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Posts Tagged ‘Big Data’

Since the mid-1990s the dominant approach to marketing is centered on the customer (cf. previous approaches emphasised production, the product and sales); more fully, the customer-centric approach evolved from a modern marketing approach, conceived somewhat earlier (1970s to early 1980s), as it sharpened the focus on the customer (*). In this era theories and concepts have developed of relationship marketing (and customer relationship management, CRM, more generally), customer experience and data-driven marketing. Retrospectively,  brand theory has been the bridge linking between the early stages of the marketing approach and the advanced customer approach, and truly to this day the brand and customer views are inter-dependent and should not be separated.

In the past twenty years we have further witnessed intensive developments in digital technologies (e.g., computer information processing, Internet and communication). Their effects on marketing and retailing now call into debate whether the technologies still constitute a progression in the execution of the customer-centric approach or already its evolution into a new approach, entering an era of “digital marketing”. This question is at the core of a recent article in the McKinsey Quarterly magazine  titled “The Dawn of Marketing’s New Golden Age” (Issue 1 of 2015). The authors (Jonathan Gordon, New-York City, and Jesko Perrey, Düsseldorf) outline five forces driving this new age: science, substance, story, speed and simplicity.

The picture emerging from the article entails consumers conducting most or all of their interactions with companies through digital portals or applications on computer-based appliances and mobile devices, and communicating among themselves and with companies about products and services in social media platforms; companies on their part analytically employ huge streams of data associated with their customers (active as well as prospects) to perform automated processes for selling to and servicing the customers. What we are about to see is a formidable enhancement on a large-scale of digital methods and programmes already familiar from the past few years. The engine of marketing will be increasingly powered by modelling, segmenting and predicting customer preferences and behavioural actions with little need for day-to-day human inspection and intervention.

Managerial thinking usually views instruments, data and methods as the tools for executing a well-specified strategy, as in customer-oriented marketing. Undoubtedly the new digital technologies have been vital for engaging customers at an individual level on a large-scale (e.g., one-to-one marketing, personalising and customising). But there are strong signs that in the new golden age the digital technologies, their tools and data-driven methods, will become the essence, the fundamental way in which marketing and retailing function, and not just as a means to an end. They will not be used to perform a customer-driven strategy — they will be the strategy in and by itself. That is what a new digital approach to marketing could mean. McKinsey & Co. already seem to adopt and support that kind of marketing empowered by Big Data, and they are not alone in this attitude. However, a prognosis for such a new age of marketing should be put up to a debate in business circles and in consumer or social circles.

Science has made significant contributions in extracting meaningful information and insights from large amounts of data for marketing purposes. It is the primary engine of the new age foreseen by the authors. The broader impact of science now spans from measurement by sensors and cameras (e.g., in smart and wearable devices) through analytics and modelling to the utilities and services that apply the derived information. Scientific advancement in the area of Big Data enable the automated estimation of multiple statistical models and handling of their results in marketing platforms. Just two examples of applications are (a) customised recommendations based on learned preferences of users; and (b) geo-location and mapping utilities that can direct shoppers to relevant stores in their vicinity. Yet science in marketing has also led to the development of more sophisticated models and better optimization and estimation techniques even before Big Data. The authors note that advanced analytic capabilities also play an important role in managerial decision-making by enabling quicker responses (e.g., in the area of hospitality, noticing trends and changes in hotel room reservations).

It is completely agreed that managers should be trained and encouraged to base their decisions more on information derived from research and analyses of customer and marketing data than on intuition. For achieving that aim managers need to understand better analytics and their outputs, and wisely combine their insights with knowledge from their practical experience. But a problem arises when more processes are channelled to automation and managers are not required to interfere and make decisions. Definitely when a company needs to handle transactions, calls and other activities from hundreds-of-thousands to millions of customers, automation of procedures is essential to let the marketing system work, but keeping an open eye by managers is as essential, particularly to make sure that customers are well-served. Automation is desirable to the extent that it allows decision-makers to devote their time to more complex issues requiring their judgement while not sacrificing the quality and sensibility of processes automated. Human reason and sense of fairness are still valuable.

Of course not every marketing and service process is automated (as yet); customer service representatives (CSRs) are required to navigate the information provided to them on any individual customer to decide on the best approach or solution for helping him or her. Information in the customer profile may include characteristics and recommendations produced by prior modelling and analytic processes. It should be the responsibility of the CSR finally to utilise the information and choose the best-apparent mode of action. The CSRs can be presented with a few feasible alternatives for a type of service or other assistance requested and should be trained how to assess and choose the most appropriate solution for the situation at hand and the customer served. As the authors Gordon and Perrey importantly observe, “Knowing what can be automated, when judgement is required, and where to seek and place technical talent are becoming increasingly central to effective marketing leadership”. Taking a position that employees, from CSRs to managers, are inadequate evaluators or judges of information who are bound to make mistakes, and therefore their decisions are better computer-automated, is misguided. It may get the opposite negative outcome where employees rely on the information system to provide also the best solution and not think for themselves which possible solution is the most appropriate or the most effective.

