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Posts Tagged ‘Conjoint’

It is increasingly evident that consumers no longer care to wait for companies to have their say on new products. Consumers want to be heard earlier in the process of developing products and exert more influence on the products they are going to use. The Internet, particularly Web 2.0 and its interactive methods and tools, is clearly playing a key role in facilitating and enhancing this mode of consumer behaviour.

The engagement of consumers in the process of new product development (NPD) can be viewed as a facet in the broader phenomenon where consumers are mixing production and consumption activities, known as ‘prosumption’. Tapscott and Williams contend in their book on “Wikinomics” (1) that many consumers seek to turn from passive product users into active users who also participate in the creation of the products they use and influence their design and function. But the type of involvement hereby referred to goes beyond the personal design of selected features of product items by consumers for their own use, as applied in mass customization; the contribution made by consumers (‘prosumers’) collaborating with companies in NPD is meant to positively affect many consumers other than themselves.  Tapscott and Williams suggest that companies should encourage their customers to contribute in more profound and significant ways to the design of products that may thereafter be marketed to many more users.

Agreeably, consumers differ in the extent and quality of contribution they are capable to make as function of their knowledge and skills in the domain of every product, and therefore consumers should be invited to collaborate in forums and with methods more appropriate for them. The forms of collaboration may vary from consumer participation in NPD research to generating ideas in social media forums and up to more extensive proposals of technical designs of product prototypes. As collaboration gets more advanced and significant it can greatly help — in addition to co-creating improved products — also to produce closer and more valuable relationships between a company and its consumers or customers. More advanced collaboration has the power to elevate relationships to a form of “partnership” and to increase the level of their strength and intimacy between a company and its more loyal customers.

In an instructive and interesting paper on Internet-based collaborative innovation, Sawhney, Verona, and Prandelli present methods which they classify by the nature of collaboration (breadth and richness) and the stage of NPD in which the given level of consumer involvement is applicable (e.g., front-end idea generation and concept development, back-end product design and testing)(2):

  • Deep-rich information at the Front-End stages: Discussions in virtual communities of social media that encourage exchange of ideas allow companies to capitalise on social or shared knowledge of consumers. Another method that relies on consumer-to-consumer communication is Information Pump, a type of “game” through which a company can reveal and better understand the vocabulary of consumers in describing product concepts vis-à-vis expressions of needs;
  • Reach a broad audience at the Front-End stages: Web-based conjoint analysis and choice techniques can be applied among consumer samples to gather and analyse relatively less rich but well-structured information about consumer preferences;
  • Deep-rich information at the Back-End stages: Web-based toolkits for exercising users’ innovation let the more expert consumers configure or design original product models of their own creation, working in a specially built environment and with computer-aided design tools — this approach relies on knowledge of individuals;
  • Reach a broad audience at the Back-End stages: Particularly applicable to digital products (e.g., software, web-based or mobile applications, video games) where prototype or experimental beta versions can be tested online; however, visual-simulated depictions of alternative virtual configurations of advanced prototypes can be applied to test and evaluate the acceptance of a wider range of tangible products.

In the virtual world of the Internet, unlike the physical world, there is a less rigid trade-off between breadth of access to consumers and richness of information (e.g., small focus groups versus surveys of large samples); this advantage is stated by Sawhney et al. “…Internet-based virtual environments allow the firm to engage a much larger number of customers without significant compromises on the richness of the interaction. ” This advantage is particularly demonstrated in social media forums.

It should be emphasised, nevertheless, that new methods of collaboration should not come in replacement of  NPD research methods; research-based methods and non-research methods of consumer-company interaction can wonderfully complement each other and should continue to be applied in parallel to answer different requirements of the NPD process for consumer informational input and aid. In a leading paper for the new age of NPD research, “The Virtual Customer” (3), Dahan and Hauser describe state-of-the-art research methods and techniques for different stages of the NPD process. They distinguish, for example, between (a) conjoint types of measurement techniques and models that are most suitable for guiding product design at an early stage (feature-based), and (b) a method applicable for testing the appeal and purchase potential of candidate prototypes (integrated concepts) at a more advanced stage of product development. The latter method in particular takes the advantage of displaying images of virtual prototypes (e.g., SUV car models) to consumers , supplemented by additional product and price information, in an online survey for testing  reaction (choice) before going to production. They also explain in great detail unorthodox methods such as the Information Pump and Securities Trading of Concepts.

