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Posts Tagged ‘Interaction’

Oftentimes references to and appraisals of product design (e.g., on websites, in magazines) concentrate on the aesthetics of the product’s visual appearance. The importance of this facet of visual design of products is now well acknowledged, particularly in attracting consumers to them. This is of no dispute. Visual design, however, has an informational capacity and it can communicate to consumers on other aspects embedded in the product or reflected from it.  These facets are functional, symbolic (personal, social) and ergonomic (affecting ease-of-use) that may be inferred from visual design or appearance of a product. They deserve no less attention than aesthetics in discussions of product design from a marketing point-of-view.

Product design did not gain much awareness or interest from marketing and consumer scholars until the mid-1990s. The researchers Peter Bloch and Robert Veryzer made each this critical observation in separate articles in 1995 as they started to conceptualise the meanings and roles of product design with respect to consumer behaviour.  Bloch referred to the powers of good design in attracting consumers, communicating to them, and adding value by enhancing the quality of their usage experiences (1). Veryzer wrote of the low relevance consumer researchers attributed to product design and aesthetics (e.g., superficial styling, related primarily to works of art) that impeded the progress of consumer research to that time in these areas. He set out to start developing a theory on the contributions of product design to consumer-product interactions, and how different considerations (e.g., aesthetic, functional, communication) affect varied consumer reactions (e.g., understanding the product, aesthetic response)(2). Both Bloch and Veryzer recognized the importance of the communicative functions of visual product design beyond aesthetics.

The aesthetics of appearance of a product ascribe to its beauty, evoking visual appeal. It relies on physical properties in the design, such as form, size (proportions), texture (materials) and colours, and how they combine or belong together (i.e., a holistic view, unity of design elements). professional designers may relate to harmony and balance. More commonly, innate preferences of people are shaped by Gestalt rules pertaining, for example, to symmetry, similarity, proximity, repetition and closure. An aesthetic pleasing appearance  increases consumers’ attraction to look at products longer, hold and obtain them.

Nevertheless, the visual design of a product can tell consumers beyond experiencing its aesthetics and appeal. Design of a product entails generally the composition and arrangement of components and overall configuration of the product. Only some of the components are readily visible to consumers (i.e., on surface); many others most relevant to the product’s orderly functioning are hidden from them, and for a good reason. Thereof appearance plays a vital role in communicating to consumers about the function and usage of a product. While it is widely accepted that “form follows function”, one should observe that in many cases form tells people how a product can or should be used (i.e., from a consumer perspective, function is determined by form). In its communicative role, design incorporates important visual and iconic cues about product use and mode of operation. The visual comprehensibility of a product is therefore vital to successful consumer-product interaction (2).

  • Features of products (e.g., electric, electronic and digital), and how to activate them, should be easily identifiable; symbols need to be self-explanatory as much as possible or be easily learnt. The consumer should be able to make basic operations without reading a manual, especially if he or she is experienced with that type of product (e.g., setting parameters and taking a photo shot on a camera). Manuals are more often refered for performing more complex or specialised tasks. Consumers expect to receive fundamental information about the product from its appearance.

Crilly, Moultrie, and Clarkson elaborate on Shannon’s model of communication, as formerly interpreted in the context of product design.


  • The source is the designer or design team
  • The transmitter is the product by its (visual) design
  • The channel is the environment in which consumer-product interaction occurs
  • The receiver entails the perceptual senses of the consumer
  • The destination is the consumer’s faculty for response, incorporating cognitive, affective and behavioural responses.

The researchers concentrate on cognitive responses to visual product design, and identify through a literature review three categories: (a) Aesthetic Impression is the sensation that results from perception of attractiveness; (b) Semantic Interpretation pertains to what a product seems to say about its function, mode-of-use and qualities; and (c) Symbolic Association relates to what a product may say about its owner or user (personal and social significance attached to the design). Decoding the “design message” from appearance and making judgements thereafter is part of cognitive response (3). Crilly et al. note that different types of emotions may stem from all cognitive categories; moreover, there are considerable interdependencies between cognitive and affective responses, where cognition is leading to affect and affect is influencing cognition.

Crilly and his colleagues suggest that aesthetic impression should account for objective qualities of design as well as subjective experiences of consumers. As a second dimension they distinguish between information and concinnity (harmonious arrangement of elements) originating in design. Information may objectively refer to the level of contrast between elements comprising the product’s design against its surroundings or among the elements themselves, while subjective information reflects a degree of novelty perceived by consumers, arising from deviation of the design from forms familiar to them. Novelty induces greater interest, but care should be taken because excessive deviations might cause greater difficulty for consumers to identify the correct category a product belongs in (e.g., by comparing to familiar prototypes), leading to confusion. Concinnity at an objective level would indicate whether the design is in good order (e.g., following Gestalt rules); subjectively, it reflects the extent to which the design makes sense to viewers (i.e., easier to understand, assign meaning, based for example on cultural norms or comparison to other relevant objects).

Semantic interpretation and symbolic association may play a more complex or nuanced role in communication from design (Veryzer recommended distinguishing between aesthetic and communicative roles). The semantics of design pertain primarily to qualities of the product, mode-of-operation and ease-of-use. Physical properties are relevant mainly with respect to how a product should be handled (e.g., its density, stability, fragility). Most importantly, Crilly et al. refer to how consumers may infer from visible components of the product — its layout, feature buttons or switches, levers etc. — how to operate it correctly and more effectively, and how easy using the product is going to be. Among the examples they give: a grooved handle may suggest in what direction it should be turned and how much force should be applied, or flashing switches signal they should be switched off.

The semantics implied from design may refer in particular to affordances (what a product is suitable for or made to do, given its form); constraints (what a product is limited in doing and should not be forced to do); and mappings (how a user’s actions relate to corresponding behaviour of the system). Mapping suggests in this context an interesting aspect of visual compatibility that seems desirable between ‘handles’  for operating a product and its form and response — buttons of a gas stove arranged to fit the layout of burners in the stove itself; levers in an electric-car-seat-control-panel for moving the seat arranged to represent the seat itself.

Think for a moment of TV sets, but not the current flat screens; reflect instead on TV “boxes” from past decades, before the 1990s. This domain demonstrates so well how technology and tastes in design have changed side-by-side over the years. The TV sets from the 1940s to 1970s were casted in a wooden “box” housing.  The TV set was perceived to a great extent as a piece of furniture in the house, and very likely it was designed in wood to match better in look with other furnitures. Early on owners used to put their TV set in a cabin with doors, as if they were not sure about its nature and wanted to conceal it in a furniture. From the 1950s the attitude changed and people were more open and happy to show the innovative technological appliance in their house. From the late 1970s the wooden housing was replaced with injection-moulded plastic. At first frames still adopted a wooden look but the appearance has gradually changed to black and grey-metalic look. The trend transformed from reflecting craftmanship and traditional warm appearance to modern cool appearance that puts technology a front.

Through several decades control panels were usually visible on the right-hand side of the screen. In the 1990s, as remote control handsets became more prevalent, control panels were reduced and became less apparent. This was partly done to leave more space for larger screens (e.g., 26”). Then came the flat screens (plasma or LCD, >32”), and control panels vanished from the front of TV.  Some controls may be found on the TV back but most selections and tuning the viewer is expected to perform on the remote control.  This is the second important change in TV sets: they leave no visual cues for their mode-of-operation easily accessible on the product itself, relying on its remote accessory. The TV sets are now made to take least space possible in the house. Manufacturers of flat screen TVs give priority to a “clean” visual design outwards and their advanced technology inwards. But from a communicative perspective, one may ask if this is the better user-friendly approach. It could be more comfortable and re-assuring for users to place a few controls (e.g., power, sound, channel buttons) on a front panel below the screen rather than hide them on the TV back.

