with advertising creatives mirro rs the problem creatives often have in communicating with consumers. Researcher s and creatives sometimes have trouble seeing the point of view of the consumer, w ho, of course, is thei r target audience (Young, 2000). Creatives agree on the most important area of research in creating an advertisement: defining the target audience a nd its needs. However, creatives also believe research is too frequently used to ev aluate advertisements, which, from a creative point of view, is counterpr oductive to the advertising creative process (Kover, 1995). According to Morgan (1984) there are tw o arguments regarding copy testing; copy research stifles creativity and worse yet kills good ideas, while the other argument states

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9 that copy research protects our investment in advertising and allows us to separate the winners from the losers. As an example Morgan (1984) cites the Campbell Soup Companys campaign Soup Is Good Food When the initial commercial in the campaign was copy-tested it was a disaster. The spot received the lowest score ever for a Campbells commercial. Luckily the campaign was already in its west coas t test markets. The commercial was a huge success, creating an awareness level of over 50 percent, exactly what was hoped for. The campaign is one of the most successful ever created. Why did the testing method fail? First the spot was tested for immediate sales, which the commercial was to have little or none. Second, the commercial was designed to be less intrusive in order to wear in because an enormous amount of media dolla rs was spent in a short period. Morgan (1984) goes on to say: If you want to nurture and promote great advertising from your agency your chances of doing so may be increased if you consider the following suggestions: Recognize that advertising is an extremely complex, multidimensional phenomenon and rarely, if ever, are two situations exactly the same. Keep a flexible array of testing methodologi es available so that the specific issues related to the advertising objectives can be addressed. Do not let your testing methodol ogy drive the creative direction. Remember that time and media weight may also be related to a campaigns effectiveness; thus in-market testing may be your best bet to evaluate long-term effectiveness (p. 35). OMalley (1987) suggested a contrasting st yle between those involved in strategy versus those in creativity. He remarked th at successful advertising comes from two contrasting styles of proble m solving--what psychologists call convergent and divergent thinking. Convergent thinking makes deductions and draws logical conclusions from

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10 information, which is what we expect from a strategist. Divergent thinking moves outward from specific information to a more broadly based generalization, which is what we expect from creatives. Pe rcy et al. (2001) stated: Creative executions spring from a creative idea. These creative ideas may come from a variety of sources, and manifest themselves in any number of ways. But in the end, a creative idea must be consiste nt with the communication strategy and brand position. It is very important th at creative thinking does not begin until a creative brief has been agreed upon. It is the creative brief that helps guide the direction the creative execution must take in order to satisfy the strategy. With the creative brief in hand, the cr eative team assigned to the campaign can go to work. (p.238) Hirschman (1989) reports that account executives are focused on fulfilling the clients communications goals. These goals may include building brand awareness and creating favorable attitudes. The advertisemen t is viewed as a vehicle to execute a given marketing strategy to deliver a positive impr ession about the product to the consumer. What an account executive would call an a ppropriate advertisement is one that is consistent with the strategy. The copywriter and art director, howev er, share a different communication goal for the advertisement. Their goals are to demonstrate their own creative talents and express their own aesthe tic viewpoints. For the art director and copywriter, the advertising appr opriateness is associated more with the artistic expression of an advertisement. Because account ex ecutives play more of a boundary-spanning role between client and creatives, they bear mu ch of the frustration and grief over the advertisement when it is too far from the strate gies envisioned by the cl ient. The result is that creatives are stereotyped--often unfai rly--by account executives as being more concerned with winning awards than selli ng the clients product or service.

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11 Creative Brief Once a strategy has been agreed upon, a creativ e brief is prepared. The briefing is a dialog at the start of the cr eative process. A brief is not the ad in longhand but a statement of the problem, whose purpose is to guide and inspire the creative team. It is a statement of purpose, usually not more than one page (Robinson, 1997) A creative brief is a bridge between brilliant strategic thinki ng and effective advertis ing, which is capable of affecting a change both on a rational a nd emotional level in the consumer (Steel, 1998). At many agencies the creation of a creative brief is the re sponsibility of an account planner or executive and others do not partic ipate. However creative people should be active participants as their thinking will im prove not only the quality of the brief but serve as a catalyst in the developm ent of the creative (Steel, 1998). While many agency staff members particip ate in the development of a marketing plan and strategy, the execution of all this work is the responsi bility of the creatives. In most cases, a copywriter and art director join forces to co nceive the big idea. The process starts with a creative brief written by the account planner or executive. Once the brief is agreed upon, it serves as a roadmap for creative ideas (Per cy et al., 2001). The more creative and innovative the brief, the greater chance cr eatives have in developing advertising campaigns that break through the clutter. While many advertising agencies use crea tive briefs, there are arguments for and against briefs Sargeant and West ( 2001). The negatives are as follows: Some successful agencies do not use them Creatives often dont respect them The time involved to create a brief They can often complicate the assignment

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12 The positives are as follows: A brief will probably be created anyway Most great advertising campaigns are based on effective briefs The right kind of brief is can help creatives Creative briefs can help creatives to “sell” their ideas to clients Briefs organize the facts and help manage client expectations is a simple short format One theory on creative briefs is that there is no better brie f than one that essentially says start from scratch or a wide-open brief. This was the direction given to the ad agency Goodby, Silverstein, and Partners, when the California Milk Board approached the agency. All previous attempts to stop the dec line in the consumption of milk had failed. The client essentially said do whatever it ta kes, and it took only two words, got milk. The campaign was ranked by USA Today’s Adtrack as the second most popular in the United States and most importantly reversed the decline in milk consumption (Steel, 1998). While this is not an argument against briefs it does demonstrate that certain situations call for different approaches. Creative briefs can be a valuable tool in defending and selling the creative executions. While the creative can be subj ective, sound research and facts are more likely to be understood by clients, which is why it is necessary to have strong creative briefs supported with solid research. Creative briefs are short concise document s that should be focused, sustainable, measurable, and, most importantly, inspirational. At its lowest level, the document’s aim is to communicate to the creatives the obj ectives of the marketing campaign--both in terms of directly measurable results, such as the number of inquiries to be generated, or

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13 in less measurable performance terms, such as the image to be communicated to the prospect (Sargeant & West, 2001). According to Robinson (1997) in his book How to Plan Advertising objectives and elements of a creative brief should include: Why are you advertising at all? What are the objectives? What is advertising’s role? Who is the advertising aiming to influence? What do you wish to communicate about this brand? Why do you think those it is aimed at will believe it? How do you wish to say it? What tone of voice? What do you think they will say havi ng received this communication? What are you not allowed to convey about this product or must be communicated legally? A good brief according to Steel (1998) accomp lishes three main objectives. First it gives the creative team a realistic view of wh at the advertising is expected to accomplish. Second, it clearly outlines the demographics of the people the advertis ing is to reach and finally it gives a clear direction on the message that is to be communicated to the target audience. Schultz and Barnes ( 1994), in their book Strategic Advertising Campaigns, state a creative brief should be evalua ted for the following traits: Simplicity. Is the strategy easy for all pers ons who will be involved in the approval to understand? Can it be made cl earer, shorter, or better? Specificity. Is it clear, comp lete, and concise? Does it state the advertising problem faced and provides a clear solution? Durability. Will the strategy last a long time? Can it be overcome or offset by competitors quickly or easily? Is it something the advertiser can live with for several years?

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14 Advertisability. Does this strategy offer the creative and media people latitude to develop really outstanding advert ising? Is it too restric tive? In short, can great advertising come out of this strategy? (p. 159) Sutherland, Duke, and Abernethy Study Sutherland et al. (2004) stat e that there are six genera l categories of marketing information critical to the development of a successful advertising campaign: 1) the target audience demographic profile; 2) customer product usage information; 3) product performance information; 4) competitor’s pr oduct performance information; 5) marketing strategy information; and 6) the main selling point supplied by the client. Since all these informational items are im portant to creating successful advertising, we expect that art directors, copywriters, and creative directors will consider this information of great value. We also belie ve art directors, copywriters, or creative directors will perceive differe nces in the usefulness of th ese six types of information. Sutherland et al. (2004) research has determin ed that advertising agencies and creative personnel are often resistant to having the cl ient supply the main selling point. Creatives and agency personnel want and need freedom to determine a main selling point, which is why they are hired. Therefore among the six types of informational items, the main selling point was consid ered the least useful. The best creatives understand the value of good research. They know that its usefulness is limited not only to creating great ads, but as a selling tool when presenting their creative executions. But while it is impor tant to provide essential information to creatives, it is also essential not to overwh elm them with excess re search that can limit the possibilities for getting that big idea. The best account executives and planners provide creatives with the information they need--not all the information they have.

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15 Unfortunately, a number of recent studies ha ve shown that descriptive variables, such as age, sex, income, occupations, a nd race, are generally poor predictors of behavior. Consequently, these descriptive variables are less than optimum bases for segmentation strategies (Frank & Green, 1968). Causal versus Descriptive Method The Sutherland et al. (2004) research relied on descriptive factors, such as job title, comparing art directors to copywriters and cr eative directors. For this reason, we cannot offer an explanation for art directors, copyw riters, and creative directors informational needs. This study identified by causal factors clusters of ar t directors, copywriters, and creative directors with similar informational needs regardless of title. Causal factors give us the ability to make predictions about futu re informational needs based on title, gender, product experience, primary duty, years in the industry and agency size. Identifying segments by causal factors rather than descriptive factors can be called “benefit segmentation.” People are classified into segments in accordance with the benefits they are seeking. They are then compared to all other segments in terms of their demographics, volume of consumption, their brand perceptions, their media habits, their personality and lifestyle, and so forth. This provides us with a deeper understanding of the people who make up each segment (Haley, 1968). Several alternative statistical approach es can be employed, among them the socalled “Q” technique of factor analysis, multi-dimensional scaling, and other distance measures. All these methods re late the ratings of each respo ndent to those of every other respondent, and then these methods seek cluste rs of the individuals with similar rating patterns. If the items related are potential consumer benefits, the clusters that emerge will be groups of people who attach similar degrees of importance to the various benefits.

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16 Whatever the statistical approach selected, the end result of the anal ysis is likely to be between three and seven consumer segments, each representing a potentially productive focal point for marketing efforts. Each segment is identified by the benefits it is seeking. However, it is the total configuration of the benefits sought that differentiates one segment from another (Frank & Green 1968). The benefit segment approach used in this study provided re searchers with new insight into the Sutherland et al. (2004) findings. Using usefulness of information (benefit) segmentation, two homogeneous clus ters were discovere d. For the purpose of this study, information usefulness was determin ed using the six informational items from the Sutherland, Duke, and Abernethy study. The items were ranked on a 1-7 scale with 7 being the most useful. Research Questions The Sutherland et al. (2004) study determin ed the types of in formation creatives desire and obtains from clients. Using clus ter analysis, this research poses the following research questions: RQ1: Are there groups of crea tives, with unique percepti ons of the usefulness of marketing information? RQ2: What characteristics di fferentiate the groups?

