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Mass customization allows companies to provide every customer a product made according to his or her preferred specifications, delivered for a mass of customers. Building on advanced information management technology and highly flexible computer-aided manufacturing (CAM) capacity, this approach enables a company to create a large variety (scope) of “ad-hoc” customized products. The interactive capabilities of the Internet, particularly Web 2.0, make configuring and ordering the self-designed product much more accessible to the public. Different methods for customization and (personalised) recommendation of products have been developed and implemented in recent years, but only the approach known as mass customization (MC)  actually allows a consumer to  order a self-designed product item. Yet, MC  has not been adopted by companies in many consumer markets so far and programmes initiated  often survive for just a few years. The main impediment has been in lowering the costs to levels compatible with mass production. It raises doubts that MC can become a viable business practice.

An online MC programme provides the consumers with an interactive Web-based configurator or MC toolkit application for choosing their preferred attribute specifications, guiding them through the self-design process step-by-step. Graphic-rich and user-friendly interfaces help to enhance the experience for consumers. The Internet offers two important capabilities that can smooth the whole MC process: (a) gathering the preferences data from customers in real-time, and (b) transferring the information to a company’s facility from anywhere a consumer operates the toolkit on a personal computer or a mobile device connected online.

The best early example of MC implementation is probably that of the Japanese National Bicycle Industrial Company (NBIC — owned by Panasonic) that allowed consumers to order ‘tailored’ bicycles. But that was already available before the age of Internet: measures to fit a pair of bicycle to a rider were taken on a specially built physical model. Among MC applications available to consumers through the Internet in the past and present we may mention for example:

  • NikeID for designing sports footwear (running for over ten years),
  • Levi’s Orignial Spin jeans for women (terminated),
  • Chocri chocolate bars and pralines from Germany (a UK service is currently suspended),
  • Reflect.com customized cosmetics (suspended),
  • Blank Label self-designed and made-to-measure  dress shirts for men (based in Boston & Shanghai and operating for four years),
  • Lego’s Create & Share programme incorporated an MC service called byMe (terminated in Jan. 2012) that allowed users to order a box with the parts-bricks for the model they personally designed with LEGO Digital Designer — the toolkit is still available,
  • Dell’s customized personal computers (changed customization approach).

In order to derive practical utility from configuring a product consumers should arrive to the task with adequate knowledge in the product category, understanding the attributes and their consequences with regard to quality or performance, and knowing which ones are the more important. This is particularly relevant for attributes for which there is shared convention as to options or levels that predict higher quality as opposed to attributes of more aesthetic nature and preferences reliant on personal tastes. Consequently, consumers are expected to have well-defined preferences on those attributes. However, many and even most of the consumers have just low to moderate levels of knowledge in any product category (e.g., food, home appliances, technologically advanced digital products). Furthermore, it is recognised now that consumers often do not have clear and well-established preferences and they resort to constructing their preferences as they advance towards a purchase decision. That means, for instance, that low-knowledge consumers who use an MC toolkit but do not clearly know what they are looking for are more likely to be influenced by the content of attributes offered for customisation by the product configurator and its overall structure.

But there is additional complexity to consumer response in the context of customization because the condition stated above on preferences may not be sufficient. Itamar Simonson, professor of marketing at Stanford University, expands the discussion by proposing that in addition to (a) having stable and well-developed preferences, consumer response to customised offers also depends on (b) the level of ‘self-insight’ into their own preferences and own judgement of their clarity and stability. When using the aid of a recommendation agent, it suggests implications such as the ability of consumers to accurately and clearly articulate their preferences to others, correctly acknowledging the real drive to their choices (e.g., rational vs. aesthetic or affective), and properly identifying a product recommendation that fits well their preferences (1). Consumers whose state of preferences is low on both factors are especially likely to be swayed by the attributes a recommending agent chooses to emphasise. In the case of using a product designer toolkit in MC, the burden on the consumer seems even greater, more explicitly requiring him or her to accurately articulate his preferences and subsequently confirm that the outcome product one designed indeed matches what he or she wanted; a major cause for consumers to abandon before ordering is their evaluation that the outcome product’s utility is less than planned. Another important cause is frustration and ungratifying experiences while utilising a configurator to self-design the product.

