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For Shufersal, the leading food retailer operating supermarkets in Israel, it looks like the sky is the limit. This is a message strongly received from the CEO of Shufersal, Itzhak Aberkohen, in a recent interview given to Globes business newspaper (for its annual publication of consumer-based equity-ranking of brands, July 2017). Shufersal is already a major national retailer, but since the collapse and sell-off of the main competing food chain Mega last year the road ahead is clear more than ever for Shufersal to ride on to stardom. The plans presented by the retailer’s CEO are definitely leading in that direction on different fronts.

  • Note: Shufersal has also been known as ‘Supersol’ but it appears that the retailer is moving to suppress that name in favour of enhancing its Shufersal brand name. The original name chosen for the retailer almost sixty years ago was composed by joining two words: ‘Shufra’ from Aramaic meaning excellent and ‘Sal’ which means basket in Hebrew. The retailer founded the first modern American-style supermarket in Israel in Tel-Aviv in 1958. Israelis frequently name the retailer ‘Supersal’ or ‘Shufersal’. The official choice of ‘Shufersal‘ by the company should make the consumers happy while remaining as true as possible to the legacy name.

The retailing company Shufersal operates over 270 stores. They are divided into multiple sub-chains of different store formats, designed to target different consumer segments or accommodate distinct shopping situations or goals. Three main sub-chains are: “My Shufersal” (the core sub-chain of ‘classic’ supermarkets in neighbourhoods); “Shufersal Deal” (large discount stores); and “Shufersal Express” (small convenience stores in neighbourhoods). Like most food chains, the stores offer in fact not only food and drink products but a larger variety of grocery and housekeeping products, and may sell as well toiletry or personal care products. Shufersal operates in addition a channel for online or digital shopping. It also has its own brand of products carrying the retailer’s name. The CEO seeks to enhance the company’s capacities in these domains, and then extend further. An important aspect in his plan is the digital transformation of the company’s retail operations and services.

  • Note that supermarkets in various countries may selectively add in different times and locations other product ranges (e.g., books and magazines, electric home equipment, housewares).

Shufersal is now on the verge of making a strategic entry into the field of ‘pharma’ retailing with the acquisition of New-Pharm, the second-sized pharma chain in the country. The food retailer already sells toiletry products in its stores, as indicated above, but it has no access to cosmetics (e.g., perfumed lotions, make-up) and non-subscription medications (via pharmacy departments). Taking over New-Pharm would provide it with this capability through the pharma-dedicated and licensed stores. The dominant leader in pharma in Israel is Super-Pharm, which gets the respect of Mr. Aberkohen as a successful and highly professional retail competitor in that field. Shufersal should be able to get better terms for purchasing toiletry products for its supermarkets and other stores, but the addition of cosmetics and pharmaceuticals seems less fitting its current line of business. It makes sense if the retailer had department stores where one of the departments would sell cosmetics, but that is not the case of Shufersal; it would probably have to operate the pharma stores separately. Undertaking the responsibility of operating pharmacies could create even greater complications that may outweigh the benefit of margins from selling OTC medications, nutrition supplements and other devices.

The deal is still awaiting approval of the antitrust supervisor by the end of August 2017. The main obstacle comprises 6-8 flagship stores that the supervisor may not allow the food retailer to have. Aberkohen has said in the interview that the acquisition of the pharma retailer would not be worth it without those stores. There could be additional restrictions due to vicinity of “Deal” stores and “My” supermarkets to some New-Pharm stores.  Aberkohen believes that the increased variety and assortment of toiletry products the company will be able to sell together with the new categories will make an important contribution to its sales potential but will also create a more balanced competitive challenge against Super-Pharm (i.e., as two equivalent retail powers) that will benefit consumers in personal care and grooming. The suppliers are concerned, however, that the bargaining power of Shufersal will become significantly, perhaps exceedingly, stronger in toiletry, and that the retailer will link the trading terms for their presence in New-Pharm stores with presence of their products in the Shufersal stores (Globes [Hebrew], 15 August 2017).

