TITLE: Creating technology-based merchandising ideas for hair coloring through weak signals, concept optimization and mind-set segmentation AUTHORS: Gillie Gabay College of Management Academic Studies Israel Phone: 972-526-431-850 E-mail:
[email protected] Laurent Flores CRMMETRIX Université Paris II & INSEEC, Paris, France Phone: 33-1-41-05-90-10 E-mail:
[email protected] Howard Moskowitz, Ph. D. Moskowitz Jacobs Inc. White Plains, New York, USA Phone: 914-421-7400 Fax: 914-428-8364 E-mail:
[email protected] Andrea Maier, Ph.D. Research Scientist: Consumer Science FCI - Food Consumer Interaction Department Nestlé Research Center, PO Box.44 CH-1000 Lausanne 26, Switzerland Phone : + 41 21 785 8979 Email:
[email protected]
Keywords: conjoint analysis, ideation, concept development, concept-response segmentation Abstract We present an approach for the development of ideas for the merchandising of hair coloring. We employ various methods, such as ideation followed by concept development and concept-response segmentation. The Internet is used to facilitate the acquisition and prioritization of new ideas. Then, we use the experimental design of ideas to identify which perform well in the body of test concepts. We finish with a demonstration of concept-response segmentation to identify different customer „mind-sets‟. From the segmentation, in a merchandising situation, one can interact with the customer to identify the segment to which the customer belongs and in turn offer the proper product and merchandising.
1
INTRODUCTION The literature of cosmetics and fragrance research has only recently begun to pay attention to the consumer, as well as methods to accelerate innovation. In the world of cosmeticss, various publications and books deal with consumer issues. For the most part, however, the focus has been on the chemistry and physics of cosmetics products, product development issues or finally, on claims testing. The lack of details on product development processes for cosmetics, in contrast to food, may be due in part to the later development of cosmetics science compared to food science. It may also be attributed to the fact that, to a great degree, the cosmetics business has been dominated by marketers who have used intuition, rather than science (Moskowitz, 1984; 1995) Some methods, to improve product development, were introduced to the cosmetics industry more than two decades ago, using the principles of psychophysics and experimental psychology (Moskowitz, 1984). The focus of these new methods is on identifying ideas from the consumer, and then quantifying them. The methods attempt to use the voice of the consumer to arrive at product ideas that have promise in the marketplace.
The notion of weak signals Opportunities for new products abound, but they must be identified in order to create products that fit these opportunities, some of which may actually be emerging trends. How can opportunities be identified? Certainly luck and, perhaps, insight help. However, insight, by its very nature, is haphazard and hard to repeat. Insight may not be welcomed in the business world, which prides itself on discipline and methods for product development. In this paper, we discuss a knowledge-based system for opportunity discovery. The original method, called the Delphi technique, relied on the advice of experts (Jolson & Rossow, 1971). Experts were assumed to monitor their environment. If the expert could be interrogated properly by the researcher, then the thinking was that it might be possible to discern the existence of so-called „weak signals‟ in the environment. These signals, not yet trends, would presage what might be happening in the consumer environment. The ingoing belief is that each individual may sense some aspect of the environment and be attuned to some small portion of the emerging trends, as well as product needs. Yet, the individual consumer may not, as a single person, realize the scope of the opportunity or what to do. With hundreds, or perhaps thousands, of respondents participating together in an exercise to identify trends and products, the „wisdom of crowds‟ takes over. Trends not apparent to one person become apparent from the response of the crowd (Ray, 2006). The task then is to create a mechanism that will describe the opportunity by aggregating the weak signals. In some respects, the notion is similar to Wikipedia™ knowledge which emerges from the collaboration of many participants, each of whom has small amount of knowledge. The systematized compilation of that knowledge produces the bigger picture known as the Wikipedia™. Using weak signals to develop product concepts in a structured fashion With the advent of the Internet, researchers have been able to harness the response of many individuals to create new product ideas. One new approach is called collaborative filtering, wherein the respondents in a project cooperate with each other to develop the new idea. A version of this 2
approach, called brandDelphi (tm)™, based on the original Delphi Method of the 1960‟s and 1970‟s, has respondents evaluate the ideas of other respondents, and then contribute their own. The brandDelphi (tm) paradigm is an ideation method whose goal it is to grow ideas by exposing them to many individuals. If the idea is sufficiently intriguing and novel, then individuals who may not have thought of the ideas will react positively when they see it. Thus, the novel idea will emerge as relevant, because individuals will have perceived its uniqueness and value. Ideas that are not as relevant or as unique will wither as respondents reject them (Flores, Moskowitz & Maier, 2003). Even if the developer can create new ideas, the question remains about how to optimize them to create new and better product concepts. If brandDelphi (tm)™ creates the ideas, then conjoint analysis using experimental design identifies what ideas do well in the context of actual test concepts. Conjoint analysis refers to a class of methods, developed in the 1960‟s, whose goal is to identify how well ideas (i.e., concept elements) perform in the body of a test concept. Conjoint analysis mixes and matches these elements or product ideas in different combinations, instructs respondents to evaluate the concepts, and then, from their ratings, determines the contribution of each element to the overall rating. When applied to product ideas in business, conjoint analysis creates strong performing concepts, because the research procedure becomes a torture test for the different concept elements (Green & Srinivasan, 1980). Conjoint analysis has enjoyed almost a forty year history in consumer research, with numerous applications (Green, Krieger & Wind, 2001; Moskowitz, Porretta & Silcher, 2005; Wittink, Vriens & Burhenne, 1994).
