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Posts Tagged ‘Knowledge’

The strength, impact and value of a brand are embodied, fairly concisely, in the concept of ‘brand equity’. However, there are different views on how to express and measure brand equity, whether from a consumer (customer) perspective or a firm perspective. Metrics based on a consumer viewpoint (measured in surveys) raise particular concern as to what actual effects they have in the marketplace. Datta, Ailawadi and van Heerde (2017) have answered to the challenge and investigated how well Consumer-Based metrics of Brand Equity (CBBE) align with Sales-Based estimates of Brand Equity (SBBE). The CBBE metrics were adopted from the model of Brand Asset Valuator (Y&R) whereas SBBE estimates were derived from modelling market data of actual purchases. They also examined the association of CBBE with behavioural response to marketing mix actions [1].

In essence, brand equity expresses an incremental value of a product (or service) that can be attributed to its brand name above and beyond physical (or functional) attributes. Alternately,  brand equity is conceived as the added value of a branded product compared with an identical version of that product if it were unbranded. David Aaker defined four main groups of assets linked to a brand that add to its value: awareness, perceived quality, loyalty, and associations beyond perceived quality. On the grounds of this conceptualization, Aaker subsequently proposed the Brand Equity Ten measures, grouped into five categories: brand loyalty, awareness, perceived quality / leadership, association / differentiation, and market behaviour. Kevin Keller broadened the scope of brand equity wherein greater and more positive knowledge of customers (consumers) about a brand would lead them to respond more favourably to marketing activities of the brand (e.g., pricing, advertising).

The impact of a brand may occur at three levels: customer market, product market and financial market. In accordance, academics have followed three distinct perspectives for measuring brand equity: (a) customer-based — an attraction of consumers to the “non-objective” part of the product offering (e.g., ‘mindset’  as in beliefs and attitudes, brand-specific ‘intercept’ in a choice model); (b) company-based — additional value accrued to the firm from a product because of a brand name versus an equivalent product but non-branded (e.g., discounted cash flow); financial-based — brand’s worth is the price it brings or could bring in the financial market (e.g., materialised via mergers and acquisitions, stock prices)[2]. This classification is not universal:  for example, discounted cash flows are sometimes described as ‘financial’; estimates of brand value derived from a choice-based conjoint model constitute a more implicit reflection of the consumers’ viewpoint. Furthermore, models based on stated-choice (conjoint) or purchase (market share) data may vary greatly in the effects they include whether in interaction with each competing brand or independent from the brand ‘main effect’ (e.g., product attributes, price, other marketing mix variables).

A class of attitudinal (‘mindset’) models of brand equity may encompass a number of aspects and layers: awareness –> perceptions and attitudes about product attributes and functional benefits (+ overall perceived quality), ‘soft’ image associations (e.g., emotions, personality, social benefits) –> attachment or affinity –> loyalty (commitment). Two noteworthy academic studies have built upon the conceptualizations of Aaker and Keller in constructing and testing consumer-based measures:

  • Yoo and Donthu (2001) constructed a three-dimension model of brand equity comprising brand loyalty, brand awareness / associations (combined), and perceived quality (strength of associations was adopted from Keller’s descriptors of brand image). The multidimensional scale (MBE) was tested and validated across multiple product categories and cultural communities [3].
  • Netemeyer and colleagues (2004) demonstrated across products and brands that perceived quality, perceived value (for the cost), and uniqueness of a given brand potentially contribute to willingness to pay a price premium for the brand which in turn acts as a direct antecedent of brand purchase behaviour [4]. Price premium, an aspect of brand loyalty, is a common metric used for assessing brand equity.

Datta, Ailawadi and van Heerde distinguish between two measurement approaches: the consumer-based brand equity (CBBE) approach measures what consumers think and feel about the brand, while the sales-based brand equity (SBBE) approach is based on choice or share of the brand in the marketplace.