  • Take for example the domain of healthcare: Suppose that an elder patient calls her HMO to make an appointment for a clinical test. The system may suggest a medical center or clinic that is in a neighbouring town because that is the closest date available or because performing the test in that facility (out-sourced) is less expensive for the HMO. Yet especially for patients in their golden age a CSR should also consider the distance from the patient’s home and the time of day (e.g., not too early) so that it would be convenient enough and not too complicated for the patient to keep the appointment.

The article does not neglect the Substance of marketing and business overall. The authors suggest in particular the experiences of customers, the delivery of functional benefits, and the development of new products and services as the core interests of substance. In this important section they truly explain, through examples, how Big Data, analytics and digital technologies are used by companies to adapt to changes in the market and achieve customer-driven marketing goals.

In another article of McKinsey Quarterly, Getting Big Impact from Big Data (January 2015), its author (David Court, Dallas) acknowledges that the predictions of McKinsey Global Institute (MGI) on the adoption of Big Data in their report from 2012 may have been too optimistic, saying that achieving the expected impact has proved difficult. The article appears as a new effort to re-ignite the growth of Big Data implementation. Some of the explanations given for lagging behind, however, are puzzling. A general claim made in the article is that companies did not realise the expected returns because their financial investments and efforts were not big enough: “many senior managers are reluctant to double down on their investments in analytics — investments required for scale, because early efforts have not yielded a significant return.” How can managers be expected to expand their investment in an initiative if they were not convinced in earlier tests of its benefits? There should be special circumstances to convince them that if a project did not work well in small-scale it would if undertaken in large-scale. While that may be the case with Big Data projects, managers should not be blamed for not seeing it or for not trusting the claim blindly.

The article further points out that companies were not focused enough and did not plan their analytic initiatives with well-specified goals. But responsibility is also put at the doorsteps of analytic vendors and data scientists for misleading managers by making unfounded promises about the kind of valuable information they could extract (or mine) from a company’s data pools. As told by Court, it was not unusual for executives to hear the claim: “just give us your data and we will find new patterns and insights to drive your business” — yet executives became disappointed and discouraged to invest further. Notably, albeit the author’s charge about the insufficient scale of investment in Big Data, he leads to the more welcome conclusion that it is “better to pursue scale that’s achievable than to overreach and be disappointed or to scatter pilots all over the organization”.

  • Automated dynamic pricing: With regard to setting prices, this article maintains that “it’s great to have real-time data and automated pricing engines, but if management processes are designed to set prices on a weekly basis, the organization won’t be able to realize the full impact of these new technologies”. Here lurks another enigma about the new way of thinking. It is technology that should adjust to management processes which in turn accommodate the structure and behaviour of the market (e.g., consumers, shoppers) and not the other way round. For once, if prices change daily or hourly (e.g., in an online store) it is likely to be perceived by consumers as lack of stability, unreliability, an attempt to manipulate, or unfair conduct by a retailer not to be trusted. Moreover, it may not be even economically justified: if most consumers perform concentrated shopping trips in supermarkets between weekly to monthly, it should not be necessary nor beneficial to update prices much more frequently.

The third driver of the new golden age — Story — is an interesting contribution in Gordon and Perrey’s article. However, it brings up again the discussion on who creates and who owns the story of a brand or a company. It is well appreciated that consumers participate and contribute to the story of a brand. Agreeably, the story would not be able to exist without the customers. Yet composing the story should not be relinquished to consumers — the company must remain in charge of designing and presenting it. First, a brand’s story is built around its history and heritage. Second, the story is enriched by the customers’ experiences with the brand. Nevertheless, a company cannot rely on discourse of customers in digital social media networks (e.g., in text and photos) to tell the whole story. The company is responsible for developing the shared experiences and  customer interactions into a narrative and coming up with a compelling story. It may use as input its maps of customer journeys to develop the story.

Speed and Simplicity entail the measures that companies have to take to organise themselves better for the new age. These may be structural, functional and logistic measures that improve the implementation of data-driven processes and marketing initiatives (e.g., reducing layers and connecting silos, sharing data and smoothing operations, more agile product development).

  • Digital self-service, through Internet websites or mobile apps, is widespreading for product or service ordering and customer support. But managers should remember that not all consumers feel equally comfortable with these platforms and have the skill and confidence in using them; consider in particular that the proportion of people age 65 and above is forecast to rise in developed countries and may reach 20% in two to three decades). Furthermore, many people do not like to “talk” with algorithms; they prefer to talk with other people to get the assistance and advice they seek.

It is important to draw a line and respect a distinction between the customer-centric approach (“what”) and the technologies, data and methods that can be employed to implement it (“how”). There is no need to declare a new age of marketing, at least not on behalf of digital technologies or Big Data. Advancement of the latter may signal a new phase of progression in implementation of the customer approach (i.e., ‘marketing in a digital age’), but suggesting beyond that may lead to dilution of the focus on the customer. Nonetheless, time may be ripe for a mature integrated approach that is guided by a triad of Customer-Product & Service-Brand as the complex of these entities and the relations between them are at the foundation of modern marketing.

Ron Ventura, Ph.D. (Marketing)

(*) The marketing approach was already oriented towards the customer as its focal target but largely at a segment-level; it advanced strategic thinking beyond sales. Consumer marketing most progressed during this period.

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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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