Competing smarter with advanced data analytics

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

Competing

smarter with advanced

data analytics

Introduction 2

Contents

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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 market-facing 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

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

Competing in the hyperconnected economy

The ongoing research on The hyperconnected economy can be found here: www.economistinsights com/technology-innovation/analysis/hyperconnected-economy

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

Companies take to the offense with data analytics

1

Source: The Economist Intelligence Unit.

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

50 44 42 41 39 37 37 35 27 17

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 competitor-facing 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 customer- and operations-focused

Focusing data analysis on competitors

2

Source: The Economist Intelligence Unit.

Please select the primary initiative—the one that you believe is the highest priority for your company

(% of respondents who designated a primary initiative)

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

Data analytics to push point-of-sale offers Data analytics to support an ongoing competitor intelligence programme

Competitor focussed Customer and operations focussed

18 17 14 11 11 10 9 6 3 1

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

External and internal data

3

Source: The Economist Intelligence Unit.

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)

Social media data Third-party marketing analytics Data from public/government databases

Credit rating data Geolocation data Aggregated tracking data from 3rd parties

Customer data Sales transaction data Pricing data Supplier/Supply chain data Ecommerce data (internal)

CRM data Manufacturing data Mobility analytics Sensor-based data

56 44 36 33 31 29 26 24 19

46 39 35 33 33 21

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-of-business 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.”

Business challenges and data challenges

4

What were the most significant business-related challenges that your organisation faced in the execution of this initiative?

(% of all respondents)

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 cost- savings 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 (33%) 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.”

Satisfaction levels

5

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

41 33

26 25 21 19 13

7

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%

Keys to success

6

Source: The Economist Intelligence Unit.

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

42 38 34 33 33 27 23 18 9