The data directive How data is driving corporate strategy and what still lies ahead

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1 The data directive How data is driving corporate strategy and what still lies ahead An Economist Intelligence Unit report Commissioned by

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3 Contents About the research 2 Executive summary 4 Introduction: The data revolution: optimisation gain, strategic strain? 7 Case study: Turning digital risk into opportunity at Universal Music 8 Functional gains: The organisational data leaders 10 Case study: Optimising sales at Anheuser-Busch 15 Case study: Rethinking customer value at a retail bank 16 Insight-led business? A sector perspective on data 17 Case study: Transitioning from physical products to data services at Xerox 20 The outliers: Who s ahead on the data game? 21 Noise, overload and trust: The data challenges 26 Case study: Building a data-centric business at Samsung 29 Case study: Cargill, strategy and the rise of the so-called super quants 0 Conclusion: The leadership considerations ahead 1 Appendix: Survey results The Economist Intelligence Unit Limited 201 1

4 About the research The data directive is an Economist Intelligence Unit (EIU) report, commissioned by Wipro. It seeks to explore the degree to which the ongoing data revolution within business is delivering truly strategic change within companies, as opposed to more incremental optimisation gains. Although many of the issues discussed here stray into the realm of so-called big data, this report is not explicitly focussed on that topic and does not deal with any technology-related issues. Instead, it seeks to explore how the wider trend toward a greater reliance on data is affecting the strategic management of businesses at a C-suite level, across a range of industries. The findings and views expressed in this report are those of the EIU alone and do not necessarily reflect the views of the sponsor. The research draws on two primary inputs: The first is a wide-ranging survey of 18 C-suite executives or their direct reports, divided between CEOs (1%), CFOs (20%), COOs (1%), CIOs (12%), CMOs (9%), and other C-suite roles. From an industry perspective, all major sectors were represented, with the largest being manufacturing, including chemicals and aerospace (18%), retail and consumer goods (11%), technology, media and telecommunications (15%), financial services (10%), and professional services (9%). Regionally, respondents hailed from North America (48%), Europe (29%) and Asia-Pacific (24%). The second key input is 20 in-depth interviews with business executives and experts, as listed below. In addition, we conducted extensive desk research into the broader topic. James Watson is the author of the report and David Line is the editor. Kim Thomas, Sarah Fister Gale, Priyanka Mehra Dayal, and Fergal Byrne assisted with further interviews. We would like to thank all survey respondents and interviewees for their time and insights (listed alphabetically by company name): Rajendran S, chief marketing officer, Acer India David Johnston, group chief operating officer, Aimia David Almeida, vice-president: sales, Anheuser-Busch Philip Clement, global chief marketing officer, Aon Irvin Newbitt, vice-president: enterprise IT, AstraZeneca Rudy Puryear, director and global head: IT practice, Bain & Co Julian Gray, chief information officer, BP Alternative Energy Professor Andy Neely, director, Cambridge Service Alliance Mike Balay, vice-president: strategy and business development, Cargill Ged Brannan, head of Coutts Experience, Coutts Greg Nichelsen, head of business intelligence, ING Direct Christian Nelissen, director: customer analytics and decisioning, Royal Bank of Scotland Asim Warsi, vice president: mobile, Samsung India Jason Trost, chief executive officer and co-founder, Smarkets Michel Allé, chief financial officer, SNCB Holding Kenneth Cukier, data editor, The Economist 2 The Economist Intelligence Unit Limited 201

5 Paul Gathercole, vice-president: digital tools, Universal Music Christa Carone, chief marketing officer, Xerox Corporation Chief operating officer, Fortune 100 retail bank Chief financial officer, Fortune 100 chemicals business Chief financial officer, Fortune 500 retailer The Economist Intelligence Unit Limited 201