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17 CHAPTER 3 METHOD Cluster Analysis Cluster analysis has become a common tool for the marketing researcher. Academic researchers and marketing applica tions researchers rely on the technique for developing empirical groupings of persons, pr oducts, or occasions which may serve as the basis for further analysis. Such a tool is particularly relevant for the emerging discipline of marketing, which still struggles with the problems of how best to group consumers, products, media types, and usage occasions (Punj & Stewart, 1983). Used as an exploratory data analysis tool cluster anal ysis solves classification problems. The object of cluster analysis is to sort cases (people, things, events, and so forth) into groups, or clusters, so that the degree of associat ion is strong between members of the same cluster and weak betw een members of different clusters. Each cluster thus describes, in terms of the da ta collected, the class to which its members belong. It has been found to be a particul arly useful aid to market segmentation, experimentation and product positioning (Saunders, 1994). As with factor analysis, cluster analysis is a tool of discovery. It is an exploratory method that may reveal associations and st ructure in data which, though not previously evident, are nevertheless sensible and usef ul once found. Objects to be clustered are scored on several dimensions and grouped on th e basis of the likene ss of their scores. This results in objects within groups that are more similar to one another than they are to objects in different groups. The gr ouping of similar objects provides a more in-

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18 depth analysis and provides a platform between two extreme views tha t: a) all objects are unique and inviolable and b) the population is homogeneous. These two views represent a poor basis for marketing planning, whethe r the objects are cons umers or products. Cluster analysis can offer a range of alternat ive views that can be useful to marketing management (Saunders 1994). Another important use of cluster analysis is its ability to help seek a better understanding of people’s behaviors by identi fying homogeneous groups. This addresses the need for better classification of relevant characteristics of individuals (Bettman, 1979). Advantages of cluster analysis include Ability to predict. It is cogn itively easier for people to predict behavior or people or objects based on group membership--all becaus e members share similar properties. Group members share certain properties that are common, and it is hoped that the resultant classification will help provide some insight into a research topic. The classification has the effect of reducing the dimensiona lity of a data table by reducing the number of rows (cases). Organizational convenience It has no independent or dependent variable The two complementary features are High internal (within cluster) homogeneity High external (between cluster) heterogeneity There are two types of cluster analysis. The first method is the hierarchical method (binary or categorical) whose al gorithms rebuild the entire hi erarchy of the objects under analysis (the so called tree), whether in an ascending order or in a descending order. The second method is the nonhierarchical/partitioning method: the user pr eviously defines the cluster numbers in which the set of objects under analysis is divided.

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19 The two steps include: 1) creating a table of relative similarities or differences between all objects, and 2) us ing this information to combine the objects into groups. The table of relative similariti es is called a proximities ma trix. The method of combining objects into groups is called a “clustering al gorithm.” The idea is to combine objects, which are similar to one another, into separate groups. To validate this method we use a different sample of split-half. Finally, we profile to see the relationship between cluster membership and other important variables. Profile of Sample Sutherland et al. (2004) conducted a su rvey of 583 practicing creatives who subscribed to Creativity an Advertising Age publica tion and industry standard. The survey produced the following results: 583 completed questionnaires with a 17.0% response rate The response rate for art directors wa s 13.3%, copywriters 21%, and creative directors 16.7% A 17% response rate for an unsolicited, fi rst mailing, which is considered common (Fink, 1995) and comparable to results repo rted in similar large scale marketing surveys (West & Paliwoda, 1996) Sutherland et al. (2004) adjusted each res pondent’s position to reflect his/her most current position. By comparing current posit ion and responsibilities, as indicated on returned questionnaires to current positi on according to the list they purchased, Sutherland et al. identified and reclassified individuals to th eir most current position: art director, copywriter, or creative director. The final adjusted-by-ti tle sample included 131 (22.5%) art directors, 225 (38.6%) copywrite rs, and 227 (38.9%) creative directors.

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20 Respondent Profile Male copywriters were the most co mmon group among the 583 respondents. The group had an average age of 37.2 with an aver age of 13.5 years in the business. Their experience averaged 11.8 years in their position as an art director or copywriter. Male respondents were older (37.8) than females ( 35.8), and they had been in the profession longer (M=14.1) than females (F=12.3). Th ere were no significant differences in numbers of males and females in their primar y jobs as art directors, copywriters, and creative directors. There was a significant difference between copywriters, art directors, and creative directors based on their years in advertisi ng and years in the primary job. While art directors and copywriters were not significantly different in ag e, they were significantly younger than creative directors. In years of experience, crea tive directors had significantly more experience than art direct ors and copywriters, but art directors had more years of experience than copywriters. Study Design Stage 1: Cluster Analysis Objectives The objective of this study was to cluster ad agency creative personnel into groups with similar perceptions of th e usefulness of the six different types of informational needs identified in the Sutherland et al. (2004) re search. Cluster analysis was used to segment the respondents’ ratings as to the usefulness of six different types of information (i.e., variables X1 through X6). Stage 2: Cluster Analysis Design The squared Euclidean distance as the sim ilarity measure was used, given that the set of variables are metric. Distance measur es consider both the data magnitude (e.g., not

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21 very useful versus very useful) and the pa ttern, unlike correlation measures that detect only data patterns (Hair, Tath am, Anderson, & Black, 1998, p. 503). The agglomeration schedule was used to determine the presence of outliers in the dataset. Only two observations had a stage of 0, meaning they never joined a cluster. With only two possible outliers, no observations were deleted in order to preserve the structure of the dataset. Also, standardiza tion of variables is not necessary since all variables are on the same scale. Stage 3: Assumptions In cluster analysis, there are only two important assumptions: sample representativeness and multicollinerarity. Cluste r analysis is not a statistical inference technique. The sample (n=575) is large and considered representative of a wide range of advertising agency personnel. Stage 4: Driving Clusters To achieve the maximum result efficien cy, the researchers performed both hierarchical and nonhierarchical techniques in sequential st eps. Step one employed a hierarchical method to 1) determine the appropr iate number of clusters in the dataset and 2) provide initial seed points for step tw o. Step two employed a nonhierarchical method to fine tune the analysis by us ing the hierarchical results (i.e ., k clusters and centroids) as a basis for generating a final clus ter solution (Hair et al., 1998 p.503). Step 1: Hierarchical Cluster Analysis Our first and most important issue is to determine the final number of interpretable clusters. Ward’s method was chosen because it uses the sum of squares between clusters summed over multiple variables. At each clustering stage, the within-cluster sum of

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22 squares is minimized over the complete set of separate clusters in order to minimize differences within clusters. Additionally Ward’s clustering agglomera tion coefficient is an accurate stopping rule for identifying the correct number of data clusters (Hair et al., 1998). The technique is to look for large increases in the coeffi cient value in the aggl omeration schedule at each stage of the hierarchical procedure. Wh en the clustering algorithm joins two distinct clusters, the result is a large percentage change in the coeffi cient. When a large increase occurs, the prior cluster solution is selected for further analysis because its combination causes a significant decrease in similarity. To identify a stopping point, the percentage change in the coefficient between clusters wa s calculated at each step. The step at which the change diminished was the stopping point This determined a range of cluster solutions that are practical to the proposed research objectives. Our analysis looks for two to five clusters, which is reasonable give n the nature of the respondents. Table 3-1 displays the calculated percentage change in the clustering coefficient for tento twocluster solutions from the hierarchical procedure. Results showed the largest percent change increase occurred when going from two to one cluster (41.3%) and from three to two clusters (21.5%). This indicates that both the two-and three-cluster solutions joined ve ry different clusters and required further analysis for their practical significance as dis tinct groups used for classifying the participants. When we go beyond three clus ters, small coefficient changes indicate homogenous groupings and the stopping point in the present data.

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23 Table 3-1: Hierarchical cluster analysis: An alysis of Ward’s agglomeration coefficient Number of clusters Agglomeration coeffcient Change in coefficient to next level Percentage change in coefficient to next level 10 2418.2 111.7 4.6 9 2529.9 124.7 4.9 8 2654.6 152.6 5.7 7 2807.2 186.9 6.6 6 2994.1 188.7 6.3 5 3182.8 244.3 7.6 4 3427.1 284.8 8.3 3 3711.9 800.8 21.5 2 4512.7 1868.1 41.3 1 6380.8 --Two-Cluster and Three-Cluster Profiles. At this point in the analysis, the objective was not interpretation but rather ensuring cluster di stinctiveness (Hair et al., 1998). Profiles of both the twoand three-cluste r hierarchical solutions act as a guide in the selection of the final cluster solution. Table 3-2 provides the variable profile. Table 3-2: Difference between cluster centers Variable Degree of freedom F value significance Two-cluster solution X1 TA demo profile 1 204.5 .000 X2 product perf. profile 1 358.8 .000 X3 Comp. product perf. info. 1 369.1 .000 X4 Marketing strategy info. 1 324.3 ,000 X5 Customer product usage 1 253.5 .000 X6 Main selling point 1 1 45.7 .000 Three-cluster solution X1 TA demo profile 2 149.2 .000 X2 Product perf. profile 2 253.7 .000 X3 Comp. product perf. info. 2 355.1 .000 X4 Marketing strategy info. 2 269.8 .000 X5 Customer product usage 2 209.5 .000 X6 Main selling point 1 2 80.7 .000

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24 Step 2: Nonhierarchical Cluster Analysis Nonhierarchical cluster analys is methods were used to fine-tune the hierarchical results in step one and get final cluster solutions. Table 3-3: Two-cluster solution Cluster X1 TA demo profile X2 Product perf. info. X3 Competitors product perf. info. X4 Marketing strategy info. X5 Customer product usage info. X6 Main selling point from client Cluster size Final cluster centers 1 5.46 4.88 3.76 5.01 5.01 4.91 200 2 6.73 6.56 6.19 6.57 6.52 6.07 375 Statistical significance of cluster differences F Value 231.12 360.25 625.22 263.89 297.48 86.60 Significance .000 .000 .000 .000 .000 .000 Table 3-4: Twoand threeclustering variable profile: Ward’s hierarchical cluster analysis Cluster TA demo profile Product perf. info. Competitors product perf. info. Marketing strategy info. Customer product usage info. Main selling point from client Cluster size Two-cluster solution 1 5.02 4.22 3.15 4.28 4.49 4.76 99 2 6.55 6.34 5.80 6.39 6.30 5.86 426 Three-cluster solution 1 5.02 4.22 3.15 4.28 4.49 4.76 99 2 6.17 5.89 5.03 5.83 5.80 5.15 216 3 6.87 6.72 6.45 6.86 6.72 6.44 260 Table 3-4 shows two respondent groupings that have unique pe rceptions of the usefulness of all six items. Cluster 1 (n =99) respondents have lower values on each variable, particularly X3 (i.e., competitor’s product performance information), indicating

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25 a group of respondents who place li ttle value in any of the pr esent information as being useful in coming up with crea tive executions as do those re spondents grouped in cluster 2 (n=426). The three-cluster solution results in the sp litting of cluster 2 from the two-cluster solution to form similar groups: cluster 2 (n= 216) and cluster 3 (n= 260). Cluster 1 (n=99) remains stable on each centroid and also th e cluster size when compared to the twocluster solution. However, respondents in cluster 3 rate the usefulness of all six information items only slightly higher those in cluster 2. This demonstrates that these two clusters are not clearly differe ntiated by their perceptions of the usefulness of the six clustering variables. While the stopping rule supports either th e twoor three-cl uster solutions, the variable profiles indicate a similarity between clusters 2 and 3. Given the robustness of the large coefficient change and a very distinct variable profile, th e two-cluster solution demonstrates practical significance as a classi fication system. The remainder of the study (i.e., cluster interpretation, validation, and profile) was th erefore conducted only for the two-cluster solution.

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26 CHAPTER 4 RESULTS Cluster Membership and Usefulness Ratings The results of the cluster an alysis provided evidence of two clusters. A comparison of the perceived usefulness of the marketing informational items revealed that one group had significantly higher usefulness ratings across all six items (table 4-1). A repeated measures analysis of varian ce, which compared usefulness ratings of information items within cluster 1, revealed significant differences. Cluster 1 rated the target audience profile signifi cantly more useful than any other item of information. Marketing strategy information, customer product usage information, and the main selling point provided by the client and the client’s produc t performance information did not have significantly different usefulness ratings. They were equal in usefulness but less than the target audience profile and mo re useful than the competitors’ product performance information. The competitors’ product performance information was significantly less useful than a ll the other information items. Repeated measures of usefulness ratings by cluster 2 also revealed significant differences among the items. Similar to cluster 1, the target audience profile was the most useful item. Marketing strategy information, customer product usage information, and the client’s product performance information were tied at second, less us eful than the target audience and more useful than the main selling point or the competitors’ product performance information. The main selling poi nt was rated the leas t useful behind the competitors’ product performance information.