The CBBE approach in their research is applied through data on metrics from the Brand Asset Valuator model developed originally by Young and Roubicam (Y&R) advertising agency (the brand research activity is now defined as a separate entity, BAV Group; both Y&R and BAV Group are part of WPP media group). The BAV model includes four dimensions: Relevance to the consumers (e.g., fits in their lifestyles); Esteem of the brand (i.e., how much consumers like the brand and hold it in high regard); Knowledge of the brand (i.e., consumers are aware of and understand what the brand stands for); and  Differentiation from the competition (e.g., uniqueness of the brand)[5].

The SBBE approach is operationalised through modelling of purchase data (weekly scanner data from IRI). The researchers derive estimates of brand value in a market share attraction model (with over 400 brands from 25 categories, though just 290 brands for which BAV data could be obtained were included in subsequent CBBE-SBBE analyses) over a span of ten years (2002-2011). Notably, brand-specific intercepts were estimated for each year; an annual level is sufficient and realistic to account for the pace of change in brand equity over time. The model allowed for variation between brands in the sensitivity to their marketing mix actions (regular prices, promotional prices, advertising spending, distribution {on-shelf availability} and promotional display in stores) — these measures are not taken as part of SBBE values but indicate nonetheless expected manifestation of higher brand equity (impact); after being converted into elasticities, they play a key role in examining the relation of CBBE to behavioural outcomes in the marketplace.


  • Datta et al. seem to include in a SBBE approach estimates derived from (a) actual brand choices and sales data as well as (b) self-reported choices in conjoint studies and surveys. But subjective responses and behavioural responses are not quite equivalent bases. The authors may have aimed reasonably to distinguish ‘choice-based’ measures of brand equity from ‘attitudinal’ measures, but it still does not justify to mix between brands and products consumers say they would choose and those they actually choose to purchase. Conjoint-based estimates are more closely consumer-based.
  • Take for instance a research by Ferjani, Jedidi and Jagpal (2009) who offer a different angle on levels of valuation of brand equity. They derived brand values through a choice-based conjoint model (Hierarchical Bayes estimation at the individual level), regarded as consumer-level valuation. Vis-à-vis the researchers constructed a measure of brand equity from a firm perspective based on expected profits (rather than discounted cash flows), presented as firm-level valuation. Nonetheless, in order to estimate sales volume they ‘imported’ predicted market shares from the conjoint study, thus linking the two levels [6].

 

Not all dimensions of BAV (CBBE) are the same in relation to SBBE: Three of the dimensions of BAV — relevance, esteem, and knowledge — are positively correlated with SBBE (0.35, 0.39, & 0.53), while differentiation is negatively although weakly correlated with SBBE (-0.14). The researchers reasoned in advance that differentiation could have a more nuanced and versatile market effect (a hypothesis confirmed) because differentiation could mean the brand is attractive to only some segments and not others, or that uniqueness may appeal to only some of the consumers (e.g., more open to novelty and distinction).

Datta et al. show that correlations of relevance (0.55) and esteem (0.56) with market shares of the brands are even higher, and the correlation of differentiation with market shares is less negative (-0.08), than their correlations with SBBE (correlations of knowledge are about the same). The SBBE values capture a portion of brand attraction to consumers. Market shares on the other hand factor in additional marketing efforts that dimensions of BAV seem to account for.

Some interesting brand cases can be detected in a mapping of brands in two categories (for 2011): beer and laundry detergents. For example, among beers, Corona is positioned on SBBE much higher than expected given its overall BAV score, which places the brand among those better valued on a consumer basis (only one brand is considerably higher — Budweiser). However, with respect to market share the position of Corona is much less flattering and quite as expected relative to its consumer-based BAV score, even a little lower. This could suggest that too much power is credited to the name and other symbols of Corona, while the backing from marketing efforts to support and sustain it is lacking (i.e., the market share of Corona is vulnerable).  As another example, in the category of laundry detergents, Tide (P&G) is truly at the top on both BAV (CBBE) and market share. Yet, the position of Tide on SBBE relative to BAV score is not exceptional or impressive, being lower than predicted for its consumer-based brand equity. The success of the brand and consumer appreciation for it may not be adequately attributed specifically to the brand in the marketplace but apparently more to other marketing activities in its name (i.e., marketing efforts do not help to enhance the brand).