6 Executive summary In July 1995, a former hedge fund executive launched a website that aimed to upend the traditional way of selling consumer goods, starting with books but soon expanding into a diverse array of products. Dubbed Amazon.com, Jeff Bezos s business went on to disrupt not only the way people typically shop, but also the book publishing industry itself, en route to becoming the world s largest online retailer. It now serves as a prime example of how technology can enable innovative companies to fundamentally rethink the way business is done, as opposed to merely optimising existing processes. In much the same way that the internet was the fundamental disruptive technology driving corporate strategic change in the 1990s, today the myriad forms of data emerging from our internet-enabled digital world promise a similar transformation. Very few executives need convincing that having more data about their products, people and processes can be empowering and profitable. But how far has the data-driven revolution gone? How many companies have followed pioneers like Amazon into transforming their strategy and potentially their industry through data-driven insights? This study seeks to examine the progress that executives have made in using information to transform their business. It highlights some of the striking gains that are being achieved so far, as companies rush to capture all kinds of new insights. From beer companies like Anheuser- Busch using data to understand where, how and when to place its products to optimise sales, through to businesses like Universal Music using data to better identify up-and-coming artists. But the research also uncovers how much work remains to be done, in terms of genuinely making use of the transformational potential of data within business. Right now, however, most businesses are focused on using this to optimise their existing approaches and processes; far fewer have found wholly new ways of operating, or breakthrough opportunities. The report s key findings include: A strong relationship between earnings growth and strategic use of data. Although correlation does not suggest causality, this study finds sharp differences between high growth firms, based on their EBITDA performance over the past three years, as compared to their no growth peers. For example, even though both sets of firms collect lots of data, high-growth firms make far better use of it, while no-growth firms are more likely to find themselves swamped by the data they hold. Similarly, twice as many high-growth firms consider themselves highly effective at extracting insights from data, as compared to no-growth companies. Highgrowth firms have also done more to reform 4 The Economist Intelligence Unit Limited 201

7 their structures and leadership around data, suggesting greater internal maturity on this issue. In particular, they are more than twice as likely to have a well-defined data management strategy in place. All C-suite functions are finding uses for data, but so far data has proved most valuable for marketing leaders. From better ways to segment the customer base, to rethinking the ideal product mix in a retail store, marketing leaders are finding wide-ranging uses for their data to help improve how they market their company s wares. Already, 50% of chief marketing officers say they have tried and found a clear, positive difference in using data to improve their understanding of customers, as just one of a range of successful applications. This is a markedly higher proportion than their C-suite peers. The financial services sector, technology companies, and professional services firms are most prepared for the data age. One of the key indicators identified within the survey is the degree to which a company has established a well-defined data management strategy. Considering this core metric, three sectors stand out as being most prepared for the data age: the financial services sector (where 22% have this in place); the technology industry (0%); and the professional services sector (40%). By contrast, data management strategies are least often found amongst manufacturers (16%) or retailers (1%). Many companies are unsure about the extent of data-fuelled strategic transformation within their business. Companies of all kinds are finding impressive ways to use data to optimise their sales, marketing and other functions. Moreover, 68% of respondents think their strategy has improved in the past two years as a result of having more data. But only 18% see a significant improvement in strategy, and few have found ways to use data to make a genuinely transformational shift in the business. Some 5% of executives agree that data has been more useful with operational choices and actions, rather than strategic ones. Just 22% disagree, while 41% are unsure. Moreover, while 72% of companies regard themselves as effective at extracting strategic insights from data, only 12% of executives polled for this study consider their companies to be highly effective in this regard. Similarly, for what is usually a confident executive audience, unusually high proportions of survey respondents regard themselves as below average on this issue, including 20% of COOs and 17% of CFOs. Businesses are stockpiling an ever-growing range of data and expect data gathering to continue to expand rapidly. Whether social media sentiment, machine-generated data via sensors, staff s, market data or otherwise, firms of all shapes and sizes are now collecting more information than ever before. At least seven in ten companies collect syndicated third-party data, such as weather information (72%), or government data (70%), while many gather anything from internal staff data (66%) to some kind of location-based information (41%), among many other types. Two-thirds of business leaders say the range and types of data have expanded in the past two years, while about three-quarters expect this data stockpiling to expand yet further in the coming two years. Working out which data matters most is the top challenge for firms, but there are wideranging issues to overcome. For companies seeking to gain more strategic insights from their data, many hurdles await. Whether organisational silos, a lack of skills, the usual disconnects between IT and the business, or worries over data quality, few consider the challenges and gaps easy to bridge. But clarity on which data matters most, amidst the data stockpiling now under way, is what tops the list of barriers, according to 40% of respondents. Furthermore, 4% of executives worry that the quality of their decisions are actually being impaired by data overload. The Economist Intelligence Unit Limited 201 5

