Week 6 analytics and its importance

Journal of Research in Interactive Marketing Interactive, direct and digital marketing: A future that depends on better use of business intelligence Merlin David Stone Neil David Woodcock

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Article information: To cite this document: Merlin David Stone Neil David Woodcock , (2014),"Interactive, direct and digital marketing", Journal of Research in Interactive Marketing, Vol. 8 Iss 1 pp. 4 - 17 Permanent link to this document: http://dx.doi.org/10.1108/JRIM-07-2013-0046 Downloaded on: 19 January 2015, At: 06:18 (PT) References: this document contains references to 29 other documents.

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JRIM 8,1

Interactive, direct and digital marketing A future that depends on better use of business intelligence

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4 Received 16 July 2013 Revised 4 October 2013 8 December 2013 Accepted 8 December 2013

Merlin David Stone and Neil David Woodcock The Customer Framework, Ascot, UK Abstract Purpose – The purpose of this article is to explain how the management of the two areas business intelligence (BI) and customer insight (CI) needs to be brought together to support a company’s interactive marketing.

Design/methodology/approach – The article is based on the author’s work in consultancy and in assessing client company’s customer management capabilities and performance, as well as a review of some of the literature on BI and CI. Findings – The article suggests that companies need to pay close attention to the governance of BI, as a self-service approach to BI becomes increasingly used by CI teams.

Research limitations/implications – The review of literature carried out by the authors suggests that the interface between BI and CI is poorly researched and would benefit from a significant research effort.

Originality/value – The focus on the interface between BI and CI is relatively new. The authors hope that it will trigger significant research.

Keywords Data analytics, Business intelligence, Customer data management, Customer insight, IT management, Marketing information systems Paper type Viewpoint

Journal of Research in Interactive Marketing Vol. 8 No. 1, 2014 pp. 4-17 q Emerald Group Publishing Limited 2040-7122 DOI 10.1108/JRIM-07-2013-0046

Introduction The advent of interactive marketing Most corporations must now “market in a digital world”. The “always on” consumer (and business consumer too) is able, and increasingly likely, to search, enquire, interact, complain, buy and pay through mobile devices. Marketing for most corporations is becoming increasingly interactive and “always on”. Delivering an efficient (for the customer and the company), relevant (personalised) and engaging experience increasingly relies on a deep knowledge of the consumer; who they are, the devices they use to connect to the company and the content they want to see.

Modern interactive marketing demands deeper understanding of customers and their behaviour and how they like to interact with the company and the ability to deliver personalised experiences which they find useful and engaging. There are few marketing, sales and service situations where the corporation is not able – at least in principle – to gather the logistic, operational, marketing, sales and service data which tells the corporation whether the customer has been served well or not, and the number of situations where it cannot are diminishing. So long as customers have smartphones, data can be gathered from customer to support all these activities.

The volumes for accesses and transactions on the internet are large and still growing very fast, so that by the time this article is published any figures given here will

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be outdated[1]. The internet now hosts a rapidly growing proportion of human dialogue, in ways that are open to viewing and influencing by companies. Ti, combined with the growing reach and capacity of mobile telephone networks, means that dramatic changes that have taken place in the volumes, frequency and effectiveness of use of the different media by which companies and customers exchange communication and in the devices and software used by consumers to exchange communications with companies and individuals and to organise and enjoy their lives, and the problems companies have in coping with these volumes.

Interactive marketing and its associated analytics, particularly real time high performance analytics, are opening up new marketing opportunities, leading to improved marketing return on investment, and then identify why so many companies fail to obtain the expected benefits[2]. The value of the social approach has been demonstrated in many markets, e.g. in mobile telephony[3], although one must not overestimate the power of social media to engage – hence Don Schultz’s warning (Schultz and Peltier, 2013) that much social media advertising engages the already engaged.

This interactivity is not only in marketing, sales and service. The “social business”, deeply connected with its staff and suppliers, knows how to harness its collective knowledge, using social systems such as Salesforce.com’s Chatter and Microsoft’s SharePoint, enabling information once locked into one channel or department to be shared across a company. Logistics and operations flows through a company have become trackable, constantly, in every process. Non-interactivity and non-trackability will become the exception rather than the rule.