Symbolic association turns our attention from the product to its owner or user. It may involve attributes that correspond to the user’s own personality (e.g., enhance or corroborate one’s self-image) as well as reflect desirable attributes or social standing of the user to others based on product’s appearance. Those product-person symbols may be shaped by the sociocultural context of use. Symbolic associations have been classified in literature, for example, as self-expressive symbolism (supporting one’s unique personality, idiosyncrasy or distinction, and differentiation from others) and categorical symbolism (suggesting one’s group membership, including social position and status, as reflected frequently via shared consumption symbols)(3).

One of the more prominent examples given for products with strong symbolic associations are clothing garments, especially the more fashionable they are, and contingent on type (materials), purpose and style of the garment. Let us look, however, to another domain perhaps less often used as an example: Think of bright beige leather seats in a car. Such seats reflect elegance and high quality; to the car’s owner the leather may also signal softness and comfort (semantic meanings). The leather seats may symbolise elegance of the car owner himself, enhance self-importance to the owner and suggest to others who see the car on the street that the owner has to be a respected person of higher prestige. (The implied symbols seem to matter to men more than to women.)

Consumers perceive physical properties in forming impression of a product’s visual design and appraising it. But to formulate their experience or judgements they translate or map the physical terms (e.g., form, size, colour, surface and texture) onto abstract attributes. Blijlevens, Creusen and Schoorman who studied and identified three such attributes for durable products note that consumers differ, however, from professional (industrial) designers in their understanding and the attributes they use to describe a design. Design literature uses terms such as harmony, unity, symmetry, typicality, massiveness and naturalness that ordinary or design-novice consumers are not familiar with and may not understand. The undesirable implication is that consumers frequently do not grasp the meaning of appearance embedded in the product as intended by its designers (4).

  • The three attributes in the model based on consumer descriptions constructed by Blijlevens et al. are: (a) Modernity (descriptions of ‘modern’, ‘oldish/old-fashioned’ [reversed], ‘futuristic’; (b) Simplicity (‘simple’, ‘minimalistic’, ‘plain’); (c) Playfulness (‘playful’, ‘funny’). Of the three attributes, modernity coincides directly with a parallel attribute used by designers while simplicity correlates inversely with an attribute of ‘complexity’ in design literature. Yet, playfulness  is an attribute more distinctive of consumers with no attribute close enough in meaning as used by designers (regarded as more accurate and deeper attributes).
  • The researchers suggest that (i) consumers’ attributes should complement, not replace, those used by designers to provide consumer viewpoint; (ii) there should be continued effort to study the mapping of physical properties onto consumer attributes; and (iii) marketers should be cognizant of changes in tastes and fashions of aesthetics and visual design that may alter existing relations or mappings over time.

Aesthetic appearance of products is a likely source of pleasure; consumers enjoy talking about appealing and creative visual design, the more so when they have greater acumen in these matters. But the picture cannot be complete, from a marketing perspective, without relating to semantic and symbolic connotations emanating from the visual design of a product because they have important influence on consumer decisions. They are significant to the practical use of a product as well as extended psychological (self-image) and social implications of product ownership and usage.

Ron Ventura, Ph.D. (Marketing)

References:

(1) Seeking the Ideal Form: Product Design and Consumer Response; Peter H. Bloch, 1995; Journal of Marketing, 59 (3), pp. 16-29.

(2) The Place of Product Design and Aesthetics in Consumer Research; Robert W. Veryzer Jr., 1995; in NA — Advances in Consumer Research, Vol. 22, F.R. Kardes and M. Sujan (eds.), pp. 641-645, Provo, UT: Association for Consumer Research.    http://www.acrwebsite.org/search/view-conference-proceedings.aspx?Id=7824

(3) Seeing Things: Consumer Response to the Visual Domain in Product Design; Nathan Crilly, James Moultrie, & P. John Clarkson, 2004; Design Studies, 25 (6), pp. 547-577.

(4) How Consumers Perceive Product Appearance: The Identification of Three Product Appearance Attributes; Janneke Blijlevens, Marielle E.H. Creusen, & Jan P. Schoorman, 2009; International Journal of Design, 3(3), pp. 27-35.  http://www.ijdesign.org/ojs/index.php/IJDesign/article/view/535/272

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Companies are increasingly concerned with the “customer journey“, covering any dealings customers have with their brands, products and services; it has become one of the key concepts associated with customer experience in recent years.  Companies are advised to map typical journeys of their customers, then analyse and discuss their implications and consequences with aim to ameliorate their customers’ experiences.

At the foundation of the customer journey underlies a purchase decision process, but the developed concept of a “journey” now expands beyond purchase decisions to a variety of activities and interactions customers (consumers) may engage, relating to marketing, sales, and service. This broad spectrum of reference as to what a journey may encompass could be either the concept’s strength (establishing a very general framework) or a weakness (too generalised, weak-defined). Another important emphasis accepted with respect to contemporary customer journeys accentuates consumers’ tendency to utilise multiple channels and touch-points available to them, especially technology-supported channels, in their pathway to accomplish any task. Furthermore, interactions in different channels are inter-related in consumers’ minds and actions (i.e., a cross-channel journey). This post-article reviews propositions, approaches and solutions in this area offered by selected consultancy, technology and analytics companies (based on content in their webpages, white papers, brochures and blogs).

Multi-channel, omnichannel, cross-channel — These terms are used repeatedly and most frequently in association with the customer journey. Oracle, for instance, positions the customer journey squarely in the territory of cross-channel marketing. But companies not always make it sufficiently clear whether these terms are synonymous or have distinct meanings. All above descriptive terms agree that consumers more frequently utilise multiple channels and touch-points to accomplish their tasks yet “cross-channel” more explicitly refers to the flow of the journey across channels, the connectivity and inter-relations between interactions or activities customers engage.

Writing for the blog of Nice “Perfecting Customer Experience”, Natalia Piaggio (5 Feb. 2015) stresses that for better understanding the end-to-end customer experience through customer journey maps (CJMs), focus should be directed to the flow of interactions between touch-points and not to any single touch-point. She explains that customers encounter problems usually during transitions between touch-points (e.g., inconsistency of information, company is unable to deliver on a promise, the next channel transferred to cannot resolve the customer’s problem) and therefore touch-points must be considered connectedly. Oracle notes in its introduction to cross-channel marketing that companies should see the big picture and consider how devices (i.e., laptops, smartphones and tablets) are being used in tandem at different points or stages in the customer journey (whether customers use their email inbox, the Web or social media). Paul Barrett (22 Feb. 2010), an industry expert contributing to a blog of Teradata, adds a nice clarification: when talking about (multiple) channels, moments-of-truth relate to individual and separate channels; yet in a cross-channel environment those moments-of-truth are connected into a customer journey. In other words, the customer journey puts moments-of-truth in context.  Therefore, cross-channel customer journeys refer to the flow as well as inter-dependencies of channels and their touch-points engaged by a customer.

TeleTech enhances the salience of the multi-channel and cross-channel aspects of the customer journey but further adds some valuable observations (TeleTech is parent company of Peppers & Rogers Group as its consultancy arm). First, they propose an association between all three terms above when defining a customer ‘path’ or ‘journey’:

Multichannel signifies the digital and physical channels that customers use in their path to purchase or when seeking support for a product or service. Omnichannel represents the cross-channel path that customers take for product research, support and purchasing.

Notably in the view of TeleTech, “omnichannel” is more directly associated with “cross-channel”. Also noteworthy is the inclusion by TeleTech of physical and digital channels. TeleTech emphasise the need to characterise different customer personas, and construct a map for each persona of her typical journey through channels and touch-points; thereafter a company should be ready to notice changes in customer behaviour and modify the map accordingly (“Connecting the Dots on the Omnichannel Customer Journey“, 2015 [PDF]). Nevertheless, Jody Gilliam contends in a blog of TeleTech that companies should attend not only to the inter-relations between touch-points but also to the (reported) mood of customers during their interactions. It is important to describe and map the whole experience ecosystem (The Relationship Dynamic, Blog: How We Think, 19 July 2013).

  • Teradata addresses the complexity introduced by the use of multiple channels through a customer journey from an analytic viewpoint. They propose a multi-touch approach to attribution modelling   (i.e., evaluating to what extent each touch-point contributed to a final desired action by the customer). Three model types for assigning weights are suggested: unified (equal) weighting, decay-driven attribution (exponential: the later an interaction, the higher its weight), and precision (customised) weighting.