The degree of correlation between CBBE and SBBE may be moderated by characteristics of product category. Following the salient difference cited above between dimensions of BAV in relation to SBBE, the researchers identify two separate factors of BAV: relevant stature (relevance + esteem + knowledge) and (energized) differentiation [7].

In more concentrated product categories (i.e., the four largest brands by market share hold a greater total share of the category), the positive effect of brand stature on SBBE is reduced. Relevance, esteem and knowledge may serve as particularly useful cues by consumers in fragmented markets, where it is more necessary for them to sort and screen among many smaller brands, thus to simplify the choice decision process. When concentration is greater, reliance on such cues is less required. On the other hand, when the category is more concentrated, controlled by a few big brands, it should be easier for consumers to compare between them and find aspects on which each brand is unique or superior. Indeed, Datta and colleagues find that in categories with increased concentration, differentiation has a stronger positive effect on SBBE.

For products characterised by greater social or symbolic value (e.g., more visible to others when used, shared with others), higher brand stature contributes to higher SBBE in the market. The researchers could not confirm, however, that differentiation manifests in higher SBBE for products of higher social value. The advantage of using brands better recognized and respected by others appears to be primarily associated with facets such as relevance and esteem of the brand.

Brand experience with hedonic products (e.g., leisure, entertainment, treats) builds on enjoyment, pleasure and additional positive emotions the brand succeeds in evoking in consumers. Sensory attributes of the product (look, sound, scent, taste, touch) and holistic image are vital in creating a desirable experience. Contrary to expectation of Datta and colleagues, however, it was not found that stature translates to higher SBBE for brands of hedonic products (even to the contrary). This is not so good news for experiential brands in these categories that rely on enhancing relevance and appeal to consumers, who also understand the brands and connect with them, to create sales-based brand equity in the marketplace. The authors suggest in their article that being personally enjoyable (inward-looking) may overshadow the importance of broad appeal and status (outward-looking) for SBBE. Nevertheless, fortunately enough, differentiation does matter for highlighting benefits of the experience of hedonic products, contributing to a raised sales-based brand equity (SBBE).

Datta, Ailawadi and van Heerde proceeded to examine how strongly CBBE corresponds with behavioural responses in the marketplace (elasticities) as manifestation of the anticipated impact of brand equity.

Results indicated that when relevant stature of a brand is higher consumers respond favourably even more strongly to price discounts or deals  (i.e.,  elasticity of response to promotional prices is further more negative or inverse). Yet, the expectation that consumers would be less sensitive (adverse) to increased regular prices by brands of greater stature was not substantiated (i.e., expected positive effect: less negative elasticity). (Differentiation was not found to have a positive effect on response to regular prices either, and could be counter-conducive for price promotions.)

An important implication of brand equity should be that consumers are more willing to pay higher regular prices for a brand of higher stature (i.e., a larger price premium) relative to competing brands, and more forgiving when such a brand sees it necessary to update and raise its regular price. The brand may benefit from being more personally relevant to the consumer, better understood and more highly appreciated. A brand more clearly differentiated from competitors with respect to its advantages could also benefit from a protected status. All these properties are presumed to enhance attachment to a brand, and subsequently lead to greater loyalty, making consumers more ready to stick with the brand even as it becomes more expensive. This research disproves such expectations. Better responsiveness to price promotions can help to increase sales and revenue, but it testifies to the heightened level of competition in many categories (e.g., FMCG or packaged goods) and propensity of consumers to be more opportunistic rather than to the strength of the brands. This result, actually a warning signal, cannot be brushed away easily.

  • Towards the end of the article, the researchers suggest as explanation that they ignored possible differences in response to increases and decreases in regular prices (i.e., asymmetric elasticity). Even so, increases in regular prices by stronger brands are more likely to happen than price decreases, and the latter already are more realistically accounted for in response to promotional prices.