The difference between situations involving the customer (e.g. testing or buying a product) and situations involving a corporation’s own people is that while the latter can be instructed to support interactivity and trackability, customers are independent and must be motivated to do it. They cannot be told to use a particular device or application to do it – hence corporations’ interest in how to engage with social and other media that customers prefer to use.

The terms “Bring Your Own Device” and “Use Your Own Application” challenge a corporation’s media management teams. Once an organisation is committed to using all the channels consumers want to use, the main change in the nature of the marketing game is speed. We have always had to know whether a customer prefers mail, telephone or face to face, but now we need to know more about their preferences and their connecting devices to deliver the right content to them at the right time.

“Always on” marketing is differs from “campaign” marketing. The corporation must develop its people, processes and system capabilities to interact more dynamically with the consumer, in all channels – hence the use of the term “omnichannel”, referring to channels customers want to use, rather than “multichannel”, referring to channels suppliers want to use.

What marketing is affected?

The trends discussed above have led to significant changes in how marketing takes place. Interactive marketing is no longer – if it ever was – a change in how marketing communication takes place. Every aspect of marketing is affected, whether in terms of how customers are affected directly, or in terms of how marketing people work with each other, the rest of their company, or with distributors, suppliers and other partners.

This is particularly important in branding (Schultz and Block, 2012), but in fact almost

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no area of marketing has been unaffected. Table I summarises some of the main changes. Of course, there are many other changes, and some of the changes are not caused completely by the rise of information-technology based interactivity. However, the critical point to note from the perspective of this paper is that each change leads to new flows of information which, to deliver the benefits specified in the table, need to be managed properly – the subject of this article. Finally, on this point, note how one of the effects of the changes is often to blur old distinctions and remove barriers between old silos.

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Marketing area Branding Product

Price Advertising

Direct marketing Personal selling

Public relations

Sales promotion Distribution People Processes, data and systems Table I. How marketing is affected by surge of interactivity

Market research

Examples of how interactivity affects each area Marketing mix The locus of many companies’ brands has shifted from the real to the virtual world, while the brands of many others are strongly affected by what is said about them in the virtual world Customer input into product design (collaborative design) can be obtained much more quickly. Customers can design their own products more easily. Designs can be tested and revised more quickly, while problems can be identified and rectified more quickly and easily Prices can be tailored more easily to different customers. Yield management can be applied in many new areas Web site/mobile advertising is gradually usurping advertising in conventional media, allowing greater trackability and better assessment of return on investment. This is leading to a blurring of the distinction between advertising and other marketing communications methods Direct marketing has expanded out of the conventional media of mail and telephone to include virtually all marketing communications Personal selling now has much stronger information support, while improved sales management systems, sometimes integrated with response management systems, allow much more effective targeting and management of customers and prospects Electronic word of mouth, or “word of mouse”, is replacing conventional media exposure, not solely through social networks, but through all aspects of web and mobile dialogue. In some sectors, online reviews have become absolutely critical in determining whether a product will sell The effectiveness of sales promotions can be gauged much more quickly, while online channels facilitate distribution of coupons and other incentives The web has become a very important channel of distribution for many information-based products and services, as well as some physical products Marketing management Marketing, sales and service people can be much better informed about what they need to know to sell and market better, and results of their work can be obtained and distributed more easily Marketing processes can be migrated onto systems, sometimes running on the “cloud”, enhancing the effectiveness and speed of processes. Systems allow much better access to data required for decision making on everything from individual customers to strategic decisions, and then for measurement, review and calculation of return on investment Market research is increasing carried out online, while customer-initiated feedback is providing a new source of information on how customers think, feel and act

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The problems posed by recent developments There are many studies which demonstrate either the awareness by companies of the problems facing them in dealing with the high volumes of data and/or their admission that they have a long way to cope with these volumes and use them to achieve their objectives, for example, The Digital Disconnect: Joining the Dots in the Modern Media Mix, SAS Institute (2012a, b, c). This paper used the results of the SAS and Marketing Week Marketing Perspectives 2012 survey in the UK to show that despite huge changes in the available technology, marketers’ mindsets had yet to adapt to the opportunities this presented, with digital media still approached in the same way as traditional communications, with channels often used in silos, rather than activity on one channel triggering or informing communication on another. Failure to exploit the insight that digital media gives into customers’ needs and sentiments was common.