Consumers differ in the type of attributes they would want to customize, the number of attributes desirable for customization and the number of options or levels to choose from — factors that influence the purchase likelihood of a customised product. Interestingly, more knowledgeable consumers have not been found to be more inclined to purchase a customized product. Some differences in preference for layout of configurtors have been found related to variation in knowledge. For example, the less knowledgeable consumers are those who actually desire a larger number of options to choose from on attributes of personal subjective taste, because they tend to learn their preference as they look through options; high-knowledge consumers need that less. But we also have to take into account what consumers believe they know, and consumers are often wrong in that assessment (‘knowledge miscalibration’). Thus, overconfident novices are those who particularly want the higher number of levels compared with experts not sure of themselves (2).

Companies that engaged mass customization have frequently chosen a rather simple solution to these concerns: the attributes they offer for customization are primarily aesthetic, related to visual appearance of the product and much less to its actual performance. There is an over-emphasis of personalised features (e.g., posting a label of the customer’s name or an image created by her or him). Companies also tend to constrain the set of customisable attributes and offer very few of them — this is done not just for avoiding too much complication for the users  but for themselves, to leave them with more control over technical aspects of product design and the cost of making the customized products. While this may serve well the less knowledgeable consumers, it gives the impression that this is not a serious enterprise, more like a game or a ‘marketing gimmick’, which seems to lead the more knowledgeable consumers to dismiss this option for purchasing products. Even less knowledgeable customers may be disenchanted by constraints imposed in the wrong places.  Configurators should combine different types of attributes for customization that allow customers influence both functional utility and hedonic benefits (pleasure) from their product.

Companies have turned to other techniques such as recommendation agents and search assistants that would help customers find the most appropriate product model for them. A recommendation online system first probes the consumer about her or his preferences through a series of questions and then offer a set of product recommendations rank-ordered according to their match with the consumer’s preferences. This method is distinguished from MC in that it selects product versions from the existing assortment of the company and does not create a product specifically for the customer. This kind of aid satisfies the preferred balance for some consumers between the levels of perceived control they get and perceived assortment available, but it also depends on their belief that the system is more capable than themselves to find a product that matches their preferences. This may further depend on the amount of information asked for and on the type of procedure used to collect preference information. A search assistant that is common in shopping websites helps to drill through the assortment of product versions in a category and narrow it down according to attribute criteria chosen by the shopper, thus screening a smaller set of plausible alternatives. However such an assistant, that does not make recommendations, cannot be truly said to offer customization if it does not make use of preference information  from the shopper to organise his or her resulting set in a more efficient way.

Obtaining a product personally designed by the consumer may endow him or her with special positive feelings, providing an important drive to participate in such an activity. The benefits from MC pertain to the experience of designing or configuring the ‘private’ product as well as the subsequent value of the outcome product to the owner. However, researchers Franke, Schreier and Kaiser identified an extra effect they called “I designed it myself” that describes the subjective value, and elevating feeling, that arises from the consumer’s notion that she or he took part in creating the product. They suggest that this effect signifies that consumers would be willing to pay a higher price for the self-designed product compared with a similar kind of product picked off-the-shelf. The effect is contingent on an underlying sense or feeling of accomplishment of the consumer in his or her contribution to the product (e.g., that the effort invested was worthwhile, proven competency, pride). The researchers corroborate this effect in a series of experiments in terms of increased willingness-to-pay for a self-designed product and further show that it depends on the sense of accomplishment but does not exclude the role that perceived value of the outcome product has when making the purchase decision (3).

Companies that develop and implement mass customization programmes should take special care of a number of aspects of the interface consumers have with the Web-based design toolkit to improve their experience and enhance their satisfaction through the process.