The scope of the customer journey — Consensus is not easy to find on what a customer journey encompasses. On one hand, professional services providers focus on particular components of a journey (e.g., interactions, digital touch-points, purchase or service), on the other hand there are attempts to present at least an all-inclusive approach (e.g., reference to a “customer lifecycle”). It may also be said that a gap currently exists between aims to cover and link all channels and the ability to implement — some of those companies talk more openly about their challenges, particularly of including both digital (e.g., web, social media) and physical (in-store) channels, and linking all types of channels during a journey of a given customer.  Orcale relates specifically to the problem of identity multiplicity, that is, the difficulty to establish the identity of actually the same customer across all channels or touch-points he or she uses, since overcoming this challenge is essential to unfolding the whole journey (“Modern Marketing Essentials Guide: Cross-Channel Marketing“, 2014 [PDF]). This challenge is also echoed by Nice, termed as identity association (Customer Journey Optimization [webpage]).

Another key issue that needs to be addressed is whether a customer journey includes only direct interactions between a customer and a focal company through channels where it operates (e.g., call centre, website, social media) or are there other activities consumers perform towards accomplishing their goal to be accounted for (e.g., searching other websites, consulting a friend, visiting brick-and-mortar stores).

  • In a blog of Verint (In Touch), Koren Stucki refers to a definition of the customer journey as a series of interactions performed by the customer in order to complete the task. Stucki thereafter points out a gap between the straightforward definition and the complexity of the journey itself in the real world. It may not be too difficult to understand the concept and its importance for customer engagement and experience, but capturing customer journeys in practice, identify and link all channels the customer uses for a given type and purpose of a journey (e.g., product purchase, technical support) can be far more complicated. Understanding these processes is truly imperative for being able to enhance them and optimise customer engagement (“Why Customer Journeys?“, 16 Sept. 2014).
  • Piaggio (Nice) also related to the frustration of companies with difficulties in mapping customer journeys. She identifies possible causes as complexity, technical and organizational obstacles to gathering and integrating data, and the dynamic nature of consumer behaviour. She then suggests seven reasons to using CJMs. In accordance, in their brochure on customer journey optimization, Nice see their greater challenge in gathering data from various sources-channels and of different types, and integrating the data, generating complete sequences of customer journeys; three main analytic capabilities they offer in their solution are event-sequencing and visualisation in real-time, contact reasoning (predictive tool), and real-time optimization and guidance (identifying opportunities for improvement).
  • In their first out of four steps to a customer journey strategy — namely map the current customer journey — IBM state that the customer journey “signifies the series of interactions a customer has” with a brand (IBM refers specifically to digital channels). Importantly, they suggest that customer journeys should be mapped around personas representing target segments. The CJMs should help managers put themselves in their customers’ shoes (“Map and Optimize Your Customer Journey“, 2014 [PDF])..
  • In the blog of TeleTech (How We Think), Niren Sirohi writes about the importance of defining target segments and mapping typical customer journeys for each one. Sirohi emphasises that all stages and modes engaged and all activities involved should be included, not only those in which the company plays a role. Next, companies should identify and understand who are the potential influencers at every stage of the journey (e.g., self, retailer, friend). Then ideas may be activated as to how to improve on customer experiences where the company can influence (“A Framework for Influencing Customer Experience“, 16 Oct. 2014).

Customer engagement — This is another prominent viewpoint from which companies approach the customer journey. Nice direct to Customer Journey Optimization via Multi-Channels and Customer Engagement. Verint also present customer journey analysis as part of their suite of Customer Engagement Analytics (also see their datasheet). The analytic process includes “capturing, analysing, and correlating customer interactions, behaviours and journeys across all channels”.  For IBM, the topic of customer journey strategy belongs in a broader context of Continuous Customer Engagement. The next steps for a strategy following mapping (see above) are to pinpoint areas of struggle for customers, determine gaps to fill wherein customer needs and preferences are unmet by current channels and functionalities they offer, and finally strategize to improve customer experiences.

  • Attention should be paid not only to the sequence of interactions but also to what happens during an interaction and how customers react or feel about their experiences. As cited above, Gilliam of TeleTech refers to the mood of customers. Verint say that they apply metrics of customer feedback regarding effort and satisfaction while Nice use text and speech analytics to extract useful information on the content of interactions.

Key issues in improving customer engagement that professional services providers recognize as crucial are reducing customer effort and lowering friction between channels. Effort and struggle by customers may arise during interaction in a single touch-point but furthermore due to frictions experienced while moving between channels. Behind the scenes, companies should work to break down walls between departments, better co-ordinate functions within marketing and with other areas (e.g., technical support, delivery, billing), and remove silos that separate departmental data pools and software applications. These measures are necessary to obtain a complete view of customers. At IBM they see departmental separation of functions in a company, and their information silos, as a major “enemy” of capturing complete customer journeys. Ken Bisconti (29 May 2015) writes in their blog Commerce on steps that can be taken, from simple to sophisticated (e.g., integrated mapping and contextual view of customers across channels), to improve their performance in selling to and serving customers across channels, increase their loyalty and reduce churn. Genesys see the departmental separation as a prime reason to discrete and disconnected journeys; continuity between touch-points has to be improved in order to reduce customer effort (solution: omnichannel Customer Journey Management). Piaggio (Nice) suggests that input from CJMs can help to detect frictions and reduce customer effort; she also relates to the need to reduce silos and eliminate unnecessary contacts. Last, TeleTech also call in their paper on “Connecting the Dots” to break down walls between customer-facing and back-office departments to produce a more channel-seamless customer experience.

  • Technology and analytics firms compete on their software (in the cloud) for mapping customer journeys, the quality of journey visualisation (as pathways or networks), their analytic algorithms, and their tool-sets for interpreting journeys and supporting decision-making (e.g., Nice, Verint, Teradata, TeleTech while IBM intend to release their specialised solution later this year).

Varied approaches may be taken to define a journey. From the perspective of a purchase decision process, multiple steps involving search, comparison and evaluation up to to purchase itself may be included, plus at least some early post-purchase steps such as feedback and immediate requests for technical assistance (e.g., how to install a software acquired). In addition, a journey of long-term relationship may refer to repeated purchases (e.g., replacement or upgrade, cross-sell and up-sell). Alternatively, a journey may focus on service-related issues (e.g., technical support, billing). How a journey is defined depends mostly on the purpose of analysis and planning (e.g., re-designing a broad process-experience, resolving a narrow problem).

As use of digital applications, interfaces and devices by consumers grows and expands to perform many more tasks in their lives (e.g., in self-service platforms), we can expect reliance of CJMs on digital channels and touch-points to become more valid and accurate. But we are not there yet, and it is most plausible that consumers will continue to perform various activities and interactions non-digitally. Consumers also see the task they need or want to perform, not merely through the technology employed. Take for example physical stroes: Shoppers may not wish to spend every visit with a mobile device in hand (and incidentally transmit their location to the retailer). Don Peppers laments that companies have designed customer experiences  with a technology-first, customer-second approach whereas the order should be reverse. Undertaking a customer perspective is required foremost for effectively identifying frictions on a journey pathway and figuring out how to remove them  (“Connecting the Dots”, TeleTech). Excessive focus on technologies can hamper that.

Bruce Temkin (Temkin Group, Blog: Experience Matters) provides lucid explanations and most instructive guidance on customer journey mapping. However, it must be noted, Temkin advocates qualitative research methods for gaining deep understanding of meaningful customer journeys. Quantitative measures are only secondary. He does not approve of confusing CJMs with touch-point maps. His concern about such interpretation is that it may cause managers to lose the broader context in which touch-points fit into consumers’ goals and objectives. Temkin puts even more emphasis on adopting a form of Customer Journey Thinking by employees to be embedded in everyday operations and processes, following five questions he proposes as a paradigm.