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27 Table 4-1: Cluster by informational usefulness Cluster 1 Cluster 2 Significance Mean Mean df = 1 Target audience demographic profile 5.461 6.731 F = 231.12, p = .000 Marketing strategy information 5.012 6.572 F =263.882, p = .000 Customer product usage information 5.012 6.522 F = 297.477, p = .000 A main selling point provided by the client 4.912 6.074 F = 86.591, p = .000 Client’s product performance Information 4.882 6.562 F = 360.257, p = .000 Competitors’ Product Performance Information 3.763 6.193 F = 625.222, p = .000 1Indicates rank of usefulness rating. 1 = Most useful. Item s not significantly different from each other are ranked equally, (e.g. marketing strategy information and customer product usage information). Cluster Membership and Primary Duty Table 4-2 profiles the clusters by primar y duty. Primary job duty was defined as your primary job responsibility (i.e. art direction, copywriting or other). This provides evidence that there may be more variance in usefulness of information ratings among copywriters, art directors, and creative directors than between these groups. There was a significant difference (p=.007) in the components of the clusters. Cluster 1 shows a much greater number of copywriters (47.0) than cluster 2 (34.4), while cluster 2 shows a larger number of creative directors (42.7) than cluster 1 (31.0). Art directors showed little difference in numb ers between cluster 1 (22.0) and cluster 2 (22.9).

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28 Table 4-2: Cluster by primary duty Cluster 1 Cluster 2 Total n % n % % Copywriter 94 47.0 129 34.4 38.8 Art director 44 22.0 86 22.9 22.6 Creative director 62 31.0 160 42.7 38.6 Total 200 100.0 375 100.0100.0 Chi square = 9.988, df = 2, p= .007 Cluster Membership and Current Title The clusters (table 4-3) c ontained individuals with diffe rent titles, e.g. copywriters, art directors, and creative directors. The clus ters were composed of all titles, supporting the approach of not using demographics in future research in this area. Table 4-3: Cluster by current title Cluster 1 Cluster 2 Total n % n % % Copywriter 73 36.569 18.4 24.7 Sr. copywriter 25 12.568 18.1 16.2 Art director 37 18.567 17.9 18.1 Sr. art airector 10 5.025 6.7 6.1 Assoc. creative director 15 7.521 5.6 6.3 Creative director 40 20.0125 33.3 28.7 Total 200 100.0375 100.0100.0 Chi square = 29.32, df = 5, p= .000 There was a significant relationship (p= .000) between cluster membership and current position (table 4-3). Current job title was an open-ended quest ion; creatives were

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29 classified by their title. Clus ter 1 had a much greater per centage of copywriters (36.5%) than cluster 2 (18.4%), while cluster 2 has a far greater percentage of creative directors (33.3%) than cluster 1 (20%). Cluster Membership and Gender Table 4-4 shows 74.7% of cluster 1 was male compared to 67.2% males in cluster 2 while 25.3% of cluster 1 was female compared to 32.8% females in cluster 2. There was no significant relationship (p=.061) between gender and cluster membership. Table 4-4: Cluster by gender Cluster 1 Cluster 2 Total n % n % % Males 148 74.7 252 67.269.8 Females 50 25.3 123 32.830.2 Total 198 100.0 375 100.0 573 Chi square = 3.502, df = 1, p= .061 Cluster Membership and Product Experience There was no significant difference (p =488) between product experience and cluster membership as shown in table 4-5. Both clusters 1 and 2 had the most experience in packaged goods, service advertising, and other (i.e. promotions, graphic services, health-care, Hispanic etc). They also both sh ared the least experience in direct response and recruitment advertising.

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30 Table 4-5: Cluster by product experience Cluster 1 Cluster 2 Total Type product n % n % % Packaged goods 53 27.3 87 23.6 24.9 Direct response 22 11.3 38 10.3 10.7 Service advertising 51 26.3 111 30.328.8 Recruitment advertising 5 2.6 4 1.1 1.6 Other 63 32.5 128 34.8 34.0 Total 194 100.0 368 100.0100.0 Chi square = 3.435, df = 4, p= .488 Cluster Membership and Years in the Industry There was a significant difference between cluster 1 and cluster 2 in age, years in the industry, and years in the primary job. Table 4-6 shows age in cluster 2 had a much higher mean (48.12) than cluster 1 (35.02) (p=.0 00). Respondents to years in the industry in cluster 2 had a mean = 14.46, while cluster 1 had a mean = 11.53. Finally in years in the primary job, cluster 2 had a mean =12.72 and cluster 1 had a mean =10.05. Table 4-6: Cluster by years in the industry Cluster 1 Cluster 2 Significance n Mean n Mean df =1 Age 171 35.02 314 48.12 F = 12.99, p = .000 Years in industry 188 11.53 367 14.46 F = 11.61, p =.001 Years in primary Job 177 10.05 328 12.72 F = 13.67, p = 000 Cluster Membership and Number of Employees There was a significant difference (p=.015) in clusters by the number of employees in the company by which respondents were employed, as shown by table 4-7. Cluster 1 had an average of 2,137.99 employees compar ed to cluster 2 with 533.56. Cluster 1

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31 comes from larger organizations while cluster 2 comes from medium and smaller organizations. Table 4-7: Cluster by number of employees Cluster N Mean Std. Deviation 1 111 2137.99 9409.639 2 216 533.56 1693.658 Total 327 1078.18 5687.412 F=5.923, df = 1, p = .015

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32 CHAPTER 5 CONCLUSIONS AND IMPLICATIONS Summary This study was an extension of the resear ch conducted by Sutherland et al. (2004). It seeks to determine if there were groups of creatives with different informational needs. Two different clusters emerged from the study of the perceive d usefulness of the marketing informational ratings. The clusters’ usefulness ratings for the information items were significantly different. Cluster 2 rated all items significantly more useful than cluster 1. A repeated measures analysis of variance comparing usefulness ratings of information items within cluster 1 revealed significant differences. Cl uster 1 rated the target audience profile significantly more useful than a ny other item of information. Marketing strategy information, customer product usage information, and the main selling point provided by the client and the client’s produc t performance information did not have significantly different usefulness ratings. They were equal in usefulness but less than the target audience profile and mo re useful than the competitors’ product performance information. The competitors’ product performance information was significantly less useful than a ll the other information items. Repeated measures of usefulness ratings by cluster 2 also revealed significant differences among the items. Similar to cluster 1, the target audience profile was the most useful item. Marketing strategy information, customer product usage information, and the client’s product performance information were tied at second, less us eful than the target

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33 audience and more useful than the main selling point or the competitors’ product performance information. The main selling poi nt was rated the leas t useful behind the competitors’ product performance information. Demographically, the groups differed by pr imary duty, current title, years in the industry, informational usefulness, and agency size. Some art direct ors, copywriters, and creative directors had similar informational usefulness needs while others had different usefulness needs. The study esta blished that perceived useful ness of information differs, but not necessarily by title or position but by ch aracteristics. Conclusions This analysis was conducted to answer two research questions: RQ 1: Are there groups of creatives, with unique perceptions of the usefulness of marketing information? The study found there were two groups, w ith distinctly different perceived usefulness of information. Composition of groups showed a mix of copywriters, art directors, and creative direct ors in each group. This cont radicts the Sutherland et al. (2004) study that assumes the perceived in formational usefulness differs by title. RQ2: What characteristics differentiate the groups? There was a significant difference between cluster 1 and cluster 2 in age, years in the industry and years in the primary job. In age cluster 2 had a much higher mean (48.12) than cluster 1 (35.02) (p=.000). Responde nts to years in the industry in cluster 2 had a mean = 14.46 while cluster 1 had a mean = 11.53. Finally in years in the primary job, cluster 2 had a mean =12.72 and cluster 1 had a mean =10.05. These results indicate that older creatives with the most experi ence have significantly greater informational needs than less experienced creatives.

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34 The primary duty showed a significant di fference (p=.007) between clusters. Cluster 1 shows a much greater number of copywriters (47.0) than cluster 2 (34.4), while cluster 2 shows a larger number of creative directors (42.7) than cluster 1 (31.0). This suggests that creative director s and art directors have grea ter informational needs than copywriters. A significant relationship existed (p=.000) between cluster membership and current title. Cluster 1 had a much greater percenta ge of copywriters (36.5%) than cluster 2 (18.4%), while cluster 2 had a far greater pe rcentage of creative directors (33.3%) than cluster 1 (20%). This again suggests that creative directors and ar t directors have the greatest informational needs. There was a significant difference in cl usters by the number of ad agency employees. Cluster 1 had an average of 2,137.99 employees compared to cluster 2 with 533.56. Cluster 1 comes from larger agencies while cluster 2 comes from medium to small agencies. Large agency creatives may not be better informed than smaller agency creatives. While large agencies may have bigger budgets, research departments, account planners, and access to more consumer testing, these resources are not being used to their fullest potential. In summary, there was a significant diffe rence between clusters 1 and 2 based on primary duty, current title, age, years in th e industry, years in the primary job, the six informational usefulness ratings, and the number of employees. This study is important for two main reas ons. First and foremost, it suggests the existence of unique groups of creatives based on characteristics not ti tle. Older creatives with more experience found the information more useful than younger, less experienced

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35 creatives. In other words this study sugge sts that creative pe rsonnel’s perceived usefulness of the information var y, but not by job title alone. The study provides evidence that groups di ffer by primary duty, current title, age, years in the industry, years in the primary job, and the number of employees. It is not surprising that more experienced creatives, pa rticularly creative directors, have the greatest informational needs b ecause they are responsible fo r the creative output of the agency. Since they are more experienced, th ey also tend to be older. This analysis showed that the most successful and experienced creatives--those with the title of creative director and the group with the greatest info rmational needs--were in cluster 2, a group that probably has more contact with clients and thus a better unders tanding of research and the role it plays in crea tive solutions. Cluster 1, which consisted of younger, less experienced copywriters and art directors, can use this resear ch to better understand the value of a sound strategy and the creative brief in the advancement of their careers. It also establishes that agency size is a factor in the info rmational process. Creatives working at large agencies with 2,000 or more employees are less likely to value research than those working at agencies with 600 or less employees. This rebuts the argument large agencies often make in pitchi ng new business, that th eir size gives them a distinct advantage in resear ch, often hiring account planners and establishing research departments. Clients can no longe r take for granted that the re search they’re paying for at large agencies is going to use. Creative direct ors at large agencies are not as hands on as creative directors at smaller agencies. They de vote most of their time to the largest most profitable accounts leaving less time to s upervise younger creatives working on smaller, less important accounts. This gi ves younger creatives more freedom in creating ads. In

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36 contrast at smaller agencies the creative dire ctors are more involved and often participate in creating the ads. This study will provide new insights into informational needs and there usefulness to creatives. Perhaps now agency and client personnel will have a new perspective on how research is being used and by whom. Practical Implications Strategy is the key to every successful cam paign. Without it, the campaign is less likely to succeed. What to say is one of th e most important decisions in creating an advertising campaign, but the how to say it, or the advertising execution, also has a major impact (Schultz & Barnes, 1994). Professionals should use the results from this study to assure that the what to say is understood and agreed to by the creative team be fore they start the creative process or the how to say it execution. Informational needs of all creatives not only have to be met but that information must be used. Agency pers onnel can learn from this research that job title does not necessarily indicate the value crea tives put on the information they receive. Surprisingly, the study also suggests that large agencies, which usually have the greatest informational resources (e.g., research departments, larger clients with bigger budgets, account planners, and so forth), are not communicating with the creatives on the usefulness of their information. Large agen cies often use their size and resources as selling points when seeking new business. Ho wever, the study suggests that medium to small agency creatives are better informed and that these agencies are handling the information flow better than larger agencies. Younger creatives need to understand the importance of the development of a sound strategy and how that strategy can he lp in seeking that “big idea.” Younger

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37 creatives often feel the constraints of a cr eative strategy that they believe may inhibit them from coming up with the most creative so lution. Their interest is in establishing a creative reputation within the industry as quickly as possi ble, and they may believe research and testing can hinder that goal. Limitations Some of the limitations of this study include a mailing list limited to subscribers of one publication. Ideally, a broade r base would have made the results more generalizable. While this study addressed the characteris tics of agency creative personnel, it was unable to determine the success of the creativ e executions produced by those surveyed. Were creatives with the mo st informational needs produc ing the most effective ad campaigns? We also do not know the types of clients these creatives handled. Some clients will have different informa tional needs than other clients. Future Research There are several possibilities for future research in determining creatives’ informational needs. This study is based sole ly on quantitative resear ch. A future study applying qualitative methods could reveal mu ch about why informational needs vary and why the value of the information changes. A qualitative study would give us a more indepth understanding of what really goes on in the minds of creatives. Another study could be conducted to determin e if an agency’s client base plays a role in informational needs. Are there cert ain groups of clients w ho share or withhold information on a regular basis from agencies? How greatly do informational needs vary by client? Additionally, the pres ent study identified only the agen cy creative’s informational needs and demographics. Future study coul d determine if there are unique groups in

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38 account services, such as account supervisors, account planners, and account executives. On the client side, are ther e differences among marketing heads, brand managers, or assistant brand managers? Finally, a study to determine if award-winni ng advertising is pr oduced by creatives with the greatest informational needs or creatives working with less information and fewer constraints in coming up with the big id ea. What effect do cr eative briefs have on how creatives conceive their ideas? Do they aid in coming up with the most creative executions?