  • It is noteworthy that most research methods concentrate on learning from consumers about their preferences without engaging them in proposing product designs; the User Design method, however, already gives more leeway to consumers-respondents to construct their desired products using a self-design tool similar to mass customisation.

Forums or personal pages in public social media networks are widely accepted these days as an excellent arena for companies to receive ideas from consumers for new products and gather information about their product preferences and expectations. However, it is likely to turn out as a formidable task to comb and pick-up ideas of real value and practical potential for implementation from these sources as well as user-generated-content in blogs. Some good ideas may also get lost in the river of postings or comments customers upload in a company’s page on service issues, billing etc.. Dedicating a special separate page for interaction with consumers on new products, goods or services, can help to raise the level of ideas formulated and to allow peer discussions on those ideas that can lead to their further progression. But even then, the ideas proposed in such a venue may be mostly initial concepts, vague or unfocused. Such a venue is a good place to start, allowing any customer interested to contribute. Thereafter, owners of more mature or promising ideas may be referred to a company-owned virtual forum on its own website where a more advanced collaboration with the consumers-contributors may be developed.

Managing collaborative activities for NPD in a company-owned website division can offer some valuable possibilities. First, it provides better control and capabilities for moderating discussions among users or interacting directly one-to-one with the originators of product-concept proposals; it would be an environment dedicated by the company and designed by it specially for interacting with users and among themselves. Second, performing collaborative activities in this environment is likely to attract users with higher level of knowledge, competence and interest in domains of the company’s products; greater proficiency of users demonstrated in their discussions frequently leads to natural screening-out of novice and less serious users.

Third comes the sensitive issue of security and protecting intellectual property. Companies do not tend to guarantee any protection for initial ideas brought up by consumers, not even in their own websites. Particularly in forums that are founded on sharing knowledge and discussion of ideas between users, information has to remain transparent and accessible to participants and to the company. Tapscott and Williams noted that consumers get excited by the creation of their own products and enjoy it even better when they can do it together (4).  However, companies can offer some better measures to secure information such as limiting access to discussions and materials (e.g., by password permission) and preventing unauthorised extraction of content. Where proposed designs of product models are meant to be shared, originators should get the option to credit their models with their IDs. Confidentiality and rights are offered for the most progressed technical designs that are planned to be adopted by a company for manufacturing and marketing.

Fourth, a company can provide an interactive toolkit for innovation on its website for consumers-collaborators who wish to take their ideas and concepts one step or more further. With the toolkit users can apply relevant design tools to sketch plans and construct virtual 3D product models. Depending on type of collaboration program and context, users can allow their proposals to be available to other users or to the company alone. Thomke and von Hippel proposed a complete process for customer innovation that includes several iterations of developing a design with a ‘toolkit for innovation’, building a prototype, receiving feedback from the company (‘test’), and return for revisions (5). Through early iterations the prototypes built by the system would be virtual, until the design is satisfactorily advanced to manufacture a physical prototype of the product. The authors suggest that the customer-led process is likely to require fewer iterations than in a ‘standard’ NPD process, save time and money, and free the company to invest more effort in improving manufacturing capabilities.

Different schemes have been devised for collaboration programs with customers:

  • The Open Innovation Collaborative Programme of Unilever, for example, is designated for highly skilled contributors with extensive knowledge in the domains of products for which they invite proposals (list of Wants, e.g., solutions for detergents). Collaborators are referred to a special portal for submission (in co-operation with a consulting firm yet2.com that manages the review process).
  • Other programmes are more popular in nature and appear suitable to a wider audience of consumers with varied levels of expertise. Take for instance the Create & Share collaborative suite by Lego on its website. More than a decade ago Lego cleverly realised with appreciation the creativity of its leading hobbyists and enthusiasts (adults included!) who invented original models based on existing parts and suggested new forms of Lego blocks; Lego started to accept such designs and offer new models’ sets and less conventional building parts. The online suite includes today a gallery of models built by fans, message boards, and especially the Lego Digital Designer toolkit application for constructing virtual plans of fans’ own models (unfortunately Lego has terminated last year its ByME customization program that allowed users to order their own physical models).

