Accenture Marketing Transformation Customer Analyticsdwainthomas.com/download/i/mark_dl/u/4008426470/4539322627/A… · Accenture Marketing Transformation—Customer Analytics 3 In

Accenture Marketing Transformation

Customer AnalyticsCutting a New Path to Growth and High Performance

Rethink

Customers have more information and more choices than ever. Fortunately, marketers can employ powerful analytical tools to understand and engage customers today and anticipate their priorities tomorrow. The discipline of analytics can serve to identify the segments that matter, uncover useful insights about behavior and point the way to process changes that will truly make a difference in customer loyalty and profitability.

Accenture Marketing Transformation—Customer Analytics 2


In a time of changing consumer values and economic uncertainty, the marketing function has a more strategic role to play in driving growth and high performance. Are marketers ready to step up? Accenture believes so—if they are prepared to use the power of analytics to propel their organizations to the next level of performance.

Staying a Step Ahead

Consider this example: An electric utility developed plans for using two-way digital technology to control appliances at customer sites and save energy, reduce cost and increase reliability. Before investing, company executives realized they would have to improve their analytical capabilities to understand customer priorities better and segment them more effectively. In particular: what were the characteristics of customers who would be most receptive to offerings built around energy-efficiency and demand-side management?

Working with Accenture, the utility mined existing data and conducted additional research. The resulting analysis identified useful segments, such as “green-minded” customers—heavy energy users with an affinity for advice about green solutions.

Using these new, actionable segments, the utility was able to launch a highly targeted marketing campaign for its

existing energy-efficiency offerings. The results were quite favorable: The customer response rate rose by 200 percent, even as marketing costs dropped by 70 percent. Over the longer term, the company will be able to use the segmentation analysis to inform both strategy and marketing as it implements its plan.

Like this utility, companies seeking high performance today contend with a complex customer landscape. Two years of intense economic turmoil have left many people more apprehensive about their future. Even before the recession, customers’ priorities were changing at a rapid pace and in erratic paths. In one situation, people are willing to pay for convenience; in another, they base purchase decisions mainly on price. They’re health-conscious here yet self-indulgent there. They crave simplicity but also want a rich experience.


Moreover, customers in general have become more demanding, better informed and less loyal to certain brands.

Complicating the marketer’s challenge, customer segments have proliferated as well. Some new segments arise from attitudinal shifts: The greening of political debate has driven the creation of new category variations such as hybrid cars and fair-trade coffee. Other segments result from demographic shifts, such as the aging populations of many nations.

The experiences of analytical leaders suggest four principles that have proven the most effective in launching or extending an initiative in customer analytics.

New segments and growth opportunities are appearing as well in emerging economies, where more and more families progress from subsistence poverty to wage-earning status, putting more goods and services

Customers have become more demanding, better informed and less loyal.

within their reach. This dynamic spawns literally dozens of separate segments, because China differs from India and Brazil. Different income pyramids and customer priorities in each region have led to a great heterogeneity of segments. Even within “rural China,” the provinces of Inner Mongolia and Guizhou exhibit quite different dialects, customs and shopping behavior.

As these shifts in customer segments, priorities and behaviors reshape commercial markets, gaining a firm grasp of what customers want and then knowing how to address demand profitably have become essential. The discipline of analytics allows marketers to build strong capabilities in segmentation and customer insight. By analytics, we mean an integrated framework that employs quantitative methods to derive actionable insights from data, then uses those insights to shape business decisions and, ultimately, to improve outcomes.

Deployed effectively, advanced customer analytics allow marketers to play a more integral, strategic role by setting and steering the growth agenda. Powerful analytical tools can help companies create customer loyalty, improve the return on marketing investment and generate new revenue streams.

As managers become more fluent and comfortable with analytics, they can address progressively more sophisticated questions about customers, such as those shown in Figure 1. A path to differentiation among high performers, then, is to move up the analytical curve, so that they can use predictive analyses to gain insights into what a company should do in response to changing customer scenarios. This involves not just a competence with technical tools, but also an organizational focus on adjusting business processes to put the insights to fruitful use.