There are no clear boundaries to the customer journey, and doubtful if they should be set too firmly — flexibility should be preserved in defining the journey according to managerial goals.  A journey should allow for various types of activities and interactions that may help the customer accomplish his or her goals, and it should account not only for their occurrence and sequence but also for content and sentiment. A viewpoint focusing on channels and touch-points, leading further to technology-driven thinking, should be modified. An approach that emphasises customer engagement but from the perspective of customers and their experiences is more appropriate and conducive.

Ron Ventura, Ph.D. (Marketing)

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Big Data, Big Science, Data Science — This triad of concepts exemplifies the new age of utilisation of data in large Volume by companies to produce information and insights for guiding their operations, such as in marketing, to perform more effectively and profitably. Yet Big Data also means that data exhibit great Variety (e.g., types and structures), and are generated and transformed in high Velocity. The data may be retrieved from internal or external sources. To be sure, non-business organisations also utilise Big Data and Data Science methods and strategies for a range of purposes (e.g., medical research, fraud detection), though our interest is focused here on marketing, inter-linked with sales and customer service, as well as retailing.

It is not quite easy to separate or draw the line between the concepts above because they are strongly connected and cover similar ideas. Big Data may seem to emphasise the properties of the data but it is tied-in with specialised technologies and techniques needed to store, process and analyse it. Likewise, Data Science (and Big Science) may imply greater emphasis on research strategies, scientific thinking, and analytic methods, but they are directed towards handling large and complex pools of data, namely Big Data. Nonetheless, we may distinguish Data Science by reference to occupation or position: Professionals recognized as “data scientists” are identified as distinct from all other business analysts or data analysts in this field – data scientists are considered the superior analysts, experts, and mostly, the strategists who also connect between the analytic domain and the business domain.

The Trend Lab VINT (Vision – Inspiration – Navigation – Trends), part of Sogeti network of experts (Netherlands), published an instructive e-book on Big Data. In the e-book titled “No More Secrets With Big Data Analytics” (2013), the team of researchers propose a logical linkage between these concepts while relating them to Big Business. Big Science was conceived already in the early 1960s (attributed to atomic scientist Alvin Weinberg) to describe the predicted rise of large-scale scientific projects. It was not associated necessarily with amount of data (typical contexts have been physics and life sciences). Big Data as a concept emerged nearly ten years ago and turned the spotlight on data. Data Science is introduced by VINT as the toolbox of strategies and methods that allows Big Data to bring us from Big Science to Big Business. Data Science is “the art of transforming existing data to new insights by means of which an organizsation can or will take action” (p. 33). Originally, Big Science emphasised a requirement of scientific projects that is true today with regard to Big Data projects: collaboration between researchers with different areas of expertise to successfully accomplish the research task.

  • The researchers of VINT note that some scientists disapprove of connotations of the word “big” and prefer to use instead the term “extreme” which is in accordance with statistical theory.

The VINT e-book cites a profile for the position of data scientist suggested by Chirag Metha (a former technology, design and innovation strategist at SAP). In the headline Metha stated that the role of a data scientist is not to replace any existing BI people but to complement them (p. 34; BI=Business Intelligence). He defined requirements from a data scientist in four areas: (a) deep understanding of data, their sources and patterns; (b) theoretical and practical knowledge of advanced statistical algorithms and machine learning; (c) strategically connecting business challenges with appropriate data-driven solutions; and (d) devise an enterprise-wide data strategy that will accommodate patterns and events in the environment and foresee future data needs of the organisation. Therefore, primary higher-level contributions expected from a data scientist include the capacity to bridge between the domains of business and data/analytics (i.e., translate business needs to analytic models and solutions and back to [marketing] action plans), and an overview of data sources and types of data, structured and unstructured, and how to combine them properly and productively.

The pressure on companies to implement data-driven marketing programmes is growing all the time. As one company becomes publicly commended for successfully using, for instance, feedback on its website and in social media to create better-tailored product offerings, it gains an advantage that puts its competitors under pressure to follow suit. It may also inspire and incentivize companies in other industries to take similar measures. Such published examples are increasing in number in recent years. Furthermore, companies are encouraged to apply individual-level data of customer interactions with them (e.g., personal information submitted online, stated preferences and tracking page visits and item choices made on viewed pages) in order to devise customized product offerings or recommendations for each customer. Already in the late 1990s the grocery retailer Tesco leveraged its business in the UK and gained a leading position by utilising the purchase and personal data of customers gathered through their loyalty Clubcard to generate offerings of greater relevance to specific customer segments they identified. Amazon developed its e-commerce business by recommending to individual customers books related to those they view or purchase based on similar books purchased by other customers and on customers’ own history of behaviour.

A key challenge facing many companies is to implement an integrative approach that enforces a single view of the customer across organisational functions and channels. Thus, marketing programmes and operations must be coordinated and share data with sales and customer service activities. Moreover, data of interactions with customers, and consumers overall (as prospects), need to be examined and incorporated across multiple channels — offline, online, and mobile. This is a mission of utmost importance for companies these days; ignoring or lagging behind on this mission could mean losing ground in a market and relevance to customers. This is because customers’ experience extends over different stages of a journey in their relationship with a company and across multiple alternative channels or touchpoints they may use to fulfill their objectives. They expect that data that become available to companies be employed to improve in some way their customer experience anywhere and anytime they interact with the company. For companies, it definitely requires that they not only gather but also analyse the data in meaningful and productive ways. Whether the interactions occur in-store, over the phone, on a company’s website, in social media networks, or through mobile apps, customers consequently expect those interactions in and between channels to be smooth and frictionless. As for companies, they need to be able to share and join data from the different channels to obtain a comprehensive view of customers and co-ordinate between channels.

  • The American leading pharmacy retailer Walgreens established a platform for monitoring, analysing and managing its inventory jointly across all of its outlets, over 8,000 physical stores and four online stores, so as to allow shoppers to find, purchase and collect products they need in as a seamless manner as possible. They integrate point-of-sale data for customers with data from additional sources (e.g., social media, third-party healthcare organisations) in order to improve patient care.
  • Procter & Gamble, which does not have direct access to sales data as retailers, created an independent channel of communication with consumers; with the help of Teradata, they use personal data provided by consumers online and other data (e.g., social media) to put forward more personalised product offerings for them.

An additional important aspect is the need to join different types of data, both structured (e.g., from relational customer databases) and unstructured (e.g., open-end text in blog posts and social media posts and discussions). Data that companies may utilise become ever more heterogeneous in type, structure and form, posing greater technical and analytical challenges to companies, but also offering better opportunities. Companies may also consider using digital images, voice tracks (i.e., not only for verbal content but also tone and pitch), and all sorts of traffic data (e.g., electronic, digital-online and mobile, and even human-physical traffic in-store). For example, suppose that a company identifies photo images posted by its customers online and recognizes that the images include objects of product items; it then may complement that information with personal data of those customers and various interactions or activities they perform online (e.g., company’s websites, social media) to learn more about their interests, perceptions, and preferences as reflected through images.

  • The US airliner JetBlue uses the Net Promoter Score (NPS) metric to trace suspected problems of customer satisfaction, and then utilise survey data and content from social media networks, blogs and other consumer-passenger communications to identify the possible source and nature of a problem and devise an appropriate fix (an award-winning initiative by Peppers & Rogers).

But there is reason for some concern. In a report titled “Big Data: The Next Frontier for Innovation, Competition, and Productivity” (2011), McKinsey & Co. Consulting Group cautioned of an expected shortage in highly advanced analytic professionals and data-proficient managers. They estimated that by 2018  organisations in the US alone could face a shortage of 140,000 to 190,000 people with “deep analytical skills”. Nonetheless, the report also predicts a shortage of 1.5 million managers and analysts “with the know-how to use the analysis” of Big Data and its effective application for decision-making.  The first part seems to refer to the professional-technical level whereas the second part points to utilisation of Big Data at the business level. Thus, McKinsey & Co. appear to be even more concerned by inadequate ability of companies at a managerial level to benefit from the business advantages, such as with marketing-related objectives, that Big Data can produce. Data Scientists may be counted in both categories of this forecast, but because they need to be simultaneously expert analysts and business-savvy they could belong more closely with managers.