8 Those companies that claim to be best at extracting insights from data are not necessarily looking to their CIOs to lead on this initiative. While the CIO is the default leader of choice across the vast majority of firms surveyed, there is a spike to preference towards the CEO and other C-suite leaders among those companies best at extracting strategic insights from data. This group is also nearly eight times as likely to have a data management strategy; and four times as likely to have changed the way they make strategic decisions. As with the high-growth firms sub-set, they are typically far more likely to see scope for radical transformation of the business through better use of data. 6 The Economist Intelligence Unit Limited 201

9 Introduction The data revolution: optimisation gain, strategic strain? There is much hype about the strategic impact of data, and many new bets on it will undoubtedly pay off. However, while some companies are making strategic changes based on data, as distinct from optimisation gains, many are yet to do so. Discussions about the transformational power of data have been hard to miss in recent years. Indeed, speculation over the impact of all aspects of data, including so-called big data, has become so frequent and over-hyped that it is difficult to separate the signal from the noise. It is now two years since the McKinsey Global Institute released its landmark study on the subject, which marked an inflection point for business. 1 McKinsey argued that the subject was now relevant for leaders across every sector, with business on the cusp of a tremendous wave of innovation, productivity and growth as a result of data. In many respects, the current data gold rush is reminiscent of the latter part of the 1990s and the rush to get to grips with the emerging Internet era. Today, just as then, profound promises are being made about the strategic impact of data, but for the time being examples of the potential coming to fruition remain few. This report agrees wholeheartedly that many of these new bets on data will pay off. However, for the majority of businesses, much evolution lies ahead. Philip Clement, the global chief marketing officer of Aon, an insurance company, gives the analogy of how Amazon.com, in the mid-1990s, promised to become one of the biggest global retailers. It seemed like a joke at the time, he says. It was true, but it just took 15 years longer than anyone imagined. This is not to suggest that the issue of data is not a priority in boardroom discussions today. This survey of over 00 C-suite executives, spanning a range of industries and functions, suggests that only a tiny fraction (%) are not currently prioritising the collection of data. Whether social media feeds, machine-generated data via sensors and telematics, call-centre recordings, staff s, syndicated information, or data from other more exotic sources, a high proportion of companies are storing increasing 1 Big data: The next frontier for innovation, competition and productivity, McKinsey Global Institute, May 2011 The Economist Intelligence Unit Limited 201 7