  • First measure that may be taken is to create at least two versions of a configurator, one that would be more suitable for more proficient higher-knowledge customers and another for amateur lower-knowledge customers. More generally, it is advisable to give users a greater degree of flexibility in choosing the complexity of configuring the product that matches the level of difficulty they think they can handle. In other words, a firm may allow some control to users in choosing whether they wish to set only aesthetic properties (e.g., visual appearance) of the product or also selected functional attributes, how many attributes to configure, etc..  Additional measures can be to invite users to show their creativity in features of visual design (enhances the sense of contribution) and recommending options on functional features of the product.
  • Second, a company may target customers who are already more inclined to participate in other types of collaborative activities of product design and development, seeking the feelings of accomplishment, challenge and also enjoyment from this type of engagement (e.g., tie them together as LEGO used to do in its Create & Share programme). These customers may be valuable advocates that bring more followers to MC.
  • Third, a variety of aids should be applied to provide users with explanations, examples or illustrations of the options for configurations, warnings about attribute combinations that would not work well, and a graphic demonstration that helps the user to realise how the product builds up.

In spite of discouraging hurdles in the past decade, it would be wrong to conclude that mass customization could not grow and expand. Yet, some changes may have to occur in the future that make it more advantageous for both companies and consumers to exchange benefits of assortment with personal customization. It may also take more time to find out for which product types consumer preferences can be more usefully answered through MC. Nonetheless. 3D-printing and MC may complement and push forward the utilisation of each other, depending on the level of autonomy consumers wish to have in co-creating their products. Technology is most likely to keep advancing, making the self-design experience easier and more gratifying, but technology will not solve all issues at stake and it is vital to continue studying and experimenting to better understand the human-side of consumer expectations of, processing capacity, and response to MC programmes as well as the ensuing 3D-printing.

Ron Ventura, Ph.D. (Marketing)

References:

(1) Determinants of Customers’ Responses to Customized Offers: Conceptual Framework and Research Propositions, Itamar Simonson, 2005, Journal of Marketing, 65 (Jan.), 32-45.

(2) The Role of Idiosyncratic Attribute Evaluation in Mass Customization, Sanjay Puligadda, Rajdeep Grewal, Arvind Rangaswamy, and Frank R. Kardes, 2010, Journal of Consumer Psychology, 20 (3), 369-380

(3) The “I Designed It Myself” Effect in Mass Customization, Nikolaus Franke, Martin Schreier, and Ulrike Kaiser, 2010, Management Science, 56 (1), pp. 125-140.

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Questions, questions, and more questions — as a survey interview goes on questions start to sound or look too much alike. The respondent may gradually pay less attention to the next question and its response options, replying more automatically and less reliably. If the respondent is truly fed up she may quit before the end of the questionnaire — abandoning a survey is even easier in self-administered online surveys than hanging up on an interviewer in a phone survey. It is a key problem researchers in marketing and other fields of social sciences are grappling with in the past decade, next to the non-response problem (i.e., whereby individuals included in the original sample don’t even get to start the questionnaire).

Researchers are struggling to return somehow to the days (1960s to 1980s) when surveys in many countries were still something new and intriguing, a unique and attractive channel for people to voice their opinions, preferences and attitudes. But since then surveys have become far more frequent, and many consumers have become more experienced answering them. This is an advantage when a respondent is more familiar with a type of question and understands better its logic to give a correct, candid or reliable answer. It is a clear disadvantage if the respondent loses interest in survey questions because they seem too familiar and predictable, whereof he or she may not read questions fully, may give answers haphazardly, and sometimes may try to outsmart the question. New media channels may also be perceived now as more attractive for consumers wherein to express their views. Truly, researchers have been concerned by this issue for many years and have always worked on devising techniques to increase respondents’ interest and engagement through the process of completing a survey in different modes. But the approach of “gamification” imported into marketing research lately provokes controversy and doubts: How far should researchers go in their effort to make surveys more likeable, appealling or entertaining to consumers-respondents?

The concept of “gamification” means the introduction of custom features or characteristics of games into survey questionnaires, applying particularly to self-administered questionnaires on the Internet. Deterding and his colleagues define “gamification” as the “use of game design elements in non-game contexts” (1), as in the contexts of advertising and research. The technology-driven inspiration for this approach comes from video games, as directly addressed by Deterding et al., yet gamification borrows its elements also from the broader framework of games. Gaming in general is different from playing, by setting goals to achieve and rules to follow, and creating a competitive challenge for the players.