The facilitating role of the internet During the past decade, consumer researchers have been fortunate to tap into the reach and capabilities of the Internet. As more individuals connect to the internet, the researcher can conduct ideation sessions using either brandDelphi (tm)™ or a host of other methods i.e., Decision Analyst, Inc., Brain Juicer, Inc.). These ideation systems work by accumulating the inputs of many hundreds or thousands of respondents and having one respondent interact with the ideas of another. Such ability would not have been possible two decades ago, when the researcher working in ideation had to rely solely on the results of a few focus groups. Experimental design of ideas, the aforementioned conjoint analysis, has also benefited from the reach and capability of the Internet. Instead of working with a few dozen or a hundred respondents, all of whom evaluate the same set of systematically varied concepts, the Internet has provided a method by which individuals can react to tailored combinations of concepts, assign their ratings, and have an individual model created to show what ideas perform best (Moskowitz, Gofman, Katz, Itty, Manchaiah & Ma, 2001).
Invention, innovation, and the competitive world of cosmetics companies When it comes to products themselves, the major cosmetics companies have been successful in sustaining growth and leadership in the world through continuous innovation of products, brand revitalization, and international expansion. Their brand portfolio has been strong in all the major categories (skin care, hair care, fragrances) and channels. Their size has allowed them to forge closer relationships with trade partners so that their brands got favorable shelf space placement in both mass markets and in prestige outlets. However, these companies face two major challenges in the future (Seung-Joo, 2004). 3
1. Growth in core markets is slowing down. In 2004, cosmetics market growth in Western Europe was 1.8% and North America grew by only 2.5%, whereas Japan experienced a negative growth of 1.1%. Future growth is expected to come from the emerging markets, especially China, Russia and Brazil. However, product positioning in those markets may have significant room for improvement. It is possible that western brands are too high end for the majority of middle class consumers in these markets. 2. Competition is stiff. Several competitors, e.g. L'Oreal. P&G, Unilever, Avon, Henkel, Beiersdorf and a host of local players in the mass market segment, compete in a number of categories with little differences in shelf space. Furthermore, P&G‟s recent acquisition of Wella poses threats for companies in the hair care segment. The foregoing situations of slow growth and stiff competition become clarion calls for the cosmetics industry to create new products, through invention and innovation. In his classic work Schumpeter (1934) states that the involvement with innovation has three main aspects. Invention is the creation of new products or new processes and their development. Innovation is the process of merchandising the invention. Imitation is the adoption of the same innovation by competitors turning it, in the extreme case, into a commodity. Whereas invention creates something new, innovation is the penetration of the invention into use. The success of the invention is therefore, measured by technological criteria. Whereas commercial criteria determine the success of the innovation (Sharma & Chrisman, 1999). Development and management of innovation are key capabilities within the cosmetics company's efforts, or indeed any company‟s efforts, to implement a successful business strategy. Product innovation is often at the foundation of a business strategy. Along the line of innovation, cosmetics companies continually compete on improving product performance. The key in improving performance is technology-based innovation (Lumpkin & Dess, 1996). Deutsche (2000) puts forth the notion that in services business, competitive advantage depends on innovation. Services embedded in consumer goods, such as merchandising services, may also be part of this thrust towards competitive advantage. Innovation in merchandising cosmetics Global markets comprise increasingly complex environments in which products are merchandised. To differentiate itself and its products from those of competitors, a company must innovate to create added value for its customers (Hamel & Prahalad, 1993). For example, a cosmetics company that will produce less harmful hair color with superior grey coverage in recyclable containers may gain a competitive advantage over its competitors in the early years of the 21st century. Identifying weak signals and trends allows cosmetics companies to remain ahead of the game and differentiate themselves from competitors by developing added value and by protecting their added value innovation from imitations. A company that continuously and effectively innovates will be able to use these abilities in gaining continued competitive advantage (Mone, McKinley, & Barger, 1998). Innovation and creativity are key success factors in promoting growth and productivity (Reynold, Hay, & Camp, 1999). Cosmetics companies that invest more in innovation yield greater returns (Price, 1996). Given the thrust in product innovation and superiority, how can the company translate that effort into merchandising innovation?
4
In saturated consumer goods markets such as cosmetics, innovation goes beyond product performance, to optimize the product/store interface with perceived winning consumer benefits (Ben-Rechav Gabay, 2000). Such benefits delight consumers and gives them what they want, or perhaps surprises the consumer and gives them something that they will later demand from all manufacturers (Ben-Rechav Gabay). Innovations may affect future strategies by focusing the company on shopping experiences as a way to differentiate the company from its competitors. Innovation in shopping experiences, especially the merchandising of products using high tech methods, is becoming a hallmark of the cosmetics industry, and especially in the category of hair coloring. A search with Google Scholar© reveals that since 1981, 25 inventions that provide consumers with a hair coloring or hair styling interactive modeling experience were registered. One example of this merchandising through hi tech was registered by Nagamachi in 2002: The image system and method for; analyzing hair, predicting achievable end colors and recommending hair color agents‟ is based on recipient-specific input. The patent includes methods for: identifying an achievable end hair color based upon at least one starting hair value of a recipient, outputting an image for a hair color analyzing system, providing a hair coloring product to a consumer, and recommending a hair coloring agent to achieve said desired end hair color. It is clear from the foregoing that the merchandising of hair coloring products has moved from considerations of the product on the shelf to the „experience economy‟, where high tech provides a store experience for the customer (Pine & Gilmour, 1999). Scope of this study This study deals with the creation of new hair products, using both ideation by brand Delphi ™ and by experimental design of ideas with conjoint measurement (IdeaMap.Net). It ends with the creation of a merchandising product for hair coloring, in the spirit of the trend discussed above towards high tech in merchandising hair coloring products. The original research was done in collaboration with a manufacturer, to identify new opportunities for subsequent, targeted development. Proprietary information has been omitted from this report, allowing us to focus on the methodological approaches of ideation concept optimization and segmentation. The studies are presented in two parts. The first part deals with the creation of elements for hair coloring products and their merchandising, using the ideation method of collaborative filtering. The second part deals with the creation of a “new to the world” product ideas and the segmentation of respondents into different groups, based upon their mind-sets.
