An report by the Economist Intelligence Unit. Competing smarter with advanced data analytics. Sponsored by

An report by the Economist Intelligence Unit Competing smarter with advanced data analytics Sponsored by

Contents Introduction 2 1 2 3 4 5 6 Companies take to the offense with data analytics 4 Focusing data analysis on competitors 5 External and internal data 6 Business challenges and data challenges 7 Satisfaction levels 8 Keys to success 9 Conclusion 10 1

Introduction In June and July 2015, with sponsorship by SAP, The Economist Intelligence Unit (EIU) carried out a survey of more than 300 executives who are familiar with their company s data analytics practices. The goal was to assess trends in the use of marketfacing advanced analytics. The sample includes 50% C-level executives and represents companies from Asia-Pacific, North America, Western Europe and Latin America. All of the respondents are from companies with at least US$500m in annual revenue, with half of them reporting US$1bn or more. To add insights to the survey findings, the EIU conducted interviews with several advanced analytics practitioners. This Executive Summary describes the top findings of this research. The survey found that companies are moving beyond first-generation big data applications based on internal assets and are reporting considerable success with innovative market-facing initiatives that use a wide range of transactional and external data. Competitor-focused initiatives are given the highest priority, with customer- and operations-focused measures comprising a significant number of initiatives. The survey also found that the biggest technical challenge was the need to identify and integrate multiple data types from both internal and external sources. When it comes to internal challenges within an enterprise, data and analytics silos stand out, largely because market-facing advanced analytics initiatives tend be driven by individual lines of business. Despite these challenges, executives overwhelmingly rate these advanced analytics initiatives as successful and point to multiple simultaneous benefits. This broad success is driving continued innovation and experimentation, with technical challenges seen as minor obstacles compared with the need to select the right initiative and the right team. 2

Competing in the hyperconnected economy The EIU, with sponsorship from SAP, is conducting a major research programme on The hyperconnected economy. This describes the quantum leap in linkages among people and companies being driven by mobility, social media, the Internet of Things and other emerging technologies. One of the important outcomes of hyperconnectivity for business is the creation of new fields of competition. Data are being developed within companies, from public sources, by third-party vendors that provide multiple linkages on products, pricing, branding and sales. From proactive pricing to tracking the branding of competitors products, hyperconnected data present a new basis for competition. The ongoing research on The hyperconnected economy can be found here: www.economistinsights. com/technology-innovation/analysis/hyperconnected-economy 3

1 Companies take to the offense with data analytics In version 1.0 of data analytics, most companies focused on internal initiatives such as operating efficiencies. But with increased computational power and new data sources, they are experimenting with offensive moves. The number and variety of initiatives is very broad and Ben Alves, Market Intelligence and Customer Analytics Manager at Autodesk, doesn t find that at all surprising. Everything comes back to big data, he says. There s more and more of it available, and more and more companies are finding unique and creative ways to create insights from those data. It s in their blood to be constantly pushing the limits. Proactive price optimisation stands out as the most common market-facing data analytics initiative, but seven others are cited by between 35% and 44% of the respondents, with the median number of initiatives being four. A number of forces have combined to generate this diversity. First, innovation in this space is typically driven by lines of business, each with its own needs. Second, emerging big data tools are flexible and often cloud-based, making it easier for business users to experiment with new applications even when they can t predict return on investment (ROI). And third, lessons learned from this experimentation accumulate, encouraging innovation in different areas. It s a very innovative space and it s early days yet, says Mr Alves. Whether companies are testing, evaluating or piloting, there s a lot of innovation going on and you need to see if it s the right fit for you. The interpretation that much of this activity entails innovation and experimentation is supported by the fact that only 17% of respondents say they have developed ongoing competitor intelligence programmes, indicating that they are not yet ready for comprehensive approaches. Has your company launched any of the following market-facing advanced data analytics initiatives? (% of all respondents) External and internal data to support a proactive price optimisation Data to track competitors brand performance, awareness and market share Predictive analytics to support market demand forecasting Social media to track trending of competitors products and brand Data analytics to push point-of-sale offers Data analytics or social media to target customers of competitors Market and internal data to support product/service launches, etc. Third party data to generate and track sales leads through the marketing funnel Geospatial analytics to optimise outlets, manufacturing, distribution, etc. Data analytics to support an ongoing competitor intelligence programme 17 50 44 42 41 39 37 37 35 27 Source: The Economist Intelligence Unit. 4