Shufersal’s CEO seems to have little regard for its follower Mega under a new ownership. Most of the chain, neighbourhood supermarkets (“Mega City”, 127 stores), was bought from a holding company (“Alon Blue Square”) in a rather bad state by a medium-sized food retailer of discount warehouse-like stores (“Bitan”) in May 2016. Other discount stores were sold and distributed among some smaller discount retail chains. Since then a few more supermarkets of Mega were apparently sold or closed. Bitan has roughly more than doubled the total number of stores in its ownership since acquiring Mega (on a scale from 70-80 to 180-190). Aberkohen argues that Bitan seems to be taking hold of the operation of Mega City but there is still much work ahead to re-organise its whole retail business. Occasional signs in the stores imply that the new owner is still grappling in effort to manage the additional supermarket chain. There will also come a time to deal with the effort and redundancy of keeping two unconnected brands of the two sub-chains of discount stores and supermarkets (“Bitan Wines” and “Mega City”, respectively).

Mr. Aberkohen has no greater regard for the other discount food retailers (the more familiar and popular of them is “Rami Levy” with 44 stores, increasing by 10 stores in the past year). In his view, Shufersal does not consider itself as opposed to Rami Levy or the other players; it is engaged in its own plans and mission with a focus on innovation. A key to success in the long-term, in his opinion, is an emphasis on managing existing (‘same’) stores and innovation, not adding more and more floor area. He thus maintains that while the competitors, particularly Bitan/Mega, are so busy handling the additional space in new stores, Shufersal will have the time it needs, as a window of opportunity, to create innovation (e.g., Internet, robotics) and gain an advantage of 3-5 years ahead.

  • So far consumers have not gained in terms of cost of shopping from the deal of selling Mega. According to Israeli business newspaper “Calcalist” there are worrying signs to the contrary. Mega under its new ownership has not been pressuring prices downwards (attributed to financial obligations of its owner Nahum Bitan), and Shufersal that had identified this weakness, took the opportunity to raise prices in its stores while gaining in bargaining power vis-à-vis its suppliers. A rise in prices (i.e., index of barcoded products) and an increase in sales revenue in the food retail sector (including non-barcoded outlets) point to a change in trend from 2014-2015.

The CEO of Shufersal is looking forward to digital transformation of retailing and shopping experiences, involving innovation both in online self-service customer-facing platforms and in the preparation and delivery of online orders. He expects great advances in the operation of logistic centres where robots and humans will take part in collating products from shelves for online orders and packing them for dispatch and delivery to customers. Three centres are in development. Enthusiastically, he proclaims that the online apparatus will involve a lot of automation, digital (features) and robotics.

Shufersal is clearly adopting the new language of data-driven marketing, Big Data, and digital automation of interactions with its customers-shoppers. The company is said to pull together to that aim its information systems, supply chain, and data pools from its customer loyalty club and club of credit card holders. This will enable it in the future to customise offers and services much better to its customers. Aberkohen talks of providing services to suppliers based on their platform of big data but he may have to think more in terms of collaboration, especially with the stronger manufacturing suppliers (i.e., sharing data on shopping patterns in exchange for support and aid in resources for analysing the data using advanced tools and methods of data science). Aberkohen believes that in the future we will see fewer stores, and smaller ones, due to transition of shoppers to online ordering and direct delivery to their homes or offices (currently online orders account for 12% of sales at Shufersal).

Moreover, the CEO is expecting a considerable expansion in ranges of products the retailer will make available to its customers via online shopping. This will include also orders from overseas (e.g., through partners in the US). He refrains from likening Shufersal to Amazon but is surely getting inspiration from the international online master. It could relate to: (a) A wide variety of products that a retailer can offer on the Internet (besides, Amazon could be getting more deeply engaged in food retailing with the recent pending acquisition of Whole Foods); (b) Employing robotics and humans in logistic centres; and (c) Advanced and dynamic analytics to customise offers to shoppers.

  • The measure of consumer-based brand equity of Globes/Nielsen is based on three key metrics: willingness to recommend, intention to buy tomorrow, and favourability. The top brand of food chain stores is Rami Levi (discount stores). This position may be credited to the personal character and initiative of Mr. Levi and his high media profile (e.g., proclaiming to fight and act for the good of consumers). Shufersal is in the second-best position in the eyes of consumers. The original brand of Bitan is ranked 7th whereas Mega City has fallen down to the ungracious 11th place (one before last).