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39 REFERENCES Bettman, J. R. (1979). An information processing theory of consumer choice Reading, MA: Addison-Wesley. Buzzell, R. D. (1964). Predicting short-term changes in market share as afunction of advertising strategy. Journal of Marketing Research 1 (August) 27-32. Davies, M. A. P. (2000). Using an analytic hierarchy process in advertising creativity. Creativity and Innovation Management 9 (2) 100-108. Fink, A. (1995). The survey handbook Thousand Oaks, CA: Sage Publications. Fletcher, W. (1990). The ma nagement of creativity. International Journal of Advertising 9(1),1-12. Frank, R. E. & Green, P. E. (1968). Numerical taxonomy in marketing analysis: A review article. Journal of Marketing Research V (February), 83-98. Ghiselin, B. (1985). The creative process Berkeley, CA: University of California Press. Hair, J., Tatham, R., Anders on, R. E., Black, W. (1998). Multivariate data analysis New York, NY: Maxwell Macmillian Intl. Haley, R.I. (1968). Benefit segmentation. Journal of Marketing 32, 30-35. Hirschman, E. C. (1989). Role-based mode ls of advertising creation and production. Journal of Advertising 18(4), 42-53. Johar, G. A., Holbrook, M. B., & Stern, B. B. (2001). The role of myth in creative advertising design: theo ry, process and outcome. Journal of Advertising, 30(2), 125. Koslow, S., Sasser, S. L., & Riordan, E. A. (2003). What is creative to whom and why? perceptions in advertising. Journal of Advertising Research 43, 28-39. Kover, A. J. (1970). Creativity and structure in advertising agencies Unpublished Ph.D. dissertation, Yale Univer sity, 1970. New Haven, CT Kover, A. J. (1995). Copywriters, implicit th eories of communications: An exploration. Journal of Consumer Research, 21(4), 30-45.

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40 Kover, A. J., & Goldberg, S. M. (1995). Th e games copywriters play: Conflict, quasicontrol, a new proposal. Journal of Advertising Research, 35(4), 52-62. Morgan, A. I. (1984). Point of view: who’s killing the great advertising campaigns of America? Journal of Advertising Research 24(6), 33-35. O’Malley, J. (1987). Creative briefing. How to plan advertising London: Cassell, 77-85. Percy, L., Rossiter, J. R., & Elliot, R. (2001). Creative execution. Strategic Advertising Management, 15 43-54 Punj, G., & Stewart, D. W. (1983). Cluster an alysis in marketing research: Review and suggestions for application. Journal of Marketing Research 20, 134-148. Robinson, C. (1997). Creativ e briefs and briefings How to plan advertising London: Continuum Books. Sargeant, A., West, D. C. (2001). Creative briefing. Direct & Interactive Marketing 8, 299-233. Saunders, J. (1994). Cluster analysis. Journal of Marketing Management 10, 13-28. Schultz, D. E., & Barnes, B. E. (1994). Strategic advertising campaigns Chicago, IL: NTC Business Books. Steel, J. (1998). Truth, lies and advertising, th e art of account planning New York, NY: Wiley & Sons. Sutherland, J., Duke, L., & Abernethy, A. (2004). A model of marketing information flow. Journal of Advertising 33(4), 39-52 Vaughn, R. L. (1983). Point of view: Creatives versus researchers: Must they be adversaries? Journal of Advertising Research 22(6), 45-48. Wackman, D. B., Salmon, C. T., & Salmon, C.C. (1986). Developi ng an advertising agency-client relationship. Journal of Advertising Research 26(6), 21-29. West, D. C. (1993). Cross-national creative personalities, processes, and agency philosophies. Journal of Advertising Research, 33(5), 53-62 West. D. C., & Palidowa, S. J. (1996). Adve rtising client agency relationships: The decision-making structure of clients. European Journal of Marketing 7, 30-38. Young, C. E. (1975). A technique for producing ideas Lincolnwood, IL: NTC Business Books, 53-54.

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41 Young, C. E. (2000). Creative differences be tween copywriters and art directors. Journal of Advertising Research 40 19-26. Zinkhan, G. M. (1993). Crea tivity in advertising. Journal of Advertising. 22(2), 1-3.

PAGE 50

43 BIOGRAPHICAL SKETCH After deciding to pursue a career in adve rtising, Phil Willet attended the Ringling School of Art and received a certificate in graphic design in 1979. He then moved to New York, working for 15 years at major ad agencies creating print and broadcast advertisements for national and regional accounts. He was employed first as an art director, and then as a creative director. After deciding he would like to teach, Phil returned to college to complete his Bachelor of Science degree at the University of Florida, (UF) in Gainesville receiving his degree in 2002. He received his Master of Advertising degree from UF in May 2005.


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ARE THERE GROUPS OF ADVERTISING CREATIVE
WITH UNIQUE PERCEPTIONS OF THE USEFULNESS OF
MARKETING INFORMATION?
















By

PHIL WILLET


A THESIS PRESENTED TO THE GRADUATE SCHOOL
OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT
OF THE REQUIREMENTS FOR THE DEGREE OF
MASTER OF ADVERTISING

UNIVERSITY OF FLORIDA


2005

































Copyright 2005

by

Phil Willet















ACKNOWLEDGMENTS

I would like to give special thanks to Dr. John Sutherland for his guidance and

support not only in the writing of this thesis, but throughout my entire enrollment at the

University of Florida. I would also like to thank my supervisory committee chair (Dr.

Lisa Duke Cornell) and member (Professor Elaine Wagner) for their assistance. Special

thanks go to Brian Parker for his contributions.
















TABLE OF CONTENTS

page

A C K N O W L E D G M E N T S ................................................................................................. iii

LIST OF TABLES ....................... .... .... .. ................. vi

ABSTRACT .............. ..................... .......... .............. vii

CHAPTER

1 IN TR OD U CTION ............................................... .. ......................... ..

O v e rv ie w ...................................... .................................... ................. .
R research P u rp o se ...................................... .............................. ................. .
Study Outline ...................................... ................................. ......... 2

2 LITERA TURE REVIEW .......................................................... ..............3

Creative Process and Problem Solving..................................................................... 3
Creative Process in A dvertising......................................................... ............... 8
C reativ e B rief. ....................................................................... .. ............. .11
Sutherland, Duke, and Abernethy Study ....................................... ............... 14
Causal versus Descriptive Method .......................................... ...............15
R research Q u estion s .............................. ........................ .. ........ .... ............16

3 M E T H O D .......................................................................... 17

Cluster A analysis .................................................................... ... ....... ...... 17
Profile of Sam ple ....................................................... ................. 19
R espondent Profile .................................... ............... .... ....... 20
S tu d y D e sig n ...................................................................... .. 2 0
Stage 1: Cluster Analysis Objectives ...................................... ............... 20
Stage 2: Cluster A analysis D esign ............................................. ............... 20
Stage 3: A ssum options .......................................... .. .. ..... .......... .....21
Stage 4: D driving C lusters................................ ........................................ 2 1
Step 1: Hierarchical Cluster Analysis....................................... ............... 21
Step 2: N onhierarchical Cluster Analysis............................................... 24









4 R E S U L T S .......................................................... ................ 2 6

Cluster Membership and Usefulness Ratings..........................................................26
Cluster Membership and Primary Duty ......................................... ...............27
Cluster M membership and Current Title ................. ....... ...... ..................... ............... 28
Cluster M membership and Gender ........................ ..... ................... ................. 29
Cluster M membership and Product Experience ................................. ................ 29
Cluster M membership and Years in the Industry .................................. ............... 30
Cluster Membership and Number of Employees................................ .................. 30

5 CONCLUSIONS AND IMPLICATIONS ...................................... ............... 32

S u m m ary ........................ ................. ................
C o n c lu sio n s........................................................................................................... 3 3
Practical Im plications ....................................... .... .... ...............36
L im station s ...................... ............... ....................................................... 37
F utu re R research ................................................................37

R E F E R E N C E S ................................................................39

BIO GR A PH ICA L SK ETCH .......................................................................... ..42
















LIST OF TABLES

Table pge

3-1 Hierarchical cluster analysis: Analysis of Ward's agglomeration coefficient .........23

3-2 D difference betw een cluster centers ............................................... ............... 23

3-3 T w o-clu ster solution ........... .............................................................. .. .... .. ..... .. 24

3-4 Two- and three- clustering variable profile: Ward's hierarchical cluster
a n a ly sis ......... ..... .......... ..................... .......... ......................................... 2 4

4-1 Cluster by informational usefulness.................................... ......................................27

4-2 C lu ster by prim ary duty ......................................... .............................................2 8

4-3 C luster by current title......... ..................... ..................................... ............... 28

4-4 Cluster by gender ......................... ........ .. .. ..... ............... 29

4-5 Cluster by product experience........ .......... ................. .................. 30

4-6 Cluster by years in the industry......... ................. ........................... ............... 30

4-7 Cluster by num ber of em ployees....... ................... ......................... ............... 31















Abstract of Thesis Presented to the Graduate School
of the University of Florida in Partial Fulfillment of the
Requirements for the Degree of Master of Advertising

ARE THERE GROUPS OF ADVERTISING CREATIVE
WITH UNIQUE PERCEPTIONS OF THE USEFULNESS OF
MARKETING INFORMATION?

By

Phil Willet

August 2005

Chair: Lisa Duke Cornell
Major Department: Advertising

Our study was a secondary analysis of data collected by Sutherland, Duke, and

Abernethy on market information flow. Their study focused on the usefulness of types of

information that art directors, copywriters, and creative directors wanted and obtained

from clients. Out study aimed to answer 2 questions their study left unanswered:

* Are there groups of creative, with unique perceptions of the usefulness of
marketing information?

* What characteristics differentiate the groups?

Units of analysis were of 583 completed questionnaires of employed creative. The

participants included art directors, copywriters, and creative directors.

We did a cluster analysis of perceived usefulness ratings of 6 marketing-

information items to explore for unique groups. Two unique groups emerged with

different perceptions of the usefulness of the marketing information. One group valued

all six information items significantly more than the other group. A demographic









comparison of the groups revealed that creative with more experience perceived the

information as more useful than did the younger, less-experienced creative.














CHAPTER 1
INTRODUCTION

Overview

Advertising, as we know it, cannot exist without creativity. Creativity plays a role

at many stages of the advertising process, including the planning stage (Zinkhan, 1993).

