Predictive Analytics: The BI Crystal Ball

Transcription

1 May 2008

2 Page 2 Executive Summary Companies are beginning to reap the benefits of tapping into data about their customers, products, risk management, strategic initiatives, and process efficiencies in order to become more predictive about their businesses. Much of this capability has previously been unavailable or has not been addressed by traditional BI technologies and practices. Best-in-Class companies are not focusing on any one method or technology specifically, but are taking multiple approaches to becoming more predictive and improving performance. These approaches include several areas of the business: Customer management. The need to increase customer satisfaction, retention, up-sell / cross-sell opportunities Risk management. The ability to predict harmful events before they occur, and uncover financial anomalies and fraudulent behavior before it affects performance Process management. The establishment of process analysis measures that allow for the prediction of process changes that will yield new efficiencies and performance improvement Growth management. Understanding the effects of growth on the business before they happen; this includes activities such as workforce planning, supply chain management, and infrastructure planning Product planning. The ability to understand market and customer demand earlier in order to increase speed to market and establish positioning, pricing, and market awareness before the competition Best-in-Class Performance Aberdeen used three key performance criteria to distinguish Best-in-Class companies: Research Benchmark Aberdeen s Research Benchmarks provide an indepth and comprehensive look into process, procedure, methodologies, and technologies with best practice identification and actionable recommendations Return on Marketing Investment (ROMI) during the past 12 months. Best-in-Class companies have achieved an average of 3.5-times ROMI during the past 12 months, compared to 1.4-times ROMI by Industry Average companies, and -0.9-times ROMI by Laggards. Change in the ability to detect and act upon harmful events, before company performance is affected, during the past 12 months. Best-in-Class companies improved their ability to detect risk by an average of 2.5-times during the past 12 months, compared to Industry Average companies that neither improved nor decreased their ability to detect and act upon harmful events, and Laggards that experienced a decrease in this ability by 1.1-times during the past 12 months. Percent change in customer retention rate in the past 12 months. Seventy-six percent (76%) of Best-in-Class respondents have achieved a customer retention rate of 90% or higher during the past 12

3 Page 3 months, compared with 12% of Industry Average companies and just 4% of Laggards. Competitive Maturity Assessment Survey results show that the firms enjoying Best-in-Class performance shared several common characteristics: Best-in-Class companies have improved visibility of predictive analytic measures to management at a mean average rate of 36% increase in the past 12 months, versus 9% among Industry Average companies, and -1% among Laggards. Fifty-nine percent (59%) of Best-in-Class companies receive information for predictive analysis within a business day or less from actual business activity, versus 45% of all other respondents combined. Best-in-Class companies have improved their ability to detect fraud significantly more than all other respondents. Required Actions In addition to the specific recommendations in Chapter Three of this report, to achieve Best-in-Class performance, companies must consider several areas of the business as potentially being addressed by predictive analytics. Best-in-Class companies have identified several areas where they have already started projects (Figure 1). Figure 1: Top Ten Business Areas Addressed by the Best-in-Class 80% 60% 69% 64% 61% 59% 40% 36% 32% 31% 29% 27% 25% 20% 0% Customer Service/Relations Competitive Analysis Sales Analysis Product Marketing Field Marketing Research and Development Corporate Growth Management Human Capital Management Pricing and Currency Change Expense Management