Figure 1: Moving up the analytical curve

Sophistication of Intelligence

Optimization

Predictive Modeling

Forecasting/extrapolation

Statistical Analysis

Alerts

Query/drill down

Ad hoc Reports

Standard Reports

What is the best that can happen?

What will happen next?

What if these trends continue?

Why is this happening?

What actions are needed?

What exactly is the problem?

How many, how often, where?

What happened?

Competitive Advantage

Predictive Analytics

Descriptive Analytics


As these shifts in customer segments, priorities and behaviors reshape commercial markets, gaining a firm grasp of what customers want and then knowing how to address demand profitably have become essential.

A new basis for competitive advantageIn a multi-polar world—a volatile, interdependent, globalized marketplace where upstart rivals can emerge quickly from any corner—competitiveness at speed remains imperative. But many of the previous bases for competition are no longer viable. Companies offer mostly similar products and use comparable technology. Proprietary technologies can be copied quickly. Physical location matters less when customers use the Internet to search and transact.

What’s left as a basis for competition is to gain a deep understanding of customer priorities by individual segment and to make the smartest business decisions possible about serving those segments. In this new environment, companies with weak

segmenting and customer insight capabilities will struggle. Companies that master customer analytics will find abundant new opportunities for profitable growth and will be able to make wise decisions about investing marketing resources.

Analytics can have a big payoff. Accenture research confirms that high-performance businesses—those that substantially outperform competitors over the long term and across economic, industry and leadership cycles—are five times more likely to use analytics strategically compared with low performers.1 High performers know that technology on its own cannot make a company into an effective analytical competitor. Their managers are also wise to the limits of equating analytics with the collection and storage of data. High-performing companies have embedded strong analytical muscle and made it central to the execution of their strategy.

The value of customer analytics has risen in part because past behavior is no longer necessarily a good predictor of future behavior. Credit scores that tick down when a homeowner loses his job have served in the past as useful predictors of likely backache or depression problems; will they be equally useful now that scores have fallen widely in the wake of high unemployment and stagnant incomes? Business-credit scores as well have become a weak indicator of repayment ability in the eyes of some large lenders. Banks may now have to rely instead on cash flow and collateral to determine the creditworthiness of a small business.2

The challenge of turning data into actionable insights has become exponentially more difficult even as it becomes more essential. The deluge of data promises to increase from new sources such as social-networking websites, mobile devices and built-in


sensors. Retail giant Wal-Mart already handles more than 1 million customer transactions every hour and Facebook, the social-networking site, hosts 40 billion photos.3 According to the research firm IDC, despite the economic downturn, the volume of digital data generated in 2008 increased three percent more than forecasted and is expected to double every 18 months.4 As more people enter the working and middle classes, they become literate, further fueling information growth.

The increasing importance of customer-centric strategies, combined with the advances in the technologies available, has led a new generation of analytical leaders to demand more insights about customers before making final decisions on target segments, price points, product features, service levels and channel partners. In a recent Accenture survey of 400 senior executives responsible for the marketing function at large enterprises, customer analytics

Analyzing Your Analytics Capabilities

Can you identify the gems in •your customer portfolio?

Do you know which are •your highest-value segments? Where future growth will come from?

How do you identify new •potential customers in emerg-ing markets?

As you expand abroad, how •are you managing the prolif-eration of segments?

What skills and tools do you •have in place to generate cus-tomer insights?

What processes are in place •to turn insight into action across the company?

How do you balance cost to •acquire and cost to serve?

How are you defending the •current customer base and extracting the most value from it?

How are you responding to •changing consumer priorities and behaviors?

was cited by 65 percent as critical to their marketing strategy—more than any other capability.5 Many were planning to increase investment over the coming year in such areas as developing customer data management and systems (41 percent); improving customer segmentation and insight (41 percent); and analyzing market and customer trends (38 percent). Tellingly, marketers at companies that grew revenue in the past year were significantly more likely to describe their analytics capability as above average than marketers at companies that lost revenue: 68 vs. 58 percent.

Data and modeling are no substitute for talking with customers and seeing the market for oneself, of course. And a few executives with enough experience, intuition and luck may choose strategies that pay off without the benefit of data and analysis. But most of us should be using facts to improve

decisions. The discipline of analytics augments personal experience and judgment by providing a quantitative structure to qualitative observations.