10 Figure 1 From data to insights Organisational effectiveness at translating new data sources/types of data into insightful information to drive strategic change Highly effective Somewhat effective Neither effective nor ineffective Somewhat ineffective Highly ineffective Source: Economist Intelligence Unit survey amounts of data in the belief that it will be of value to them. McKinsey s 2011 report already highlighted that 15 out of 17 sectors in the US had more data stored per company than the information contained in the US Library of Congress. Moreover, while it is true that 72% of executives polled consider themselves effective in translating these data sources into insightful information that can drive changes of strategy (Figure 1), only a small minority (12%) consider themselves highly effective in this regard. As this report will argue, there are many impressive examples of how companies are using data to optimise many aspects of their business. However, there is far less evidence that corporate leaders are systematically using it to strategically transform their companies or business models. This is the continuum, argues Kenneth Cukier, The Economist s data correspondent and author of the recently released Big data, 2 By being a data-driven company, are you using data to optimise what you re doing today, or are you taking this information to do something totally new? Case study: Turning digital risk into opportunity at Universal Music 2 Big Data: A Revolution That Will Transform How We Live, Work, and Think, Viktor Mayer-Schonberger and Kenneth Cukier, March 201 What a difference a few years make. Back in 2006, Universal Music made headlines with a lawsuit filed against MySpace, the entertainment-oriented social network, for copyright infringement. Since then, the world of music has gone increasingly digital, with wholly new music distribution models emerging, even as MySpace has faded. But Universal Music has been constantly evolving over the past decade too, using data to help it shift from a defensive risk-oriented strategy to an offensive, opportunity-led one. In many respects, this is a very literal transition: the same data team that once worked to contain digital piracy risks has been transformed into a source of insight for signing and marketing new hits. This Digital Tools team, lead by Paul Gathercole, has spearheaded the creation of a powerful data platform, which now helps to guide executives on their decisions and investments. This platform draws on diverse data, from online music services streaming data, to social media and web sentiment analysis, to physical sales, to stars concert and TV appearances, among other things, and it is reshaping how the company works and thinks. Our data team now handles queries from all parts of the business, such as asking for a precise breakdown of how popular a given artist might be across various digital and analogue channels, explains Mr Gathercole. As this data platform has grown in scale and capability, it is in turn changing the business. The key thing that can change people s perceptions is the nuance you can get from the data, says Mr Gathercole. It s a long path, but Universal has changed a lot in the last three years and the degree of openness to data has changed dramatically. The questions we receive are better and meatier, which is indicative of an organisation educating itself and raising its ability to use and converse about data meaningfully. 8 The Economist Intelligence Unit Limited 201

11 Of course, there are many examples of companies who ve made data the core of their business models and who couldn t exist without it. These firms are predominantly within the high-tech sector and financial services sector whether Google, Amazon, or one of many hedge funds that compete on their ability to uncover insights from information. Overall, a small core of businesses are genuinely rethinking what data means for them, and how to organise themselves around it. But apart from these outliers, there are relatively few that have worked out how to truly reshape their business around data. Quite simply, for many companies today, the real data revolution still lies ahead. This is the breakout moment, believes Mr Cukier. Everyone has been learning about it, figuring out what it can do, but for most companies, they haven t done it yet. The Economist Intelligence Unit Limited 201 9

12 1 Functional gains: The organisational data leaders So far, CMOs are reporting the most clear gains from their companies data strategies. Across the sweep of companies surveyed for this report, a diverse array of data sources are being tapped into, as the rush to stockpile information gathers pace. From syndicated third-party data, such as market data (which 72% of companies say they collect; Figure 2), to open data supplied by governments (70% collect), to staff s or messaging (66% collect), to contact centre data (62% collect), collection from all sources is on the rise. Customer-related information is also increasingly common, from social media feeds (42% collect) through to location-based information of some kind (41% collect). And this is hardly all. When asked to specify some of the types of information collected, respondents listed examples ranging from competitor information and web sentiment data, through to point of sale feeds, machineto-machine data, news and media, loyalty information, search engine analysis, census bureau data, and RFID tags, to name just a few. Overall, two-thirds (67%) of executives say the range and types of data they rely on to make decisions have expanded over the past two years; and three-quarters (74%) expect this trend to Figure 2 From data to insights Non-traditional data collection trends Collects Plans to collect Does not collect Don t know Syndicated data from third-party data providers (eg, market data, weather, etc) Open data (eg, data released by governments) Staff data (eg, s, calendars, instant messaging, etc) Contact centre data (eg, audio conversations, text chats, customer s, etc) Machine generated data (eg, sensors, smart grid, RFID, network logs, telematics, etc) Social media (eg, Facebook, Twitter, YouTube, blogs, etc) Location-based information (eg, GPS, mobile logins, etc) Source: Economist Intelligence Unit survey 10 The Economist Intelligence Unit Limited 201