Shufersal’s own brand currently captures about 20% of total sales. The CEO aims to increase this share to a level of 40%-50% to be in par with similar retail chains overseas. The retailer will have to walk on a thin rope when cutting down purchases of branded products from national manufacturers without ruining relations with them. Shufersal already offers milk, cheese and meat (beef) under its private label (a precedent in Israel), yet the CEO admits they still value and need their relationship with the leading national producer of these food products (Tnuva). In the past Shuferal has also had a bitter battle with another producer of dairy and other food products (Strauss). Other categories in which the retailer markets under its name include baby diapers and milk formulae; the CEO has the full intention to add more product types to this list and expand the shelf space and volume assigned to Shufersal’s own brand. The proposition according to Aberkohen is to bring quality products at value-for-money. Shufersal has taken additional strategic steps in recent years to tighten their control over the display of products in their stores: assigning their own workers to place most products on shelves in-store instead of allowing representatives of suppliers to do so, and bringing-in most products to stores independently from their logistic centres.

The CEO of Shufersal is cognizant that many consumers do not strive to shop in large discount stores that are usually located at the outskirts of cities or in industrial areas. Often enough consumers prefer convenience to lower cost. People who work long hours, including young adults early in their career, and even students, cannot afford the time or pass over the option of shopping in those stores. It may be added that for older consumers (e.g., pensioners), discount stores may simply be out of reach, especially if one does not drive. Supermarkets in shopping malls (so-called ‘anchors’) are also considered by Aberkohen as obsolete. These consumers-shoppers prefer visiting (at least during the week) a supermarket or even a convenience store in their neighbourhood — they are too pressed in time with duties or other engagements to bother about the somewhat higher cost (Mr. Aberkohen brings his own daughter as an example). Nevertheless, if the neighbourhood stores do not work out as a practical option, they will probably order online.

To top the list of the plans of Shufersal’s CEO, he sees the retailer engaged in a variety of peripheral services consumers may like to have at easy reach such as non-banking financial services (e.g., loans), insurance, travel (including holidays abroad), and optometric (eye-glasses). Some of the services are likely to be made available only online (e.g., insurance, travel), next to additional shopping options Shufersal expects to generate. Although Aberkohen does not refer specifically to the mobile channel, it is reasonable that much of what he describes in relation to an online channel is necessarily applicable these days in a mobile channel.

Shufersal’s CEO has high aspirations for the retail company he leads. Aberkohen’s plans may change not only the consumption culture in the country, as he maintains, but also the nature and character of the company itself. Hence, Shufersal’s management will have to watch carefully what areas it is about to enter and how qualified the company is to make those extensions. They will have to consider, for example, how to integrate the business areas of New-Pharm into the portfolio of Shufersal. They should not underestimate the trouble that discount retailers can cause them. Moreover, as Shufersal makes more moves to fortify its retail business, its management must act with sense and sensibility amid tensions that such moves cause, and are likely to continue to cause, with suppliers as well as consumers. The expansion and addition of products and services for the benefit of consumers is a positive venture, but Shfuersal still has to convince them as such, every day.

Ron Ventura, Ph.D. (Marketing)

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Human thinking processes are rich and variable, whether in search, problem solving, learning, perceiving and recognizing stimuli, or decision-making. But people are subject to limitations on the complexity of their computations and especially the capacity of their ‘working’ (short-term) memory. As consumers, they frequently need to struggle with large amounts of information on numerous brands, products or services with varying characteristics, available from a variety of retailers and e-tailers, stretching the consumers’ cognitive abilities and patience. Wait no longer, a new class of increasingly intelligent decision aids is being put forward to consumers by the evolving field of Cognitive Computing. Computer-based ‘smart agents’ will get smarter, yet most importantly, they would be more human-like in their thinking.

Cognitive computing is set to upgrade human decision-making, consumers’ in particular. Following IBM, a leader in this field, cognitive computing is built on methods of Artificial Intelligence (AI) yet intends to take this field a leap forward by making it “feel” less artificial and more similar to human cognition. That is, a human-computer interaction will feel more natural and fluent if the thinking processes of the computer resemble more closely those of its human users (e.g., manager, service representative, consumer). Dr. John E. Kelly, SVP at IBM Research, provides the following definition in his white paper introducing the topic (“Computer, Cognition, and the Future of Knowing”): “Cognitive computing refers to systems that learn at scale, reason with purpose and interact with humans. Rather than been explicitly programmed, they learn and reason from interactions with us and from their experiences with their environment.” The paper seeks to rebuke claims of any intention behind cognitive computing to replace human thinking and decisions. The motivation, as suggested by Kelly, is to augment human ability to understand and act upon the complex systems of our society.