In other words, there was a sizeable gap between the importance placed on digital channels and the ability to execute through them.

Marketing in the value chain Some companies find that their ability to make the best use of digital and interactive marketing is hampered by the relationship between marketing and the rest of the corporation. We believe that this problem is related to the way the role of marketing has been portrayed in the strategic literature, particularly the value chain literature.

Porter’s (1985) value chain idea was first published in 1985 was developed with reference to competitiveness. Marketing and sales was the fourth and service the fifth and final of the primary activities (after inbound logistics, operations and outbound logistics). These were supported by firm infrastructure, human resource management, technology development and procurement.

The concept was both helpful and damaging to marketing, sales and service.

It helped by focusing managers on the contributions made by the different activities to profit. It damaged by seeming to relegate sales, marketing and service to the status of “downstream activities”, undertaken after goods or services were produced. The model did not cover the importance of marketing in organising the whole corporation’s activities to service customer needs.

Now marketing is central to the value chain Porter’s book was written nearly 30 years before this article. Then, the information needed to organise the whole corporation as suggested above was simply not available, at the right time, in the right depth and quality. Then, it might have been necessary to carry out value chain activities in the piecemeal, sequential way that Porter suggested.

Today, information systems allow data from all stages of the supply chain to support critical processes for managing the corporation’s partners, suppliers and customers, as shown in Figure 1.

However, the damage was done, and lasting. Thus, in a recent global study commissioned by The SAS Institute (Economist Intelligence Unit, 2012), focusing on the relationship between chief marketing officers (CMOs) and their peer senior executives (known as the C-suite), showed a serious lack of alignment in most companies between the views of both marketing’s role, with other C-suite executives considering the CMO to have a smaller role in company success, even in marketing areas, than the

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Information collection and use Core Customers Business Partners & Processes Technology Development Partners

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Suppliers

Primary Operations Inbound Business (Production, Outbound Functions (Purchasing, Inventory (Distribution) Logistics) Management)

Figure 1.

A modern version of a marketing-driven value chain

CRM

NPD Margin

SRM

Marketing and Sales

Service

Drivers of Value in a Generic Firm (Porter 1985) SRM = Supply Chain Management, CRM = Customer Relationship Management, NPD = New Product Development

Source: Value activities, core processes and the value chain (Roberts and Zahay, 2013, p. 37)

CMO did, and the need for CMOs to do a better job convincing their C-suite colleagues that marketing is a significant contributor to business value.

A key question raised by Porter was, “What makes for competitive advantage, in the way the whole corporation works?” Today, we might ask “What is it about a corporation’s DNA that makes it competitively successful?” The answers to these questions relates not to a corporation’s strategies or policies, but to its capabilities and how it deploys them. However, many companies have not even developed one of the most basic capabilities required for interactive marketing, the single customer view.

As ours and other studies have shown, this is not the case.

The single customer view One of the most important recent studies in this area is Clark’s (2012), which concluded that there were notable differences in company’s expectations of the value to be extracted from the surge of new data (Big Data) and the development of a single customer view (SCV) (SCV, in which a company’s knowledge about each customer is summarised, ideally fully documented, in a single data record which is accessible to all those making decisions about and managing the customer).

The benefits from investment in SCV, Big Data and high performance analytics (HPA) were difficult to prove and in the economic and regulatory climate priority tended to be given to regulatory compliance and projects with better risk profiles. Those companies that had committed to a SCV strategy and were the furthest along the path were seeing the greatest returns and were most determined to retain this advantage. The more advanced companies also stressed that SCV is not an IT project but should be seen as a part of implementing a customer focused strategy. Organisations that were striving to achieve a single customer view were starting to tackle the “Big Data” challenge.

Companies investing in a SCV strategy were seeing significant benefits by using a Big Data approach involving data integration and sophisticated analytics to generate

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Business intelligence (BI) – the engine room of interactive marketing Marketing is recovering from relegation to downstream status to being a key integrator of information to ensure that a company stays competitive. Behind this lies the corporation’s BI, which is defined as a combination of technologies, architectures, people, processes and methodologies that transform raw data into useful business information. In marketing, the main BI technologies used are reporting, online analytical processing, analytics (past and predictive), data and text mining.