However, the situation may not improve as quickly as sought. The problem may be that young people are not attracted, not encouraged, and are not educated and trained enough to obtain high proficiency and skills in the exact sciences of mathematics and statistics, at least not at a growing pace that the industry may require. This problem seems to be imminent particularly in Western countries. Popular areas of studies such as technology, computer sciences and business administration can not compensate for lack of sound knowledge and skills in mathematics and statistics as far as utilisation of Big Data in marketing in particular and management in general is concerned. Yet business students, MBAs included, are more inclined to stay away rather than embark on their courses and tasks in statistics and data analysis; and the number of graduates in exact sciences is not increasing fast enough (in some cases even decreasing).  Here are some figures indicative of the problem that may help to illuminate it:

  • In the latest PISA exams carried out by the OECD in 2012 for school students aged 15-16, seven out the ten top ranking countries (or economies) in math are from the Far East, including Shanghai and Hong-Kong of China, Singapore, Republic of Korea, and Japan. Three European countries close the top list: Switzerland, adjacent Lichtenstein, and the Netherlands. Their scores are above the mean OECD score (494), ranging between 523 and 613.
  • Western countries are nevertheless among the next ten countries that still obtain a score in math above the OECD mean score, including Germany, Finland, Canada, Australia, Belgium and Ireland. But the United Kingdom is in 26th place (score 494) and the United States is even lower, in the 36th place (481). Israel is positioned a bit further down the list (at 41st, score 466). [34 OECD members and 31 partner countries participated].

  • In Israel, the rate of high school students taking their matriculation exam in math at an enhanced level (4 or 5 units) has changed negatively in recent years. It ranged in the years 1998-2006 from 52% and up to 57% but since 2009 and until 2012 it dropped dramatically to 46% of those eligible to a matriculation certificate, according to a press release of the Israeli Central Bureau of Statistics (CBS). It is noted by CBS that this decrease occurs in parallel with an increase in the total number of students who obtain the certificate, but this suggests that effort was not made to train and prepare the additional students to a high level in mathematics.

  • In statistics published by UNESCO on the proportion of academic  graduates (ISCED levels 5 or 6 — equivalents of bachelor to PhD) in Science fields of study, we find that this proportion decreased from 2001 to 2012 in countries like Australia (14.2% to 9%), Switzerland (11.5% to 9%), Republic of Korea (9.8% to 8.5%), UK (17.4% to 13.7%), and Israel (11.7% to 8.5% in 2011).
  • This rate is stable in the US (8.6%) and Japan (though low at 2.9%), while in Finland it has been relatively stable (10%-11%) but shifting down lately. Nice rises are observed in Poland (5% to 8%), Germany (13% to 14.5%), and the Netherlands (5.7% to 6.5%); Italy is also improving (up from 7.5% to 8%). [Levels of ISCED scheme of 1997; a new scheme enters this year].

The notion received is that supply of math and science-oriented graduates may not get closer to meet market demand by companies and non-business organisations in the coming years; it could even get worse in some countries. Companies can expect to encounter continued difficulties to recruit well-qualified analysts with potential to become high-qualified data scientists, and managers with good data and analytics proficiency. Managers and data scientists may have to work harder to train analysts to a satisfying level. They may need to consider recruiting analysts from different areas of specialisation (e.g., computer programming, math and statistics, marketing), each with a partial skill set in one or two areas, continue to train them in complementary areas, and foremost oversee the work and performance of mixed-qualification teams of analysts.

Big Data and Data Science offer a range of new possibilities and business opportunities to companies for better meeting consumer needs and providing better customer experiences, functionally and emotionally. They are set to change the way marketing, customer service, and retailing are managed and executed. However, reaching the higher level of marketing effectiveness and profitability will continue to command large investments, not only in technology but also in human capital. This will be a challenge for qualified managers and data scientists to work together in the future to harvest the promised potential of Big Data.

 

Ron Ventura, Ph.D. (Marketing)

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Consumers like to talk about the brands in their lives. Brands may be connected to their personal history or to a narrative which describes their current lifestyle; people can tell others about a variety of brand experiences they have had, for better or worse. Consumers use likewise word-of-mouth information they receive from friends and relatives, but not only from them. They refer to product reviews, user-generated blogs, as well as stories, opinions and suggestions conveyed in forums of digital social networks from people they may not know so well but consider convincing or trustworthy. The proliferation of user-generated content through Web 2.0 and mobile applications did a great deal to facilitate the spread of word-of-mouth (WOM) and increase consumer reliance on this type of information. However, it does not preclude the still dominant transfer of brand-related WOM offline between people more closely connected in face-to-face meetings and phone conversations.

But brands do not exert WOM to the same extent. Some brands get more of such informal publicity than others. The question thence becomes: What characteristics of a brand make it more interesting, important or relevant to consumers to talk about with friends, family and others? In such discourse consumers could be mainly in the role of providers or receivers of information, and they may share personal experiences, viewpoints, and recommendations, or conversely warnings, regarding any brand.

Researchers Lovett, Peres, and Shachar (2013) took the challenge of investigating the relations of brand characteristics to stimulation of WOM shared among consumers, and they offer some interesting insights, especially on the differences between offline and online channels. At start, it should be clarified that drivers for engaging in WOM are originated in the consumers for satisfying their personal needs; the brand characteristics may be seen as operational instruments that link with the drivers that stimulate brand-related WOM. The researchers identify three main drivers in their guiding theoretical framework:

  • Social driver — Concerned with a need of consumers to express themselves to others, showing their uniqueness, for self-enhancement, and out of desire to socialize with others;
  • Emotional driver — Associated with excitement and pleasure of satisfaction (emotional sharing);
  • Functional driver — Related to the need to obtain information and the tendency to provide information to others, moderated by aspects such as complexity and knowledge.

The researchers collated information on over six hundred US national brands of products and services as well as corporate and retailer names (covering the period of 2008-2010). The brands spanned across 16 broad product categories (e.g., beverages, children’s’ products, clothing, department stores, cars, media and entertainment).

  • Data sources on brand characteristics included a consumer survey in the US (primary source) and several datasets of proprietary research programmes (secondary sources), the major of them is the Young and Rubicam Brand Asset Valuator (characteristics corresponding to brand equity “pillars”:  Differentiation, Relevance, Esteem, and Knowledge).
  • The level of WOM generated about a brand (operated as count of mentions of a brand) was modelled and analysed separately in offline conversations and online settings or forums. Data of brand mentions in face-to-face and in phone conversations were obtained from the TalkTrack project of Keller and Fay (a diary-based survey) whereas data on online WOM were adopted from the Nielsen McKinsey Incite tool (a search engine that can retrieve brand mentions in settings such as discussions groups, blogs and microblogs). [The count of brand mentions was modelled under the assumption that it follows a Negative-Binomial distribution.]

We will take here a quick look at results and insights from the research that I find the more revealing and interesting, with an emphasis on distinctions between offline and online channels:

The Social Driver — A brand that is better differentiated from competitors can make an easier and more effective vehicle for a consumer to express his or her own uniqueness to others. Greater brand differentiation contributes to more brand mentions offline and online. Yet, the positive effect on WOM online is stronger. There could be greater motivation for consumers to utilise brands for highlighting their uniqueness when communicating online because they can address much larger audiences than offline, and a reference to the relevant brand can efficiently deliver the message, particularly when cues of visual appearance or sound cannot be used. Brand differentiation is a newly studied characteristic in relation to WOM in this research project.

The volume of brand WOM also increases with higher perceived quality of the brand’s products, and is larger for more prestigious, premium brands. Associating with brands of higher quality products (represented by Esteem) can serve to demonstrate the consumer’s expertise in a category — it has a positive effect on WOM offline and online, but the effect online is twice as large.  A premium brand characterization, that reflects a higher social status, has a significant effect only in an online channel. Enhancing one’s self-image through expertise or social status, as with highlighting personal uniqueness, is possibly felt more needed by consumers in the less intimate interactions that take place online with people whom they are less familiar with than those they interact with face-to-face or on the phone. A consumer may have more to “prove” to or impress “friends” who are known primarily and even solely as members in his or her virtual social network.