The game elements to be utilised can range from interactive graphic features, scenarios and scenes, objectives and rewards, to explicit rules that have to be followed. However, a gamified application (e.g., survey question or task) adopts only a subset of design elements shared by games, that makes the applications look and feel like a game but is usually not a full-fledged game (2). The problem starts with ambiguity about the choice of elements to be imported into survey research and how to implement them. It is a concept still in formation and researchers appear to give it different interpretations:

  • Using interactive visual response options and features for a playful design (rather than gameful);
  • Creating scenarios (story-like) that set a more interesting and realistic context in which respondents should answer the actual question;
  • Formulating questions as problems or challenges using the logic of games;
  • Designing a questionnaire  in a set-up that mimics a video game (e.g., the respondent chooses a personal avatar, screens appear like scenes from an adventure game, awards like bonus points are given as the respondent progresses through the questionnaire).

In the first option, playful features include for instance sliders instead of standard  scales (i.e., ticking circles), animation, and drag-and-drop response items (e.g., for ranking or sorting them into categories). While such features may resemble isolated interactive elements that appear in video games, they lack other fundamental components of a game. In the second and third options we may find more game logic without the visual appearance of a video game (perhaps evoking instead ‘visualization’ by the respondent). Using an alternative terminology in the field of gaming, some of the methods employ more ‘game mechanism’ while others emphasise more ‘game thinking’. Which of these aspects is more relevant and important with respect to research? And how closely should we stick to the ‘video game’ as a reference for gamification? The fourth option for implementation of gamification appears much less compatible with the objectives of marketing research projects than the preceding options . The methods that use scenarios, game-like problems and ‘playful’ interactive response tools exhibit potential to improve (online) surveys but their planning and implementation need to be carefully considered and thought-out because of the ways in which they may alter the context, meaning, and scale of measurement of responses.

In order to illustrate benefits to be gained vs. pitfalls at risk when ‘gamifying’ survey questions, I refer to an instructive award-winning research paper by Rintoul and Puleston (3). They tested the effects of ‘gamified’ question formats against standard formats in an online survey, while comparing response patterns between Western countries (US, Australia) and Asian countries (China, India, Japan, Korea, and Singapore). Let us consider just two examples here:

  • When examining what olympic sport events people like to watch, a standard question asks “What are your favourite Olympic events?” But respondents may be requested to imagine a scenario in which they play a more important role that induces them to be more engaged in choosing sport events: “Imagine you were in charge of the TV coverage of the London 2012 Olympics and they broadcast only the events You liked to watch, can you draw a list of all the events you would show on TV?” Australians listed in the scenario version more than double the number of events on average than in the standard version (6.8 versus 3.3). Large lifts were also found in Asian countries where the “base” number of events was the smallest (e.g., China: from 2 to 8 events). The scenario proposed in this study seems legitimate as it merely adds some flare of interest in a situation that is not truly likely to happen in reality. There is the danger, however, that when the scenario is too peculiar or defines a condition too specific the researcher would no longer be safe to infer the validity of those consumer responses in other conditions. (Note: Scenarios opening with keywords like “suppose” and “imagine” have been used in surveys before the age of ‘gamification’, for instance in conjoint studies).
  • Rintoul and Puleston test a ‘gamified’ scale with a ‘smooth’ slider in two visual versions: one version used just the slider, the other version included an additional animated feature, a person figure that changes his posture according to the position of the ‘slider’ handle on the scale — the person rises and applauds when the highest level is chosen on one end to that person remaining seated leaning backwards ‘asleep’ when the lowest level is chosen on the other end. The researchers show that the animated version reduces differences in response patterns between countries. The concern is that respondents would be affected by animation, trying to experiment with the figure or ‘please’ it. In this study the ‘applauding person’ did not lead to higher frequency of the top-level; yet, conspicuously, more responses in the US and Australia were attracted by the person ‘asleep’ at the low end of the scale than in the simple version. (Note: The ‘rating’ scale did not show numeric levels.)