A shown in Figure 2, BI usually requires integrating data from many source systems into a data warehouse, the creation of data models to support that integration and of metadata definitions (e.g. how an “active customer” is defined using different variables), analysis, and providing resulting data and analyses to wherever it is needed, whether by reports or individual data transfers (e.g. data about an individual customer during a transaction), on whatever platform or device users need.

This figure illustrates the phases through which data (on the left hand side) must pass before it is translated into insights to support action, The figure gives examples of BI Data scientists

BI Data architecture & governance

BI & Business

Data

Data Warehouse

Insight Generation

Action

Outcomes

Data architecture, quality & governance

Full CDR, clean data, single version of truth

Insight models and service

Insight Delivery and visualpresentation

Pilot, test, learn, scale

Internal (owned) data (e.g. sales, web)

Data Quality Data Governance

Reports,Dashboards Drill down

Welcome, attrition Personalised content algorythms Next Best Action Modeling Channel Optimization Forecasting

Key performance indicators & time scales

Self serve reporting Deployment Prompt feeds to operational systems Data quality management reports

Measure success Scale

Deployment services

Unstructured Data (e.g. social)

Segmentation Matching & Load

Analytic services

3rd party data Providers (e.g. overlays)

Data Cleansing & quality services

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customer insight (CI), primarily to improve customer segmentation and to target marketing campaigns more effectively. While the concept of a complete SCV was seen as a useful aspiration, changes in technology, markets and customer requirements meant that not all companies see it as completely achievable or desirable.

The most common strategy was to move incrementally towards a SCV, each step being dictated by the cost/benefit analysis and the availability of scarce analytical skills. A minority of companies were content with a product silo approach as they believed that there was little overlap of customers across products. However, a large majority of companies had a project underway to move towards a SCV although most admitted that achieving a full SCV was some way off.

Commercial imperatives

Brand sentiment Qualitative input (e.g. internal social, research

Source: The Customer Framework (2013)

Figure 2.

The modern architecture of marketing BI

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the kinds of insight that businesses need to generate and resulting actions that they need to take, but given the changes that we identified in Table II, a depiction of the complete architecture would require a much larger diagram.

At the heart of successful (interactive) marketing lies a clear view about what BI is needed for marketing to work and how this BI needs to be created and rapidly deployed to create the insight that marketers need now and will need in the future. A recent study of 510 companies on the impact of BI systems (Ramesh et al., 2010) identified several areas critical to marketing as those that most companies believed to have been improved

by BI systems – time savings, single version of the truth, better strategies and plans, better tactics and decisions.

The maturing of marketing BI A corporation’s ability to determine its BI needs and then implement the required BI is an essential part of the maturing of BI management and of marketing. Just as corporations take time to absorb and deploy the best marketing techniques, so they take time to learn how to develop and use BI. Work has been carried out on BI maturity by many writers, including Williams and Thomann (2013) who propose a four stage maturity model that stresses changes in how information is used, with each stage bringing increasing value, and The Data Warehousing Institute’s model (Eckerson, 2007), in which maturity is evaluated using eight areas: scope, sponsorship, funding, value, architecture, data, development and delivery. Each of the eight aspects is graded with the following five grade scale: infant, child, teenager, adult, and sage. Gartner’s maturity model for business intelligence and performance management (Burton, 2007a, b; Hostmann et al., 2006; Rayner and Schlegel, 2008) has five levels of maturity: unaware, tactical, focused, strategic, and pervasive. Assessment includes people, processes, metrics and technology. Gartner shows that many companies’ departments have different maturity levels.

Key aspects of marketing BI maturing are: .

The development of a strong data culture (commitment to ensuring that the right BI is available to support decisions and actions).

The biggest challenges with data are [. . .]

Dealing with the huge array of data sources/large volumes Finding business analysts good enough to produce and communicate insights Deploying insights practically in the business Getting marketers to understand how to use it Measuring the impact/ROI Table II. Data challenges

Remains a significant problem (%)

We are resolving this issue (%)

Already resolved, or not an issue (%)

54

43

3

49

41

11

49

41

11

45 42

50 47

5 11

Source: World Federation of Advertisers/The Customer Framework Survey May 2013 Base 47 Companies

The evolution of the relationship between BI people, traditionally located in the IT department but increasingly embedded in commercial decision making units, and users in marketing, sales and service.