There’s gold in (some of) those motorcycle riders Companies that are advanced in using analytics have built up richly detailed views of their customers. With multiple touch points feeding in data, algorithms help managers understand how their products and services are used. Behavior such as monthly cell phone usage, payment histories, needs articulated in surveys or focus groups, demographic and income data generated in-house or acquired from outside sources—all sorts of data provide the raw material for segmentation. Suppliers in business-to-business markets also frequently possess, or can construct, rich data on customer


preferences and behavior that can help create segments that are far more useful than “small/medium/large” customers.

Progressive Insurance has put analytics and modeling to profitable use in defining new segments. Mining widely available insurance industry data, Progressive defined narrow groups, or cells, of customers—for example, motorcycle riders age 30 and above, with college educations, credit scores over a certain level and no accidents. For each cell, the company performed a regression analysis to identify the factors that most closely correlate with the losses that group engenders. It then set prices for the cells that would enable the company to earn a profit across a portfolio of customer groups and used simulation software to test the financial implications of those hypotheses. With this approach, Progressive was among the pioneers to profitably insure customers in traditionally high-risk categories.

Beyond the basic statistics, companies can use modeling to predict how they will respond to certain offerings. A mobile phone carrier, for instance, can track games that a customer plays on his handset. From the type of game played, the carrier can learn that certain customers have a fondness for numerical puzzles and will respond favorably to a text message offering a discount voucher for a soon-to-be-released puzzle.

Cupid’s equationWeb-based businesses are fertile ground for capturing customer interaction data and using algorithms to make valuable recommendations to customers. Many companies migrated to the Internet by simply plunking a traditional business model into a website. So there is ample opportunity

to differentiate a web-based business by making customer analytics the foundation of the strategy.

The online dating industry is a case in point. Most dating websites don’t reveal the details of their method or data involved in making a match. In contrast to this opaque approach, OkCupid, founded by four mathematicians, uses statistical tools to analyze how the mating game plays out on its site and publishes the results of its number-crunching on a blog with charts to walk members through the process.

OkCupid members answer questions and the answers are analyzed by several sets of algorithms to calculate the level of compatibility with other users. For example, OkCupid analyzed the first messages sent by users to would-be mates to determine which ones were most likely to get a response. Messages with words like “fascinating” and “cool” had a better success rate than those with “beautiful” or “cutie.”6 These data-baring tactics have helped OkCupid grow the largest membership in online dating.

Applied customer analytics can benefit old-line industries that rely on brick-and-mortar assets as well. Grocery chains collect an enormous amount of transaction data at checkout, yet many grocers still give this data to third parties to buy it back in the form of relatively generic analysis.

Likewise in the aviation industry, every airline has a customer loyalty program, but all offer roughly similar benefits and only some have used the underlying data to create a distinctive offering for certain segments. One or two airlines could break away from the pack with an analytics-based loyalty program, yet currently they are perhaps too preoccupied with fuel costs and mergers to seize this opportunity.

Four principles for reaching the right customers It’s easy to spend time and resources on the wrong data or a tangential issue. To help winnow the options, the experiences of analytical leaders suggest four principles that have proven the most effective in launching or extending an initiative in customer analytics. These are the principles against which marketers and other executives can test their current operations as they take a fresh look at the elusive customer.

1. Analyze what’s most useful and actionable, not just what’s at hand.

All too often, we find that managers use data to justify what they were going to do anyway, or they review only data that’s already assembled because it’s easy to access.

Consumer packaged goods manufacturers, for instance, have long had access to checkout scanner data from third-party vendors. But relying solely on that data and the executive summaries that come with it has yielded few brilliant insights.

A few packaged goods manufacturers have blazed their own analytics trails, taking time to vet the data, augment it where necessary and put it to use in addressing new segments with better offerings. Kraft Foods made an early foray into customer analytics when it collaborated a decade ago with Information Resources, Inc., and Nielsen to understand the food buying behavior of Hispanics in the United States. That led Kraft to create a line of cheeses for Hispanic shoppers, many of whom became loyal to the Kraft brand. And those cheeses gradually found a broader market among consumers who liked to cook different ethnic cuisines.