Part 1 – Coming up with promising ideas through collaborative filtering
Part 1 comprised the creation of ideas through the process of collaborative filtering. For this portion of the project, a total of approximately 500 respondents participated. They were selected from a group of 8,000 individuals who had agreed to participate in Internet-based studies. The respondents were sent email invitations to participate in a study dealing with hair coloring, but told nothing else. 5
The response rate mirrors the typical 5% - 10% response rate one typically gets for studies of this type, where the incentive is a sweepstakes. Upon clicking on the link embedded in the invitation, the respondent was led to the study. It began with a short presentation of the topic. The respondent was told that she was to be participating in a study on hair coloring, dealing with several topics. After that, the respondent completed a short classification questionnaire, and then participated in the main body of the study, which was to elicit ideas about three aspectscreate of hair coloring These the (Figure ‘mind1).set’ or ‘aufgabe’
The three questions to be addressed
Figure 1: The three questions used to guide respondents in the ideation process
Respondents went through three exercises in randomized order. The first dealt with information on the package. The second dealt with the store experience. The third dealt with the packages in the store. For each of the exercises, the respondent focused on the specific topic. For that topic, the respondent first looked at a number of ideas given by respondents who had previously participated, and selected those that were relevant. The respondent then provided new ideas. Finally, the 21 new respondent rated several of the ideas given by previous participants. Respondents then provided ideas. We see an example of these two exercises ( Figure 2) for the store experience. Finally, the respondent rated some of the ideas given by previously participating respondents. Figure 2: Screen for selecting ideas and providing ideas in the ideation phase.
Over time, as increasing numbers of respondents participated, the database of ideas became increasingly richer. Some ideas were given by certain respondents; they grew as other respondents 6
saw them and rated them as being important. Other ideas were thrown out by one respondent, but failed to ignite the interest of other respondent. They eventually stopped appearing because they were not selected. In a sense, the approach imitates the natural selection of genes. Some ideas are fortunate to be interesting, and flourish; others just simply fade away because they really do not thrive. We see examples of the ideation and collaborative filtering in Table 1. Altogether, 618 ideas emerged from the three sets of questions (package display, obstacle/solution, and store display, respectively). Each idea has three different parameters associated with it 1. The first parameter is Sel, short for Selected. The Sel number is the total number of time that the element was selected, i.e., chosen, as directed (see Figure 2). All elements chosen fewer than 10 times were eliminated, simply because either they were not really well accepted by other respondents when presented, or because they did not appear a sufficient number of times to demonstrate customer interest in them. 2. The second parameter is SelP, short for the percentage of times that an item was selected when it was presented. High values of SelP mean that the item is interesting most times it appears, and is selected. In Table 1 we show a range of Sel P. values. 3. The third parameter is Rate, the recall weight that is assigned to each element rated by the respondent. We have a continuum of ratings. For illustration purposes, we selected 4-6 elements from different rating values. This strategy gives a range of strong performing elements versus weak performing elements. The elements provide a rich matrix of ideas. Perhaps lurking within the set are some really strong performers. Those will emerge in the second phase, when we work with experimentally designed concepts. Although it is tempting to stop at this first step, when we have some strong performing ideas, the real task of concept development is just beginning. The full table of ideas must be edited and the concept elements refined so that they make sense to the product developer and merchandiser. Despite the incomplete nature of the table, exercises of this type extract nuggets of ideas that can be incorporated into concepts that have meaning to consumers. Table 1: Partial results from collaborative filtering and ideation. There are 4-6 ideas for each level of Rate, to show the different types of ideas. The ideas are presented in exactly the spelling that they had in the study. StoreInf = store information, ObsSol = obstruction/solution, PackDisp = package display. Which Question Idea Consumers should be able to get more coupons to save StoreInf money on their favorite brands. ObsSol Better gray coverage Would like different size packages for people with short hair and people with long hair. I always end up with ObsSol some product left because I have very short hair. ObsSol Larger sizes, it takes two boxes to do mine.
Sel SelP Rate 11 28
39 50
8.9 8.9
29 11
42 31
8.8 8.8 7
StoreInf StoreInf ObsSol StoreInf ObsSol
StoreInf ObsSol
StoreInf PackDisp
PackDisp PackDisp StoreInf PackDisp PackDisp ObsSol
Natural looking that really colors grey and doesn't fade Would like to purchase the exact same conditioner that is in the coloring kit separately. When its on sale, they are usually out of my color – stock up on the popular colors when planning a sale True color shown I used to use a brand that cost 4x what I'm using now with the same results--put a cap on those rising prices Shades that do not fade or turn brassy after a few shampoos. 2. More accurate description of what shade it will turn your hair color to Shade is always darker than anticipated. should be able to tell more accurately how it will truly look I would like have multiple swatches of hair (6-9 inches long) so I can hold it next to my hair and look in a mirror to tell whether I like it or not. Show if colors can be mixed, to make a totally different color. I would like to see packaging available in a clear bag so I can see the color and have a sample swatch of the final color. I would like to have the gloves fit better. They fall off they way they are made now. whether animal testing was done Have a mirror available to compare with your own hair color most stores racks are overcrowded...hard to find the shade u are looking for Changing names of colors is confusing
10
77
8.5
26
58
8.5
22 41
51 55
8.5 8.0
22
31
8.0
20
65
8.0
72
59
8.0
39
51
8.0
11
55
7.5
37
27
7.5
31 10
30 59
7.5 7.0
16
40
7.0
42 12
33 36
7.0 6.9
A careful analysis of the ideas developed from the brandDelphi (tm)™ ideation showed that one general „idea‟ looked quite promising. The exact nature of the idea was not clear, but it appeared to have something to do with a machine, or a chart that provides prediction or feedback about the hair. An examination of this different idea showed that when the notion of a computer, or some feedback device or even feedback step was given to the respondents, they rated it high. Thus, the notion was born of a color-computer for colored hair, to help the respondents know what to do and what they might expect. It would remain for the conjoint portion of the project to delineate the features of this machine, but Table 2 shows some of the ideas that led up to the machine. It is important to re-state that the development of products in this way is not rigid. We do not follow the ideation in a literal sense, but rather happen upon ideas that are interesting, resonate with respondents to the level that respondents can conceive of them. From a business perspective, these ideas ought to have commercial potential, as well as be technically and economically feasible for the corporation.