Relevant stature is positively related to responsiveness to feature or promotional display (i.e., consumers are more inclined to purchase from a higher stature brand when in an advantaged display). Consumers also are more strongly receptive to larger volume of advertising by brands of higher stature and better differentiation in their eyes (this analysis could not refer to actual advertising messages and hence perhaps the weaker positive effects). Another interesting finding indicates that sensitivity to degree of distribution (on-shelf availability) is inversely associated with stature — the higher the brand stature from consumer viewpoint, larger distribution is less attractive to the consumers. As the researchers suggest, consumers are more willing to look harder and farther (e.g., in other stores) for those brands regarded more important for them to have. So here is a positive evidence for the impact of stronger brands or higher brand equity.

The research gives rise to some methodological questions on measurement of brand equity that remain open for further deliberation:

  1. Should the measure of brand equity in choice models rely only on a brand-specific intercept (expressing intrinsic assets or value of the brand) or should it include also a reflection of the impact of brand equity as in response to marketing mix activities?
  2. Are attitudinal measures of brand equity (CBBE) too gross and not sensitive enough to capture the incremental value added by the brand or is the measure of brand equity based only on a brand-intercept term in a model of actual purchase data too specific and narrow?  (unless it accounts for some of the impact of brand equity)
  3. How should measures of brand equity based on stated-choice (conjoint) data and actual purchase data be classified with respect to a consumer perspective? (both pertain really to consumers: either their cognition or overt behaviour).

Datta, Ailawadi and van Heerde throw light in their extensive research on the relation of consumer-based equity (CBBE) to behavioural outcomes, manifested in brand equity based on actual purchases (SBBE) and in effects on response to marketing mix actions as an impact of brand equity. Attention should be awarded to positive implications of this research for practice but nonetheless also to the warning alerts it may signal.

Ron Ventura, Ph.D. (Marketing)

Notes:

[1] How Well Does Consumer-Based Brand Equity Align with Sales-Based Brand Equity and Marketing-Mix Response?; Hannes Datta, Kusum L. Ailawadi, & Harald J. van Heerde, 2017; Journal of Marketing, 81 (May), pp. 1-20. (DOI: 10.1509/jm.15.0340)

[2] Brands and Branding: Research Findings and Future Priorities; Kevin L. Keller and Donald R. Lehmann, 2006; Marketing Science, 25 (6), pp. 740-759. (DOI: 10.1287/mksc.1050.0153)

[3] Developing and Validating a Multidimensional Consumer-Based Brand Equity Scale; Boonghee Yoo and Naveen Donthu, 2001; Journal of Business Research, 52, pp. 1-14.

[4]  Developing and Validating Measures of Facets of Customer-Based Brand Equity; Richard G. Netemeyer, Balaji Krishnan, Chris Pullig, Guangping Wang,  Mahmet Yageci, Dwane Dean, Joe Ricks, & Ferdinand Wirth, 2004; Journal of Business Research, 57, pp. 209-224.

[5] The authors name this dimension ‘energised differentiation’ in reference to an article in which researchers Mizik and Jacobson identified a fifth pillar of energy, and suggest that differentiation and energy have since been merged. However, this change is not mentioned or revealed on the website of BAV Group.

[6] A Conjoint Approach for Consumer- and Firm-Level Brand Valuation; Madiha Ferjani, Kamel Jedidi, & Sharan Jagpal, 2009; Journal of Marketing Research, 46 (December), pp. 846-862.

[7] These two factors (principal components) extracted by Datta et al. are different from two higher dimensions defined by BAV Group (stature = esteem and knowledge, strength = relevance and differentiation). However, the distinction made by the researchers as corroborated by their data is more meaningful  and relevant in the context of this study.