To the credit of Rintoul and Puleston, their approach is well-reasoned, moderate in its application of ‘gamified’ elements, and realistic. But as advocates of ‘gamification’ techniques they tend to play down the potential problems with those techniques. Some of their visual instruments seem to alter the type of scale or mix between measurement scales where the properties of those new instruments are not properly understood (e.g., sorting items into Likert-type categories, the “water tap” scale, rating scales using faces and text with no numeric levels). A critical question should be raised with regard to every gamified feature: how relevant is it to the mental construct, behaviour, or decision situation studied (e.g., a scenario, a time constraint instructsing “answer within 2 minutes”); at the very least it should not conflict with the focal construct. There is also concern whether additional items chosen when using pictorial images truly reflect respondents’ preferences or interests or were they chosen just as part of the ‘game’? Utilisation of ‘gaming’ features in an artificial way and of little relevance to the original issue studied can seriously hurt the attainment of the research objectives.

Another study that compared four conditions of design format found just modest support for the application of gamifying elements (4). The formats tested were text-only, decoratively visual (DV) , functionally visual (FV, i.e., interactive visual features), and gamified questionnaire. The researchers, Downes-Le Guin and colleagues, address the argument that sliders and other visual features help to combat the problem of ‘straightlining’ in Likert-type scales by increasing response variation between items (an issue addressed also by Rintoul and Puleston). They found limited support for such improvement in the FV condition and none in the DV condition. The FV and gamified designs showed some benefits in increasing enjoyment and interest but no improvement in data quality. The gamified version adds in fact a kind of back-stage view from a game-video to a question display similar to FV. Even when some gaming features like choice of avatar are added, it still functions not substantially more than as a stage decoration while bearing no relevance to the content of questions. It cannot be too surprising that this design has demonstrated little effectiveness.

If researchers wish to introduce characteristics of games right into the content of questions, they may borrow concepts from the fields of game theory, decision making, particularly through the prism of behavioural economics, and simulations. These areas have aspects that are particularly relevant to marketing (competition) and consumer behaviour which can be used to make questions more interesting, intriguing (e.g., as a puzzle), relating more closely to realistic market situations, and being of greater self-relevance (e.g., by quoting or bidding prices for products). There are also limitations to this line of questioning. Questions posed as game-like problems are usually more difficult to comprehend and answer, and take more time to do so. In addition, problems presented in experiments of behavioural economics often reveal biases in judgement that need to be addressed or accounted for. Therefore, the level of sophistication has to be balanced against the ability and willingness of respondents to “solve” the problem-questions.

This course of “gamified” research should have more reason to look for examples and ideas in the domain of “serious games” (e.g., applied in training and education) than video games for fun and entertainment. There are important areas of marketing research where advanced models and techniques of gamification can indeed contribute such as: (1) studying reactions to scenarios of customer experiences in service delivery, or (2) simulation of shopping trips in stores and malls.

Introducing gamified elements and techniques to marketing survey questionnaires can help to improve respondent behaviour, receiving a more favourable attitude towards the survey and smoothing their progress through the questionnaire. The benefits, however, are not clear-cut and are subject to much debate. The contribution of gamification depends greatly on how it is implemented — not all interpretations are welcome from a research perspective. There is justified concern that an entertainment-driven approach will lead to superficial game-like applications that only distract consumers-respondents from answering questionnaires accurately and sincerely. From playful interactive features to more advanced mechanisms like scenarios and problems with game-logic that also encourage more thinking, it is important to keep the gamified design sensibly relevant and related to the domain of the question.

May you all my readers have a Joyful and Successful New Year 2013

Ron Ventura, Ph.D. (Marketing)

Notes:

(1) “From Game Design Elements to Gamefulness: Defining Gamification”; Sebastian Deterding, Dan Dixon, Rilla Khaled, & Lennart Nacke, 2011; Conference Proceeding MindTrek ’11 (ACM)http://85.214.46.140/niklas/bach/MindTrek_Gamification_PrinterReady_110806_SDE_accepted_LEN_changes_1.pdf