8
Table 2: Some of the ideas that relate to a prediction or feedback device for use when coloring one’s hair. The ideas are sorted by ‘Rate’. Which Question PackDisp ObsSol
StoreInf ObsSol
StoreInf ObsSol
ObsSol
PackDisp
StoreInf
PackDisp
ObsSol
PackDisp
Idea Sel SelP Rate A new way to show colors, the current display system has very inaccurate colors. 9 56 8.8 I like the idea about the machine 10 29 8.7 Boxes should show either more samples of what your color will be compared to the color it is, or say on the box if your current color is medium to light brown, blond, etc., your new color will be... 10 48 8.7 Know what the true color result will be, not the picture of the model on the box, as her hair was stripped. 23 55 8.6 It would be great if there was some kind of machine where you could scan your current hair color into it, then input the various shades to get a true idea of what the new shade would look like on your own hair, rather than just the generic colors 47 59 8.6 I would like for them to have a chart on the outcome if your hair is this color then it will look like this, etc. 23 43 8.5 I would like a machine that would analyze my hair color and tell me what color to use to lighten it or add highlight, etc. 26 68 8.5 A machine that you could scan your current color into and then input the various shades of hair color to get a true idea of what the new shade would look like on your current color. 83 65 8.5 I would like to have a monitor display where I can insert my personal complexion, eye color and natural hair color and get suggestions for what kind of color would be best for me 35 49 8.5 A computerized program in store that would do the same thing as the first item above matching a persons individual coloring with different hair coloring 20 39 8.5 Not knowing the actual color it will be on your own hair. Charts, staff, people who know what will or will not happen 60 49 8.4 I would like to have an interactive leaflet with variations of hair, eye and complexion color to match different hair color 63 50 8.2
Part 2 – Experimental design of concepts to identify „what works‟ The second part of the project used the ideas to identify which specific elements perform well in the body of a concept, as well as looked for respondents with different mind-sets. This second effort was also implemented on the web, using specifically written programs that mix and match the elements, create the test combinations, present them to the respondent, acquire the ratings, and then process the data (Moskowitz & Gofman, 2007). 9
To summarize the approach, ideas emerging from the brandDelphi (tm)™ in the first part of the project are not necessarily in the format that one needs them. These ideas or elements: 1. Should be selected on the basis moderate to high performance, and selection frequency upon presentation. Ideas should be interesting but not overly obvious. 2. Should communicate some tangible new feature or benefit to the customer, rather than just be simple phrases, e.g., „does a good job‟, 3. Should avoid being a description of the consumer‟s emotional state, because that description does not immediately lead to a product concept, or even to a positioning concept that has a tangible benefit. 4. Should be edited after emerging from the brand Delphi (tm), and therefore not be followed slavishly. 5. Could be new ideas, based on one‟s insight after looking at the elements that emerged from the ideation exercise. We can see from the phrasing in Tables 1 and 2 that most of the elements are somewhat incomplete thoughts, sometimes in the rough consumer shorthand that could profit from some editing. In order to prepare the elements for concept evaluation, we edited the elements, selecting a set of moderate to strong performers. Where necessary, we added other elements that were not originally in the set provided from brandDelphi (tm)™. Many of the ideas come from the elements in brandDelphi (tm)™, but others are modified ideas,developed by R&D or by marketing. In Table 3, we see the final set of 24 elements , which are more polished and categorized into six distinct silos. These seven silos are only one of the alternative layouts for the elements (see www.IdeaMap.net for a list of the designs). Table 3: The final set of 24 elements, divided into six silos with four elements each, as well as the average Interest utility from the 480 respondents who participated. The elements are sorted with silo in descending order of interest utility. The results were obtained through the application of conjoint analysis, specifically IdeaMap.Net®.
A2 A1 A3 A4
B2 B3 B1
Base Size Additive constant Silo 1 - What it is ..Automatic color computer A Hair Color Computer Genie…let it 'SCAN YOUR HAIR' and you'll be amazed at the way it will help you An interactive "Hair Station" just like a computer…complete and detailed hair coloring information right at your fingertips An Automated Teller for Hair…choosing a hair color has never been easier A interactive attachment for your Lap top… connect via the Internet to a Master Hair Color computer Silo 2 - How to interact with the device Enter your personal info such as natural hair color, eye color and skin tone as well as your preferences and it will surely tell you what your next hair color should be Select your personal options, hit Go when done... get results in less than a minute Tells you everything you need to know to make your hair color transitions
Total 480 43
13 7 5 -2
8 5 4 10
B4
C1 C2 C3 C4
D1 D3 D4 D2 E1 E2 E3 E4
F1 F3 F2 F4
smooth Send an Instant Message to a stand-by consultant and get the answer you need Silo 3 - User friendly features Features a Product Info and interactive Demo, so you can see how a color would look on you before you actually purchase it A touch screen panel makes it easy to navigate through the system quickly A user friendly menu option…provides answers to all questions Photo section…photos of women with similar physical attributes to see what types of shades or highlights they've used Silo 4 - The output At the end of the session the computer will recommend the best product for you The system will recommend the best color for your hair based on your features and latest hair coloring trends Helps you make the right choice…the system will provide you with several product options to achieve your goal At the end of the session several product options will be displayed…you only need to choose Silo 5 – Knowledge Benefits to the user Get a personalized report printed out…take it home with you Learn how to make a gradual change, not a drastic one Learn how to maintain strong shiny hair…in spite of coloring it Connect to some of the country's top rated salons…and learn their best kept secrets Silo 6 – How to access the machine Print out a coupon for what you plan to purchase from Hair Color product web sites Answer a quick survey about your hair habits and lifestyle…and get a rebate on your next Hair Color purchase Become a member of the „price savings club‟…you‟ll save on all your hair coloring necessities Insert your credit card at the start of the session…and you‟ll get charged based on the time you spent
-2
9 5 4 2
5 4 3 2 6 4 4 3
4 4 0 -16
Running the conjoint analysis study Conjoint analysis of ideas methods run differently from the ideation session. The approach is to identify how the elements from the ideation perform in the body of a test concept. The conjoint study follows these steps: 1. Polish and categorize ideas into silos. The ideas from the ideation session were assessed to identify what new opportunity appeared to exist. The emerging opportunity was for a machine that could provide a quick estimation of what one‟s hair might look like after the dying experience. It was not clear what the features of the machine would be, or how to merchandise. Thus it was necessary to develop both the different aspects of the machine (features, merchandising, etc.), and the elements for the different silos. Table 3 presents the silos and