 

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For over fifteen years companies are gradually shifting from providing customer service by live person-to-person channels to computer-based, automated and self-service modes. In the past three years the momentum seems to have even increased to replace bilateral human interactions with human-computer interactions — human on the customer’s side, computer on the company’s side. The trend is evident in a variety of sectors, including manufacturers and dealers providing maintenance and repair of goods, inherently service providers (e.g., mobile and Internet telecom, health, insurance, tourism), and retailers. The servicescape is definitely changing, and the repercussions are still unfolding (e.g., customer adaptation, social-related, customer-company relationships).

We can identify several stages a company may go through in reducing its direct human interaction with customers — transitioning from face-to-face to phone touchpoints, then to self-service on the Internet and by mobile applications. In-between companies have applied methods such as IVR on the phone and Web-based live chats with human customer service representatives (CSR). But the latest technological advance in computer-based self-service entails a potentially more extensive substitution of intelligent virtual agents for human service agents. It means that a larger variety of issues handled thus far via phone conversations and live chats on the Internet would be resolved by chats, at different levels of sophistication, with virtual agents.

Certainly a customer would not want to rush out to a physical branch of the serving company for resolving every problem with a product or service when it can be settled by a phone call to the company’s call centre. Some enquiries and technical issues can furthermore be resolved by means of e-mail or Web-based interfaces and resources without talking to anyone — many customers prefer nowadays to make a phone call to a company only after they have exhausted their options to solve their problem by means of self-service. There are clear advantages (e.g., convenience, control, independence) in using the computer-based modes of service. However, in cases where problems cannot be resolved effectively by self-service, and reaching a live representative of the company is made harder, it may engulf a wider gap between the company and its customer, possibly inducing frustration and anger. It may be even worse for consumers who are less computer-orientated and have difficulties using those online tools (e.g., tasks that may seem obvious or easy-to-do to Millennials [Generation Y], and to a large extent to Generation X consumers, are less likely to be so for earlier generations born before 1960).

Companies have a strong incentive of cost reduction to reduce or limit forms of human personal service  — various estimates suggest that the cost of interaction may fall from $10-12 when face-to-face to $5-6 by phone to less than a dollar by e-mail, online live chats or social media, and even less in human-computer interactions that do not involve a human on the company’s side. The face-to-face channel seems to fare the worst. Some companies eliminate branches for meeting with customers, reduce their accessibility or span of services provided face-to-face, and generally de-motivate customers to come and see their representatives for service.

  • A few examples: (a) A mobile telecom company that receives customers at its service centre only for acquiring new phones or leaving a phone for repair at the lab but not for issues related to changing a service package or billing; (b) An airline that prefers customers to arrange and order flights by phone and better on the Internet, and de-motivates them to come and make their travel arrangements face-to-face; (c) A ticket agency (e.g., live concerts) whose office is unaccessible, relying only on phone and online contacts.

While consumers are more willing to utilise computer-based self-service tools and resources, and are doing so more frequently, this does not mean they are ready to give up access to a person from the company. That is a wrong interpretation by certain companies who make it more difficult for customers to access their representatives, by phone or face-to-face. The last thing a company should do is to let its customers feel that it is not interested in hearing or seeing them in person. Consumers should have the privilege to choose how to receive their service. Otherwise, it is a slippery slope whereby a company may distance itself too far apart from its customers.

  • There are a number of cases where acquisition and service are intermingled. For instance, when (a) consultation is required prior to a purchase decision; (b) the buyer is a repeated customer; and (c) a purchase transaction is made online for a product or service consumed or experienced in the real physical world.

Intelligent virtual agents (IVA) may operate in several forms with regard to their level of exchange with users. They all rely on advanced methods of artificial intelligence and abilities to interpret natural language, and they may also utilise knowledge gained through Big Data analytics (e.g., of previous customer enquiries) to improve their quality of response (e.g., Watson by IBM, Siri by Apple, Optus by IntelliResponse). In one form, that may be described as less dynamic, a user poses a question in his or her own words, to which the IVA replies with the most accurate answer it could find from existing content in the company’s knowledge base (e.g., product profiles, service procedures, bill structure). The information, provided in text, is standard for any similar question on the same topic. The agent may assume the still image of a real person with a first name. A more sophisticated form of chatbot is an animated figure that behaves more like a live agent and can actually speak. The content may not differ from that given by the agent described formerly but it gives a more realistic “lively” feeling of speaking with another human being.