The Emotional Driver — Being excited about a brand seems as a very plausible motive to arouse consumers to talk about it. Lovett and his colleagues indicate that excitement, a brand personality trait, has not been studied yet in the context of WOM.  As expected, brands that evoke more excitement lead consumers to engage more in WOM about the brand, both offline and online. While the effects of excitement are similar between the channels, there is a distinction between them, as addressed below, with respect to the emotional driver in general.

The researchers expected that a higher level of WOM would be generated when satisfaction with a brand is very high or very low. Their model results showed, however, that only very low satisfaction yields a peak in WOM, and that as satisfaction rises the level of WOM drops (i.e., a relationship described by a monotonic descending concave curve). The finding that very low satisfaction induces consumers to talk (critically) more about a brand is frequently supported in other studies.

  • The proposition about the effect of very high satisfaction may have not been supported, according to the researchers, because it has confounded with the effects of esteem and excitement included in their model and not in previous research. But one cannot ignore that the dataset included satisfaction scores for just a third of the brands analysed, as reported, and scores for the remaining 2/3 of brands with missing data were imputed based on the distribution of the available scores. Consequently, it is hard to conclude based on the evidence whether the effect of high satisfaction indeed exists.

The Functional Driver — This driver has two dimensions: obtaining information and providing information through WOM. Consumers often require assistance when learning complex product information (e.g., prior to purchase) or dealing with complex technical details and instructions (e.g., for correct product utilisation). Complexity matters primarily to those who wish to obtain information. This research reveals that greater complexity is related to more brand mentions only in offline conversations. That is, more immediate, direct and intimate interactions offline between consumers are adopted as more suitable for discussing together and clarifying information that is complex and more difficult to comprehend about products. It may be added that such conversations are also more likely to be held between consumers who know each other better, and that allows for a better flow of interaction. Less complex information can be obtained from online forums. Online conversations, as the authors argue, tend to be asynchronous, and entail longer delays in responding to questions that may hinder clarification of confusing matters and information exchange. Complexity is another characteristic included in this study yet not in previous research in the context of WOM.

Interestingly, consumers also engage more in WOM on younger (i.e., newer) brands when communicating offline but not online —  brands possibly perceived as innovative, intriguing, exciting or still ambiguous appear also to be more appropriate to talk about in person.

From the perspective of those who provide information, producing and disseminating WOM on brands would depend on how knowledgable consumers feel they are on the subject.  The results confirm that brands that are perceived to be more familiar to consumers and better known are more likely to be talked about, similarly offline and online.

The researchers further extracted and compared the relative importance of each main driver between the two settings of offline and online channels. The social driver is the most important stimulant of online WOM followed by the functional and lastly the emotional driver. In contrast, in offline conversations the emotional driver is the most important, followed by the functional driver, and relatively the least important driver is social. Notably, while the emotional driver has a positive effect in both types of channels, it is more prominent in driving brand mentions in conversations offline. These differences exemplify the difference in nature between offline and online interactions — offline interactions are more intimate and open between people, more accommodating to share excitement and satisfaction, whereas online interactions are less personal, tend to promote “broadcasting” information to many people and social signalling with verbal cues.

  • The different nature of offline and online channels may also be evident in an almost complete separation between lists of leading brands (top 1o) in number of their brand mentions between those two settings: Offline we find Coca-Cola, Verizon, Pepsi, Wal-Mart, Ford, AT&T, McDonald’s, Dell Computers, Sony, and Chevrolet. Online, on the other hand, arrived on top the brands of Google, Facebook, iPhone, YouTube, Ebay, Ford, Yahoo, Disney, and Audi. Only Ford is on both lists. The contrast between “new” and “old” or “physical” and “virtual” brands speaks for itself.

The models furthermore demonstrate the positive role of brand equity in encouraging consumers to talk more about a brand. Stronger brands — more encompassing in their areas of activity and influencing many more people — command more conversation (e.g., information exchange and sharing opinions). First, we may recognize an implicit effect of brand equity on WOM through factors represented in the models such as perceived quality, differentiation, knowledge, and visibility that contribute to enhancing the equity of a brand. Second, nonetheless, the researchers included in their two models a control variable of brand equity, represented as the inclusion of brands in the list of 100 top brands constructed by Interbrand. It is thereby confirmed that brands on this list enjoy higher WOM. One should keep in mind, however, that being more frequently the subject of conversation, offline or online, is evidence of greater importance and relevance of a brand, and in turn may increase its equity further, when WOM is positive, but may also decrease its equity when the WOM is negative.

The authors acknowledge some limits of their research. In particular: (1) The brands included are the most talked about in the US (i.e., covering reduced variation in level of WOM over brands); (2) The models refer to “offline” and “online” in wholesome as types of channels — more research is needed to investigate effects on WOM in separate online spaces like the blogosphere and social media networks; (3) Since the units of information are brands rather than individual consumers, the ability to describe and explain the processes in which consumers exchange, produce or obtain WOM information on  brands is impaired, inviting more research in this respect.

Marketing communication managers may use the results (effect estimates) and insights from these models of WOM to identify characteristics of brands in their responsibility that can be expected to yield more WOM and learn of gaps between actual and expected levels of WOM when planning where and how to invest their effort for evoking more WOM on their brands. However, it is most important for marketers, as Lovett, Peres, and Shachar stress in their article, to keep offline and online channels distinguished and plan their measures for each environment separately — what may work well in an online environment can prove ineffective offline, and vice versa. In each environment it is necessary to emphasise different aspects and goals and take appropriate measures.

Ron Ventura, Ph.D. (Marketing)

Reference:

On Brands and Word-of-Mouth; Mitchell Lovett, Renana Peres, & Ron Shachar, 2013; Journal of Marketing Research, 50 (August), pp. 427-444.

The authors won a grand award for their research project in a joint-competition of the Wharton Customer Analytics Initiative and the Marketing Science Institute.

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Mapping the customer journey is often suggested as a vital step for better understanding customer experiences, before appropriate measures can be planned for improving on them. At the core of a “customer journey” is the purchase decision process, yet the evolved concept of “journey” encompasses broader aspects of customer behaviour and experience. Particularly with respect to consumers, the term “process” may have seemed to many (e.g., practitioners, managers) as too technical and logical while a “journey” is perceived as more imaginative and more likely to be imbued with emotion. There is still a significant parallel between the two concepts, yet the concept of journey has been extended in some important ways and emphasises the following aspects:

  • More frequently, the relation of a consumer with a company or a brand does not end with the act of purchase (transaction) of its product, good or service — following the purchase decision process there are likely to be additional immediate activities like further enquiries about product usage, feedback to the company or exchange of impressions with friends and family; in many cases, especially for on-going services and durables, there are continued interactions of customer service and technical support.
  • In any task concerned with purchase or usage customers more often engage multiple channels and touchpoints to complete their tasks and accomplish their goals (e.g., visiting a company’s Web site, a product & price comparison online portal, and a brick-and-mortar store before buying, interacting with a company by Facebook and e-mail to receive technical assistance).
  • Processes entailed in a “customer journey” tend to be cyclic rather than uni-directional processes with clear start and end points — there is continuity or flow from one purchase episode to the next such that if a subsequent purchase of a similar or related product is made from the same company customer loyalty can develop, but there are also possible cycles and repetition of activities performed by a consumer during a single purchase decision process.