Consumers who collaborate with companies should be rewarded for their more significant contributions of ideas and products designs. On the one hand, the reward does not have to be monetary, cash-in-hand (some may not even want to be perceived as paid contributors/employees). On the other hand, companies should not get satisfied by relying on enjoyment of contributors and their feelings of self-fulfillment and accomplishment. Furthermore, a company should not appear to be relinquishing its duties in generating genuine ideas and developing new products to its customers. First, many customers will be happy to receive credit by name in recognition of their contribution in the company’s publications and websites. Second, contributors can be rewarded with special gifts or privileges in obtaining and using their own-designed products and other products of the company. Monetary prizes will probably continue to be distributed to winners in competitions.

Collaboration for innovation changes the relations between a company and its consumers or customers because it gets them to work together, co-creating new products that thereof better fit consumer needs and wants. Particularly activities that engage consumers in developing concepts and designing products have the better potential of narrowing gaps between companies and customers.  Research, collaboration in other ways, and internal development by professional teams within the company should be used together in integration in NPD activities.Collaboration shifts the balance of control more towards the consumers, but companies who learn how to share knowledge and competencies with the latter can gain in improving innovation practices, increasing value, and not least, enjoying stronger customer relationships.

Ron Ventura, Ph.D. (Marketing)

Notes:

(1) “Wikinomics: How Mass Collaboration Changes Everything“, Don Tapscott and Anthony D. Williams, 2006, Portfolio.

(2) “Collaborating to Create: The Internet as a Platform for Customer Engagement in Product Innovation”, Mohanbir Sawhney,  Gianmario Verona, & Emannuela Prandelli, 2005, Journal of Interactive Marketing, 19 (4), pp. 1-14 (DOI: 10.1002/dir 20046).

(3) “The Virtual Customer”, Ely Dahan and John R. Hauser, 2002, The Journal of Product Innovation Management, 19, pp. 332-353.

(4) Ibid. 1.

(5) “Customers as Innovators: A New Way to Create Value”, Stefan Thomke and Eric von Hippel, 2002, Harvard Business Review, 80 (April), pp. 74-82.

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Competition in health-related industries (i.e., health care services, pharmaceutical, biotechnology) has been increasing continuously in the past two to three decades. The health business has also become more complex and multilayered with public and private institutions, individual doctors and patients, as players. Consequently, decision processes on medical treatment may become more complicated or variable, being more difficult to predict which treatment or medication will be administered to patients. For example:

  • For many medical conditions there are likely to exist a few alternative brands or versions of the same type of prescribed medication. Depending on the health systems in different countries, and on additional situational factors, it may be decided by a physician, a health care provider and/or insurer, or a pharmacist what particular brand of medication a patient would use. In some cases the patient may be allowed to choose between a more expensive brand and an economic brand (e.g., original and generic brands, subsidised and non-subsidised brands).
  •  There are plenty of over-the-counter (OTC) medications, formulae and devices that patients can buy at their own discretion, possibly with a recommendation of a physician or pharmacist.
  • Public and private medical centers and clinics offer various clinical tests and treatments (e.g., prostate screening, MRI scanning, [virtual] colonoscopy), often going above the heads of general/family physicians of the concerned patients.
  • In more complex or serious conditions, a patient may choose between having a surgery at a public hospital or at a private hospital, depending on the coverage of his or her health insurance.

In the late 1990s, professionals, executives and researchers in health-related areas have developed an interest in methods for measuring preferences that would allow them to better understand how decisions are made by their prospect customers, especially doctors and patients (“end-consumers”). This knowledge serves (a) to address more closely the preferences of patients or requirements of physicians, and (b) to channel planning, product development or marketing efforts more effectively. In particular, they have become interested in methods of conjoint analysis and choice-based conjoint that have already been prevalent in marketing research for measuring and analysing preferences. Conjoint methods are based on two key principles: (a) making trade-offs between decision criteria, and (b) decomposition of stated preferences with respect to whole product concepts (e.g., a medication) by means of statistical techniques into utility values for levels of each attribute or criterion describing the product (e.g., administering 2 vs. 4 times in 24 hours). The methods differ, some argue quite distinctly, in terms of the form in which preferences are expressed (i.e., ranking or rating versus choice) and in the statistical models applied (e.g., choice-based conjoint is often identified by its application of discrete choice modelling). An important benefit for pharmaceutical companies, for example, is gained in learning what characteristics of a medication (e.g., anti-depressant) contribute more to convincing physicians to prescribe it, versus factors like risks or side-effects that lead them to avoid a medication.

The product concepts presented are hypothetical in the sense that they are specified by using controlled experimental techniques and do not necessarily match existing products at the time of study. This property is essential for deriving utility values for the various levels of product attributes studied, and to allow prediction by simulation of shares of preference (“market shares”) for future products. The forecasting power of conjoint models is considered their major appeal from a managerial perspective. In addition, conjoint data can be used for segmenting patients and designing refined targeted marketing strategies.