The planning stage is where the information gathering begins. Many advertising-agency

staff members are involved in this process, but executing the strategy and producing the

actual campaign it is the responsibility of the copywriters and art directors (Percy,

Rossiter, & Elliot, 2001).

Those working to create advertisements, must know as much as possible about the

brand and its targeted consumers. Creative people are always looking for some spark that

will ignite their creative juices. The more unique and interesting the facts about the

product, brand, and consumer, the more likely the creative are to find that big idea

(Percy et al., 2001).

What information do creative ad agency personnel find useful in creating the

advertising that corporations spend billions of dollars on annually? Do these perceptions

of the usefulness of the information vary by job title, experience, or agency size?

Research Purpose

The purpose of our study was to determine if there are groups of creative with

different perceptions of the usefulness of marketing information in the creation of

advertising. We took the findings of Sutherland et al. (2004) classified them by

demographics, and did a more in-depth analysis of the results.









To address the question of information usefulness, Sutherland et al. (2004)

surveyed art directors, copywriters, and creative directors to determine if persons from

these areas differed in their perceived usefulness of marketing information. While an

important study in the area of creativity, the Sutherland et al. (2004) study classified

individuals on the basis of job title and function. These are demographic or descriptive

variables with the assumption that persons in these positions (copywriter, art director,

creative director) were similar to each other yet different from the other positions. From

an analysis of variance perspective, the assumption was that each group (copywriters, art

directors, copywriters) had less within-group variance and more among-group variance.

In other words, it assumed that differences among copywriters were fewer than

differences between copywriters and art directors.

According to Haley (1968), the problem with classifying individuals by

demographics is that they are descriptive variables, not causal variables. Attitudes (need

and perceived usefulness) cause behavior and are more appropriate for creating

homogeneous groups. Some copywriters may share perceived usefulness of information

with art directors and creative directors, and vice versa. Our study aimed to reanalyze the

Sutherland, et al. (2004) data to determine if unique groups of creative with similar

perceived usefulness ratings were not represented or accounted for by comparing

individuals by title orjob function (e.g., copywriters, art directors, and creative directors).

Study Outline

Our study addressed 2 research questions:

* Are there groups of creative, with unique perceptions of the usefulness of
marketing information?

* What characteristics differentiate the groups?














CHAPTER 2
LITERATURE REVIEW

Creative Process and Problem Solving

The process of creating ads has two basic functions: 1) writing the copy and

2) laying out a print ad or developing a storyboard for a commercial. A team of creative

(consisting of a copywriter and art director) usually carries out these creative functions.

In smaller agencies, however, the process can rest with a single individual (Percy et al.,

2001).

Before the 1960s, art directors and copywriters worked separately. William

Bernbach made one of the most significant changes in the process of creating an

advertisement (Young, 2000). In the 1960s, Bernbach instituted creative teams, each

consisting of a copywriter and art director. This revolutionized the advertising business

and is believed to have greatly improved the quality of creative work. Combining the

talents of an art director and a copywriter enhanced the ability to create great and

powerful advertising. These teams could work as a sounding board for each other's ideas

and provide consensual validation (Young, 2000).

When we try to define great advertising, we start with advertising that sells; but the

definition needs to go well beyond this. Great advertising has equity because it adds

something to the brand or company that is greater than immediate sales. Bill Backer of

the Philip Morris Company suggested that their annual report should list under assets a

line that reads, "Marlboro Man estimated value--incalculable. Great advertising should

have legs, able to work over a long period of time (Morgan, 1984 p.34).









Advertising campaigns, particularly those brought about by outstanding creative

executions, are usually the result of what is termed a "big idea" (Schultz & Barnes,

1994). A winning creative idea (one that stands out from the crowd and is memorable),

can have an enormous impact on sales. Winning creative ideas can greatly increase sales

(Buzzell, 1964). Exceptionally creative imaginative ideas also greatly affect hiring and

firing of advertising agencies, and help agencies win creative awards. These awards, in

turn, enable agencies to generate more business and hire and retain better staff

(Wackman, Salmon, & Salmon, 1986). A small but growing trend among clients is to

remunerate agencies based on creative effectiveness.

The process of conceiving effective advertising is idiosyncratic and unpredictable.

Creatives use their abilities in something comparable to an artistic production:

copywriters and art directors engage in an intensely personal process (Kover, 1970).

Advertising that gets results bears the imprint of the copywriter and/or art director and his

or her life, needs, and perceptions. Ghiselin (1985) described the creative mindset as:

A certain amount of eccentricity, some excess, taint, or "tykeishness" is often
prized by creative minds as a guarantee of ability to move apart or aside, outside.
Drugs or alcohol are sometimes used to produce abnormal states to the same end of
disrupting the habitual set of the mind, but they are of dubious value, apart from the
dangers of addiction, since their action reduces judgment, and all the activities they
provoke are hallucinatory rather than illuminating. What is needed is control and
direction (p. 52).

But to think of advertising writers and art directors as simply one group may be

misleading. Important differences exist in the attitudes and perceptions of these creative.

The ability to easily process between verbal and visual information, distinguishes art

directors and copywriters. Art directors and copywriters process various kinds of

information in different ways and therefore see the world differently. A better

understanding of the process of producing an effective advertisement is more likely to be









achieved only as a result of a better understanding of the differences in how art directors

and copywriters operate, rather than through a generic definition of the term "creativity"

(Young, 2000). To understand why some advertisements are more creative than others,

we first need to understand why perceptions of creativity differ from person to person

(Koslow, Sasser, & Riordan, 2003).

According to Young (2000), two-thirds of creative realize that major differences

exist in how writers and art directors conceptualize advertising. Roughly half of the

creative agree that you can tell the difference between a commercial created by an art

director and a commercial created by a writer. Art directors, more than copywriters, cite

originality, uniqueness, and the visual and graphic look of the advertising, followed by

the attention-getting power and memorability of their favorite commercials. In contrast,

copywriters appear to value more highly the persuasiveness of the commercial,

particularly in terms of its credibility, its intelligence value, and how well people can

relate to it.

Creatives with less time in the business are more likely to be driven by the desire to

be fresh and original (Young, 2000). With experience, however, creative are more

likely to understand the limitations of their own professional perceptions in terms of how

they make a connection with the consumer. In particular, more experienced creative

appear to become increasingly sensitive to the importance of visual effectiveness in

television advertising. As a positive note for advertising researchers, it also appears that

with experience some creative may become more accepting of the role that research can

play in the creative process (Young, 2000).









It is no surprise that creative are misunderstood, given the widely dispersed

perspective in creativity research in both the academic and practitioner realms. Creatives

often resist testing and will often rebel when given too many rules and mandatories.

However creative may be different because they have been insulated more than any

other members of the advertising agency. By being buffered from the daily account,

budget, and clientele, they may be able to contribute to the strategic thinking with an

open mind other agency personnel can't. An original creative strategy, in the form of a

creative brief is a stimulus for the creative team in its efforts to create a successful and

memorable campaign (Koslow et al., 2003).

Agencies, however, that do nothing more than execute a dull and overused strategy

are often left with boring, unconvincing commercials that do not cut through the clutter.

They essentially waste millions in media dollars.

To communicate effectively, the creative staff working on an account needs to have

adequate knowledge about the target audience, product, and market. Little research has

examined differences in information needs among creative directors, art directors, and

copywriters (Sutherland et al., 2004). Creating effective advertising cannot be

accomplished without proper research, market, and consumer information--the basis for

any type of advertising campaign (Schultz & Barnes, 1994). One of "the major inputs

received by real-world advertising creative is a client background document" which

typically contains information about "the company, the product and the competitive

environment" (Johar, Holbrook, & Stern, 2001, p. 4). This information is used to develop

the creative brief. Once art directors and copywriters receive the brief, they begin their

brainstorming process. According to Davies (2000), "Poor briefing has been attributed to









poor creative solutions because the creative are less likely to be on the same wavelength

of their clients, or alternatively clients may be indecisive, causing much unnecessary re-

work (p. 101).

When it comes to the creative process, creative ideas often appear mysteriously,

even to the person responsible. Nevertheless, in most businesses, these ideas are the result

of organizational processes (Kover & Goldberg, 1995). The key organizational issue in

the creative process is how to balance and optimize the degree of freedom that creative

need to produce the big idea. Studies have suggested in the management of creativity that

supervisors should shift between involvement and detachment throughout the process.

Control and freedom should be alternated strategically throughout the process. In the

early idea-generation stage, there should be complete freedom, but during the execution

process, control is necessary.

One of the best-known, proven methods of conceiving creative ideas was

developed by Young (1975). Young suggested that a new idea is nothing more than a

new combination of old elements. Young, in his book, A Technique for Producing Ideas,

developed a five-step approach to creating new ideas:

* First, the gathering of raw materials--both the materials of the immediate problem
and the materials which come from the constant enrichment of your store of general
knowledge

* Second, the working over of these materials in the mind

* Third, the incubation stage, where something besides the conscious mind does the
work of synthesis

* Fourth, the actual birth of the idea--"Eureka! I have it!" stage

* Fifth, the final shaping and development of the idea to practical usefulness









This simple formula Young developed in 1940, while at J. Walter Thompson, has

worked for many successful creative and is one of the best-known and widely accepted

methods of developing creative ideas.

Creative Process in Advertising

The battle between agency creative and researchers is a never-ending struggle in

the advertising business, which represents the conflict of art versus science (Vaughn,

1983). Creative people have an innate rebelliousness, which inevitably leads them to

dislike taking orders. It is imperative, however, for managers to show understanding of

creative people and their ideas, but they must retain overall control and make few

concessions (West, 1993). Some research has indicated professionals in "creative

industries" (e.g., advertising, architecture, and film) tend to be insecure, egotistical,

stubborn, rebellious, poor timekeepers, perfectionists, fame seekers, and not possessing

out-of-the-ordinary intelligence (Fletcher, 1988). The difficulty that researchers have in

communicating with advertising creative mirrors the problem creative often have in

communicating with consumers. Researchers and creative sometimes have trouble

seeing the point of view of the consumer, who, of course, is their target audience (Young,

2000).

Creatives agree on the most important area of research in creating an

advertisement: defining the target audience and its needs. However, creative also

believe research is too frequently used to evaluate advertisements, which, from a creative

point of view, is counterproductive to the advertising creative process (Kover, 1995).

According to Morgan (1984) there are two arguments regarding copy testing; copy

research stifles creativity and worse yet kills good ideas, while the other argument states









that copy research protects our investment in advertising and allows us to separate the

winners from the losers.

As an example Morgan (1984) cites the Campbell Soup Company's campaign Soup

Is GoodFood. When the initial commercial in the campaign was copy-tested it was a

disaster. The spot received the lowest score ever for a Campbell's commercial. Luckily

the campaign was already in its west coast test markets. The commercial was a huge

success, creating an awareness level of over 50 percent, exactly what was hoped for. The

campaign is one of the most successful ever created. Why did the testing method fail?

First the spot was tested for immediate sales, which the commercial was to have little or

none. Second, the commercial was designed to be less intrusive in order to wear in

because an enormous amount of media dollars was spent in a short period. Morgan

(1984) goes on to say:

If you want to nurture and promote great advertising from your agency your

chances of doing so may be increased if you consider the following suggestions:

* Recognize that advertising is an extremely complex, multidimensional
phenomenon and rarely, if ever, are two situations exactly the same.

* Keep a flexible array of testing methodologies available so that the specific issues
related to the advertising objectives can be addressed.

* Do not let your testing methodology drive the creative direction.

* Remember that time and media weight may also be related to a campaign's
effectiveness; thus in-market testing may be your best bet to evaluate long-term
effectiveness (p. 35).