(2)  Ibid. 1

(3) “Beyond Colour and Movement: Measuring the Impact of Dynamic Answer Formats on Respondent Behaviour”; Duncan Rintoul and Jon Puleston, 2012; AMSRS Conference (Australian Marketing and Social Research Society) [See another paper related to this research presented at ESOMAR Asia ’12 Conference). http://iibsor.uow.edu.au/consulting/UOW109801.html (Step down the page of Rintoul to “Downloads”) / See also an article with more examples by Deborah Sleep, “Improving Online Market Research through Gamification”, The Guardian: Media Network blog, posted 15 Aug., 2012  http://www.guardian.co.uk/media-network/media-network-blog/2012/aug/15/online-market-research-gamification

 (4) “Myths and Realities of Respondent Engagement in Online Surveys”; Theo Downes-Le Guin, Reg Baker, Joannne Menchling, & Eric Ruylea, 2012, International Journal of Market Research, 54 (5), pp. 1-21 http://www.marketstrategies.com/user_area/content_media/Online_Survey_Engagement.pdf

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The Internet has introduced a range of new avenues and tools for people to obtain products they need or want, changing their behaviour as consumers. The online impact can be expressed in different ways; for example, consumers may effortlessly and often more quickly obtain requested products that are unavailable at stores in their vicinity; more conveniently gather information on several suppliers and compare their offers; and consequently often may order online at lower costs than buying in brick-and-mortar shops (after accounting of course for delivery costs). Not less importantly, however, consumers apply the Web as a rich resource of information for conducting “market research” on a product category before going to buy at a store or order online. It is no wonder that many traditional retailers, small and large, regard electronic retailing (e-tailing) as a threat to their businesses. Furthermore, if one considers also the spreading use of mobile hand-held devices and apps designed to provide consumers with updated information and tools to help them with their purchase decisions as-they-go, the pressure on retail stores could only be growing.

While e-commerce is encroaching on the sales at traditional stores on the streets and in shopping centres, traditional retailing is, as yet, not at an existential risk, and stores are not near disappearing from our street scenery. The overall share of online sales out of total retail sales in the US stood at 5% in 2008 and is expected to rise to 8% by next year  (2013); Forrester expect the share to  plateau at 10% (1). In Europe, during 2011 Britain was leading (even over the US!) with an online share of 12%, followed by Germany (9%), Switzerland (8.7%), Denmark and Norway (~8%), and France (7.3%). Britain, Germany, and France account for 71% of the European online retail sales (2). Nonetheless, retail stores of certain categories (e.g., computers, electronics, entertainment media) are already under stronger pressure and are at greater risk, and can expect to face more structural and operational changes in coming years. Retailers in other fields also cannot remain complacent as shares of online sales are growing over time (e.g.. share in Britain grew from 8.6% in 2008 to 12% in 2011). Even more crucial for traditional retailers to take to their attention, Forrester point to the significant impact the Internet has as an information resource on offline sales: If we take explicit online sales + online-influenced sales in the US, they were estimated to account for 40% of total retail sales in 2009, and may reach 54% by 2013 (3).

Moreover, many consumers no longer check the relevant information they seek on the Internet at home and then come to a brick-and-mortar shop — they bring the Internet with them to the shop on mobile devices like smartphones and tablets. According to a recent survey commissioned by Google, 31% of consumers in the UK report checking product information on their smartphones as they stand in front of the shelf display at a store (US: 35%, Israel: 39%)(4). In addition, shoppers may use apps that provide them more efficient access to market information and tools they can utilize during decision-making in-store. They may also share information and consult with friends in social media networks while at the store. Store owners and managers need to acknowledge the additional information shoppers may access in-store as well as how using digital technologies and interactive tools influences the course of their shopping visits.

One way to face the digital and interactive technologies is by trying to fight them off, insisting that the traditional stores, with their facilities and staff, offer better solutions to consumers’ needs and expectations. This fight, unfortunately, is likely to be futile because those technologies are already widely available, well-rooted in the way of life of many consumers, and especially the younger generation (born after 1990) is not about to give them up. A better way would be to bring the advanced technologies into the store, integrating them cleverly with the overall design of the store scene. They may be assimilated with facilities and fixtures of the store and as part of the services the store offers its patrons.