Interest in application of conjoint methods in a health context has grown in the past decade. According to a review research of conjoint studies reported in 79 articles published between 2005 and 2008, the number of studies nearly doubled from 16 in 2005 to 29 in 2007. The researchers estimated that by the end of 2008 the number of published studies would reach 40. The most frequent areas of application have been cancer (15%) and respiratory disorder (12%)(1). However, applications of conjoint techniques can be found also for guiding policy making and the design of health plans in a broader context of health-care services provided to patients (e.g., by HMOs).

Most conjoint studies in health (71%) apply choice experiments and modelling, becoming the dominant approach (close to 80%) particularly in 2008. A typical study includes 5 or 6 attributes with 2 or 3 levels for each attribute. Most studies in a choice-based approach involve 7 to 8 scenarios (choice sets) but studies with 10-11 or 14-15 scenarios are also frequent (2). A choice scenario normally includes 3 to 5 concepts from which a respondent has to choose a single most prefered concept.

Interpretation of conjoint studies among medical doctors needs a special qualification to be distinguished from studies of patients or consumers. That is because the physicians make professional judgements about the most appropriate treatment option for their patients.  Therefore, it is less appropriate to relate to personal preferences in this context. It is more sensible and suitable to talk about decision criteria that physicians apply, their priorities (i.e., represented by importance weights), and requirements of physicians from pharmaceutical or other treatment alternatives available in the market.

Including monetary cost in conjoint studies on products and services in health-care may be subject to several complications and limitations. That may be the reason for the relatively low proportion of articles on conjoint studies in health that were found to include prices (40%)  (3). For instance, doctors do not take money out of their own pockets to pay for the medications they prescribe, so it is generally less relevant to include price in their studies. It may be sensible, however, to include cost in cases where doctors are allowed to purchase and hold a readily available  inventory of medications for their visiting patients in their private clinics (e.g., Switzerland). It may still be useful to examine how sensitive doctors are to the cost of medication that their patients will have to incur when prescribing them. However, this practice may be additionally complicated because the actual price patients pay for a specific medication is likely to change according to the coverage of their health plan or insurance. It is appropriate and recommended to include price in studies on OTC medications or health-related devices (e.g., for measuring blood pressure). Aspects of cost can be included in studies on health plans such as the percentage of discounts provided on medications and other types of clinical tests and treatments in the plan’s coverage.

An Example for a Conjoint Study on Health-Care Plans:

A choice-based conjoint study was conducted to help a health-care coverage provider assess the potential for a new modified heath plan it was considering to launch. Researchers Gates, McDaniel and Braunsberger (4)  designed a study with 11 attributes including provider names (the client and two competitors), network of physicians accessible, payment per doctor visit, prescription coverage, doctor quality, hospital choice, monthly premium, and additional attributes. Each respondent was introduced to 10 choice sets where in each set he or she had to choose one out of four plans. This setting was elected so that in subsequent simulations the researchers could more accurately test scenarios with existing plans of the three providers plus a new plan by the client-provider. The study was conducted among residents in a specific US region by mail. Yet beforehand a qualitative study (focus group discussions) and a telephone survey have been carried out to define, screen and refine the set of attributes to be included in the conjoint study. 506 health-care patients returned the mail questionnaire (71% response rate out of those in the phone survey who agreed to participate in the next phase).

The estimated (aggregate) utility function suggested to the researchers that the attributes could be divided into two classes of importance: primary criteria for choosing a health plan and secondary considerations. The primary criteria focused on access allowed to doctors in the region of residence and cost associated with the plan, representing the more immediate concerns to target consumers in the market in choosing a health-care plan by a HMO. It was mainly confirmed in the study that consumers are less concerned by narrowing the network of doctors they may visit, as long as they can keep their current family physician and are not forced to replace him or her with another on the list. Respondents appeared to rely less on reported quality ratings of doctors and hospitals. Vision tests and dental coverage were among the secondary considerations. Managers could thereby examine candidate modifications to their health plan and estimate their impact on market shares.