The advantages of this new breed of IVA are not to be underestimated. The IVA can save customers considerable time that is often needed in reviewing multiple results for a search query, referring the user to various pages from a company’s knowledge base. The IVA is also more flexible and efficient than the anachronistic method of pre-edited FAQ. The IVA can construct a relevant answer ad-hoc on a much larger variety of issues than a typical FAQ and it is much faster and more accurate in providing the correct relevant answer than a user searching the company’s resources. Yet, a virtual agent’s answers are based only on information that is pre-existent in the digital library of the company — if a customer asks for more details on a topic that are not available in advance, the agent may revert to repeat itself (links to related or additional details may be enclosed in an answer, thus excusing the user from posing the next question). The virtual agent also seems to provide standard answers not related to a specific personal problem described by the customer (e.g., particular monetary figures in the customer’s recent bill). For that purpose, the virtual agent should promptly escalate the call to a live CSR; the question remains, how readily IVAs are configured and able to do so.

Hence, IVAs at least at this stage may be able to promise consistency of relevant answers but not real ingenuity. Other aspects that also remain debatable are, for example, the ability of IVAs to identify the correct context of questions posed in natural language and their sensitivity to the mood of customers as a chat proceeds. These capabilities call upon a combination of experience and intuition that human representatives should still have the advantage in exercising over intelligent virtual agents.

In a main feature article in Fortune magazine (August 2015), Geoff Colvin discusses the impact that 21st century’s technological changes, particularly advance of automated computer and robotic systems, have on members of society, whether as employees or as consumers (1). He is critical of a spiral of underrating humans versus computers which may lead further to degrading human touch. In response, Colvin proposes areas of activities that humans should insist on continuing to perform, no matter the abilities of computers: remain in charge (e.g., be accountable to others, making judicial decisions); work together to set collective goals; sustain an advantage in satisfying deep interpersonal needs (e.g., in doctor-patient relations).

Colvin refers to a study by a research firm where employers were asked about the skills they expect to seek in five to ten years. We may predict those would be mainly analytic, business and financial-related (e.g., note warnings of a shortage in decision scientists). Yet, according to the study cited the future skills more demanded by employers include relationship building, teaming, co-creativity, brainstorming, cultural sensitivity, and ability to manage diverse employees. These stated priorities are partly at odds with employers’ own inclination to be more reliant on computer systems for service and allowing less leeway to customer-facing employees to act on their own judgements. Social interaction and empathy are expected to be in high demand in the 21st century. However, social interaction may regress when people become increasingly occupied with their smartphones and invest more in interacting with others through social media networks; and empathy, as Colvin shows, appears to be actually in decline among college students since 1990. Colvin concludes in suggesting that people should take the challenge by computers as an opportunity and work harder on their social skills and value as humans.

Forrester Research issued recently a brief report on the changed characteristics of young contact centre agents from the Millennial generation and how to accommodate them in the workplace (2). It is a new breed of (live) agents who are well-seasoned users of computer devices and computer-based tools and applications, an experience that shapes their approach to digital technology in leisure as well as at work. They have their own “philosophy”: any information they may need is stored in some repository these days (online or offline) and their skills should be directed to finding it. There is therefore no need for them to memorise facts and procedures. At work, they seem reluctant to learn details of products and services the way workers of previous cohorts have done. They prefer to learn where to find the information, being free of memorising details of product support. That clearly poses a challenge to professionals who develop the applications that agents should use for delivering computer-assisted service. Forrester proposes going towards the new agents with tools that reenforce their information search and navigation capabilities  (e.g., improved knowledge management, context-wise tools). Additionally, it is advisable to provide them hardware such as touch screens which they are so familiar with and comfortable operating (e.g,, as on their smartphones), and compatible graphic interface.