11
elements. Some of these elements came directly from the brandDelphi ™ study after being edited, but many others came from ideas inspired by the brandDelphi ™ elements. 2. The elements were combined according to an experimental design, with the property that each respondent evaluated 49 different combinations. Across the full set of 49 combinations each respondent eventually evaluated all of the 28 elements three times. The elements were set up to appear in different combinations, with the property that the elements were statistically independent of each other. The test concepts comprised a minimum of two and a maximum of four elements, with at most one element from any silo in any single concept. This fractional factorial design is set up to allow dummy variable regression, with an estimate of the absolute utility value of each element. That is, the zero is meaningful, allowing ratios to be estimated. 3. The respondents for this second phase of the development project were invited from the same panel as had been used for the ideation above. The respondents were incentivized by a first prize of $100 and two second prizes of $50 each. A total of 10,000 invitations were sent, with 480 respondents completing. The 4.8% response rate is typical for this type of study. 4. When the respondents clicked on the link embedded in the invitation e-mail they were led to the orientation page of the interview (Figure 3). Figure 3: Orientation page for the concept evaluations of Part 2.
5. Each respondent evaluated a unique set of 49 test concepts, developed from the experimental design. The basic design remains unchanged, but the elements were permuted so that the combinations differed across respondents. This permutation strategy ensures that any unsuspected bias in the test combinations would be minimized, since the elements were combined in many different ways. Any specific combination in which the elements synergized or suppressed would thus not affect the results (Moskowitz & Gofman, 2004) 6. Interviews on the Internet lasted 15-20 minutes in total, sufficiently short to maintain respondent interest (MacElroy, 2000). Furthermore, studies of this type usually interest the respondents because the combinations vary. Respondents who logged in to participate show a relatively high completion rate, around 70%. 12
Analyzing the results to identify what features of the hair product drive interest Respondents participating in these studies do not know that the product they are evaluating is „new to the world‟. The orientation page does not even talk about a new machine, but rather about hair color products and the shopping experience. Furthermore, it is almost impossible for a respondent to outguess the experimental design because the concepts vary so much. Despite the apparently complex nature of the interview, the analysis of the data to identify „what wins‟, is fairly straightforward. Keep in mind that each respondent evaluated 49 combinations, in which the concept elements each appeared three times in a statistically independent fashion. The ratings for each respondent could, therefore, be related to the presence/absence of the 28 elements by ordinary least-squares regression (Box, Hunter & Hunter, 1978; Systat, 1997). We followed this set of steps to create the in individual-level model: 1. For each respondent, create two dependent variables. The first is the original rating of interest on the 1-9 point scale. The second is a transformed value, with ratings of 1-6 transformed to the value „0‟, and ratings of 7-9 transformed to the value „100. The latter, binary transform, follows the conventions of market research, which focuses on the membership of a respondent in a class, rather than the intensity of feeling experienced by the respondent. The transformed value „0‟ means that the respondent rejects the particular test concept, whereas the value „100‟ means that the respondent accepts the concept. A respondent either accepts or rejects any specific concept, based upon the magnitude of the rating assigned to the specific concept. 2. For each respondent relate the presence/absence of the 28 concept elements to the ratings, using the method of ordinary least squares. Each dependent variable generates its own equation, of the form: Rating = k0 + k1(Element 1) + k2 (Element 2) .. k28(Element 28) 3. When the equation uses the 9-point rating scale as the dependent variable, the equation is the persuasion model. It is only used for segmenting the respondents on the basis of the pattern of their 28 coefficients. 4. When the equation uses the 0/100 binary values as the dependent variable, the equation is the interest model, which is used to understand the nature of how the respondents think about the different concept elements, and will be used to name the three segments that emerge from the study. 5. The additive constant for the interest model, k0, shows the conditional probability for a single respondent or percent of respondents across the full set of respondent who would rate a concept as interesting, i.e., 7-9 on the 9-point scale, even without elements. Of course all concepts comprised 2-4 elements by design, so the additive constant is a purely estimated parameter. Nonetheless, it is an important baseline. High additive constants mean that the concept has a chance of success based upon the fundamental interest in the product idea. In contrast, low additive constants mean that the concept must be carried by the elements, because the basic 13
interest is low. High scoring elements can remedy initially low basic interest, as long as the right elements are used in the concept. 6. The element coefficient or utility, ki, (i.e., the impact value for each of the 28 elements. The utility), shows how each element drives interest. Each element generates its own utility value from the regression model. The norms for these element utilities from thousands of studies are as follows, and should be viewed as qualitative guides to the importance of the elements: A utility value greater than 15 is exceptionally strong, probably breaks through any clutter to drive interest, and definitely ought to be included in the concept. A utility of 10 – 15 indicates much impact and should be included in the concept. A utility of 5-10 indicates an impact that makes a moderate difference. A utility value of 0- 5 plays little role. A negative utility value reduces the number of respondents interested in the concept and should be avoided in the concept. Typically we look at elements with utility values of 8 or higher, corresponding to those significant at the 95% confidence interval, in order to be conservative. 7. We see the element utilities from the interest model in Table 3. Keep in mind that the utility value is the average utility from the 480 respondents, because each respondent generated an individual interest model. The additive constant is 43, meaning that close to half of the respondents would be interested in this new product ideas, even without the elements being present. 