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.