The experiences of analytical leaders suggest four principles that have proven the most effective in launching or extending an initiative in customer analytics.

Four Principles Underlying Successful Customer Analytics • Analyze what’s most useful and actionable, not just what’s at hand.

• But don’t ignore databases close by if they can be mined with fresh eyes.

• Restructure processes and bring on new people to take full advantage of the new insights.

• Embrace a rapid “test-and-learn” approach.



Netflix owes its success in online video rental in part to the creation and deployment of a recommendation engine based on proprietary software. Cinematch, as the engine is called, analyzes subscribers' choices and feedback on the videos they have seen—more than a billion ratings and reviews of videos they loved and hated—to recommend videos in ways that optimize both the subscriber’s taste and Netflix’s inventory. The company had to build that database from scratch and convince customers to participate, but it was well worth the effort. Netflix is now working to improve Cinematch’s effectiveness for new subscribers for whom Netflix has only sparse data.

Online media and social networks represent huge analytical opportunities as well. Much of this information arrives in new, unstructured formats, but some big companies including eBay and Sprint have been able to tap the data in creative and useful ways.7

EBay, for instance, used online tracking technologies to identify customers who browsed or shopped for clothing products on its site. With the help of the start-up 33Across, eBay then analyzed data from social-networking sites to map out the connections between the customers eBay had identified and other Web surfers, to serve up ads at the right time and place. So if an eBay customer shared a movie review with a friend, 33Across identified that connection and placed a cookie, or anonymous string of tracking data, on the friend’s browser so that he later could be targeted with a relevant ad when visiting certain sites. Sprint tested this approach to promote the launch of its Palm Pre smart phone and quadrupled related online sales.

2. But don’t ignore databases close by if they can be mined with fresh eyes.

Existing streams of data—from warranty claims, complaint letters and emails, call center conversations—often deserve a fresh look to help address customer-related issues. Industrial products or automotive companies, for instance,

could find opportunities to mine the calls that come in for words such as “fire” or “failure.” More organizations are coming to realize that their operational data can be an important asset.

Cablecom, a Swiss telecoms operator, has reduced defections from one-fifth of subscribers a year to fewer than 5 percent by crunching the data it already had. Its software spotted that although customer defections peaked in the thirteenth month, the decision to leave was made much earlier, around the ninth month, as indicated by things such as the number of calls to customer support services. So Cablecom offered certain customers special deals seven months into their subscription and reaped the rewards.8

Returning to the insurance industry, Delta Dental of California realized that by analyzing years of claims data, it could begin to understand patterns of behavior among insured customers and the dentists it pays: Are a particular dentist’s patients developing more problems than others? Are root canals more common in some areas than others? Another health insurer realized that it could identify older insured customers at risk of diabetes from inactivity and now works to head off the disease through a program called Silver Sneakers Steps (run by Healthways, a disease management company), which uses a pedometer to measure daily steps taken.9

3. Restructure processes and bring on new people to take full advantage of the new insights.

Inertia or outright resistance among pockets of the organization may stop a promising analytics initiative before it fulfills its potential or even gets off the ground. Process changes may appear too daunting or require a consensus among too many players. A segmentation exercise may have been commissioned by one functional group and then ignored by others. Or executives may not have delved deeply enough into their current processes to realize that these may be inconsistent with the priorities of new customer segments.

Cultural obstacles need to be addressed head on. A retailer might have a successful loyalty program that generates vast amounts of data that the company uses to tailor promotions to customers. However, if the marketing organization is structured by product categories, each category manager acts in the best interest of his or her category, even if it hurts store performance overall. There is a pool of money to offer customers, but category managers want to use it in their own areas, ignoring overall profitability. The retailer will need to change the organizational structure and incentives in order to fix this fragmented approach.

Differentiated treatments of customer segments will often require different sales tactics, service needs and messaging—an entirely new blueprint for a separate customer experience. Identifying these organizational implications early on is essential for acting successfully on the insights produced by analysis.