8. There is one very strong performer, with a utility of +13, meaning that with this element alone an additional 13% of the respondents will change their vote from disinterested (rating 1-6) to interested (rating 7-9). The element is A2, A Hair Color Computer Genie…let it 'SCAN YOUR HAIR' and you'll be amazed at the way it will help you. There is also an element that dramatically detracts from acceptance, with a utility of -16, meaning that 16% of the respondents switch from interested to not-interested. This element is F4, Insert your credit card at the start of the session…and you'll get charged based on the time you spent. The results from the Total Panel revealed only one breakthrough idea. The question for developers is whether or not this one breakthrough idea comes from the fact that none of the ideas is very good, or comes instead from the interaction of segments of respondents, with different, often opposing ideas about what they like. Segmentation means that we might find the breakthrough ideas, but must divide the respondents by the profile of how they respond to the different elements. Segmenting respondents on the basis of the pattern of their utilities Methods of segmenting respondents has been dealt with in other papers (Green & Krieger, 1991). The approach has been used to segment actual fragrances based on the pattern between liking and sensory attributes (Moskowitz, 1986). With some minor changes in assumptions, we can segment respondents on the basis of the patterns of their utilities, rather than on the sensory characteristics of the product. Thus, it becomes possible to identify respondents with similar „mind-sets‟, for a specific product category, such as hair coloring. The approach is straightforward. Each respondent generates an individual persuasion model, which shows how the intensity of feeling towards the hair coloring product varies with the presence/absence of the 28 concept elements. We can cluster the respondents based upon the pattern of their utilities, so that each cluster comprises individuals with similar patterns of persuasion utilities. The k-means clustering method is straightforward, with the measure of distance being the 14
parameter (1-R) with R = Pearson correlation between two respondents based upon their 28 persuasion utilities) (Systat, 1997). The output of clustering comprises groups of respondents whose patterns of utilities differ across clusters, but are similar within a cluster. Segmentation requires looking at different segmentation results, such as two segments, three segments, four segments, etc., and finding that minimal set of segments that appear to be different in terms of the elements that have the most impact. For these data, the two-segment solution was difficult to interpret. That is, the elements that rose to the top „told different stories‟. The three-segment solution seemed more reasonable and interpretable. The segments seemed more homogeneous with respect to the elements that performed well in a segment. Table 4 shows the high performing, winning elements of each segment, defined by utility values of 8 or more, which are shaded. In these types of studies, the standard error of the interest utility value tends to be about 3.5 to 4.0, depending upon the particular element. Thus, a conservative value of two standard errors, or 8 points, would be significantly greater than 0. The three segments are the following: 1. Segment 1 (54%) wants easy to use, high technology, and reliability. This segment has the highest additive constant, 50, meaning that even without any elements in the concept to drive acceptance, about half of them are predisposed to be interested in the new product. They have three breakthrough elements, which deal with the product and one of which deals with easy to find stores. A strong performing concept will do well primarily because of the basic interest in the product, not necessarily because of the breakthrough ideas. 2. Segment 2 (25%) wants to give, and get information about themselves, with respect to hair coloring. Furthermore, they want to turn information giving/getting a process, not simply a rapid 1-2-3 affair. In contrast to Segment 1, the additive constant for Segment 2 is only 32, meaning about one out of three of them is predisposed to be interested. For the respondents in this segment it is the elements that must do the work. Fortunately, 11 of the 28 elements are breakthrough for these respondents. A strong performing concept for them will do well because of the messaging, not because of the basic interest.
3. Segment 3 (21%) wants results. They want information, primarily what the product will do for them. Information becomes valuable in terms of what it delivers. They have a middle value for the additive constant, 38, meaning that they are more predisposed to the idea than Segment 2, but the elements still have to do the work. Altogether 13 of the 28 elements are breakthrough for Segment 3. Table 4: Winning elements for the three segments emerging from the hair coloring study.
Base Size Additive constant Total Panel A Hair Color Computer Genie…let it 'SCAN YOUR HAIR' and you'll be amazed at the way it will help you
Tot Seg1 Seg2 Seg3 480 257 122 101 43 50 32 38
13
13
9
17 15
Features a Product Info and interactive Demo, so you can see how a color would look on you before you actually purchase it Enter your personal info such as natural hair color, eye color and skin tone as well as your preferences and it will surely tell you what your next hair color should be Segment 1 – Easy to use A Hair Color Computer Genie…let it 'SCAN YOUR HAIR' and you'll be amazed at the way it will help you Enter your personal info such as natural hair color, eye color and skin tone as well as your preferences and it will surely tell you what your next hair color should be Segment 2 - Information givers and getters Features a Product Info and interactive Demo, so you can see how a color would look on you before you actually purchase it A touch screen panel makes it easy to navigate through the system quickly A user friendly menu option…provides answers to all questions Photo section…photos of women with similar physical attributes to see what types of shades or highlights they've used Print out a coupon for what you plan to purchase from Hair Color product web sites Get a personalized report printed out…take it home with you Learn how to make a gradual change, not a drastic one Become a member of the 'price savings club'…you'll save on all your hair coloring necessities Answer a quick survey about your hair habits and lifestyle…and get a rebate on your next Hair Color purchase A Hair Color Computer Genie…let it 'SCAN YOUR HAIR' and you'll be amazed at the way it will help you Connect to some of the country's top rated salons…and learn their best kept secrets Segment 3 - Results oriented - what does it do for ME? At the end of the session the computer will recommend the best product for you The system will recommend the best color for your hair based on your features and latest hair coloring trends A Hair Color Computer Genie…let it 'SCAN YOUR HAIR' and you'll be amazed at the way it will help you At the end of the session several product options will be displayed…you only need to choose Helps you make the right choice…the system will provide you with several product options to achieve your goal
9
4
20
6
8
12
0
10
13
13
9
17
8
12
0
10
9
4
20
6
5
0
17
3
4
-2
17
5
2
-3
15
-2
4
-6
14
16
6 4
5 4
13 13
-1 -8
0
-9
11
11
4
-4
10
16
13
13
9
17
3
4
9
-8
5
2
-1
19
4
3
-5
18
13
13
9
17
2
0
-5
17
3
1
-4
17 16