2 Focusing data analysis on competitors A pattern emerges when respondents are asked to cite the initiative that is the highest priority for their company. The top three initiatives are all competitor-focused, including proactive price optimisation, tracking competitors with social media and tracking competitor brand performance and market share. Competitor-focused initiatives are one of the main drivers for organisations to integrate external data with internal data at the outset, says Dr Amy Shi-Nash, Chief Data Science Officer at DataSpark, Singtel s analytics subsidiary. There s an element of self-defence to it the thinking is, If I can use data better than my competitors, not only will I not be left behind, but I can also seize the competitive advantage. On the other hand, there is also considerable activity spread over several categories of customer-/operations-focused initiatives, with the overall total nearly equally split between the two types. The higher priority attributed to competitorfacing initiatives may result in a greater allocation of resources, which may partly explain the fact that satisfaction is higher with competitor-focused initiatives. The proportion of executives who report being somewhat and very satisfied with their primary initiative is 93% for competitor-focused initiatives and 78% for those that are customerand operations-focused. Please select the primary initiative the one that you believe is the highest priority for your company (% of respondents who designated a primary initiative) Competitor focussed External and internal data to support a proactive price optimisation Social media to track trending of competitors products and brand Data analytics to track competitors brand performance, awareness and market share Predictive analytics to support market demand forecasting Third party data to generate and track sales leads through the marketing funnel Geospatial analytics to optimise outlets, manufacturing, distribution etc. Market and internal data to support product launches, promotions, and offers Data analytics or social media to target customers of competitors 6 Data analytics to push point-of-sale offers 3 Data analytics to support an ongoing competitor intelligence programme 1 Customer and operations focussed 18 17 14 11 11 10 9 Source: The Economist Intelligence Unit. 5

3 External and internal data The survey finds that companies are combining many types of data to carry out their advanced analytics initiatives. Most of them mix multiple types of internal and external data. The survey found that the average initiative uses three internal and two external data sources for a total of five. Moreover, every type of internal and external data included in the survey is being used by a significant number of respondents, the lowest being sensor-based data with 19% and aggregated third-party tracking data with 21%. While the power of advanced market-focused analytics is greatly enhanced by this ability to integrate disparate data sources, this is also the root of the most important challenges. There s an overwhelming amount of internal and external data available for analysis, and companies are struggling to capture and process all of this data into a format that balances analysts need for speed and computational power without overburdening the organisation with enormous hardware and storage costs, says Amy Gershkoff, Chief Data Officer at Zynga. But those that successfully capture the wide array of available data integrating it into a unified, easy-to-use database, hiring terrific analytical talent and empowering that talent to uncover actionable insights have a crucial competitive advantage. The survey confirms that the need to access and integrate internal and external data from multiple sources and technologies are the principal challenges confronting advanced data analytics initiatives. The top four challenges all involve either identifying or integrating different types of data and are cited by between 37% and 43% of respondents. Accessing, cleaning and integrating data from different technologies are also significant hurdles. Which of the following transactional data sources did your organisation use to support this initiative? (% of all respondents) Which of the following external data sources did your organisation use to support this initiative? (% of all respondents) Customer data Sales transaction data Pricing data Supplier/Supply chain data Ecommerce data (internal) CRM data Manufacturing data Mobility analytics Sensor-based data 19 36 31 29 26 24 44 56 Social media data 46 Third-party marketing analytics 39 Data from public/government databases 35 Credit rating data Geolocation data Aggregated tracking data from 3rd parties 21 Source: The Economist Intelligence Unit. 6

4 Business challenges and data challenges When asked which business-related challenges are the biggest obstacles to the successful execution of advanced analytics initiatives, executives most frequently point to data and analytics silos within their organisations (43%). Other top challenges include gaining sufficient executive support, analysing data across silos to develop a holistic view, and lack of personnel with sufficient data expertise (all 41%). All of these challenges appear to stem from the fact that new and innovative data analytics initiatives are most commonly driven by lines of business, which is not where data analytics expertise usually resides. Several factors are behind this trend. Line-ofbusiness owners are often the first to perceive needs and the first to recognise the benefits of innovation. Moreover, a range of new tools gives them access to advanced analytics independent of their enterprise IT functions. Sales units can use both big data and data-mining tools to categorise customers and develop new products to maximise profits, says Atlas Lu, Vice President of China Airlines Information Management division. Managers can use business intelligence tools to quickly analyse current operations data and facilitate new strategic planning, while IT personnel maintain clear lines of communication and supplement missing data. And finally, the expected cost of initial forays into big data is generally low enough that line-of-business owners do not need to demonstrate ROI for an experimental initiative. In fact, demonstrating ROI is the least important challenge, cited by only 10% of executives. The situation can change once experimental innovations have proven successful, since at this point proponents have an interest in broadening support and resources and this generally requires support from enterprise leaders. There is substantial reason for optimism on that front. By using relevant marketing analytics, we can find hidden and unforeseen patterns among large amounts of internal and external data to build our initiatives, says Mr Lu. Our hope is that the relevant personnel can use this method to examine current market and sales strategies, developing new ones to improve service quality across the board. What were the most significant business-related challenges that your organisation faced in the execution of this initiative? (% of all respondents) Data and analytics silos within the organisation Analysing data across analytics silos to develop a holistic view Sufficient executive support Using personnel with sufficient data expertise Engaging business users, through self-service functions or otherwise Sufficient financial resources Providing decision-makers with analytics-based insights Finding the right analytics software Demonstrating sufficient ROI on the project 10 16 32 43 41 41 41 Source: The Economist Intelligence Unit. 7