O'Malley (1987) suggested a contrasting style between those involved in strategy

versus those in creativity. He remarked that successful advertising comes from two

contrasting styles of problem solving--what psychologists call convergent and divergent

thinking. Convergent thinking makes deductions and draws logical conclusions from









information, which is what we expect from a strategist. Divergent thinking moves

outward from specific information to a more broadly based generalization, which is what

we expect from creative. Percy et al. (2001) stated:

Creative executions spring from a creative idea. These creative ideas may come
from a variety of sources, and manifest themselves in any number of ways. But in
the end, a creative idea must be consistent with the communication strategy and
brand position. It is very important that creative thinking does not begin until a
creative brief has been agreed upon. It is the creative brief that helps guide the
direction the creative execution must take in order to satisfy the strategy. With the
creative brief in hand, the creative team assigned to the campaign can go to work.
(p.238)

Hirschman (1989) reports that account executives are focused on fulfilling the

client's communications goals. These goals may include building brand awareness and

creating favorable attitudes. The advertisement is viewed as a vehicle to execute a given

marketing strategy to deliver a positive impression about the product to the consumer.

What an account executive would call an appropriate advertisement is one that is

consistent with the strategy. The copywriter and art director, however, share a different

communication goal for the advertisement. Their goals are to demonstrate their own

creative talents and express their own aesthetic viewpoints. For the art director and

copywriter, the advertising appropriateness is associated more with the artistic expression

of an advertisement. Because account executives play more of a boundary-spanning role

between client and creative, they bear much of the frustration and grief over the

advertisement when it is too far from the strategies envisioned by the client. The result is

that creative are stereotyped--often unfairly--by account executives as being more

concerned with winning awards than selling the client's product or service.









Creative Brief

Once a strategy has been agreed upon, a creative brief is prepared. The briefing is a

dialog at the start of the creative process. A brief is not the ad in longhand but a

statement of the problem, whose purpose is to guide and inspire the creative team. It is a

statement of purpose, usually not more than one page (Robinson, 1997). A creative brief

is a bridge between brilliant strategic thinking and effective advertising, which is capable

of affecting a change both on a rational and emotional level in the consumer (Steel,

1998).

At many agencies the creation of a creative brief is the responsibility of an account

planner or executive and others do not participate. However creative people should be

active participants as their thinking will improve not only the quality of the brief but

serve as a catalyst in the development of the creative (Steel, 1998).

While many agency staff members participate in the development of a marketing

plan and strategy, the execution of all this work is the responsibility of the creative. In

most cases, a copywriter and art director join forces to conceive the "big idea." The

process starts with a creative brief written by the account planner or executive. Once the

brief is agreed upon, it serves as a roadmap for creative ideas (Percy et al., 2001). The

more creative and innovative the brief, the greater chance creative have in developing

advertising campaigns that break through the clutter.

While many advertising agencies use creative briefs, there are arguments for and

against briefs Sargeant and West (2001). The negatives are as follows:

* Some successful agencies do not use them
* Creatives often don't respect them
* The time involved to create a brief
* They can often complicate the assignment









The positives are as follows:

* A brief will probably be created anyway

* Most great advertising campaigns are based on effective briefs

* The right kind of brief is can help creative

* Creative briefs can help creative to "sell" their ideas to clients

* Briefs organize the facts and help manage client expectations is a simple short
format

One theory on creative briefs is that there is no better brief than one that essentially

says start from scratch or a wide-open brief. This was the direction given to the ad agency

Goodby, Silverstein, and Partners, when the California Milk Board approached the

agency. All previous attempts to stop the decline in the consumption of milk had failed.

The client essentially said do whatever it takes, and it took only two words, got milk. The

campaign was ranked by USA Today's Adtrack as the second most popular in the United

States and most importantly reversed the decline in milk consumption (Steel, 1998).

While this is not an argument against briefs it does demonstrate that certain situations call

for different approaches.

Creative briefs can be a valuable tool in defending and selling the creative

executions. While the creative can be subjective, sound research and facts are more

likely to be understood by clients, which is why it is necessary to have strong creative

briefs supported with solid research.

Creative briefs are short concise documents that should be focused, sustainable,

measurable, and, most importantly, inspirational. At its lowest level, the document's aim

is to communicate to the creative the objectives of the marketing campaign--both in

terms of directly measurable results, such as the number of inquiries to be generated, or









in less measurable performance terms, such as the image to be communicated to the

prospect (Sargeant & West, 2001).

According to Robinson (1997) in his book How to Plan Advertising, objectives and

elements of a creative brief should include:

* Why are you advertising at all? What are the objectives? What is advertising's
role?

* Who is the advertising aiming to influence?

* What do you wish to communicate about this brand?

* Why do you think those it is aimed at will believe it?

* How do you wish to say it? What tone of voice?

* What do you think they will say having received this communication?

* What are you not allowed to convey about this product or must be communicated
legally?

A good brief according to Steel (1998) accomplishes three main objectives. First it

gives the creative team a realistic view of what the advertising is expected to accomplish.

Second, it clearly outlines the demographics of the people the advertising is to reach and

finally it gives a clear direction on the message that is to be communicated to the target

audience.

Schultz and Barnes (1994), in their book Strategic Advertising Campaigns, state a

creative brief should be evaluated for the following traits:

* Simplicity. Is the strategy easy for all persons who will be involved in the approval
to understand? Can it be made clearer, shorter, or better?

* Specificity. Is it clear, complete, and concise? Does it state the advertising problem
faced and provides a clear solution?

* Durability. Will the strategy last a long time? Can it be overcome or offset by
competitors quickly or easily? Is it something the advertiser can live with for
several years?









* Advertisability. Does this strategy offer the creative and media people latitude to
develop really outstanding advertising? Is it too restrictive? In short, can great
advertising come out of this strategy? (p. 159)

Sutherland, Duke, and Abernethy Study

Sutherland et al. (2004) state that there are six general categories of marketing

information critical to the development of a successful advertising campaign: 1) the target

audience demographic profile; 2) customer product usage information; 3) product

performance information; 4) competitor's product performance information; 5) marketing

strategy information; and 6) the main selling point supplied by the client.

Since all these informational items are important to creating successful advertising,

we expect that art directors, copywriters, and creative directors will consider this

information of great value. We also believe art directors, copywriters, or creative

directors will perceive differences in the usefulness of these six types of information.

Sutherland et al. (2004) research has determined that advertising agencies and creative

personnel are often resistant to having the client supply the main selling point. Creatives

and agency personnel want and need freedom to determine a main selling point, which is

why they are hired. Therefore among the six types of informational items, the main

selling point was considered the least useful.

The best creative understand the value of good research. They know that its

usefulness is limited not only to creating great ads, but as a selling tool when presenting

their creative executions. But while it is important to provide essential information to

creative, it is also essential not to overwhelm them with excess research that can limit

the possibilities for getting that big idea. The best account executives and planners

provide creative with the information they need--not all the information they have.









Unfortunately, a number of recent studies have shown that descriptive variables,

such as age, sex, income, occupations, and race, are generally poor predictors of

behavior. Consequently, these descriptive variables are less than optimum bases for

segmentation strategies (Frank & Green, 1968).

Causal versus Descriptive Method

The Sutherland et al. (2004) research relied on descriptive factors, such as job title,

comparing art directors to copywriters and creative directors. For this reason, we cannot

offer an explanation for art directors, copywriters, and creative directors informational

needs. This study identified by causal factors clusters of art directors, copywriters, and

creative directors with similar informational needs regardless of title. Causal factors give

us the ability to make predictions about future informational needs based on title, gender,

product experience, primary duty, years in the industry and agency size.

Identifying segments by causal factors rather than descriptive factors can be called

"benefit segmentation." People are classified into segments in accordance with

the benefits they are seeking. They are then compared to all other segments in terms of

their demographics, volume of consumption, their brand perceptions, their media habits,

their personality and lifestyle, and so forth. This provides us with a deeper understanding

of the people who make up each segment (Haley, 1968).

Several alternative statistical approaches can be employed, among them the so-

called "Q" technique of factor analysis, multi-dimensional scaling, and other distance

measures. All these methods relate the ratings of each respondent to those of every other

respondent, and then these methods seek clusters of the individuals with similar rating

patterns. If the items related are potential consumer benefits, the clusters that emerge will

be groups of people who attach similar degrees of importance to the various benefits.









Whatever the statistical approach selected, the end result of the analysis is likely to be

between three and seven consumer segments, each representing a potentially productive

focal point for marketing efforts.

Each segment is identified by the benefits it is seeking. However, it is the total

configuration of the benefits sought that differentiates one segment from another (Frank

& Green 1968).

The benefit segment approach used in this study provided researchers with new

insight into the Sutherland et al. (2004) findings. Using usefulness of information

(benefit) segmentation, two homogeneous clusters were discovered. For the purpose of

this study, information usefulness was determined using the six informational items from

the Sutherland, Duke, and Abernethy study. The items were ranked on a 1-7 scale with

7 being the most useful.

Research Questions

The Sutherland et al. (2004) study determined the types of information creative

desire and obtains from clients. Using cluster analysis, this research poses the following

research questions:

* RQ1: Are there groups of creative, with unique perceptions of the usefulness of
marketing information?

* RQ2: What characteristics differentiate the groups?














CHAPTER 3
METHOD

Cluster Analysis

Cluster analysis has become a common tool for the marketing researcher.

Academic researchers and marketing applications researchers rely on the technique for

developing empirical groupings of persons, products, or occasions which may serve as

the basis for further analysis. Such a tool is particularly relevant for the emerging

discipline of marketing, which still struggles with the problems of how best to group

consumers, products, media types, and usage occasions (Punj & Stewart, 1983).

Used as an exploratory data analysis tool cluster analysis solves classification

problems. The object of cluster analysis is to sort cases (people, things, events, and so

forth) into groups, or clusters, so that the degree of association is strong between

members of the same cluster and weak between members of different clusters. Each

cluster thus describes, in terms of the data collected, the class to which its members

belong. It has been found to be a particularly useful aid to market segmentation,

experimentation and product positioning (Saunders, 1994).

As with factor analysis, cluster analysis is a tool of discovery. It is an exploratory

method that may reveal associations and structure in data which, though not previously

evident, are nevertheless sensible and useful once found. Objects to be clustered are

scored on several dimensions and grouped on the basis of the likeness of their scores.

This results in objects within groups that are more similar to one another than they

are to objects in different groups. The grouping of similar objects provides a more in-









depth analysis and provides a platform between two extreme views that: a) all objects are

unique and inviolable and b) the population is homogeneous. These two views represent

a poor basis for marketing planning, whether the objects are consumers or products.

Cluster analysis can offer a range of alternative views that can be useful to marketing

management (Saunders 1994).

Another important use of cluster analysis is its ability to help seek a better

understanding of people's behaviors by identifying homogeneous groups. This addresses

the need for better classification of relevant characteristics of individuals (Bettman,

1979). Advantages of cluster analysis include

* Ability to predict. It is cognitively easier for people to predict behavior or people or
objects based on group membership--all because members share similar properties.

* Group members share certain properties that are common, and it is hoped that the
resultant classification will help provide some insight into a research topic. The
classification has the effect of reducing the dimensionality of a data table by
reducing the number of rows (cases).

* Organizational convenience

* It has no independent or dependent variable

The two complementary features are

* High internal (within cluster) homogeneity
* High external (between cluster) heterogeneity

There are two types of cluster analysis. The first method is the hierarchical method

(binary or categorical) whose algorithms rebuild the entire hierarchy of the objects under

analysis (the so called tree), whether in an ascending order or in a descending order. The

second method is the nonhierarchical/partitioning method: the user previously defines the

cluster numbers in which the set of objects under analysis is divided.









The two steps include: 1) creating a table of relative similarities or differences

between all objects, and 2) using this information to combine the objects into groups.

The table of relative similarities is called a proximities matrix. The method of combining

objects into groups is called a "clustering algorithm." The idea is to combine objects,

which are similar to one another, into separate groups. To validate this method we use a

different sample of split-half Finally, we profile to see the relationship between cluster

membership and other important variables.