Suppose you enter a DIY (Do-It-Yourself) store, and as you step into the entry hall, to your right there are three stands with flat screens on top of poles at 45° angle. Each information post lets you look at an interactive floor plan and possibly search for the location of products you seek that will be flagged on the store diagram. You move on to the department of working tools like electric drillers. On a screen attached at eye-level you may choose to watch a short video of the machine in operation, rotate the product to be shown from different angles with annotation for its different parts, and possibly read additional technical information on the side bar. Only some of the products can be displayed in-store, but for some other products you find a QR (square) code that directs you to an online information page for the product on your smartphone.  You no longer have to run after information — it follows in your footsteps wherever you go.

This scenario is not truly fictional — these technological features are already applied to some extent by retailers worldwide. Watch for example for the new FuelStation store by Nike in London (14 March 2012): The sports company seems to have taken the new interactive digital approach to the full. First, the store has a dynamic fixture display whose appearance changes with the movements of shoppers passing by. Second, the store features advanced displays such as augmented reality tools that display interactive animated product information and iPads embedded in walls that show product and event information. Furthermore, shoppers can access Nike Web-based store on the iPads and order products.

In a post last year on the crisis of retail chains for music/entertainment media (7 April 2011) I attempted at sketching some outlines for the design and organization of future stores in this field. Two themes in particular, I suggested, could make stores better adapted to new patterns of behaviour among consumers — personal customization and social interaction. Customers should be given good reasons to come to a store instead of compiling their songs or other media collections online at home, whether acting alone as individual buyers or acting in groups of friends.

During a visit to a brick-and-mortar store, customers may end-up making their purchase of a product in an online store, whether the retailer’s own Web-based store or a competing e-store. Writing in the blog “The Scholarly Kitchen” (12 Jan. 2012), Kent Anderson describes how he switched from buying books he found at the Waterstones store in London to ordering online using the Amazon app on his iPhone, either their print or Kindle editions. His motive: not carrying as many as a dozen books with him and not paying the British prices in pounds.  Anderson, an executive in academic publishing, sees an important shift in the future functioning of retail stores with promising opportunities for serving customers in new ways: ” Imagine the opportunity presented for redesigning retail spaces to support these behaviors — QR codes to online review centers, instant price-matching opportunities (even to the company’s own Web site), and so forth.”  It is in the retailers’ interest to offer their own references to recommended information sources of their choice, particularly linking with their own online store. Bookstores, specifically, should accommodate customers who prefer e-books and provide them “reading terminals” to browse digital editions at the store with options to make the purchase at the retailer’s online store or at the cashier desk.

An application of these concepts for a primarily service provider is nicely demonstrated by the Royal Bank of Canada (RBC). Last year RBC launched a new style of banking Retail Store (1 February 2011) that combines interactive digital devices for self-service with more traditional advisory by human professional staff. In the branch-store clients may watch video guides on financial plans and services, receive customized investment recommendations with interactive and visual illustrations, view profiles of the branch experts, and more. The Retail Store applies Microsoft Surface technology and exhibits comfortable and spacious sitting and working areas for clients to create a blend of “shopping” and “banking” retail experience.

This is the place to emphasise that it is not at all intended to suggest that the retail store scene should be surrendered to digital and interactive technologies (e.g., Nike may have stepped too far). Fashion stores, for instance, have properties of space, design, and merchandise display that attract shoppers to walk around, touch and pick clothing items, and try them on, having an experience that an online store website cannot really create for them. Designing a store loaded with digital features will only make it too similar to online alternatives for shopping, diluting most of its physical and experiential advantages. But some interactive facilities can be embedded in the store so that shoppers are allowed to see more information on the fashion items, how it may look on models out-of-store, or even simulate how it would look on themselves. The technological features should be applied to let customers look at the merchandise in more ways, enriching their shopping experience, and possibly establishing links with their online experiences on the store’s website. Technology should not replace human staff — they can complement each other for different customers and in different situations.

Retailers can no longer afford to ignore the various ways and practices of consumers in using digital and interactive technologies, especially retail chains and large/department stores. Adopting and embedding these technologies in physical stores is about creating continuity and integration: letting customers extend their digital activities into the store, making their shopper experiences online and offline connected and less divergent, linking and coordinating the channels operated by a retailer for interacting with the customers (e.g., physical store, online, mobile apps), and using common visual, audio and other symbols that help to enhance the retailer’s brand as a whole.