Understanding natural language has been for a long time a human cognitive competence that computers could not imitate. However, comprehension of natural language, in text or speech, is now considered one of the important abilities of cognitive computing systems. Another important ability concerns the recognition of visual images and objects embedded in them (e.g., face recognition receives particular attention). Furthermore, cognitive computing systems are able to process and analyse unstructured data which constitutes 80% of the world’s data, according to IBM. They can extract contextual meaning so as to make sense of the unstructured data (verbal and visual). This is a marked difference between the new computers’ cognitive systems and traditional information systems.

  • The Cognitive Computing Forum, which organises conferences in this area, lists a dozen characteristics integral to those systems. In addition to (a) natural language processing; and (b) vision-based sensing and image recognition, they are likely to include machine learning, neural networks, algorithms that learn and adapt, semantic understanding, reasoning and decision automation, sophisticated pattern recognition, and more (note that there is an overlap between some of the methodologies on this list). They also need to exhibit common sense.

The power of cognitive computing is derived from its combination between cognitive processes attributed to the human brain (e.g., learning, reasoning) and the enhanced computation (complexity, speed) and memory capabilities of advanced computer technologies. In terms of intelligence, it is acknowledged that cognitive processes of the human brain are superior to computers inasmuch as could be achieved through conventional programming. Yet, the actual performance of human cognition (‘rationality’) is bounded by memory and computation limitations. Hence, we can employ cognitive computing systems that are capable of handling much larger amounts of information than humans can, while using cognitive (‘neural’) processes similar to humans’. Kelly posits in IBM’s paper: “The true potential of the Cognitive Era will be realized by combining the data analytics and statistical reasoning of machines with uniquely human qualities, such as self-directed goals, common sense and ethical values.”  It is not sufficiently understood yet how cognitive processes physically occur in the human central nervous system. But, it is argued, there is growing knowledge and understanding of their operation or neural function to be sufficient for emulating at least some of them by computers. (This argument refers to the concept of different levels of analysis that may and should prevail simultaneously.)

The distinguished scholar Herbert A. Simon studied thinking processes from the perspective of information processing theory, which he championed. In the research he and his colleagues conducted, he traced and described in a formalised manner strategies and rules that people utilise to perform different cognitive tasks, especially solving problems (e.g., his comprehensive work with Allen Newell on Human Problem Solving, 1972). In his theory, any strategy or rule specified — from more elaborate optimizing algorithms to short-cut rules (heuristics) — is composed of elementary information processes (e.g., add, subtract, compare, substitute). On the other hand, strategies may be joined in higher-level compound information processes. Strategy specifications were subsequently translated into computer programmes for simulation and testing.

The main objective of Simon was to gain better understanding of human thinking and the cognitive processes involved therein. He proclaimed that computer thinking is programmed in order to simulate human thinking, as part of an investigation aimed at understanding the latter (1). Thus, Simon did not explicitly aim to overcome the limitations of the human brain but rather simulate how the brain may work-out around those limitations to perform various tasks. His approach, followed by other researchers, was based on recording how people perform given tasks, and testing for efficacy of the process models through computer simulations. This course of research is different from the goals of novel cognitive computing.

  • We may identify multiple levels in research on cognition: an information processing level (‘mental’), a neural-functional level, and a neurophysiological level (i.e., how elements of thought emerge and take form in the brain). Moreover, researchers aim to obtain a comprehensive picture of brain structures and areas responsible for sensory, cognitive, emotional and motor phenomena, and how they inter-relate. Progress is made by incorporating methods and approaches of the neurosciences side-by-side with those of cognitive psychology and experimental psychology to establish coherent and valid links between those levels.

Simon created explicit programmes of the steps required to solve particular types of problems, though he aimed at developing also more generalised programmes that would be able to handle broader categories of problems (e.g., the General Problem Solver embodying the Means-End heuristic) and other cognitive tasks (e.g., pattern detection, rule induction) that may also be applied in problem solving. Yet, cognitive computing seeks to reach beyond explicit programming and construct guidelines for far more generalised processes that can learn and adapt to data, and handle broader families of tasks and contexts. If necessary, computers would generate their own instructions or rules for performing a task. In problem solving, computers are taught not merely how to solve a problem but how to look for a solution.