Profile of Sample

Sutherland et al. (2004) conducted a survey of 583 practicing creative who

subscribed to Creativity, an Advertising Age publication and industry standard.

The survey produced the following results:

* 583 completed questionnaires with a 17.0% response rate

* The response rate for art directors was 13.3%, copywriters 21%, and creative
directors 16.7%

* A 17% response rate for an unsolicited, first mailing, which is considered common
(Fink, 1995) and comparable to results reported in similar large scale marketing
surveys (West & Paliwoda, 1996)

Sutherland et al. (2004) adjusted each respondent's position to reflect his/her most

current position. By comparing current position and responsibilities, as indicated on

returned questionnaires to current position according to the list they purchased,

Sutherland et al. identified and reclassified individuals to their most current position: art

director, copywriter, or creative director. The final adjusted-by-title sample included 131

(22.5%) art directors, 225 (38.6%) copywriters, and 227 (38.9%) creative directors.









Respondent Profile

Male copywriters were the most common group among the 583 respondents. The

group had an average age of 37.2 with an average of 13.5 years in the business. Their

experience averaged 11.8 years in their position as an art director or copywriter. Male

respondents were older (37.8) than females (35.8), and they had been in the profession

longer (M=14.1) than females (F=12.3). There were no significant differences in

numbers of males and females in their primary jobs as art directors, copywriters, and

creative directors.

There was a significant difference between copywriters, art directors, and creative

directors based on their years in advertising and years in the primary job. While art

directors and copywriters were not significantly different in age, they were significantly

younger than creative directors. In years of experience, creative directors had

significantly more experience than art directors and copywriters, but art directors had

more years of experience than copywriters.

Study Design

Stage 1: Cluster Analysis Objectives

The objective of this study was to cluster ad agency creative personnel into groups

with similar perceptions of the usefulness of the six different types of informational needs

identified in the Sutherland et al. (2004) research. Cluster analysis was used to segment

the respondents' ratings as to the usefulness of six different types of information (i.e.,

variables Xl through X6).

Stage 2: Cluster Analysis Design

The squared Euclidean distance as the similarity measure was used, given that the

set of variables are metric. Distance measures consider both the data magnitude (e.g., not









very useful versus very useful) and the pattern, unlike correlation measures that detect

only data patterns (Hair, Tatham, Anderson, & Black, 1998, p. 503).

The agglomeration schedule was used to determine the presence of outliers in the

dataset. Only two observations had a stage of 0, meaning they neverjoined a cluster.

With only two possible outliers, no observations were deleted in order to preserve the

structure of the dataset. Also, standardization of variables is not necessary since all

variables are on the same scale.

Stage 3: Assumptions

In cluster analysis, there are only two important assumptions: sample

representativeness and multicollinerarity. Cluster analysis is not a statistical inference

technique. The sample (n=575) is large and considered representative of a wide range of

advertising agency personnel.

Stage 4: Driving Clusters

To achieve the maximum result efficiency, the researchers performed both

hierarchical and nonhierarchical techniques in sequential steps. Step one employed a

hierarchical method to 1) determine the appropriate number of clusters in the dataset and

2) provide initial seed points for step two. Step two employed a nonhierarchical method

to fine tune the analysis by using the hierarchical results (i.e., k clusters and centroids) as

a basis for generating a final cluster solution (Hair et al., 1998 p.503).

Step 1: Hierarchical Cluster Analysis

Our first and most important issue is to determine the final number of interpretable

clusters. Ward's method was chosen because it uses the sum of squares between clusters

summed over multiple variables. At each clustering stage, the within-cluster sum of









squares is minimized over the complete set of separate clusters in order to minimize

differences within clusters.

Additionally Ward's clustering agglomeration coefficient is an accurate stopping

rule for identifying the correct number of data clusters (Hair et al., 1998). The technique

is to look for large increases in the coefficient value in the agglomeration schedule at

each stage of the hierarchical procedure. When the clustering algorithm joins two distinct

clusters, the result is a large percentage change in the coefficient. When a large increase

occurs, the prior cluster solution is selected for further analysis because its combination

causes a significant decrease in similarity. To identify a stopping point, the percentage

change in the coefficient between clusters was calculated at each step. The step at which

the change diminished was the stopping point. This determined a range of cluster

solutions that are practical to the proposed research objectives. Our analysis looks for

two to five clusters, which is reasonable given the nature of the respondents. Table 3-1

displays the calculated percentage change in the clustering coefficient for ten- to two-

cluster solutions from the hierarchical procedure.

Results showed the largest percent change increase occurred when going from two

to one cluster (41.3%) and from three to two clusters (21.5%). This indicates that both

the two-and three-cluster solutions joined very different clusters and required further

analysis for their practical significance as distinct groups used for classifying the

participants. When we go beyond three clusters, small coefficient changes indicate

homogenous groupings and the stopping point in the present data.









Table 3-1: Hierarchical cluster analysis: Analysis of Ward's agglomeration coefficient
Change in Percentage change
Agglomeration coefficient to next in coefficient to
Number of clusters coeffcient level next level
10 2418.2 111.7 4.6
9 2529.9 124.7 4.9
8 2654.6 152.6 5.7
7 2807.2 186.9 6.6
6 2994.1 188.7 6.3

5 3182.8 244.3 7.6
4 3427.1 284.8 8.3

3 3711.9 800.8 21.5
2 4512.7 1868.1 41.3
1 6380.8 -- -

Two-Cluster and Three-Cluster Profiles. At this point in the analysis, the

objective was not interpretation but rather ensuring cluster distinctiveness (Hair et al.,

1998). Profiles of both the two- and three-cluster hierarchical solutions act as a guide in

the selection of the final cluster solution. Table 3-2 provides the variable profile.

Table 3-2: Difference between cluster centers
Variable Degree of freedom F value significance
Two-cluster solution
X1 TA demo profile 1 204.5 .000
X2 product perf profile 1 358.8 .000
X3 Comp. product perf info. 1 369.1 .000
X4 Marketing strategy info. 1 324.3 ,000
X5 Customer product usage 1 253.5 .000
X6 Main selling point 1 1 45.7 .000
Three-cluster solution
X1 TA demo profile 2 149.2 .000
X2 Product perf profile 2 253.7 .000
X3 Comp. product perf info. 2 355.1 .000
X4 Marketing strategy info. 2 269.8 .000
X5 Customer product usage 2 209.5 .000
X6 Main selling point 1 2 80.7 .000









Step 2: Nonhierarchical Cluster Analysis

Nonhierarchical cluster analysis methods were used to fine-tune the hierarchical

results in step one and get final cluster solutions.

Table 3-3: Two-cluster solution
X6
X5 Main
X1 X2 X3 X4 Customer selling
TA Product Competitors Marketing product point
demo perf. product strategy usage from Cluster
Cluster profile info. perf. info. info. info. client size
Final cluster centers
1 5.46 4.88 3.76 5.01 5.01 4.91 200

2 6.73 6.56 6.19 6.57 6.52 6.07 375

Statistical significance of cluster differences
F Value 231.12 360.25 625.22 263.89 297.48 86.60

Significance .000 .000 .000 .000 .000 .000


Table 3-4: Two- and three- clustering variable profile: Ward's hierarchical cluster
analysis
Main
Customer selling
TA Product Competitors Marketing product point
demo perf. product strategy usage from Cluster
Cluster profile info. perf. info. info. info. client size
Two-cluster solution
1 5.02 4.22 3.15 4.28 4.49 4.76 99

2 6.55 6.34 5.80 6.39 6.30 5.86 426

Three-cluster solution
1 5.02 4.22 3.15 4.28 4.49 4.76 99

2 6.17 5.89 5.03 5.83 5.80 5.15 216

3 6.87 6.72 6.45 6.86 6.72 6.44 260



Table 3-4 shows two respondent groupings that have unique perceptions of the

usefulness of all six items. Cluster 1 (n=99) respondents have lower values on each

variable, particularly X3 (i.e., competitor's product performance information), indicating









a group of respondents who place little value in any of the present information as being

useful in coming up with creative executions as do those respondents grouped in cluster 2

(n=426).

The three-cluster solution results in the splitting of cluster 2 from the two-cluster

solution to form similar groups: cluster 2 (n=216) and cluster 3 (n=260). Cluster 1 (n=99)

remains stable on each centroid and also the cluster size when compared to the two-

cluster solution. However, respondents in cluster 3 rate the usefulness of all six

information items only slightly higher those in cluster 2. This demonstrates that these two

clusters are not clearly differentiated by their perceptions of the usefulness of the six

clustering variables.

While the stopping rule supports either the two- or three-cluster solutions, the

variable profiles indicate a similarity between clusters 2 and 3. Given the robustness of

the large coefficient change and a very distinct variable profile, the two-cluster solution

demonstrates practical significance as a classification system. The remainder of the study

(i.e., cluster interpretation, validation, and profile) was therefore conducted only for the

two-cluster solution.














CHAPTER 4
RESULTS

Cluster Membership and Usefulness Ratings

The results of the cluster analysis provided evidence of two clusters. A comparison

of the perceived usefulness of the marketing informational items revealed that one group

had significantly higher usefulness ratings across all six items (table 4-1).

A repeated measures analysis of variance, which compared usefulness ratings of

information items within cluster 1, revealed significant differences. Cluster 1 rated the

target audience profile significantly more useful than any other item of information.

Marketing strategy information, customer product usage information, and the main

selling point provided by the client and the client's product performance information did

not have significantly different usefulness ratings. They were equal in usefulness but less

than the target audience profile and more useful than the competitors' product

performance information. The competitors' product performance information was

significantly less useful than all the other information items.

Repeated measures of usefulness ratings by cluster 2 also revealed significant

differences among the items. Similar to cluster 1, the target audience profile was the most

useful item. Marketing strategy information, customer product usage information, and the

client's product performance information were tied at second, less useful than the target

audience and more useful than the main selling point or the competitors' product

performance information. The main selling point was rated the least useful behind the

competitors' product performance information.









Table 4-1: Cluster by informational usefulness
Cluster 1 Cluster 2 Significance
Mean Mean df = 1
Target audience demographic 5.46 6.731 F = 231.12, p = .000
profile
Marketing strategy 5012 6572 F =263.882, p =.000
information
Customer product usage 5.012 6.522 F = 297.477, p = .000
information
A main selling point provided 4.912 6.074 F= 86.591, p =.000
by the client
Client's product performance 4 2 562 F 360.257 p .000
Information
Competitors' Product 3
Competitors' Product 3.763 6.193 F = 625.222, p = .000
Performance Information
Indicates rank of usefulness rating. 1 = Most useful. Items not significantly different
from each other are ranked equally, (e.g. marketing strategy information and customer
product usage information).

Cluster Membership and Primary Duty

Table 4-2 profiles the clusters by primary duty. Primary job duty was defined as

your primary job responsibility (i.e. art direction, copywriting, or other). This provides

evidence that there may be more variance in usefulness of information ratings among

copywriters, art directors, and creative directors than between these groups.

There was a significant difference (p=.007) in the components of the clusters.

Cluster 1 shows a much greater number of copywriters (47.0) than cluster 2 (34.4), while

cluster 2 shows a larger number of creative directors (42.7) than cluster 1 (31.0). Art

directors showed little difference in numbers between cluster 1 (22.0) and cluster 2

(22.9).









Table 4-2: Cluster by primary duty


Cluster 1


Cluster 2


Total


n % n % %

Copywriter 94 47.0 129 34.4 38.8

Art director 44 22.0 86 22.9 22.6

Creative director 62 31.0 160 42.7 38.6

Total 200 100.0 375 100.0 100.0

Chi square = 9.988, df = 2, p= .007

Cluster Membership and Current Title

The clusters (table 4-3) contained individuals with different titles, e.g. copywriters,

art directors, and creative directors. The clusters were composed of all titles, supporting

the approach of not using demographics in future research in this area.