The conjoint methods offer professionals and managers in health-related organizations research tools for gaining valuable insights into patient preferences or criteria governing the clinical decisions of doctors on medications and other treatments. These methods can be particularly helpful in guiding the development of pharmaceutical products or instruments for performing clinical tests and treatments when issues of marketing and promoting them to decision makers come into play. As illustrated in the example, findings from conjoint studies can be useful in policy making on health-care services and designing attractive health plans to patients. This kind of research-based knowledge is acknowledged more widely as a key to success in the highly competitive environs of health-care.

Ron Ventura, Ph.D. (Marketing)

Notes:

(1)  Conjoint Analysis Applications in Health – How Are Studies Being Designed and Reported? An Update of Current Practice in the Published Literature Between 2005 and 2008, D. Marshall, J.F.P. Bridges, B. Hauber, R. Cameron, L. Donnalley, K. Fyie, and F.R. Johnson, 2010, The Patient: Patient-Centered Outcomes Research, 3 (4), 249-256

(2) Ibid. 1.

(3) Ibid. 1.

(4) Modeling Consumer Health Plan Choice Behavior to Improve Customer Value and Health Plan Market Share, Roger Gates, Carl McDaniel, and Karin Braunsberger, 2000, Journal of Business Research, 48, pp. 247-257 (The research was executed by DSS Research to which Gates is affiliated).

Additional sources:

A special report on conducting conjoint studies in health was prpared in 2011 by a task force of the International Society for Pharmaeconomics and Outcomes Research. The authors provide methodological recommendations for guiding the planning, design, and analysis and reporting conjoint studies in health-related domains.

Conjoint Analysis Applications in Health – A Checklist: A Report of the ISPOR Good Research Practices for Conjoint Analysis Task Force, John F.P. Bridges, and A. Brett Hauber et al., 2011, Value in Health, 14, pp. 403-413

http://www.ispor.org/taskforces/documents/ISPOR-CA-in-Health-TF-Report-Checklist.pdf

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Measuring the value or equity of a retailer name from a customer’s point-of-view is usually challenging because of the diversity of products from various brands retailers offer on display and additional dimensions of performance that are specific to the retail store environment. For a long time retailers are not merely distributors that bring forward products to the consumers but offer products in their own names; furthermore, experiences of shoppers on-site of the store have a stronger influence on their purchase decisions. Thus, assigning customer values to retail names is often not a simple matter.

A similar problem with respect to the diversity of products may arise when trying to measure the value of a manufacturer corporate name to consumers, if the manufacturer uses its corporate name as an umbrella or even as a higher-level endorser for a wide range of products of different types. In this condition consumers may become confused as to which type of product they should refer and find it difficult to generalise their value judgements too broadly. Particularly, when trying to translate the subjective value to monetary terms, as often exercised with conjoint models of preference, one cannot plainly specify a price range that will be relevant to various types of products (e.g., TVs, stereo systems, washing machines, etc.) because the over-arching corporate name is too abstract. One has to conduct an evaluative study for each product category separately to obtain valid and relevant evaluations. The evaluation problem becomes several times more complex for a retail chain by accounting for the internal competition between manufacturers’ brands and a retailer’s own brand, and the other facets of the shopper experience in store (e.g., design and atmosphere, convenience, service).

  • As a case in example consider the branch of fashion retail. Castro, a leading Israeli homegrown fashion retail chain, has expanded greatly over the past 15 years (operating around 100 stores) and is a well-known and favourable name in many homes. According to a survey by Israeli business daily paper TheMarker with market research firm “Meida Shivuki” (27 Jan. 2012 (1)), Castro is the most familiar fashion retailer in Israel, remaining stable in this position six years in a row. Nearly half of adult Israelis have purchased an item or two in one of Castro’s stores in the past year. Fox, a low-cost retailer, is second, and Zara, a global Spanish-originated retailer, is in the third place (fourth year in a row). However, newly coming international retailers like H&M and Gap are tailing Castro, and more international brands like American Eagle or Banana Republic are expected to arrive soon. H&M climbed in awareness from 18.5% to 27% in six months, while Castro withdrew a little from 68% to 64%. Competition in the local fashion arena is becoming fierce, maintains TheMarker. In this setting, we may ask how well the value of Castro, from a consumer perspective, fares against rival international retail brands. One may also question what is the “attraction power” of Castro in terms of willingness-to-pay, and does it have to drag itself into a price war with the rivals to win?

In a conjoint analysis or choice study, brand is usually defined as one of the attributes describing a product, with several different brand names suggested as options (e.g., a choice set with four alternative products, each from another brand). This approach provides a single-numeric measure of value for each brand that some criticize as of too limited scope. Hence, further analyses on the subjective brand-equity values are advised, such as translating them also to monetary values of brand premiums by accounting also for consumers’ price sensitivity.