The emergence of the topic of governance in the development and use of BI.

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The latter has become particularly important as increasingly sophisticated data extraction and analysis tools become available. Despite the complexity of their internal workings, they are much easier to use than earlier software, giving rise to “self-service BI”, allowing managers at all levels, from planning through to operational decision makers to access and analyse data whenever and wherever they need.

However, the history of relationships between marketing and IT people has not been good. A Nakata et al. (2011) show in their study of the area of product planning, building on their earlier study of the more general relationship (Nakata and Zhu, 2006). They also argue that too tight an integration can hamper innovativeness, so that in situations where rapid innovation is key to competitiveness, it may be better not to insist on too close an integration. This echoes the familiar complaint of BI people, that “users have all got their own spreadsheets”. They have them because they deliver what the BI people cannot!

11

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The rise of insight and the self-service problem In many corporations, users of BI are organised into departments like “consumer insight” or “customer intelligence”, replacing the older “market intelligence” or “marketing information”, reflecting the move from less targeted forms of marketing towards precision marketing and creating personalised customer experiences. However, the BI community is not always good at managing CI users. CI has emerged as a powerful BI user, but is still learning what to do with so much data, whether owned or non-owned (such as data arising from social media), structured or unstructured (e.g. voice, text, video). For some corporations, markets – customer needs, channels used competition, devices – are changing fast. Capabilities are changing too – witness the move towards real-time or near real-time marketing.

The CI community is right to demand better self-service, but an unrestrained and unplanned drive to self-service may lead to wrong conclusions due to poor understanding of how data arises or to use of the wrong analyses or tools. Improved planning and governance is needed to cope with a powerful, self-servicing CI community. Self-service should be rolled out in a careful, prioritised and targeted way, using criteria of need and competence. BI’s experts, who know the software and are in command of metadata development and data definitions, should work across both communities. Their knowledge of what works well and what does not is crucial to the interface. Governance is needed at all levels of the organisation.

Where BI and CI communities move in different directions or have different priorities, CI users do not get the support they need, while the BI community may take the route of technological optimisation rather than meeting users’ needs. If this is so, the company should either slow down deployment of new BI technologies, and/or ensure better alignment in plans and operational delivery for the two communities.

Applying maturity modelling to the relationship between them helps, whether to look backwards to see how the relationship developed or forwards to see how to improve it,

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towards the nirvana of CI and BI marching in step, along a road where each step’s value is understood by the business and supported throughout it.

As excellent BI self-service tools become common and as users such as CI become more competent at using them, the question, “Who guards the guardians?” must be asked. In many corporations, the BI community’s role is to support users with data and tools for “self-serve” data access and analysis. But what if users get it wrong? What happens if they are looking in the wrong direction when a tsunami comes, as many financial services companies were? Or should the BI community abdicate its role as custodian of ensuring that users use data correctly, in the face of a well organised CI community which professes expertise in using BI in marketing, sales and service, keeping the BI community busy with demands for new self-service tools to model, analyse, explore, view, measure, collaborate, virtualise, and then write back results, citing the advantages of more agile decision-making, reduced delays and frustration for users, and so on, and reports that show a strong correlation between self-service and good business results? These questions can only be resolved by partnership between IT and user communities.

However, partnering processes often evolve over years, without careful consideration and analysis of how well they work and meet the demands of the user community or of the governance needed to supervise the partnerships. This must change.

Meanwhile, wherever the question “how far should self-service users be allowed to go” is asked, in circumstances where users are highly motivated to use self-service tools and where central BI teams are limited in resource (which seems to be the norm!), then a good strategy is for the central teams to use modern toolsets to provide users with many different ways of selecting, analysing and forecasting using given data sets, but for the central BI team to maintain some control over the content of the dataset, so that the corporation as a whole can be sure of the validity of the results.

Recommendations for management Strategy Interactive marketing is strongly linked to, and one output of, an effective BI operation.

Self-service BI in marketing typically receives strong support from users in corporations which are data-rich (e.g. they have very large volumes of data about individual customers and their transactions) but where the central team develops a very comprehensive but unrealistic view about what BI is needed to support marketers, leading to long planning and delivery projects that absorb management attention and risk being outdated by the time the planned enhancements to BI are delivered.