5 Satisfaction levels Survey respondents report high levels of satisfaction with their big data analytics initiatives. Overall, 80% say they are satisfied, including 23% very satisfied and 57% somewhat satisfied. These results are supported by a broad range of specific benefits that executives report. Reduced costs are the most frequently cited benefit-surprising, as reduced costs were not among the top objectives of respondents advanced analytics initiatives. To some extent this may reflect unexpected costsavings from parallel actions such as moving to cloud-based analytics platforms. Another consideration is that reduced costs are easy to recognise while other benefits can take time to appear. But China Airlines Atlas Lu cautions that seeking cost reductions can be a distraction. Our goal [with data analytics initiatives] is to find hidden information with potential for results that surpass all imagination, says Mr Lu. Through data analytics we can identify our customers consumption habits, stimulate purchasing behaviour and increase corporate earnings on a basis of increased customer loyalty-reaching our long-term goal of corporate sustainability. Cost reductions are not our main concern. Aside from cost-savings, respondents point to multiple benefits from both competitor-focused and customer-focused efforts. New business opportunities (%) and increased revenues from existing lines of business (26%) are ranked second and third, but additional customers and increased market share are also cited by more than one in five respondents. Competitive advantage is about more than just sizeable increases in bottom-line revenues and top-line cost reductions-even though one or both of those goals is usually the primary impetus for organisations to undertake large-scale data integrations, agrees Amy Gershkoff of Zynga. It provides seismic strategic benefits to the organisation, including the ability to forecast shifts in the industry, determine the optimal new products to develop, identify the need to shift brand positioning and much more. What were the greatest benefits achieved by the initiative? (% of all respondents) Reduced costs New business opportunities Increased revenues from existing lines of business New, additional customers Increased market share Improved operations Increased customer satisfaction Deeper market or competitive insights 7 13 19 21 26 25 41 Source: The Economist Intelligence Unit. 8

6 Keys to success The high degree of satisfaction with past and current analytics initiatives has engendered optimism about the future. More than 90% of respondents say that they are likely to pursue further market-facing advanced analytics initiatives. The executives surveyed have clearly learned from their experiences and are now ready to innovate further. They report that selecting the right data-driven initiative and assembling the right team to execute it are the most important success factors. This is another indication that considerable experimentation is still ongoing. Collaborating, garnering senior executive support and choosing the right technology are also important success factors cited by at least one-third of respondents. There are two main talents you need from [your team], says Ben Alves of Autodesk. First, they need to be able to understand what s being done with the data at a high level and to figure out ways of how it can be beneficial to the pilot, group or company and communicate that business strategy to the data scientists. Second, you need someone to encourage buy-in, capable of explaining how these tools can be beneficial not to a single group but to the whole organisation. Priorities for market-facing advanced analytics over the next 12-18 months are just as varied as they have been in the recent past. Various competitor-focused initiatives are anticipated by between 36% and 41% of respondents, followed closely by customer-/operations-focused projects ranging from 30% to 36%. Which of the following factors are most important in determining the success of market-facing data initiatives? (% of all respondents) Selection of the right data-driven initiative Having a team with the right skills Selection of best technology/software Obtaining senior executive support Collaboration of data specialists with business stakeholders or lines of business Access to suitable internal data Access to suitable external data Skills and patience in integrating data Sophisticated analysis and interpretation of data 9 18 42 38 34 27 23 Source: The Economist Intelligence Unit. 9

Conclusion First-generation big data applications focused on internal initiatives such as supply-chain optimisation or customer segmentation because that was where the data were and could be used. As companies gain expertise, and as software grows more sophisticated, industry leaders are now expanding their data priorities to include marketfacing initiatives. These are external analyses, sometimes leveraging external data sources that are used to undercut competitors pricing, build new business opportunities and increase revenues. However, these more complex initiatives create commensurate challenges. Data and analytics silos, multiple data sets and the integration of externally curated data are the primary problems. The initial benefit is cost-reduction, as data enables more efficient approaches and as the move to cloud lowers direct costs. But users cite further benefits, including increased revenue, new business opportunities and the ability to cross-sell existing products to customers. In sum, data are no longer just about analytics, they are about creating a whole new enterprise. The keys to success are finding the right initiative, mobilising qualified personnel and selecting the right software and technologies. High levels of satisfaction are found in these early users, with four out of five satisfied with their current initiatives, and nine out of ten planning market-facing data initiatives in the near future. 10

Whilst every effort has been taken to verify the accuracy of this information, neither The Economist Intelligence Unit Ltd. nor the sponsor of this report can accept any responsibility or liability for reliance by any person on this report or any of the information, opinions or conclusions set out in the report. Cover: Shutterstock 11

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