Print out a coupon for what you plan to purchase from Hair Color product web sites Answer a quick survey about your hair habits and lifestyle…and get a rebate on your next Hair Color purchase An interactive "Hair Station" just like a computer…complete and detailed hair coloring information right at your fingertips An Automated Teller for Hair…choosing a hair color has never been easier Become a member of the 'price savings club'…you'll save on all your hair coloring necessities Tells you everything you need to know to make your hair color transitions smooth Enter your personal info such as natural hair color, eye color and skin tone as well as your preferences and it will surely tell you what your next hair color should be Select your personal options, hit Go when done... get results in less than a minute
4
-6
14
16
4
-4
10
16
7
4
7
13
5
2
6
13
0
-9
11
11
4
3
-1
11
8
12
0
10
5
6
-3
9
Improving the chances of sales success by finding the segments in the customer population The segmentation results shown in Table 4 provide the manufacturer and the trade with an opportunity to fine-tune the development of the new product and its merchandising. One of the recurring questions, however, is how to find these segments in the population? Traditional methods use data mining techniques, requiring many respondents (Rudd, 2000). Data mining works by searching for assignment rules that put people into the segments based upon a decision rule. The variables used by the decision rule come from external information about the respondent, which the respondent may have provided previously, or patterns of purchases that the individual may have made over time. The objective is to increase the chances of correctly classifying a new individual as a member of one of the three segments, and by so doing present the prospect with a better offer, whether an improved product or shopping experience. DISCUSSION Over any reasonable amount of time in many industries, particularly in the cosmetics industry which faces slow growth and stiff competition, advantage is viewed as a process driven by innovation (Jacobson, 1992). Innovation provides the innovators with all the advantages attributed to first enterers (Kanter, 1999). Innovation in the past four decades was the major factor in the evolution of cosmetics causing movement through the industry life cycle and changing the industry structure (Porter 1980). The cosmetics industry innovation mostly involved developing better chemistry for new or improved products. Over the last decade, cosmetics companies also provided superior service, superior after sale service and support. Product innovation creates value by creating new products or enhanced versions of existing products that customers perceive as having more utility. Higher utility provides an innovative company with uniqueness that its competitors lack. Utility, as shown here, embeds much more than chemistry or service development. By the discovery knowledge system presented here, we showed that customers perceived the highest utility as related to new store experience, specifically, to harnessing technology as a unique offering that may promote innovation. 17
Although promoting innovation can be a source of competitive advantage, the rate of failure in innovations is high. One study relating to chemical products suggested that only about 20 percent of major R&D projects succeed (Mansfield, 1981). In an in depth study of two chemical companies, only about 60 percent of projects were completed. Out of them, only 30 percent were commercialized, while only 12 percent earned profits (Mansfield, 1981). Along the same lines, another study concluded that only 11 percent of R&D projects produced commercially successful products (Stevens & Burley, 2003). This evidence suggests that only 10 to 20 percent of major R&D projects give rise to commercially successful products. Hill and Jones (2007) presented five reasons underlying failures in innovation. 1). Unclear market demand. 2). New products that are not well adapted to customer needs, in quality or in design. 3). A lack of fit between the specific product and the marketing strategy. 4). Slowness to market and 5). Technology myopia when there is no market demand for the technological product despite the glorious technical features it may possess. In order to increase the rate of success, innovating companies have to do a better job than their competitors in identifying and satisfying customer needs. Achieving responsiveness to customer needs is integral to achieving superior innovation. To achieve it, a company must give customers what they want, when they want it. The requisite for responsiveness is, therefore, listening to customers, investigating and identifying their needs. The early bird who identifies the needs and wants of its customers and satisfies those needs quickly, will be perceived by the market as innovative and responsive, resulting in successful commercialization. Cosmetics companies can use the internet-enabled approach we demonstrated here to rigorously yet straightforwardly discover weak signals of trend and test concepts. Such companies will increase the rate and the spectrum of their innovations. Through the discovery of trends, these companies can identify strong needs. Since customer needs are variegated, responsiveness to customer needs also requires customization of goods and services tailored to the unique tastes of customer groups, as demonstrated by segmenting respondents by their mindsets. This approach turns its users into proactive innovators. They can anticipate changes in trends when they are still a weak signal rather than respond to the change in trends when it becomes evident to all players. Our approach allows comprehensive analysis of numerous concepts for innovation using information from the ultimate customer. It provides powerful insights overnight. Affordable iterations are feasible, thus assuring the fine tuning of concept designs and features in accordance with market demand for each segment. This is a results oriented approach by which companies gain competencies for innovation, leading to the development of good concepts. Cosmetics companies using this approach will no longer focus mainly on improved chemistry of products, but will innovate in store experiences, in service, and in a wider proliferation of products and experiences based on direct communication with consumers. We expect a paradigm shift from 'product economy' to the new world of 'experience economy', going beyond static to provide new ideas for experience in the selling environment. The internet-enabled brandDelphi™ and conjoint approach suggested here provides innovators with an increased rate of innovation, better commercialization, better marketing mix, responsiveness, customization and shorter time to market, all critical innovation competencies. Thus, cosmetics companies can hold the knowledge necessary to effectively and consistently 18
innovate with enhanced responsiveness that will result in higher rates of success. New commercialized products or processes will better satisfy customer needs, further driving brand loyalty and premium pricing options. Since the approach is internet-enabled, it can be ported world wide in a cost effective fashion. Global companies will simultaneously communicate with customers across global markets to examine differences in weak signals and trends. We speculate that if such systematic approaches as the one shown here become widespread, weak signals will be tracked and detected early to build and optimize new product concepts. Acknowledgment Dr. Howard R. Moskowitz, Dr Laurent Florès and Gillie Gabay wish to thank Linda Lieberman, MJI, for editing this manuscript and generally keeping them on production schedule.