This problem can be avoided by maintaining a very strong focus on the strategies that need to be supported by BI, having a clear view of the priority of these and by ensuring that senior management has a very clear view of what BI is being created and how it benefits their corporation. This requires a clear articulation of the marketing or consumer strategies that must be supported by BI, for example: . to win higher quality customers through precision marketing acquisition strategies; . to engage high value consumers with deeper personalised experiences to improve retention; and . to increase shopping basket size through the use of “omnichannel” real-time prompts.

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The main areas in which these strategies need to be articulated are: .

Customer management (optimising recruitment, retention, development and efficiency strategies). .

Channels; communications and sales (e.g. digital, web, social, mobile, near real-time CRM, catalogue, internal sales and contact centre, field sales). .

Promotions (promoting the right products to the right customers in the most cost-effective way, to produce the best results). .

Product management (optimising product mix, bundles, own/brand and management). .

Pricing (setting the right prices to make the most revenue/profits and satisfy customers). .

Purchasing (acquiring the right products for customers and markets). .

Logistics/supply chain/payment/finance (the flow through the business and delivery to customer). .

Process (how efficiently, transparently and speedily things are being managed, speed to market). .

Colleagues (how well people are performing, where improvement is most required).

These cover all areas of the Porter value chain model, not just marketing. We have published an example of how customer management strategies can be articulated straightforwardly, allowing BI requirements to be articulated in turn (Stone and Woodcock, 2012).

Critical planning work required To articulate BI requirements, a corporation should determine: .

Which strategies and models are most valuable to the business. .

Which ones are becoming more or less important, taking into account market sector, channel and IT developments. .

Which ones are most dependent on improved BI. .

Which ones are easiest to obtain. .

Which ones are most reliably improved with data (e.g. data quality, recency, frequency).

This is the preliminary work for much more detailed work, which should result in a catalogue of BI requirements, whereby each deliverable (e.g. report or analysis) is specified in terms of priority, content, timing and mode of delivery (e.g. central or local, prior programmed or self-service). The catalogue should be reviewed, probably annually, to ensure that interactive marketing gets what it needs.

Focusing on people aspects Different competencies are required, as follows: .

Data architecture and governance competencies are the foundation of good BI. Excellent data, with clearly defined meanings and a “single version of the truth� while difficult to maintain in a large organisation, builds trust throughout

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the organisation. Data architects and governors must understand the strategies BI supports, govern data quality and hold functions to account for por use of data (be it capture, use or application).

Data scientists must to be able to work with different types of data; structured and unstructured; combining transactional, research and social data to deliver analysis and early insights of commercial value. They must be technically skilled and build a deep knowledge of the data, its structures, limitations and worth.

The relationship management that links BI with the commercial organisation must be strong. Relationship managers must be respected by the commercial teams who should involve them in “top table” discussions so that they understand commercial issues and opportunities. They can then both brief the data scientists and help interpret and challenge results, before “selling” the insights to the commercial teams. They also have a role in helping the commercial teams deploy insights and measure their impact.

Recent research has revealed the shortage of skills and the relatively poor provision in this area by academic and other educational institutions (Watson, 2012; Wixom et al., 2011; e-skills UK, 2013). Table II below shows recent research carried out in conjunction between the Worldwide Federation of Advertisers (WFA) and The Customer Framework. It illustrates the importance of all of these roles.

Recommendations for researchers Although the literatures on both BI and interactive marketing are well developed, the academic literature bringing them together is weak. Much of the business literature comes from suppliers and tends to be high level. This is an excellent opportunity for researchers to make a strong contribution to business theory and practice.

One approach to this research would be to build on the work by Nakata and Zhu (2006) and Nakata et al. (2011), to focus on the competencies and organisational structures required for specialists in interactive marketing and BI to work together more productively. We were disappointed that their themes seem not to have been taken up by other researchers as much as might have been expected, particularly in relation to the areas that are the focus of this present paper. Amongst the exceptions to this is the work by Rapp et al. (2010). It is their warning that the rapidity of changes in the external environment moderates the positive relationship between customer-linking capability and customer relationship performance that leads us to a second area of recommended research.