One electric utility that aspired to expand sales in energy efficiency products and services undertook a needs segmentation study that showed the variety of motivations among customers, including the desire to conserve energy, save on monthly bills and protect the environment. All well and good, but the utility found itself unable to act immediately on these insights. Its call center did not have interactive voice-response scripting capabilities; direct marketing channels did not have the ability to print different mailers. The utility had to solve these basic process problems before it had a hope of executing a set of energy-efficient offerings to the newly defined segments.

This highlights why analytics must be integrated with everyday business decisions and processes and why it may require hiring people with a more analytical bent. The field should not be relegated to a few pricing and promotions experts or limited to a few isolated applications. Instead, analytics should be used routinely as a natural part of daily work, bringing rigor and discipline to such questions as: How do we predict customer care needs?

Use Customer Analytics to:• Build a foundation for highly-targeted marketing

• Improve the ROI of marketing campaigns

• Improve cross-selling through segmentation schemes that reveal growth potential of specific groups

• Decrease customer churn by isolating loyalty drivers and optimizing retention offerings

• Improve decision making through dashboard reporting that integrates business intelligence

• Anticipate shifts in customer priorities

Keep an Eye Out for:• Expanding masses of customer data, from multiple sources, that can confound attempts to zero in on essential insights

• Varying levels of analytical sophistication within the firm

• Organizational barriers to turning insights into practical actions

• Lack of business intelligence and tools to move from ad hoc to ongoing analysis


“A hit, a very palpable hit” Royal Shakespeare CompanyTheater companies, like any other business, rely on customer loyalty for their long-term success. The Royal Shakespeare Company (RSC) in the United Kingdom, despite its global reputation, realized that it needed to reach a much broader and more diverse audience, while retaining its core, loyal customers. The RSC needed to expand its audience base and encourage more repeat visits by theatre patrons while diversifying the artistic program.

Improving audience analytics was critical to achieving these goals. If the RSC better understood its audience, it could use this information to help plan productions and target its marketing efforts more effectively. By selling more tickets and raising the share of regular patronage, the RSC would achieve a stronger financial return, which would bolster funding appeals to patrons and sponsors.

Gaining a better understanding of its audience posed a daunting challenge for the RSC. Its basic ticketing database offered few insights into the demographics and behavior of its current and potential audience members. So the RSC worked with Accenture to profile the current and potential audience, an effort that served as the foundation for a comprehensive marketing strategy for the theatre’s Stratford-upon-Avon and London audiences.

The project started by examining more than two million records over seven years of box-office transactions, creating an audience database that could be easily and quickly segmented by customer behavior. Drawing on an alliance relationship with Acxiom, Accenture factored in more demographics in the audience

Customer Analytics in Action

Use Customer Analytics to:

• Build a foundation for highly-targeted marketing

• Improve the ROI of marketing campaigns

• Improve cross-selling through segmentation schemes that reveal growth potential of specific groups

• Decrease customer churn by isolating loyalty drivers and optimizing retention offerings

• Improve decision making through dashboard reporting that integrates business intelligence

• Anticipate shifts in customer priorities

Keep an Eye Out for:

• Expanding masses of customer data, from multiple sources, that can confound attempts to zero in on essential insights

• Varying levels of analytical sophistication within the firm

• Organizational barriers to turning insights into practical actions

• Lack of business intelligence and tools to move from ad hoc to ongoing analysis

analyses including income, profession, age of children and lifestyle indicators. This level of data allowed the RSC to understand how the characteristics of its customers differed across the various segments.

Eight different segments of bookers emerged at Stratford-upon-Avon and six segments in London. Four Stratford segments had a significant number of return visits. These segments included “regulars” who attended performances at least four times a year and accounted for 59 percent of the RSC’s revenue, “semi-regulars,” Internet users and those who attended family shows. Customers within these segments became the marketing priority. Among London ticket buyers, the core audience group made up 17 percent of patrons and 45 percent of the financial contribution. In contrast to Stratford-upon-Avon audiences, London patrons’ decisions to attend a show were strongly influenced by casting.

With these new analytical capabilities, the RSC produced a series of targeted mailings and initiatives that have increased the core audience base by 30 percent. Specifically:

• The number of Stratford ticket buyers has increased more than 50 percent to more than 320,000.

• The number of audience members in the Stratford segment defined as regulars, which makes the greatest overall contribution to the RSC, increased by more than 70 percent, from 40,000 to more than 68,000.