Retail brick-and-mortar stores are most likely to be here-to-stay. They offer qualities of shopping experience that cannot be fulfilled online. But combining features of the physical and virtual environments in-store can complement each other and create even more effective and enjoyable shopper experiences. Online and interactive technologies should not be viewed as mere threats to traditional retailing. They should better be approached as challenges that need to be answered cleverly and creatively.

Ron Ventura, Ph.D. (Marketing)

Notes:

(1)  US Online Retail Sales Forecast 2008 to 2013, Forrester, Feb. 2009

(2) Online Retailing: Britain and Europe 2012, Centre for Retail Research, UK

http://www.retailresearch.org/onlineretailing.php

(3) Ibid. 1

 (4) “Israelies Are Looking at the Smartphone Istead of the Shelf in the Store”, TheMarker (Hebrew), 25 July 2012 (a report of findings from the Consumer Commerce Barometer research project commissioned by Google. Hebrew readers can find this article at  http://technation.themarker.com/digital/1.1785242)

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Digital online advertising is more than banners that appear on web-pages, whether static or animated with motion. It has to be, because the online interactive scene of the Internet has so much more to offer, and much more can be done to capture and activate consumers. Advertising clips are also not enough, because they usually leave people passive watching them. There is also no substantial advantage to playing a clip on the webpage than on TV because the company is still not using the online medium properly.  

Ad banners catch your eye, grab your attention at least for a second or two, but they do not succeed at achieving much more than that. They do not deliver much information, and in most cases they are meant to induce you to click the banner and transport to another webpage where you may find something more interesting, meaningful or entertaining. The problem is that with time people have become used to them, often regarding them as a disturbance or nuisance to their main interest with a webpage. They can be truly annoying on occasion, over-riding text areas on the page and making it impossible to get rid of them. Surfers in decreasing numbers become tempted or intrigued enough to click a banner (somewhere between 1%-5%). They may even purposely ignore them as punishment.  

This brings us to a new type of advertising that lets the consumer get into the plot and become an active player in it. This branch is still in its infancy but one can find some good examples from time to time. One such brilliant example is the Pleasure Hunt promotional game by Magnum ice cream (a brand of the British-Dutch Unilever). It is cute, dynamic, entertaining, and has an effective ending. (I owe my gratitude to a good friend who referred me to this gaming-ad, recommending it for a good reason).  

I will not describe the game-ad in detail in order not to spoil the fun and surprises for those who want to follow the link below and try it for themselves. A few brief comments: First, it took me a little time to learn how to pick-up more bonbons on the way — I hope you are more successful. Sometimes, however, I reached a bonbon but couldn’t get it.  Second, on some screens it seemed the full image didn’t fit into my browser frame (too tall) and it overflow.  Yet, and that’s the main point, the whole concept was intriguing. The game gives publicity to advertisers in other domains (e.g., tourism, cars, fashion, beauty-care etc.) by displaying apparent web-pages of them which appeared a bit odd in a promotion for Magnum. But Magnum probably won’t worry about it — you cannot click away because the pages are fake, you are too busy running around looking for bonbons, and eventually you will remember the advertised pleasure at the game end. The beauty of it is mainly in the way the scene changes through the game story and the different topics add interest to the game. It is not a complex game; it should not be so. It is merely a little fun, and it does it well, not distracting the consumer-player from the marketing goal at the end.   

I regard this kind of application as Engaging Advertising because of its ability to captivate and draw the consumer into the ad-application as an active player and not just observer or viewer. I think it is not truly appropriate to use terms solely like advertising, promotion or marcom in this case , in concern that it does not do this kind of marketing initiative  justice. It blends advertising with experiential marketing. It is said about TV ads that even when they include “people like us”, when they involve  sentimental scenes and familiar situations, and may be exciting, the viewer is still left outside and experiences it indirectly or vicariously. It is different when the viewer becomes part of the plot and can influence it.

And now you can try the Pleasure Hunt ad-game first-hand.

Good luck, and Bon Apetit!

Ron Ventura, Ph.D. (Marketing)

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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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