13 continue over the coming two years. In short, it is difficult to find examples of data that companies do not seek to squirrel away for possible use. But which functions within the business are actually making real use of this data? Figure Where data delivers for CMOs Top strategic aspects of role in which data has made biggest positive difference (% respondents selecting in top three choices) A marketer s delight Of all the C-suite roles examined for this report, the function that stands out as the single largest successful user of data appears to be the chief marketing officer. Of those polled, 50% report having tested and seen a clear, positive difference in using data to improve their understanding and segmentation of customers (Figure ). A further 40% see similar merit in helping to increase sales, among various other benefits. This degree of usage is well ahead of nearly all other C-suite roles. Increasing customer understanding and segmentation Increasing sales Helping assess potential demand for new products/services Increasing cross-selling efforts Improving customer service Improving return on marketing Improving pricing optimisation This is consistent with the views of many marketing and sales leaders interviewed for this report. Ged Brannan, Head of Coutts Experience at the private bank, says that his firm has invested significantly in a new technology platform to create the opportunity to capture and leverage data, as part of a new strategic focus under its latest CEO. The opportunity arising from this investment relates to significantly improving its ability to make use of both financial and non-financial information to better understand clients behavioural patterns. We want to serve the clients in the markets we want to be in, rather than trying to be all things to all people, explains Mr Brannan. The core of this is still coming in place, but Mr Brannan is working to create a more evidence-based management approach. One example of the type of opportunity that exists could be tracking the working habits of high performing staff, to discern if any traits can be more widely adapted elsewhere. At Aon, Mr Clement says the scope of how data can improve marketing is absolutely huge, as he puts it. Automotive marketers would use analytics to simply try to identify the typical characteristics of consumers who might, say, be interested in a car type - like muscle cars. Now, Gaining better grasp of competitive landscape Optimising marketing mix Improving cross channel experience Other, please specify None of the above Source: Economist Intelligence Unit survey 1 17 it s just the opposite, you literally know who the person is that likes muscle cars, he says. Across a range of interviews, many such examples emerge, especially within customer-centric organisations, of how data are helping improve customer understanding and, ultimately, sales. Even outside of the sales and marketing function, other executives are often involved in supporting such initiatives. At Royal Bank of Scotland (RBS), some of the most high-profile, data-centric initiatives of the past few years have related to trying to gain a far more advanced understanding of customers. There s been a huge shift in the organisation, driven by an understanding of how customers bring value to the bank. It might sound obvious, but once you start seeing these values, you start to actually The Economist Intelligence Unit Limited

14 understand all sorts of interesting things, explains Christian Nelissen, the bank s director for customer analytics and decisioning. This helps the bank rethink how it incentivises behaviour internally, to focus more on building the value of the customer relationship through meeting their needs and working to keep them within the bank. This means staff become interested in how happy the customer is, which changes the dynamic, says Mr Nelissen. Professor Andy Neely, director of the Cambridge Service Alliance, a group of companies and academics, adds that data provides vast opportunity to innovate on service delivery and sales. It will go a long way beyond how do I sell more strawberry pop tarts to actually how do I really deliver value to my customers, he says. Back-office boost Marketing is currently the most immediately visible user of data but other functions in the business are making wide-ranging use of data to support myriad initiatives. As businesses globalise, for example, data has made a clear positive difference to CEOs in how they plan for geographic expansion (40% say they ve benefitted from this). For four in ten CFOs, it s opened up a raft of possibilities around scenarios and forecasting. Nearly half (49%) of COOs say data has helped to bolster operational efficiency, Figure 4: Where data delivers Top strategic aspects of role in which data has made biggest positive difference (% respondents selecting in top three choices) CEO % CFO % COO % Geographic expansion 40.0 Improving scenario planning and forecasting 40. Improving operational efficiency 48.8 Identifying new business models 5.0 Improving financial close management.9 Improving product research and/or development 9.0 Gaining better insights into the competitive landscape 2.5 Improving profitability 2. Improving product/service quality 6.6 CMO % CSO % CIO/CTO % Increasing customer understanding and segmentation 50.0 Identifying new business models 1.8 Managing/controlling IT spending 45.8 Increasing sales 40.0 Identifying new revenue streams 1.8 Allowing for better business processes 44.1 Helping assess potential demand for new products/services Source: Economist Intelligence Unit survey 6.7 Identifying new markets 1.8 Improving identification of risks / risk management 28.8 Figure 5: and where it has greatest potential to deliver Top strategic aspects of role in which data has potential to make biggest difference (% respondents selecting in top three choices) CEO % CFO % COO % Improving risk management 2.5 Improving financial risk management 24.2 Delivering process innovation 1.7 Improving organisational productivity 0.0 Improving corporate reporting / dashboards 22.6 Improving resource usage 1.7 Identifying new revenue streams 25.0 Identifying cost efficiencies 21.0 Improving inventory management 24.4 CMO % CSO % CIO/CTO % Increasing cross-selling efforts. Improving medium-/long-term planning 6.4 Identifying business transformation opportunities 25.4 Optimising marketing mix 26.7 Improving scenario planning 1.8 Improving support for data-enabled strategic decisions across the business Improving pricing optimisation 26.7 Gaining better grasp of competitive landscape Source: Economist Intelligence Unit survey 27. Improving scenario planning and forecasting The Economist Intelligence Unit Limited 201