Moreover, in order to learn about the sources of brand values, we can analyse variation in brand values at the individual level vis-a-vis brand perceptions on several relevant dimensions of brand image (e.g., performance, reliability, or courteous service). Several techniques allow that, including with discrete choice modelling. It is worth mentioning in that context an unusual approach suggested more than 20 years ago of a brand-anchored model for evaluating the images of retailer brands (2). In that conjoint model, rather than including retailer names as options in a single brand attribute, retailer names are represented as options in each of several retail image attributes: For example, convenience of shopping is like at store of retailer “A”, “B”, or “C”.  This model does not offer overall values for each retailer, but it does suggest the relative values of a retailer name on each dimension of image. It’s like combining two-stage analyses proposed at the top of this paragraph in a single analysis. One conspicuous weakness of this approach is that respondents who do not know what would be the level of performance of an existing retailer on any of the dimensions will face difficulty in making reliable judgements of the retailer “portfolios” suggested to them or make a choice between them (the researchers have shown that consumers are likely to recall better the brands that score higher).

In the remaining of this post-article I suggest three alternative approaches for evaluating customer-based brand equity of retail chains in the framework of conjoint models, the first two apply a monetary currency whereas the third proposes distance as the currency of cost.

A Common Set of Products (“basket”) — In this approach we present to respondents-shoppers a well-defined set of products and ask them to suppose that they are going to shop for this set in stores of several optional retailers. Since retailers frequently offer on display a large variety of products, this set should serve as a common reference for comparison with regard to price levels. In some domains, such as food and grocery, we may be able to construct a “basket” of particular product items, including specific brands, because most stores hold the same product brands. Yet, in other domains like fashion this task could be more daunting because retailers choose to offer more differentiated clothing designs and specialise in bringing clothing items from different designer names. In the case of fashion we may have to describe in more general terms an outfit composed of several items but be specific enough about the quality and style of the items (e.g., think of dressing a mannequin with an outfit).

Applying this approach, therefore, is more domain-contingent. Our aim is to estimate the price premium that shoppers are willing to pay in order to purchase a set of products from a particular retailer. However, there is greater risk in this approach of confounding the value of a retailer with the values of products included in the set of reference.

Retailers’ Qwn Brands — Many retail chains in various domains offer products in selected categories carrying their own retail name as brand or their unique private labels available only at their chain-stores. Emphasizing the retailer’s own brand of products helps to better focus attention on the retailer on all aspects of shopping from its chain-stores. It may be seen as a special case of the first approach, only that here respondents-shoppers are advised that all product items included in the set are carrying the retailer’s name or private label. Thus, the differences in quality between retailers with respect to their own branded products can be taken into consideration by the respondents-shoppers.

This method represents a more round-up approach for assessing the monetary premium shoppers are willing to pay when buying at a particular retail chain on ground of both products identified with the retailer and the experience of shopping at its stores. Yet, it is applicable only if all retailers proposed have salient brands of their own for comparison.

Distance from a Retailer’s Nearest Store — Taking on a different perspective, this approach breaks with the common use of monetary price as the currency of cost. As implied in the first two methods described above, the monetary currency may introduce quite difficult complications in the context of retailer evaluation. Nonetheless, there are types of cost consumers are likely to incur while making purchase decisions such as time and psychic effort or stress. Particularly in the context of retail, Sorensen relates to time and angst in addition to money as the three currencies of cost shoppers may incur while looking for products they require or desire in-store (3). However, even before entering the store, another type of cost may be the distance the shopper has to make to reach his or her favourite store. Distance is often suggested also as a measure of loyalty: How far are you willing to go in order to find your favourite brand or to shop in a store of your favourite retailer?

According to this approach, a “cost” attribute would inform respondents-shoppers, for instance that “the nearest store of Retailer A is 500 meters away from you”. This type of conjoint application measures the retailer’s brand premium in terms of extra distance shoppers are willing to go to reach one of its stores (relative to a competing retail chain). It is possible that some consumers would want to go further actually to find lower prices or better value, but that perception could also be engrained in the retailer’s image. Indeed, the conjoint model alone may not tell us whether a retailer’s brand is preferred due to price/value perceptions or shopping experience aspects. On the other hand, it provides a measure of loyalty that may fit more smoothly in the context of choosing a retailer and poses no pre-conditions on the specific products each consumer may wish to buy at the store.