Table 4-3: Cluster by current title


Total


Cluster 1


Cluster 2


n % n % %
Copywriter 73 36.5 69 18.4 24.7
Sr. copywriter 25 12.5 68 18.1 16.2

Art director 37 18.5 67 17.9 18.1
Sr. art director 10 5.0 25 6.7 6.1
Assoc. creative director 15 7.5 21 5.6 6.3
Creative director 40 20.0 125 33.3 28.7
Total 200 100.0 375 100.0 100.0

Chi square = 29.32, df = 5, p= .000
There was a significant relationship (p=.000) between cluster membership and

current position (table 4-3). Current job title was an open-ended question; creative were









classified by their title. Cluster 1 had a much greater percentage of copywriters (36.5%)

than cluster 2 (18.4%), while cluster 2 has a far greater percentage of creative directors

(33.3%) than cluster 1 (20%).


Cluster Membership and Gender

Table 4-4 shows 74.7% of cluster 1 was male compared to 67.2% males in cluster 2

while 25.3% of cluster 1 was female compared to 32.8% females in cluster 2. There was

no significant relationship (p=.061) between gender and cluster membership.

Table 4-4: Cluster by gender
Cluster 1 Cluster 2 Total

n % n % %

Males 148 74.7 252 67.2 69.8

Females 50 25.3 123 32.8 30.2

Total 198 100.0 375 100.0 573

Chi square = 3.502, df = p= .061

Cluster Membership and Product Experience

There was no significant difference (p=488) between product experience and

cluster membership as shown in table 4-5. Both clusters 1 and 2 had the most experience

in packaged goods, service advertising, and other (i.e. promotions, graphic services,

health-care, Hispanic etc). They also both shared the least experience in direct response

and recruitment advertising.









Table 4-5: Cluster by product experience
Cluster 1 Cluster 2 Total

Type product n % n % %
Packaged goods 53 27.3 87 23.6 24.9
Direct response 22 11.3 38 10.3 10.7
Service advertising 51 26.3 111 30.3 28.8
Recruitment advertising 5 2.6 4 1.1 1.6
Other 63 32.5 128 34.8 34.0
Total 194 100.0 368 100.0 100.0
Chi square = 3.435, df = 4, p= .488

Cluster Membership and Years in the Industry

There was a significant difference between cluster 1 and cluster 2 in age, years in

the industry, and years in the primary job. Table 4-6 shows age in cluster 2 had a much

higher mean (48.12) than cluster 1 (35.02) (p=.000). Respondents to years in the industry

in cluster 2 had a mean = 14.46, while cluster 1 had a mean = 11.53. Finally in years in

the primary job, cluster 2 had a mean =12.72 and cluster 1 had a mean =10.05.

Table 4-6: Cluster by years in the industry
Cluster 1 Cluster 2 Significance
n Mean n Mean df=1
Age 171 35.02 314 48.12 F = 12.99, p =.000
Years in industry 188 11.53 367 14.46 F = 11.61, p =.001
Years in primary Job 177 10.05 328 12.72 F = 13.67, p = 000


Cluster Membership and Number of Employees

There was a significant difference (p=.015) in clusters by the number of employees

in the company by which respondents were employed, as shown by table 4-7. Cluster 1

had an average of 2,137.99 employees compared to cluster 2 with 533.56. Cluster 1






31


comes from larger organizations while cluster 2 comes from medium and smaller

organizations.

Table 4-7: Cluster by number of employees
Cluster N Mean Std. Deviation

1 111 2137.99 9409.639
2 216 533.56 1693.658
Total 327 1078.18 5687.412
F=5.923, df= p = .015














CHAPTER 5
CONCLUSIONS AND IMPLICATIONS

Summary

This study was an extension of the research conducted by Sutherland et al. (2004).

It seeks to determine if there were groups of creative with different informational needs.

Two different clusters emerged from the study of the perceived usefulness of the

marketing informational ratings.

The clusters' usefulness ratings for the information items were significantly

different. Cluster 2 rated all items significantly more useful than cluster 1. A repeated

measures analysis of variance comparing usefulness ratings of information items within

cluster 1 revealed significant differences. Cluster 1 rated the target audience profile

significantly more useful than any other item of information.

Marketing strategy information, customer product usage information, and the main

selling point provided by the client and the client's product performance information did

not have significantly different usefulness ratings. They were equal in usefulness but less

than the target audience profile and more useful than the competitors' product

performance information. The competitors' product performance information was

significantly less useful than all the other information items.

Repeated measures of usefulness ratings by cluster 2 also revealed significant

differences among the items. Similar to cluster 1, the target audience profile was the most

useful item. Marketing strategy information, customer product usage information, and the

client's product performance information were tied at second, less useful than the target









audience and more useful than the main selling point or the competitors' product

performance information. The main selling point was rated the least useful behind the

competitors' product performance information.

Demographically, the groups differed by primary duty, current title, years in the

industry, informational usefulness, and agency size. Some art directors, copywriters, and

creative directors had similar informational usefulness needs while others had different

usefulness needs. The study established that perceived usefulness of information differs,

but not necessarily by title or position but by characteristics.

Conclusions

This analysis was conducted to answer two research questions:

* RQ 1: Are there groups of creative, with unique perceptions of the usefulness of
marketing information?

The study found there were two groups, with distinctly different perceived

usefulness of information. Composition of groups showed a mix of copywriters, art

directors, and creative directors in each group. This contradicts the Sutherland et al.

(2004) study that assumes the perceived informational usefulness differs by title.

* RQ2: What characteristics differentiate the groups?

There was a significant difference between cluster 1 and cluster 2 in age, years in

the industry and years in the primary job. In age cluster 2 had a much higher mean

(48.12) than cluster 1 (35.02) (p=.000). Respondents to years in the industry in cluster 2

had a mean = 14.46 while cluster 1 had a mean = 11.53. Finally in years in the primary

job, cluster 2 had a mean =12.72 and cluster 1 had a mean =10.05. These results indicate

that older creative with the most experience have significantly greater informational

needs than less experienced creative.









The primary duty showed a significant difference (p=.007) between clusters.

Cluster 1 shows a much greater number of copywriters (47.0) than cluster 2 (34.4), while

cluster 2 shows a larger number of creative directors (42.7) than cluster 1 (31.0). This

suggests that creative directors and art directors have greater informational needs than

copywriters.

A significant relationship existed (p=.000) between cluster membership and current

title. Cluster 1 had a much greater percentage of copywriters (36.5%) than cluster 2

(18.4%), while cluster 2 had a far greater percentage of creative directors (33.3%) than

cluster 1 (20%). This again suggests that creative directors and art directors have the

greatest informational needs.

There was a significant difference in clusters by the number of ad agency

employees. Cluster 1 had an average of 2,137.99 employees compared to cluster 2 with

533.56. Cluster 1 comes from larger agencies while cluster 2 comes from medium to

small agencies. Large agency creative may not be better informed than smaller agency

creative. While large agencies may have bigger budgets, research departments, account

planners, and access to more consumer testing, these resources are not being used to their

fullest potential.

In summary, there was a significant difference between clusters 1 and 2 based on

primary duty, current title, age, years in the industry, years in the primary job, the six

informational usefulness ratings, and the number of employees.

This study is important for two main reasons. First and foremost, it suggests the

existence of unique groups of creative based on characteristics not title. Older creative

with more experience found the information more useful than younger, less experienced









creative. In other words this study suggests that creative personnel's perceived

usefulness of the information vary, but not by job title alone.

The study provides evidence that groups differ by primary duty, current title, age,

years in the industry, years in the primary job, and the number of employees. It is not

surprising that more experienced creative, particularly creative directors, have the

greatest informational needs because they are responsible for the creative output of the

agency. Since they are more experienced, they also tend to be older. This analysis

showed that the most successful and experienced creatives--those with the title of creative

director and the group with the greatest informational needs--were in cluster 2, a group

that probably has more contact with clients and thus a better understanding of research

and the role it plays in creative solutions. Cluster 1, which consisted of younger, less

experienced copywriters and art directors, can use this research to better understand the

value of a sound strategy and the creative brief in the advancement of their careers.

It also establishes that agency size is a factor in the informational process.

Creatives working at large agencies with 2,000 or more employees are less likely to value

research than those working at agencies with 600 or less employees. This rebuts the

argument large agencies often make in pitching new business, that their size gives them a

distinct advantage in research, often hiring account planners and establishing research

departments. Clients can no longer take for granted that the research they're paying for at

large agencies is going to use. Creative directors at large agencies are not as hands on as

creative directors at smaller agencies. They devote most of their time to the largest most

profitable accounts leaving less time to supervise younger creative working on smaller,

less important accounts. This gives younger creative more freedom in creating ads. In









contrast at smaller agencies the creative directors are more involved and often participate

in creating the ads.

This study will provide new insights into informational needs and there usefulness

to creative. Perhaps now agency and client personnel will have a new perspective on

how research is being used and by whom.

Practical Implications

Strategy is the key to every successful campaign. Without it, the campaign is less

likely to succeed. What to say is one of the most important decisions in creating an

advertising campaign, but the how to say it, or the advertising execution, also has a major

impact (Schultz & Barnes, 1994).

Professionals should use the results from this study to assure that the what to say is

understood and agreed to by the creative team before they start the creative process or the

how to say it execution. Informational needs of all creative not only have to be met but

that information must be used. Agency personnel can learn from this research that job

title does not necessarily indicate the value creative put on the information they receive.

Surprisingly, the study also suggests that large agencies, which usually have the

greatest informational resources (e.g., research departments, larger clients with bigger

budgets, account planners, and so forth), are not communicating with the creative on the

usefulness of their information. Large agencies often use their size and resources as

selling points when seeking new business. However, the study suggests that medium to

small agency creative are better informed and that these agencies are handling the

information flow better than larger agencies.

Younger creative need to understand the importance of the development of a

sound strategy and how that strategy can help in seeking that "big idea." Younger









creative often feel the constraints of a creative strategy that they believe may inhibit

them from coming up with the most creative solution. Their interest is in establishing a

creative reputation within the industry as quickly as possible, and they may believe

research and testing can hinder that goal.

Limitations

Some of the limitations of this study include a mailing list limited to subscribers of

one publication. Ideally, a broader base would have made the results more generalizable.

While this study addressed the characteristics of agency creative personnel, it was

unable to determine the success of the creative executions produced by those surveyed.

Were creative with the most informational needs producing the most effective ad

campaigns? We also do not know the types of clients these creative handled. Some

clients will have different informational needs than other clients.

Future Research

There are several possibilities for future research in determining creative'

informational needs. This study is based solely on quantitative research. A future study

applying qualitative methods could reveal much about why informational needs vary and

why the value of the information changes. A qualitative study would give us a more in-

depth understanding of what really goes on in the minds of creative.

Another study could be conducted to determine if an agency's client base plays a

role in informational needs. Are there certain groups of clients who share or withhold

information on a regular basis from agencies? How greatly do informational needs vary

by client?

Additionally, the present study identified only the agency creative's informational

needs and demographics. Future study could determine if there are unique groups in






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account services, such as account supervisors, account planners, and account executives.

On the client side, are there differences among marketing heads, brand managers, or

assistant brand managers?

Finally, a study to determine if award-winning advertising is produced by creative

with the greatest informational needs or creative working with less information and

fewer constraints in coming up with the big idea. What effect do creative briefs have on

how creative conceive their ideas? Do they aid in coming up with the most creative

executions?















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BIOGRAPHICAL SKETCH

After deciding to pursue a career in advertising, Phil Willet attended the Ringling

School of Art and received a certificate in graphic design in 1979. He then moved to

New York, working for 15 years at major ad agencies creating print and broadcast

advertisements for national and regional accounts. He was employed first as an art

director, and then as a creative director.

After deciding he would like to teach, Phil returned to college to complete his

Bachelor of Science degree at the University of Florida, (UF) in Gainesville receiving his

degree in 2002. He received his Master of Advertising degree from UF in May 2005.