Therefore, the customer journey may be not only longer than what a purchase decision process implies but also more multi-faceted and complex. To be honest, some of the extending aspects have been already suggested within the framework of the purchase decision process. For instance, post-purchase stages such as feedback and product divestment have been suggested in decision models in the 1990s (e.g., Engel, Blackwell and Miniard). Reliance on multiple information sources (marketer- and non-marketer controlled) has also been long considered  in the course of a purchase decision process. And if we concentrate on the path of a single decision process, decision models described and depicted by prominent scholar Jim Bettman in the late 1970s are all but simple, uni-directional and straightforward. Consumers frequently move back-and-forth, collect and use different pieces of information according to various decision rules, evaluate their options, and if necessary return to revise their consideration set, collect more information or re-examine their prior analysis. Those concepts and models have been tested and developed by Bettman together with his colleagues John Payne and Eric Johnson under the theoretical framework of adaptive decision-making (1993). Hence, the customer journey clearly builds on the foundations of earlier theories and models of consumer decision-making.

However, the concept of customer journey contributes several new perspectives. First, journey models give more weight to post-purchase activities compared with purchase decision models that traditionally address these activities only briefly, leaving them to be treated in other model types. Sharing opinion in social media networks, crowd sourcing for assistance, or asking for customer support from a company-provider, all these are important for business practice; accounting for these activities recognises that positive experiences in these activities build the link from one purchase to the next with the same company  (i.e., replacement, cross-sell, and up-sell). Journey maps vary nonetheless in their scope: taking a broad-view of a relationship journey with a company or focus on specific tasks and activities (e.g., enquiry about billing); considering all aspects of a purchase decision process, including any engagement with offers by competitors, or concentrating on interactions between the company concerned and its customers, as “customer journey” literally suggests.

Second, journey models appear to give more room to expression of emotions and affective reactions by customers, for example, in giving feedback or during service-related interactions with the company. Mapping studies that rely on interviews with customers even encourage such expressions. However, it should be noted that literature on decision-making, particularly in the past 10-15 years, already recognises the incorporation of both cognitive and affective components as co-influencers of decision processes.

Third, making probably the most important contribution, customer journey models address the employment of multiple channels by customers through various associated touchpoints with companies to perform purchase, usage or service tasks. This aspect appears to be driven primarily by business enterprises in response to the contemporary reality of their relations with customers. These channels furthermore are expected to be co-ordinated. In some cases, however, ambiguity arises whether each touchpoint defines an independent channel or multiple touchpoints are nested within a single channel:

  • In a brick-and-mortar store, shoppers may encounter touchpoints with the retailer in front of a shelf display (this is also a potential touchpoint with a manufacturer’s brand) and at the cashier;
  • On the internet, a customer may experience a touchpoint with a company on its main commercial website when learning about its products, but she may also transfer to the company’s blog linked to the website or launch a chat conversation from the website to ask for assistance from a service representative.
  • “Mobile” is commonly considered a channel by itself but nested within are a variety of resources and tools that can be used on the mobile devices, some of them have parallels in other modes of communication (websites, e-mail, social media), some are specially designed for mobile (e.g., apps).

Constructing a mapping diagram of customer journeys is a specialisation with its own techniques; it falls in the domain of information visualisation or graphic design and is beyond the scope of this post-article. Such maps can quickly become complicated, rich in detail, because there are many pathways that customers may follow in their journey. A common way to deal with the complexity, and in order to make journeys more accessible and vivid to managers is to identify “typical” customers with characteristic personal attributes and pathways they go through, and build accordingly exemplary profiles, also known as ‘personas’ (e.g., common in the area of user experience [UX]).

But it could matter on what type of input the profiles of these personas are based. Are methods of quantitative research for collecting relevant data from customers sufficient? Bruce Temkin, expert on customer experience and head of the consulting group by his name, recommends in his blog, Customer Experience Matters, that companies combine between input from discussions (‘think tank’) of their managers responsible for customer relationships, and data from customer research (e.g., in-depth interviews, ethnographic techniques). These steps would preferably be conducted in this order. It should be helpful, however, to use quantitative data to construct plausible journeys and identify most relevant and interesting customer personas. Surveys may not be economic and efficient as a method to collect detailed-enough data. Yet, surveys can be useful for at least characterising main stages in a journey as well as the channels and touchpoints engaged, that could still enable better generalisation or validity of the information. Even quantification of input collected during in-depth interviews can help to pinpoint more frequent activities or stages, and paths or links between them so as (1) to depict significant or salient journey scenarios; (2) to identify key segments; and (3) to construct more meaningful and realistic personas that managers can effectively rely upon in their planning. Relevant approaches and techniques may be learned from the areas of means-end chain models and path analysis, for instance of shoppers’ journeys in physical stores (i.e., a true physical journey that is nonetheless relevant in this context).

Better established maps of customer journey layout a chain of main stages as the foundation or “spine” of the journey, and then add more detail on specific activities, customer impressions and reactions, costs and benefits, etc. A map would be devised for each key segment or prototypical persona. Maps can get more complex as one tries to account for cycles in the flow of events and activities during the journey (e.g., initial exploration on a website, visit to a store, return to the website for more information, and so on). A model proposed by Forrester Research, for example, defines four primary stages in a customer journey: discovery, explore, buy, and engage. The general model distinguishes between reach channels used for discovery, depth channels appropriate for exploration, and relationship channels through which customers engage with the respective company over time (i.e., strengthening relationships). McKinsey & Co. define more explicitly their orientation: they offer a model named the “consumer decision journey”. It is a cyclic journey model which includes four main phases: (a) initial consideration set for research and learning; (b) active evaluation of alternatives; (c) the moment of purchase; and (d) post-purchase experience, which can cycle back through a “loop” of loyalty to purchase. Noteworthy about this model, it recognizes that consumers may check again new alternatives and update their consideration set during active evaluation.

The Big Data sphere is also recruited to the mission of mapping customer journeys. However, the approach taken in such applications tends to be more strictly focused on performance of particular tasks by customers with the client company (e.g., product enquiry and service). Furthermore, th0se maps seem to over-emphasise the role of touchpoints as used by customers, particularly digital ones, as the nature of data sources used dictates. Temkin (see above) criticises the interpretation of a customer journey map as a touchpoint map, as typically adopted in systems based on big data. He argues that concentrating on individual interactions is prone to lose sight of the “broader context of how that touchpoint fits within the overall goal and objectives of the customer.” Systems in the field do show links or transitions between touchpoints, but the maps provide a rather narrow viewpoint of the journey and its context.

A map may zoom for instance on a particular touchpoint such as a call centre (by phone) and show how many customers visited previously a webpage of the company and how many ended the journey at the call centre or proceeded to another touchpoint for completing their task. Conspicuous figures or pathways may start a discussion of what that means and what should be done to improve the experience. However, such applications “see” only computer-based channels or touchpoints associated with the company, that is, mapping strictly customer journeys of technological interactions with the company. What if the customer consulted a friend on the phone, responded to a TV ad, or visited a store? The effectivity of the maps relies also on strong connectivity between the different channels of communication and interaction operated by the company (e.g., PC website, mobile, phone call centre). Silos in the organisation can hamper the construction of journey maps. Finally, it is important to study not only what customers do but also how they perceive their own actions and their attitude towards them. It would help companies to tap into subjective sensitivities of customers about their behaviour and avoid infringing into areas of customer desired privacy.

Mapping the customer journey can be used to improve many aspects of decision processes and post-purchase experiences (e.g., foster linkage between physical stores and information through mobile devices). Focusing on the journey of customers for narrowly defined tasks that involve interaction with a company can help indeed in resolving concrete problems or issues in customer experience. Nevertheless, companies should also take a broader perspective to map the journeys of more elaborate processes and experiences that extend in time through a relationship with the company. Models should also avoid being too restricted to customer interactions with the company and explore interactions with other potential influencers.

Ron Ventura, Ph.D. (Marketing)

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

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

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

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

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

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

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

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

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

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

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

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

Different schemes have been devised for collaboration programs with customers:

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

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

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

Ron Ventura, Ph.D. (Marketing)

Notes:

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

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

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

(4) Ibid. 1.

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

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Social media networks are flourishing in activity. Most attention is given to Facebook that reached one billion members in the summer of this year. The lively arena of Facebook, humming with human interaction, and its potential to provide easy access to millions of consumers, has soon attracted the interest of marketers. A particular area of interest is the opportunity to study consumer perceptions, attitudes, preferences and behaviour through research activity in online social media networks, primarily in Facebook.