References Ben Rechav, Gillie Gabay. (2000), Relationship selling and Trust. Ann Harbor, Michigan: UMI Press. Box, G.E.P., Hunter, J., & Hunter, S. (1978), Statistics for Experimenters. New York: John Wiley. Deutsche Telecom (2000), “The new millennium with a capital T”, Forbes, Jan. 24, 81. Flores, Laurent., Moskowitz, Howard R., & Maier, Andrea S. (2003), From “weak signals” to successful product development: using advanced research technology for consumer driven innovation. Cannes, France: ESOMAR, Technovate. Green, P.E., and Krieger, A. M., (1991), “Segmenting Markets with Conjoint Analysis”, Journal of Marketing, 55, 20-31. Green P. E., Krieger, A. M. & Wind, Y. (2001), “Thirty years of conjoint analysis: reflections and prospects”, Interfaces, 31, S56-S73. Green, P.E. and Srinivasan, V. (1980), “A general approach to product design optimization via conjoint measurement”, Journal of Marketing. 45: 17-37. Hamel, G. and Prahalad, C. K. (1993a), “Strategy as a stretch and leverage”, Harvard Business Review, 71, (2), 75-84. ---(1993b), Competing for the future. Cambridge, MA: Harvard Business School. Hill, C. W. and Jones, G. R., (2007). Strategic Management, New York: Houghton Mifflin Company. Hitt, M. A., Ireland, R. D. and Hoskisson, R. E., (2001). Strategic Management: Competitiveness and Globalization, 4th edition, Nashville, Tenn.: Southwestern College Publishing.
19
Jacobson, R. (1992), “ The Austrian School of strategy”, Academy of Management Review, 17, 782807 Jolson, M. and Rossow, A. (1971), “The Delphi Process in Marketing Decision Making”, Journal of Marketing Research, 8 (November), 443-448. Kanter, R. M. (1999), “From spare change to real change: The social sector as Beta site for business innovation”, Harvard Business Review, 77 (3), 122-132. Lumpkin, G. T. and Dess, G. G. (1986), “Clarifying the entrepreneurial construct and linking it to performance”, Academy of Management Review, 21, 135-172 MacElroy, B. (2000), “Variables influencing dropout rates in Web-based surveys”, Quirks Marketing. Mansfield, E. (1981), “How economists see R&D”, Harvard Business Review, Nov.- Dec. 98-106. Mone, M. A. McKinely, W. and Barger, V. L. III (1998), “Organizational decline and innovation: A contingency framework”, Academy of Management Review, 23, 115-132. Moskowitz, Howard R. (1984), Cosmetics Product Testing: A Modern Psychophysical Approach. New York: Marcel Dekker, Inc. --- (1986), “Sensory segmentation of fragrance preferences”, Journal of the Society of Cosmetics Chemistry, 37, 233-247 --- (1995), Consumer Testing & Evaluation of Personal Care Products. New York: Marcel Dekker, Inc. Moskowitz, Howard R. and Gofman, Alex. (2004). System and method for performing conjoint analysis. US Patent Pending, US2005/0177398 A1 August 2005 Moskowitz, HowardR. and Gofman, Alex. (2007). Selling Blue Elephants: How to Make Great Products Before Your Customers Even Know They Want Them New Jersey:Wharton School Publishing. Moskowitz, HowardR., Gofman, Alex, Katz. Rachel, Itty, Barbara, Manchaiah, Madhu, & Ma, Zhenyu. (2001),“Rapid, inexpensive, actionable concept generation & optimization – The use and promise of self-authoring conjoint analysis for the foodservice industry”, Foodservice Technology, 1, 149-168. Moskowitz, Howard R., Porretta, Sabastiano, & Silcher, Matthais (2005), Concept Research in Food Product Design and Development. Ames, Iowa: Blackwell Publishing. Nagamachi, M. (2002), “Kansei as a powerful consumer oriented technology for product development”, International Journal of applied Industrial Ergonomic,s 33, 289-294
20
Pine, B., & Gilmour, J.H. (1999). The Experience Economy. Cambridge, MA.:Harvard Business School Press. Porter, M. E., (1980). Competitive Strategy. New York, NY: Free Press. Price, R. (1996), “Technology and strategic advantage”, California Management Review, 23, 115132 Ray, R. (2006), “Prediction Markets and the Financial "Wisdom of Crowds", Journal of Behavioral Finance, 7, 2-5 Reynolds, P. D., Hay, M., and S. M., Camp (1999), “Global Entrepreneurship Monitor”, Executive report, Babson Park,MA.: Babson College. Rudd, O. P. (2000). Data Mining Cookbook. New York, NY:Wiley and Sons. Seung-Joo, L. (2004). “A case study of L'Oreal: Innovation and growth strategy”, KDI School of Policy and Management. Sharma, P. and Chrisman, J. L., (1999), “Toward a reconciliation of the definitional issues in the field of corporate entrepreneurship”, Entrepreneurship: Theory and Practice, 23 (3), 11-27. Shumpeter, J. (1934). The Theory of Economic Development. Cambridge, MA.: Harvard University Press. Stevens G.A. and Burley, J. (2003), “Piloting the rocket of radical innovation”, Research Technology Management, 46, 16-26 Systat (1997). The system for statistics. User manual. Evanston, Ill.: Systat Corporation, Division of SPSS. Wittink, D. R., Vriens, M. & Burhenne, W. (1994), “Commercial use of conjoint analysis in Europe: Results and critical reflections”, International Journal of Research in Marketing, 11, S. 41-52.
21