While cognitive computing can employ greater memory and computation resources than naturally available to humans, it is not truly attempted to create a fully rational system. The computer cognitive system should retain some properties of bounded rationality if only to maintain resemblance to the original human cognitive system. First, forming and selecting heuristics is an integral property of human intelligence. Second, cognitive computing systems try to exhibit common sense, which may not be entirely rational (i.e., based on good instincts and experience), and introduce effects of emotions and ethical or moral values that may alter or interfere with rational cognitive processes. Third, cognitive computing systems are allowed to err:

  • As Kelly explains in IBM’s paper, cognitive systems are probabilistic, meaning that they have the power to adapt and interpret the complexity and unpredictability of unstructured data, yet they do not “know” the answer and therefore may make mistakes in assigning the correct meaning to data and queries (e.g., IBM’s Watson misjudged a clue in the quiz game Jeopardy against two human contestants — nonetheless “he” won the competition). To reflect this characteristic, “the cognitive system assigns a confidence level to each potential insight or answer”.

Applications of cognitive computing are gradually growing in number (e.g., experimental projects with the cooperation and support of IBM on Watson). They may not be targeted directly for use by consumers at this stage, but consumers are seen as the end-beneficiaries. The users could first be professionals and service agents who help consumers in different areas. For example, applied systems in development and trial would:

  1. help medical doctors in identifying (cancer) diagnoses and advising their patients on treatment options (it is projected that such a system will “take part” in doctor-patient consultations);
  2. perform sophisticated analyses of financial markets and their instruments in real-time to guide financial advisers with investment recommendations to their clients;
  3. assist account managers or service representatives to locate and extract relevant information from a company’s knowledge base to advise a customer in a short time (CRM/customer support).

The health-advisory platform WellCafé by Welltok provides an example of application aimed at consumers: The platform guides consumers on healthy behaviours recommended for them whereby the new assistant Concierge lets them converse in natural language to get help on resources and programmes personally relevant to them as well as various health-related topics (e.g., dining options). (2)

Consider domains such as cars, tourism (vacation resorts), or real-estate (second-hand apartments and houses). Consumers may encounter tremendous information in these domains on numerous options and many attributes to consider (for cars there may also be technical detail more difficult to digest). A cognitive system has to help the consumer in studying the market environment (e.g., organising the information from sources such as company websites and professional and peer reviews [social media], detecting patterns in structured and unstructured data, screening and sorting) and learning vis-à-vis the consumer’s preferences and habits in order to prioritize and construct personally fitting recommendations. Additionally, it is noteworthy that in any of these domains visual information (e.g., photographs) could be most relevant and valuable to consumers in their decision process — visual appeal of car models, mountain or seaside holiday resorts, and apartments cannot be discarded. Cognitive computing assistants may raise very high consumer expectations.

Cognitive computing aims to mimic human cognitive processes that would be performed by intelligent computers with enhanced resources on behalf of humans. The application of capabilities of such a system would facilitate consumers or the professionals and agents that help them with decisions and other tasks — saving them time and effort (sometimes frustration), providing them well-organised information with customised recommendations for action that users would feel they  have reached themselves. Time and experience will tell how comfortably people interact and engage with the human-like intelligent assistants and how productive they indeed find them, using the cognitive assistant as the most natural thing to do.

Ron Ventura, Ph.D. (Marketing)

Notes:

1.  “Thinking by Computers”, Herbert A. Simon, 1966/2008, reprinted in Economics, Bounded Rationality and the Cognitive Revolution, Massimo Egidi and Robin Marris (eds.)[pp. 55-75], Edward Elgar.