The focus on new information skills is welcome and in due time, and companies are most justified to enhance them in the young service agents. But Millennials, and others  in the same mind, should realise that their approach could be self-defeating. In order to excel at work, such as delivering an exceptional customer service, one should utilise in the best way his or her rich declarative knowledge in a domain and the practical experience one accumulated. Memorising information cannot be discarded because with expertise it means the CSR is better able to quickly provide the most effective solution to a customer’s problem. Can it be done equally well by looking up the solution or clues to it in a company’s knowledge repository? This is yet to be proven.

In the realm of keeping a sensible balance between human competence and computer technology, customer-facing employees are required to demonstrate professional aptitude (e.g., domain knowledge, proficiency in using information, responsiveness) and certain personality traits that can contribute to dialogue (e.g., reaching-out, courteous, open-minded)(3). Domain knowledge resides in one’s head (brain), not by sole reference to knowledge management systems. Thereby the human agent can develop the proficiency of using information retrieved from both own-memory and the information system as the task calls for. Companies are expected to reward exceptional CSRs. Even more advanced computer technologies may offer the agents the opposite — greater dependence on and integration with the computer system. Forrester suggests that live agents should be reserved for more complex context-sensitive conversations. If human service agents cannot demonstrate exceptional capabilities, companies will be encouraged to replace them with even-greater-intelligent virtual agents in future.

Companies as well as customers and customer-facing employees may perceive benefits in greater reliance on advanced computer technologies, for preferences or interests of each party. But there is a price to pay in company-customer relationships. What indeed is a relationship without a human factor, engaged on both sides? Companies should find it very hard to talk of a bond with their customers if they have little or no human contact with them. They should not expect too much loyalty from their customers in such conditions. The three parties have much to gain  from preserving and supporting live person-to-person service.

Ron Ventura, Ph.D. (Marketing)

Notes:

(1) Humans Are Underrated; Geoff Colvin; Fortune (Europe Edition), 1 August 2015 (Vol. 172, No. 2)  , pp. 34-41.

(2) Brief: Retool for a New Workforce Reality — New Technology for a New Breed of Agent, Forrester Research Inc., December 2014.

(3) Adopted from a 2011 post: The Human Shortages of Relationship Marketing

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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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People have difficulty assessing the value of a non-tangible asset like knowledge. How does one grasp the breadth and quality of a knowledgebase contained in an encyclopedia, especially when it is built within a website and spread across thousends of webpages? Superficial as it may sound, the look of the book volumes of an encyclopedia served consumers as an easy and immediate cue to grasp the amount of knowledge in the series as a whole and in each volume. Of even greater import, a prospect buyer could turn pages back and forth, get impressed by the many entries in a book volume, and have a quick read or glance at drawings and photos to asses the quality of content. Turning paper pages in a book does not feel quite the same as ‘jumping’ pages on a website.

Now as before, one may gather additional information on the editorial board, choice of contributors, and the editorial process of the encyclopedia for greater assurance of the quality of knowledge it offers. But it is not certain that people have appreciation for  those assurances of the quality of content and expertise of contributors. Wikipedia, the free online encyclopedia, changed the way such a knowledgebase is constructed on the Internet and created much trouble for the old-class encyclopedias like Encyclopaedia Britannica. It has particularly put into question the value of the traditional editorial process of an encyclopedia.

On the other hand, digital technologies, first on CD-ROM and later also on the Web, introduced consumers to new features and associated benefits in searching for information and browsing content in encyclopedias. Such features include primarily search engines, advanced navigation tools (interactive graphics), hyperlinks, and multimedia capabilities like hearing soundtracks and viewing videos . The tools for using knowledge are becoming more valid sources of value that publishers are able to price rather than the content of knowledge.