Each of these three approaches to measuring customer-based brand equity of retailers may be more appropriate, sensible, and easier to apply in some domains rather than others. The third approach appears for example the more suitable in the domain of fashion. However, if pricing issues arise, the  first or second approaches may be more practical albeit with some greater difficulty. That is where experience and good judgement of managers and researches comes in.

Ron Ventura, Ph.D. (Marketing)

Notes:

(1) Hebrew readers may find the original article of TheMarker at http://www.themarker.com/consumer/1.1627373.

(2) “Reliability and Validity of the Brand-Anchored Conjoint for Measuring Retailer Images, Jordan J. Louviere and Richard D. Johnson, 1990, Journal of Retailing, 66 (4). pp. 359-382.

(3) “Inside the Mind of the Shopper (The Science of Retailing)”, Herb Sorensen, 2009, Pearson Education.

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Consumers can contribute valuable guiding information to the process of new product development (NPD) in almost every step of the way. By reviewing  academic literature in the areas of NPD and marketing research or by browsing the products and services of major marketing research firms worldwide, one may find an assortment of research methods designed for providing information from a consumer perspective to support product development decisions.

A suite of methods and models developed and organized by a team of researchers at MIT (Cambridge, MA) seems to incorporate the most essential and contemporary  ingredients of a comprehensive programme for NPD research. It is comprehensive in the sense that one may find in it a method most suitable for each of three core stages of an NPD process:

  • Generating ideas for the purpose of a new product (i.e., what consumer needs or desires the product will answer) and the approach taken to achieve that;
  • Selecting attributes and features to be included in the product based on what consumers value more and value less (e.g., “must have”, “nice to have”, and “better without” features);
  • Testing product prototypes, models or concepts that already reached an advanced state of their design.

That research programme is titled the ‘Virtual Customer Initiative’. The methodological approaches may not be new in principle but they have been modified and adapted to be fit for the technology as well as the lifestyle of the 21st Century. The data gathering interface is web-based, that is, the interface with consumers is transported to the virtual world of the Web. The programme further offers new techniques for gathering data on the web that take advantage of and adapt to particular properties of the web environment.

From another perspective, prospect customers or users of a product can be introduced to products in a virtual form before a physical model item has been produced. Particularly in a pretest or test stage, consumers-respondents can see a visual design of a candidate product, possibly rotate its image to be viewed from different angles, without the need yet to produce a physical demo. This can save a considerable amount of time and money for the product development (PD) team.

Conducting NPD research in virtual settings has several attractions. Consumers spend more and more time on the Web, they become more accustomed to the conventions and styles of working with the Internet, and consumers can access the study questionnaire from home or work without arriving to a central facility or be visited by an interviewer. The latter advantage implies potentially greater convenience and ease for respondents and less cost in logistic effort, time and money for researchers and PD teams. There are however some limitations: Consumer panels from which samples are drawn for studies are often still not sufficiently representative of the target populations; without supervision respondents can freely abandon the questionnaire at anytime; and, a self-administered questionnaire (SAQ) must be clear and easy to understand its instructions and informational prompts without guidance or assistance from an interviewer.

Dahan and Hauser (2002) classify the six methods in the suite along two dimensions: products described as “feature-based” or “integrated concepts”, and using “fixed design” vs. “adaptive design” (three levels).

  • A feature-based method manipulates and measures the values of features composing a product whereas a method of integrated concepts is concerned with preferences for the whole products as given.
  • Adaptive designs for constructing products are recognized as computationally more sophisticated designs compared with fixed designs — adaptive designs are flexible and dynamic,  capable of altering the product stimuli for each customer-respondent in accordance with his or her previous responses. The goal is to produce as small as possible a set of products, and thus a shorter questionnaire, for each respondent, while maintaining a sufficiently efficient design for estimating model parameters (e.g., feature part-worth values).

Dahan and Hauser recommend the use of adaptive algorithms in order to decrease burden on consumers-respondents and increase the likelihood that they complete a shorter questionnaire. Nonetheless, they add that interfaces also have to be interesting and engaging so as to attract and persuade the respondents to stay on to the end of the questionnaire.