2. The examples given above are described in IBM’s white paper by Kelly and in: “Cognitive Computing: Real-World Applications for an Emerging Technology”, Judit Lamont (Ph.D.), 1 Sept. 2015, KMWorld.com

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

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

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

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

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

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

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

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

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

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

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

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

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

Ron Ventura, Ph.D. (Marketing)

References:

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

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

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

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Looking over thirty years back, it is quite fascinating to reflect how our customs of viewing films at home have shifted during that period: We started in the early 80s with rental of videotapes at local library stores to be played on our good-old VCR; next, we explored the variety of films offered on multiple movie channels of cable and satellite TV networks; then moved in the 90s from videotape to DVD rental; later-on discovered the convenience of vide0-on-demand (VOD) services launched by our cable and satellite TV providers; and more recently we may have started to select and stream films online via the broadband Internet. Some changes to go through in just three decades! Even when excluding options that involve the acquisition of film copies (e.g., on DVD), much activity in this field is apparent. And all that has come evidently at the expense of cinema theatres whose numbers in city centers have diminished significantly, and urban social life has changed with it.

An important and interesting player in the field of film viewing at home is Netflix Inc. (www.netflix.com). The company started in 1997 in the US with an exceptional rental service of films — order online, receive and return a DVD by mail. Yet in 2007 Netflix moved into the domain of video streaming and it is now a growing part of its business. An early move by Netflix has gained it advantages that other Internet companies try to challenge.

Last December the Fortune Magazine selected Reed Hastings, founder and CEO of Netflix, as Businessperson of the Year 2010  (1). He arrived first ahead of fifty nominees rank-ordered by Fortune. The selection was based on reader’s choice, financial-based metrics (e.g., stock performance, revenue growth, and profit growth), and additional off-the-book factors related to marketing and managerial leadership and style. Hastings scored first on stock performance (during 2010 Netflix share price more than tripled on NASDAQ from $50 to ~$175 climbing at a steady rate) and ranked third in reader’s choice.

Four main reasons emerge from the story on Hastings and Netflix that seem to explain better how he became deserving of the award:

Hastings had a foresight already in the early 2000s that DVDs and the service concept of rental are going to lose favour with consumers and therefore decay before too long. Consumers would want access to films that is much more convenient, fast and direct. Led by this vision, he has been willing to take risks exploring, developing and testing different types of solutions for delivering film videos to consumers at their homes that rely on Internet broadband technology. Significantly, Hastings did not let himself get locked on a solution just because the company has spent that much resources on developing a prototype if he eventually realized it was not the right approach after all. For instance, Netflix developed a branded device of a hard drive type to which consumers would be able to gradually (i.e., rather slowly) download a whole film and watch later. But YouTube that went on air in 2005 quickly convinced him that the hard drive approach is obsolete and that the solution should be in the realm of live streaming, so he dropped the hard-drive concept and changed course. Netflix did not come first with streaming and was inspired by YouTube but it still had to find a way to efficiently stream full feature films. Later on Hastings was also dissatisfied with another branded device, realizing that his customers should not be burdened with proprietary hardware from Netflix. Instead, its current solution is in software applications, giving customers flexibility to stream films to a variety of devices (e.g., computers, TVs, tablets, smartphones). In an interview to Fortune Hastings said with regard to his decision to give up Netflix-branded device: “But if you are not genuinely pained by the risk involved in your strategic choices, it’s not much of a strategy” (p. 54).

Furthermore, Hastings did not refrain from introducing the new mode of content delivery simultaneously with the incumbent rental of DVDs by mail even though the new service could hurt or ‘cannibalize’ business from the existing one. Companies who plan to introduce a new product are often cautioned of cannibalizing a product they already offer to fulfill a similar purpose or function for the consumers. Accordingly, many companies that look to expand their product lines test carefully the sources of demand for the new introduction. Managers that become attached to a successful product they previously helped bring about are reluctant to do something that may help “killing it”. Watching out of cannibalization is in place when the company aims to increase variety of models to different consumers’ tastes or merely prove it is not freezing. However, cannibalization can be justified as in the case of Netflix when Hastings projected that the days of DVD rental where in any case numbered. Then progressing a transition proactively, letting the new solution grow at the expense of the old is the more appropriate course of action.

  • The customer base of Netflix has started to expand impressively in 2005, growing from 5 million subscribers to nearly 10 millions by the end of 2008, and is estimated in the end of 2010 at 20 million subscribers.
  • Unfortunately the chart of Fortune does not show how the ratio of subscribers between “renters” to “streamers” changes, but they do report that in the 3rd quarter of 2010 66% of customers used streaming for at least 15 minutes compared with 55% at the beginning of the year and only 37% in mid-2009.