• Preseason targeted mailings for London audiences sold out much earlier than previously. This allowed the RSC to more accurately predict its revenues and work toward attracting more diverse audiences.

• The RSC has identified a new and valuable London-based segment known as “newcomers.”

“The audiences to target were so clear cut,” said Mary Butlin, head of market planning for the RSC. “We could tell from the analysis exactly when to communicate with different groups to maximize response. As well as the campaign planning being much faster and more fact-based, it is easier to predict likely response even in London, which is notoriously difficult.”



How do we reequip the sales force? How far should we push customer self-service? How should we monitor customer service phone calls?

4. Embrace a rapid “test-and-learn” approach.

When data is incomplete and a market is evolving quickly, speed may be more important than completeness. An iterative, “test-and-learn” approach to developing initial segment profiles makes the most of existing data. This can work especially well for firms with relatively immature analytical capabilities as a base step for deeper analytical initiatives down the road.

One obvious benefit of a rapid approach is the ability to quickly focus on key issues at minimal cost. For instance, you don’t need to invest in a data enterprise warehouse but can use operational data stores, which are the inputs to the warehouse.

Another benefit is being able to gauge the attractiveness of potential offers before customers have actually bought

One obvious benefit of a rapid approach is the ability to quickly focus on key issues at minimal cost.

them. The science of experimental design enables rapid test and learn from different combinations of dimensions—what channel, message, frequency, pricing, assortment and so on. In contrast to data mining—after-the-fact analysis of customer behavior—experimental design uses mathematical formulas to select and test a subset of combinations of variables that represent the complexity of all the original variables. Marketers can define and control the independent variables before putting them into the marketplace, trying out different kinds of stimuli on customers rather than observing them as they naturally occur.

Putting customer analytics to work takes time, effort and concentrated thinking. Managers will encounter a variety of challenges, starting with data challenges, such as scrubbing or augmenting incomplete, low-quality data. And data from multiple sources will have to be integrated in ways that allow marketers to construct useful customer segments and insights.

Organizational challenges crop up when multiple functions or stakeholders have competing interests, or when multiple efforts to segment customers occur at the same time. Senior management will then need to step in to coordinate these efforts and devise the right segmentation framework.

There may be institutional or regulatory challenges as well, such as firewalls that make it difficult to get a complete view of the customer. Banks in many countries are required by law to have firewalls between checking account and credit card services; as a result, those core-banking executives cannot access useful credit card data for their own customers.

Despite the effort involved, most companies have little choice but to embrace analytics if they want to keep in step with, or a step ahead of, the changing customer landscape. More data and new analytical tools become available every day. Analytically savvy people abound among the younger generations in business and they will


gravitate to the firms that value their work. Customers themselves value offerings that precisely address their needs and desires, and all else equal they seek out providers whose offerings fit like a glove. Whatever the customer issue at hand, analytics can help to solve it.

How Accenture Can HelpWe have continually increased our analytic sophistication and capabilities—from batch reporting in the 1970s, to pioneering information management and performance management solutions in 1992, to the creation of a dedicated information management services organization in 2005.

Today, Accenture works with businesses, government agencies and other public service organizations to develop the predictive capabilities these fast-changing times demand. We offer a full spectrum of services from strategy to execution to outsourcing of key business processes.

The Accenture Customer Relationship Management (CRM) service line helps companies drive growth and achieve high performance by evolving from traditional descriptive customer analytics to predictive analytics. Our CRM professionals have deep skills in segmentation and data management, combined with extensive experience in deploying advanced analytical methodologies to improve revenue, profitability and return on marketing investment.

Accenture also leverages the enterprise, functional and industry capabilities of our Accenture Analytics team. With more than 20,000 professionals with analytics experience, a network of innovation centers, and a powerful alliance with industry leader SAS, Accenture Analytics has the knowledge, scale and global footprint clients need for better decision making and improved business outcomes to accelerate high performance.

Proprietary Accenture Innovations for Customer Analytics

• Accenture Segmentation Methodology: Encompasses nine distinct steps in developing a segmentation model (value, behavior or needs) each of which has unique objectives and deliverables.