We may distinguish two tracks of research:

  • One track entails the collation and analysis of personal content created by network members with minimal or no intervention of companies. This track falls mainly within the domain of Big Data analytics that evolved dramatically in the past few years and keeps growing. Analytic processes may include text mining in search of keywords and key phrases in discussions, frequencies of “like”s, and movement between pages.
  • The other track, that is the focus of this post-article,  includes interaction between a company and consumers, usually within a community or forum set-up by the company in its corporate name or in the name of one of its brands (e.g., its “page” on Facebook). This activity may take the form of regular discussions initiated by the company (e.g., introducing an idea or a question on topic of enquiry to which members are invited to comment) but also invitations to participate in surveys and moderated focus-group discussions online.

Online marketing research is prevalent for at least ten years now and the methods associated with this field, including surveys, experiments and focus-group discussions, continue to improve. However, the belief taking hold among marketers that they can reliably and transparently shift their research studies to the environment of social media is illusive and misleading (see articles in The New-York Times, TheMarker [Israel]).

Advantages in speed and cost may be tempting marketers to replace established methods with new techniques accustomed to social media or attempt at launching the former from within social media networks. But social media has distinctive features, particularly in structure of information and the coverage of its audiences, that do not allow an easy and simple transition into the new environment, at least not so much as turning traditional marketing research methods redundant.

The problem starts with the “rules of game” typical in a social media network. The codes or norms of discourse between members in the network do not generally fit well with the requirements of rigorous tools of research for data collection. Questions in surveys usually have specially designed structures and format and are specific in defining what the respondent is asked about. They are formulated to achieve satisfactory levels of validity and reliability. The social network on the other hand gives utmost freedom of expression in writing entries or comments. It tries to avoid constraining members into particular modes of reply. Questions prompted to members are usually written in everyday friendly language, the less formal as possible. One may normally post one to three questions at most in such mode of discussion. It lacks any discipline that robust research usually demands. The mode of questioning normally feasible within the pages of the social media website may be acceptable for some forms of qualitative research but, reasonably, it takes more than a few questions to properly investigate any topic.

A marketer may get some idea of direction where consumers or customers are driving at in their thoughts and feelings by scrutinizing their answers subjectively and individually. But it would be presumptuous to derive quantitative estimates at any reasonable level of accuracy (e.g., purchase intentions and willingness-to-pay).

  • Critics of surveys argue that the reliability of responses is often compromised when respondents attempt to second-guess what the client of the survey wants to hear or they are subject to “social desirability”, that is, they are trying to give the answer believed to be approved by others. However, this problem is not any less susceptible to surface in comments in the setting of social media. When writing in their own words in the less formal setting of a social media community, members may feel more free to express their opinions, preferences, thoughts and feelings; yet they are still expressing what they are ready to share. Furthermore, the social media is a great venue for people to promote the way they wish to be perceived by others, that is, their “other-image”, so we should not assume that they are not “fixing” or “improving” on some of their answers about their preferences, attitudes, the brands they use, etc.

One may use a web application to upload a short survey questionnaire embedded in his or her own page or as a pop-up window. The functionality of such surveys is rather limited, with only a few questions, and is usually more of a gadget than a research tool. The appropriate alternative for launching a more substantive study is to invite and refer participants to a different specialised website where an online survey is conducted with a self-administered questionnaire or a remote focus-group session can be carried out. Here we should become concerned: Who answers the survey questions or takes part in a study? Who do the participants represent?

This concern is a more critical issue in the case of surveys for quantitative research than in forms of qualitative research. Firms are normally allowed and able to address members of their own pages or communities who are “brand advocates” or “brand supporters”. The members-followers are most likely to be customers, but in addition to buying customers they may also include consumers who are just favourable towards the brand (e.g., for luxury brands). If the target population of the research that the marketer wishes to study matches this audience then it is acceptable to use the social media network as a source, and at least for a qualitative study it can be sufficient and satisfactory. However, for a quantitative study it is vital to meet additional requirements upon the process of selection or sampling of participants in order to allow valid inferences. Unfortunately, the match is in many cases inadequate or very poor (e.g., the pool of accessible members covers only a faction of the customer-base with particular demographic and lifestyle characteristics). For quantitative research the problem is likely to be more severe because the ability to draw probabilistic samples is limited or non-existent, and recruitment relies mostly on self-selection by the volunteering members.

The field of online research is still in development where issues of sampling from panels for example are still debatable. There are also misconceptions about the speed of online surveys because in practice one may need to wait even for a week for late respondents in order to obtain a better representative sample. Yet advocates of marketing  research through social media networks like Facebook try, quite immaturely, to pave the way into this special territory facing even more difficult methodological challenges.

There are certainly advantages to focusing research initiatives on the company’s customers, particularly in matters of new product development. Customers, and possibly even more broadly “brand supporters”, are likely to be more ready and motivated to help their favourite company, contributing their opinions and sharing information about their preferences. They are also likely to have closer familiarity with the company or brand and obtain better knowledge of its products and services than consumers in general. Hearing first what its own customers think of an early idea or a product concept in development makes much sense to help putting the company on the right track. However, as the configuration of a product concept becomes more advanced and specific, more specialised research techniques are required to adequately measure preferences or purchase intentions. Wider consumer segments also need to be studied. Even at an early stage of an idea there is a risk of missing on real opportunities (or vice versa) if an inappropriate audience is consulted or insufficient and superficial measurement techniques are used. Using the responses from “brand supporters” in a social media network can be productive for an exploratory examination to “test the water” before plunging in with greater financial investment. But such evidence should be evaluated with care; relying on the evidence from social media for making final decisions can be reckless and damaging.

Nevertheless, marketers should distinguish between interactions and collaboration between a company and its customers and research activity. Not every input should be quickly regarded as data for research and analysis. First of all, the mutual communication between customers or advocates and a company/brand is essential to maintaining and enhancing the relationship between them, and the company therefore should encourage customers to interact and furthermore contribute to its function and performance. Hence, when product users offer their own genuine ideas for new products or product improvements (e.g., hobbyists and enthusiasts who develop and build new Lego models) their contributions are welcome, and the better ones are implemented. And when a company (Strauss food company, Israel) gives feedback on ideas by its followers on its Facebook page as to which ideas are inapplicable, to be applied “maybe another time”, as well as in initial review, this activity is commended. But these interactions belong in the domain of collaboration, not research. Survey-like initiatives in Facebook may aid in enforcing a feeling of partnership between a company and its customers (commented to TheMarker by Osem food company).  A debate extended on this issue of “partnership” questions whether the reward to originators of successful ideas is only a sense of achievement and contribution or should they receive also material rewards from the benefiting companies.

Social media networks seem foremost appropriate as a source for qualitative research. If those who advocate performing marketing research in Facebook refer primarily to qualitative types of research, then it seems reasonable and more often may be admissible. It is also generally appropriate for exploratory and preliminary examinations of marketing initiatives but when done with caution in view of the limitations of the social media forums. It is much less appropriate as a venue and source for quantitative studies.

While interesting and valid studies can be conducted on how consumers behave in social media websites (e.g., on what subjects they talk, with whom, and the narrative of discourse they use), using a social media network as a source of research on other topics is a different matter. When done for marketing purposes, there are ethical issues regarding analytics of personal content in social media that could not be discussed in the current post. Primarily at stake is the concern whether companies are entitled to analysing content of conversations between consumers-members, suggesting that they are spying on and eavesdropping to network members. Even in discussions on the company’s page the utilization of analytic techniques may not be appropriate or effective. Access to background information on members who activate web apps on the company’s page (with their permission) is another contentious issue. For most users, this is the kind of privacy they have to give up for participating in a network free of charge, but to what extent will consumers agree to go on like this?

The use of social media networks for marketing research, as well as analytics, is therefore more complex and less straightforward than many marketers appear to perceive those activities. Foremost, explorations in social media should not be viewed head-on as a substitute for the more traditional methods of marketing research.

Ron Ventura, Ph.D. (Marketing)

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