 Netflix was looking for ways to improve on its film recommendation system to its customers. Being able to customize film offers that better fit customers’ preferences based on existing customer knowledge has become a core competence requisite, making search for relevant films by customers easier and more pleasurable. Rather than investing all the effort in-house or outsourcing it to some expert BI company, Hastings took a brave initiative and announced in 2009 a competition, the Netflix Prize of 1 million dollars. Talented engineers, computer scientists, statisticians etc., organized in teams, joined the challenge to develop a new enhanced model of personalized film recommendations. The full story of the competition is beyond the scope of this post; in view of the positive way it worked out it may be concluded that Hastings allowed talented professionals from around the world participate in an important enterprise of Netflix, with opportunities to enhance their careers and win a hefty prize, and in return received a powerful sophisticated model that would improve the service performance of Netflix. There is also reason to expect the competition story added value to the brand (e.g., a story in Fortune).

  • Two points are interesting noting about the model of the winning team (BellKor). The model conceptualized a map of film titles grouped in “regions” where titles in a region may share in common a genre, actors, or specific aspects of content and style.
  • First, the spatial spread of film titles is based on data of customer characteristics, films they viewed and ratings assigned, if any. As such, the inclusion of titles in the same region may not be immediately interpretable based on some objective classification of genres but reflects similarities between films as perceived by the customers.
  • Second, distances between any two titles can matter. Thus, while a film title is likely to match better with another title in the same region than from other regions, if the first film is close to a border it may make a better match with a second film just across the border in a neighbouring region than with a third film that is located in the same region but farther on the other end. 

A fourth factor that may have helped Hastings to succeed with Netflix can be associated with his own character and style of management. Apparently, in a pervious company he founded and managed he has been recognized as a very aggressive boss, impatient and somewhat erratic. It earned him the nickname ‘animal’, courtesy of one his senior managers, McCord, who admits that at first he was reluctant to rejoin Hastings at Netflix. Hastings said to Fortune that one of the problems was he never spent time to build a distinct culture to his previous software company. At Netflix he is reportedly more attentive to other people, willing to listen to their ideas in brainstorming even if he does not agree , and cares more about the development of high-esteemed professional culture that relies on trust in the integrity and commitment of employees (it is a “no perks” company according to McCord, all their fun is from building products).

This is definitely not all of the story of success of Netflix. Probably more contributing factors may be suggested, and there also are challenges that Netflix will have to confront in the near future to maintain its strengths in the market. Notably, streaming applies not only to feature films but also to a wide variety of TV programme series.

In the past three years Netflix was smart and quick to secure agreements for rights to stream content, particularly new films, with leading production studios such as Paramount, Lion Gate and MGM (in consortium via their joint venture Epix). Competition on rights with studios as well as TV networks (e.g., ABC, CBS, HBO) promises to be a hot topic for the foreseeable future in this field. Just in December Netflix signed with ABC. Amazon is already showing interest in offering streaming to its subscribers and is seeking attractive agreements. Netflix is also likely to face resistance from cable and satellite TV networks who feel threatened by their activity. Limitations in availability of films and programmes for streaming will curtail its advantage to customers vis-a-vis rentals and could also reduce the effectiveness of recommendations of content for access by streaming.   

 There is likely to be a continued quest for capacity to stream ever-increasing amounts of video data via the broadband Internet. It is estimated that Netflix already captures 20% of all broadband downstream traffic during peak hours in the US. If indeed consumers look at streaming as the preferred way to access and watch films and TV programmes in coming years, the contest will be more intense. Internet Service Providers already seem to make their own considerations, possibly charging their own customers by volume downstream (as already done with respect to mobile devices). So while Netflix may charge $7.99 monthly on a stream-as-you-can basis, subscribers may be required to pay an additional fee to their ISPs  (2).

On a final bright note, streaming can open new opportunities for Netflix to extend its business outside the US and Canada (started just in 2010). Less constrained by the logistics of rental by mail, it may obtain access to broadband channels to stream film and TV content to customers in more countries, perhaps first in Europe and then in other regions.

Ron Ventura, Ph.D. (Marketing)   

 (1) Reed Hasting: Leader of the Pack, Michael V. Copeland, 2010, Fortune, Europe Edition, Vol. 162, No. 9, December 6, pp. 49-56

(2)  Netflix on a roll as streaming catches on, FT.com, 28 January 2011

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