• Accenture Global Consumer Segmentation Model: An aggregate model, based on consumer data gathered in 10 countries, that defines 15 strategic cross-region segments that are critical to growth. It provides insight on consumer motives, attitudes and behaviors along key dimensions such as financial well-being, technology, entertainment and health.

• Accenture Customer Analytical Record (CAR): A patented asset consisting of a table that consolidates all data at a customer level to make the data available for modeling and analysis. CAR shortens the model development cycle by as much as 90 percent, increases the quality of the modeling data set and ensures that relevant industry-specific customer data is validated and used.

• Accenture Customer Experience Blueprint and Workbench: A process to create a model governing how, why, when and where interactions should occur with each customer, while balancing priorities such as marketing objectives, customer satisfaction targets and cost-to-serve targets.

• Accenture Customer Lifetime Value Methodology: Predicts product preference and lifetime value as well as the propensity of prospects and customers to churn. Helps to align customer needs and values at each specific life stage and improve the targeting of resources along the life cycle.

• Accenture Web Analytics: Monitors your website and gathers data on customer actions per impression, with the primary objective of generating useful statistics and reports for improving the web experience.

With this powerful combination of experience, insight, distinctive offerings and market-tested solutions, Accenture is prepared to help you compete on analytics—in any industry or geography.

To learn more about how Accenture can help you drive growth and achieve high performance through customer analytics, contact:

Nick Smith CRM service line Marketing Transformation [email protected]

Catherine Zhou CRM service line Marketing Transformation [email protected]

1. Thomas H. Davenport and Jeanne G. Harris. Competing on Analytics: The New Science of Winning Harvard Business Press, 2007, pp 46-47.

2. “When Business Credit Scores Get Murky,” The Wall Street Journal, March 18, 2010.

3. “Data, data everywhere,” The Economist, February 27, 2010.

4. John Gantz and David Reinsel, “As the Economy Contracts the Digital Universe Expands” (IDC Multi-Media Report, May 2009). http://idcdocserv.com/EMC_MMWP_Digital_Universe (accessed May 5, 2010).

5. “Onward and Up: How Marketers Are Refocusing the Front Office for Growth,” Accenture, 2010.

6. Jenna Wortham, “Looking for a Date? A Site Suggests You Check the Data,” The New York Times, February 13, 2010.

7. Emily Steel, “Marketers Watch as Friends Interact Online,” The Wall Street Journal, April 15, 2010.

8. “A different game,” The Economist, February 27, 2010.

9. Thomas H. Davenport, Jeanne G. Harris, and Robert Morison, Analytics at Work, Harvard Business Press, 2010.

Copyright © 2010 Accenture All rights reserved.

Accenture, its logo, and High Performance Delivered are trademarks of Accenture.

About Accenture CRM SolutionsAccenture’s Customer Relationship Management service line helps organizations achieve high performance by transforming their marketing, sales and customer service functions to support accelerated growth, increased profitability and greater operating efficiency. Our research, insight and innovation, global reach and delivery experience have made us a worldwide leader, serving thousands of clients every year, including most FORTUNE® 100 companies, across virtually all industries.

About AccentureAccenture is a global management consulting, technology services and outsourcing company, with more than 190,000 people serving clients in more than 120 countries. Combining unparalleled experience, comprehensive capabilities across all industries and business functions, and extensive research on the world’s most successful companies, Accenture collaborates with clients to help them become high-performance businesses and governments. The company generated net revenues of US$21.58 billion for the fiscal year ended Aug. 31, 2009. Its home page is www.accenture.com.

About Accenture AnalyticsAccenture Analytics delivers the insights that organizations need to make better business decisions, faster. Our extensive capabilities range from accessing and reporting on data to predictive modeling, forecasting and sophisticated statistical analysis. We have more than 20,000 analytics-skilled people with deep functional, industry, business process and technology experience. At the intersection of business and technology, Accenture Analytics enables organizations to achieve the business outcomes that drive high performance. For more information about Accenture Analytics, visit www.accenture.com/analytics.

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Accenture Marketing Transformation Customer Analyticsdwainthomas.com/download/i/mark_dl/u/4008426470/4539322627/A… · Accenture Marketing Transformation—Customer Analytics 3 In