I chose to focus below on four of the methods:

In Web-Based Conjoint Analysis (feature-based, fixed design), as since the inception of the CA methodology nearly 40 years ago, respondents are introduced to hypothetical product concepts described as profiles of the attributes or features under consideration by the PD team. A respondent is asked to rank-order or rate the full profiles while trading-off levels from the different attributes composing the product. The set of products is constructed in a fixed experimental design, that is, the set is determined in advance and is presented to all respondents. With a web-based application, researchers may include in addition to verbal descriptions also pictorial illustrations of product attributes and apply interactive displays that improve the communication and flow of the conjoint task.

The FastPace Adaptive Conjoint (more formally: Fast Polyhedral Adaptive Conjoint Estimation — feature-based, adaptive design) is an important and impressive recent development aimed at constructing ever smaller sets of product profiles, customised for each respondent. An advanced mathematical algorithm relatively quickly reduces the space of all possible feature combinations into a smaller set based on answers from earlier steps. The method promises to create smaller adaptive designs than achieved in the veteran Adaptive Conjoint Analysis (ACA) by Sawtooth Software Inc..  Apparently, Dahan and Hauser highlight FastPace by introducing it as the dominant approach to adaptive conjoint designs. But FastPace is not always better than ACA: FastPace has been shown to be superior particularly when it uses prior measures of attribute importance (i.e., additional questions) as ACA does, and there is a major concern of respondent wear out. Notably, the relatively new method of Sawtooth Software of Adaptive Choice-Based Conjoint (ACBC) for choice data is founded on the method of FastPace. They retain a prior set of questions before the adaptive conjoint task, and its combination of attribute-based questions and screening choice questions (an elimination phase of unacceptable products) creates a process that seems more intuitive and natural to consumers than that used in the older ACA.

Adaptive conjoint designs are beneficial for feature-based studies with more than 8 attributes. Because of the complexity of a method and interviewing procedure such as ACBC, it is advisable to consider carefully to what degree it is essential, not an overkill for the problem at hand, and especially see to it that implications of the model assumptions and limitations due to the adaptive process are well understood.

User Design (feature-based, intermediate adaptive design) works like a product configurator — it allows a respondent-product user to choose any feature from a list of available features (e.g., car gearbox: manual), drag and drop it in another list of his or her preferred product features. As a feature is added to his self-designed product the total price is updated. If the respondent regrets, he can return a feature to the availability list. And when he reaches a satisfying design and no longer wishes to make changes, he is asked for the likelihood of purchasing the designed product. It is a feature-based method with a moderate level of adaptation. This method  is advantageous particularly when there are many features to be considered, and furthermore, if there are potential interactions between attributes that need to be considered (estimating interactions in conjoint studies can have a substantial effect on the size of the design). The task is engaging because the participating customer  learns his preferences as he tries out features and builds a product to his liking. This method may be used for a preliminary exploration of preferences for plausible features before a conjoint study. Since in User Design each customer-respondent constructs only a single “ideal” product, this method is more limited when making predictions of preference shares by simulation than conjoint methods.

Virtual Concept Testing (integrated concept, fixed design) is concerned with whole product models that are already fully configured. But there are holistic aspects of a candidate product concept — its design, style and appearance — that need to be tested before a product can be approved. These holistic aspects are matters of impression and appeal that are difficult to breakdown into technical or functional features. Each product in a set is represented primarily by its brand name and a visual image (i.e., identifying the concept), and a price tag. It works similar to a conjoint study but with only two attributes: concept and price. Any additional information on specific attributes, such as ratings of performance, are pre-determined for each concept. Only prices may vary in a controlled manner. In a web-based application there is excellent opportunity to make the display of concepts more engaging and realistic with the use of rich media. Dahan and Hauser refer to an earlier study showing that preferences measured with this method are highly consistent with concept tests based on physical prototypes.

A sensible programme of NPD research reveals itself: User Design provides an initial but broad glance at configurations of features customers would like to find in the proposed type of product ; followed by Web-Based Conjoint Analysis or FastPace Adaptive Conjoint to measure more rigorously preferences for hypothetical product profiles, and estimate the values of attributes; and finally complemented with a Virtual Concept Test to examine how a candidate product model designed by the PD team fares against competing products at a target price.

For more information on these and other methods, I encourage interested readers to visit the website of ‘Virtual Customer Initiative’. You will find in the site brief explanations of each method, more detailed and technical information in published papers and white papers, demonstrations and open-source programme codes to download.

Ron Ventura, Ph.D. (Marketing)

The Virtual Customer, Ely Dahan and John R.  Hauser, 2002, The Journal of Product Innovation Management, 19, pp. 332-353

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