This second area of required research relates to the impact of the rapid developments currently taking place in both BI and in interactive marketing on relationships between the two areas. On the BI side, one of the most notable developments in recent years has been the acceleration of analysis afforded by developments such as in-memory processing (which greatly reduces the time needed to analyse very high volumes of data). In interactive marketing, a highly innovative development has been the emergence of on-line affiliate platforms (including those of the e-business giants Amazon, e-bay and Apple, but also those of specialist start-ups) that support the real-time and automated auctioning of advertising slots to individual consumers while they are using their mobile telephone to browse or use an app[4]. The consumer data this uses is not their name or even their identity (as revealed by their mobile phone

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number or e-mail address – which would not be available to a third party), but details of the site they are on, the device they are using and certain aspects of their mobile browsing history). In such a world, BI and interactive marketing are scarcely separable and become fully automated, taking us a very long way from the world of batch processing and analysis in which most of the ideas of BI and interactive marketing originated[5]. As O’Kane (2013) points out, marketers are still trying to make sense of mobile messaging as a medium, yet this represents a much earlier stage in the development of interactive marketing practice.

A third area of research which would be particularly useful to practitioners is to identify the effect of the above factors in different sectors. Is the effect the same in retailing, as in travel, in utilities as in telecommunications, in published as in broadcast media? How are the different sectors of financial services affected by these developments?

How much does the effect depend upon the underlying nature of customer relationships in each sector and how this is changing, whether due to changes in CRM technology or in the underlying product or service technology of the sector in question?

Third, we would like to see some attempt by academics to harness the substantial grey literature such as that cited in this article to identify likely future trends in the areas being researched and the future implications of the conclusions academic researchers are carrying out. Too often, academics researchers’ research is essentially backwards looking, focusing on questions such as how consumers reacted or behaved when subjected to particular influences from companies. In areas where developments are rapid, such as those covered by this article, at least some of the researches carried out by academics are likely to be out of date by the time they filter through into the world of practitioners through translation articles. Such an approach would help remedy some of the problems relating to the academic-practitioner divide signalled by Zahay (2013) and confirmed the review of internet marketing research carried out by Pomirleanu et al. (2013).

Conclusion In this article, we have reviewed the rise of interactive marketing as a key capability, requiring support by advanced BI. As corporations’ interactive marketing becomes more sophisticated, the BI required to support it must become more advanced, while marketing users must become more adept at using self-service technology. However, this carries with it the dangers of all self-service, that users may get things wrong – hence the need for a mature, well-governed relationship between BI experts and marketing users. Finally, on the horizon lie developments which may automate the exploitation of BI, so a new challenge for BI experts and marketing users may be to move to a higher level, so to speak, by focusing on building automated capabilities for the use of BI. Notes 1. For this reason it is better to refer to an online resource for the latest figures – see for example www.internetworldstats.com/ (accessed 8 December 2013).

2. For a truly excellent summary of developments, see Webber (2013). This article represents the collective opinion of the editorial board of the journal, celebrating the 25th anniversary of the Institute of Direct Marketing, for which this is the house journal. For an example of assessment of the impact of big data, see The SAS Institute (2012b), which quotes an expert prediction that the amount of data organisations in all sectors have will double every six months, while increasing in

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variety (with big growth in unstructured data), velocity (the speed with which data arrives), variability (peaks and troughs) and complexity (of hierarchies and linkages).

3. For an analysis of how churn by one customer is related to churn by many others, and the consequent importance of analysing network links between customers, whether on social or telecommunications networks (The SAS Institute, 2012c).

4. For a list of the top 20 affiliate networks, see http://mthink.com/affiliate/ (accessed 8 December 2013).

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5. A this stage of development of the approach, there is some doubt about how far the use of this technique will extend, as much depends on its productivity, but also on the understanding by marketers of how best to use the technique, as highlighted by Shields (2013).

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Further reading Stone, M., Woodcock, N. and Foss, B. (2003), The Customer Management Scorecard, Kogan Page, London.

About the authors Merlin David Stone is a Research Director at The Customer Framework and Visiting Professor at De Montfort, Oxford Brookes and Portsmouth Universities, UK. Merlin Stone is the corresponding author and can be contacted at: merlin.stone@thecustomerframework.com Neil David Woodcock is a Chairman and CEO of the Customer Framework and Visiting Executive Professor at Henley Business School, University of Reading, UK.

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