15 while about one-third (2%) of chief strategy officers are using it to help identify new revenue streams or markets or business models. And many CIOs are using it to control IT spending or improve business processes (see Figures 4 and 5 for a full overview). Across varying roles, there are wide-ranging examples of how data are being used. The CFO of a Fortune 100 chemicals company, who asked to remain anonymous due to the sensitive nature of the work, says his function has been significantly increasing its use of data to improve its competitive intelligence and thus pricing structures. As we ve begun to learn more about data opportunities, it s allowed us to better analyse the competitive set that we play against in terms of pricing, cost structures, the ingredients of their products versus ours, cost positions, and market approaches, he explains. This is all analysed in the pursuit of competitive gaps. Further downstream, it also drives other finance-led forecasting, such as how projected demand is affected by price shifts, the ability to supply that demand, and where excess capacity might lie. It gives us a better sense of our capacity to respond, he says. At BP Alternative Energy, CIO Julian Gray notes that his company is a huge user of data, across nearly every part of the business, from seismic modelling in its upstream exploration, through to in-depth farming and meteorological data in its biofuels business. All of those areas are growing immensely, he says. Inevitably, though, some functional roles take differing views on data. The CFO, for example, is often closely engaged with data issues, given the nature of the role. But as our survey suggests, CFOs often seek to act as the voice of caution on data many more see a risk that data overload provides false comfort to the business, for example. They are also far more likely to note difficulties in identifying which data are truly relevant to strategy, and which to ignore (a subsequent report will examine the role of the CFO in more depth). Tactical or strategic? This chapter has highlighted a few of the key areas where C-suite leaders are making use of Figure 6 Uncharacteristically uncertain Usage of data compared to industry peers Significantly above average Somewhat above average About average Somewhat below average Significantly below average Don t know CIO/CTO CSO CMO COO CFO CEO Source: Economist Intelligence Unit survey The Economist Intelligence Unit Limited 201 1

16 data today. But two things stand out from an analysis of this. The first is that for a cohort of typically confident and bullish executives, today s leaders are unusually humble about their grasp of data. Just 15% of CEOs consider themselves significantly above average in their use of data, while nearly the same amount think they re below average (Figure 6). One in five COOs regard themselves as below average, as do 17% of CFOs all unusually high proportions. The second, more important point that stands out from the survey, and which is confirmed by interviews for this report, is that the use of data today often centres on optimising existing processes and tactics, rather than driving genuine strategic transformation. For example, although one in two executives agree that strategic decision-making has improved since they began prioritising data, there is far less certainly about the degree to which data are more useful for day-to-day operational issues versus genuinely strategic ones. About one in three (5%) agree that greater use of data has been more pertinent for operational choices and actions, rather than strategic ones, while only about one-fifth (22%) disagree. The biggest proportion (41%) remains undecided. It s within the day-to-day perspective where the data use is more heavily focussed right now, as opposed to the longerterm strategic planning, says the chemical company CFO. David Johnston, now group chief operating officer at Aimia, a loyalty-scheme management company, agrees that a lot of the data in business today is being used to help optimise processes, versus more fundamental change. Nevertheless, he sees a growing number of examples where it is helping spark new products, ideas or businesses. Take one of his company s latest offerings, a partnership between Yahoo and Aimia s loyalty brand Nectar, which offers customers points in exchange for search-related advertisements that are far more precisely targeted. We can deliver far more targeted adverts, because we know your purchasing behaviour, explains Mr Johnston. This also enables advertisers to more explicitly match their before and after advertising performance against actual sales for a given audience. This is one of several other opportunities and new business models we see out there, says Mr Johnston. Figure 7 Data-driven strategy Corporate planning and decision making Strategic planning Strategic decision making Highly data-driven 25 2 Somewhat data-driven At the time of being interviewed, Mr Johnston was the company s executive vice president, president and chief executive officer for EMEA. Occasionally or partly data-driven Rarely data-driven Source: Economist Intelligence Unit survey The Economist Intelligence Unit Limited 201