Last month the Encyclopaedia Britannica announced that it was ending its print edition after 244 years since its establishment. The last edition is the 2010 version which contains 32 volumes and entails entries on contemporary topics as global warming and the Human Genome Project. From now 0n the publisher will concentrate on the web-based encyclopedia. The president of Britannica, Jorge A. Cauz, says it is continuously updated, is much more expansive, and includes multimedia (1). A DVD version is also available. Many if not most entries are already available for access free-of-charge on the Web. The paid-for online edition also offers, for instance, links to external websites trusted by Encyclopaedia Britannica, accsess to an archive of historical documents, atlas and maps, and statistics.

The Encyclopaedia Britannica takes pride in 4,000 experts carefully chosen by the editorial board for writing articles in their domains of expertise. Furthermore,  Britannica (as well as World Book or Groiler)  publish scholarly peer-reviewed articles as acceptable in academic literature. Wikipedia in contrast is a “crowd source” knowledgebase where almost anyone is invited to write an entry ; other members in the community of authors enter comments and corrections or may add information. The whole process is subject to approval by an editorial supervisor. Nonetheless, the inclarity regarding the credentials of authors sometimes shows in incoherent articles that are complex yet poorly structured, featuring occasional inconsistencies, and including frequent notes demanding clarifications or corroborative citations. Wikipedia often appears excessively dynamic to the extent that an intelligent reader may feel uncertain in relying on its information. Any encyclopedia is best referred to as an initial source for becoming familiar with a concept, but learners should then look for additional and more comprehensive sources of knowledge to develop their own knowledge of the domain and obtain more perspectives. With Wikipedia it is imperative to seek other information sources on the domain of interest (e.g., learning for general knowledge beyond a basic idea, writing a school/college paper, preparing a work project).

The advantages of the traditionally composed encyclopedias may be obvious to their editors and publishers but they seem to be much less clear to their audiences. Moreover, users of knowledge sources online are not so willing to pay for those advantages as information is abundant on the Internet for free and the users, especially the younger ones, lack either the skills for critics and scrutiny or the motivation to apply them properly.

The print edition of Britannica is offered for $1,400 [£1,200](2) and is purchased in recent years almost solely by institutions. The DVD version is sold for $40 [£40]! Annual subscription to Britannica Online stands at $70 [£50].  Even if we assume that a DVD is adequately accurate for three years, buying three editions over a period of 10 years will come to a nominal cost of £120, tenth of the price of a print edition which may be reliable enough for the extended period of time. Meanwhile, buying ten annual subscriptions to a continuously updated knowledgebase would cost us at present prices nominally £500. Take into consideration that the present value of payments made years ahead is lower than their nominal monetary values given above, that digital sources include many dynamic features unavailable in print, and that fresher information can be obtained online, then one realises how much less consumers are required to pay for the content of knowledge in Britannica in the new digital media compared with the old print medium.

Let us clear-up the price differences between media a little further. In the 1990’s it cost  $250 on average to produce a set of books (3). It has left approximately $1,000-1,200 of selling price to account for the value of knowledge delivered in Encyclopaedia Britannica. Scholars and experts have always been strongly interested in writing entries in Britannica, willing to receive just small fees. This can explain in part how it will have been possible in coming years to reduce the price to consumers. But if in those years  knowledge was possibly over-priced, now knowledge may become under-priced.

When Encyclopedia Britannica finally issued an encyclopedia in digital format on CD-ROM in 1989, it was done under the name of its “sister” Compton (i.e., in fear of diluting its own brand). The CD was provided free to purchasers of the print encyclopedia, but offered at a hefty price of $895 to those interested only in the new digital format (4). The cost of the physical CD is immaterial and so $900 may account for the value of knowledge (including writing, editing, plus configuring content in a digital, multimedia format). That was not received well by consumers who thought it was much above its value to them, and even more so under the name of Compton. Microsoft who were interested in publishing a digital encyclopedia, but not in writing one, were rejected by both Britannica and World Book before associating with a failing encyclopedia (Funk & Wagnall). And yet its multimedia CD-ROM Encarta sold successfully in the mid 1990 for just $100 (5) (it was also distributed as a free add-on to Windows). (more…)

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