17 While some companies are basing strategic planning and decision making on data, some are yet to leverage data for strategic use. About one-third of executives overall consider their company s strategic planning to be highly data driven, although 42% say it is somewhat datadriven (Figure 7). Fewer think the same with regards to strategic decision-making, where just 25% say it is highly data-driven and 0% reckon this is only occasionally or rarely the case. This is not to say that they don t recognise the importance: four in ten consider increased data volumes to be highly important for such strategic issues, while only a tiny minority think it unimportant. Instead, this further showcases the fact that few companies have got to grips with how to rethink their companies around data, even as they rush to collect more of it. Case study: Optimising sales at Anheuser-Busch For global beer brand Anheuser-Busch, the science of selling beer has long been as rigorous a process as its efforts to create compelling new craft brews. As early as 2004, the company was cited as a leader for its effort to create BudNet, its data platform for tracking sales. But over the past two years, explains David Almeida, the company s vice-president of sales, it has invested substantially to develop this platform further. It now means that I can look at any given account, see a detailed plan of the store and all our SKUs [stock-keeping units], with related data on local households from a range of sources. I can then use the data to choose the optimal assortment of beer for that store, knowing which SKUs will perform better, what the best display options are, and the best configuration of brands for that demographic, explains Mr Almeida. We think this data will separate us from our competitors. Of course, as he readily highlights, there s a constant challenge of ensuring that this data doesn t overwhelm sales. Or, more crucially, that niche fads are not chased at the expense of mainstream sales. One of the biggest challenges has been the move towards growing craft segments, which leads to more SKUs and more choice, but which pose a complexity challenge, he says. By blindly chasing this high-growth, but niche, market, Mr Almeida says the industry has hurt sales by crowding out shelf space for mainstream brands. Our analysis shows that even craft beer buyers only buy such brands 0-40% of the time. For the rest, they re buying premium or discount beers, so the craft infatuation loses shoppers when core brands are out of stock more often. All this has led to greater refinement of the firm s portfolio approach across its 500 SKUs. I m trying to generate an environment where the vast majority of our decisions are based on data, and knowing what will work, says Mr Almeida. The Economist Intelligence Unit Limited

18 Case study: Rethinking customer value in retail banking All businesses have a core of most valuable clients. The challenge lies in identifying who these are, and then considering how best to reshape the business around them. This has been one of several data-intensive exercises that a major European retail bank has gone through in recent years. We all know the 80:20 rule, but we ve reviewed the distribution of our revenues across our customer base, in order to split these into distinct cells, explains the bank s COO, who asked to remain anonymous given the sensitive nature of its work. The resulting findings were pretty astounding, he says, and have reset long-held assumptions within the leadership team by showing the high concentration of revenue within a relatively small number of customers. It really highlights the fact that about half of your customer base essentially generates no revenue, he explains. Many companies segment customers like this, but today s technology is allowing for far deeper analysis. We analysed the bottom 50% of customers, to see what products they use, how many ATM withdrawals they make and how many phone calls we get from them. It proves that these customers take up a substantial amount of operational capacity, says the COO. The normal reaction is that you always need the volume to cover your fixed costs, but that s absolutely wrong. They simply do not generate enough revenue to do so. This poses a challenge to management. Given that no bank could fire half of its customer base, how can this issue be addressed? How does the bank identify valuable clients from those who only use the bank as a secondary account? And how do you restructure your business for those clients that do matter most? These are the kinds of questions that are now being grappled with internally. But it is clear that such insights have reset core internal assumptions: Seeing results that no-one thought possible has really changed mindsets, says the COO. And we all have preset ideas about what reality looks like. 16 The Economist Intelligence Unit Limited 201

19 2 Insight-led business? A sector perspective on data High-tech, financial services and professional services companies are the most advanced in using data and hence are getting more out of it, while some sectors such as retail are constrained by poor industry conditions. Although it is not difficult to find examples of data-centric companies in nearly any industry, it is also obvious that some sectors have embraced data more rapidly than others. The most highprofile are Silicon Valley s high-tech companies. But they are not alone. As this survey highlights, many companies within financial services and professional services are highly proficient in making use of data. Overall, these three sectors are the most likely to have created a well-defined data management strategy, or are marshalling resources toward this (see Figure 8). At Aon, for example, data continues to reshape how its products and services are conceived and developed. Whether proxy data on housing starts feeding into construction insurance, or more granular life insurance models based on a better Figure 8 Data-driven industries 60 Manufacturing Financial services Professional services Retail and consumer goods Technology, media and telecoms We have a well-defined data management strategy that focuses resources on collecting and analysing the most valuable data We understand the value of our data and are marshalling resources to take better advantage of them We collect a large amount of data but do not consistently maximise their value We collect data but they are severely underutilised We do not prioritise data collection Source: Economist Intelligence Unit survey The Economist Intelligence Unit Limited

20 For more on this, see Big data: The next frontier for innovation, competition and productivity, McKinsey Global Institute, May 2011 grasp of life expectancy, its products are built on the back of data. Ten years ago we already had an unbelievable ability to deal with data, but our business decision making and acumen hadn t caught up with it, says Mr Clement. It is changing our business model and the fact is that data and the analytics on that data are becoming of equal value to the work we have traditionally done. This is a fundamental change for a company like ours. This in turn is reflected in Aon s workforce. In contrast to a decade ago, statisticians, actuaries, meteorologists, and other data specialists make up a far higher proportion of its staff. At a macro level, demand for data skills is currently concentrated in a few industries, such as finance or biotech (with consequent shortfalls in such talent widely forecast). The pharmaceutical company AstraZeneca, for example, is now reshaping both its process for discovering and developing new drugs, as well as the way it commercialises these, around data. This is drawing on wide-ranging and complex information, from individuals genetic data at one extreme, through to industry insights about prescriptions and clinical efficacy, explains Irvin Newbitt, the company s vice-president for enterprise IT. Still getting started By contrast, other sectors are not yet consistently prioritising data-led initiatives or skills. For example, our survey shows that while both the manufacturing sector and the retail and consumer goods sector collect large volumes of data, they more often admit to not consistently maximising use of this resource than their peers in finance or high-tech. Across a range of data types, barring some exceptions, these sectors less often routinely collect information; and when they do, they less frequently analyse it for insight. For example, Figure 9 The collectors Types of data collected and analysed, by industry 80 Manufacturing Financial services Professional services Retail and consumer goods Technology, media and telecoms Collects Collects and analyses Social media (eg, Facebook, Twitter, YouTube, blogs etc) Collects Source: Economist Intelligence Unit survey Collects and analyses Machine generated data (eg, sensors, smart grid, RFID, network logs, telematics, etc) Collects Collects and analyses Location-based information (eg, GPS, mobile logins, etc) Collects Collects and analyses Contact centre data (eg, audio conversation, text chats, customer s, etc) Collects Collects and analyses Staff data (eg, s, calendars, instant messaging, etc) Collects Collects and analyses Open data (eg, data released by governments) Collects Collects and analyses Syndicated data from third-party data providers (eg, market data weather, etc) 18 The Economist Intelligence Unit Limited 201

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