4 Page 4 Table of Contents Executive Summary...2 Best-in-Class Performance...2 Competitive Maturity Assessment...3 Required Actions...3 Chapter One: Benchmarking the Best-in-Class...6 Business Context...6 The Maturity Class Framework...7 The Best-in-Class PACE Model...8 Best-in-Class Strategies...9 Chapter Two: Benchmarking Requirements for Success...11 Competitive Assessment...12 Capabilities and Enablers...13 Chapter Three: Required Actions...21 Laggard Steps to Success...21 Industry Average Steps to Success...21 Best-in-Class Steps to Success...22 Appendix A: Research Methodology...24 Appendix B: Related Aberdeen Research...26 Figures Figure 1: Top Ten Business Areas Addressed by the Best-in-Class...3 Figure 2: Top Three Business Benefits Expected from Predictive Analytics Investment...6 Figure 3: Top Five Business Pressures Driving Predictive Analytic Capability Focus...7 Figure 4: Top Five Best-in-Class Predictive Analytic Strategies...9 Figure 5: Best-in-Class Maturity with Predictive Analytics...10 Figure 6: All Other Respondent s Maturity with Predictive Analytics...10 Figure 7: Best-in-Class Process Capabilities...13 Figure 8: Best-in-Class Organizational Management Capabilities...14 Figure 9: Best-in-Class Knowledge Management Capabilities...15 Figure 10: Internal and External Data Unstructured Data Integration is a Planned Best-in-Class Capability...16 Figure 11: Internal and External Unstructured Data Sources (Currently in Use and Planned)...16 Figure 12: Best-in-Class Performance Management Capabilities...17 Figure 13: Best-in-Class Technology Management Capabilities...18 Figure 14: The Top Best-in-Class Current and Planned Technology Investments for Predictive Analytics...18 Figure 15: Predictive Analytics Solution Selection Criteria...19

5 Page 5 Tables Table 1: Top Performers Earn Best-in-Class Status...8 Table 2: The Best-in-Class PACE Framework...8 Table 3: The Competitive Framework...12 Table 4: The PACE Framework Key...25 Table 5: The Competitive Framework Key...25 Table 6: The Relationship Between PACE and the Competitive Framework...25

6 Page 6 Chapter One: Benchmarking the Best-in-Class Business Context Increasingly, companies are realizing the value of using data and information to align their current actions with their future objectives. Organizations are under pressure to predict the future more accurately than ever before, both in terms of becoming more proactive within shifting market dynamics, and achieving improved performance through a better understanding of customer behaviors and attitudes, assessment of risk, improvement of process efficiencies, and planning for product development, pricing, and market positioning. In April and May of 2008, Aberdeen Group investigated the spectrum of predictive analytic capabilities through a primary survey research program. This study, based on survey responses from over 280 organizations, uncovers the strategies, actions, technology investments, and services that Best-in-Class companies are utilizing to improve performance through gaining predictive knowledge about their business. When asked to identify the top three expected business benefits to be gained from predictive analytics capabilities, respondents generally agreed that a combination of low-hanging opportunities were ripe for the picking (Figure 2). Fast Facts 40% of all respondents expect predictive analytics to yield benefits toward predicting harmful events before they affect the company Yet only 19% of respondents feel that predictive analytics will assist with identifying noncompliant activity and enabling corrective action Figure 2: Top Three Business Benefits Expected from Predictive Analytics Investment 0% 20% 40% 60% Ability to cross-sell / up-sell customers during an interaction vs. waiting for the next interaction 49% 54% Ability to detect sales, marketing and other opportunities as they occur as opposed to 'following the market' 49% 56% Improved ability to detect harmful events before they affect the business 41% 38% Best-in-Class All Others While companies are indeed focusing efforts on achieving improved customer performance (30% of all respondents identified elevate customer satisfaction

7 Page 7 / loyalty as the top business pressure) and are also focused heavily on improving market leadership (29% of all respondents listed evaluate growth strategies as a top pressure) the level of activity directed toward limiting risk does not support the expected gains in this area (Figure 3). Figure 3: Top Five Business Pressures Driving Predictive Analytic Capability Focus 40% 30% 20% 30% 29% 25% 19% 15% 10% 0% Elevate customer Evaluate growth satisfaction/loyalty strategies Find process inefficiencies Identify and act Need to maximize upon harmful allocation of situations before resources performance is affected The improvement of customer interactions and the resulting benefits of achieving gains in customer loyalty and retention are among the top drivers for companies that are currently or planning to invest in predictive analytic capabilities. Interestingly, while 40% of respondents see the improved ability to detect harmful events before they affect the company as a topthree business benefit expected from predictive analytic initiatives (see Figure 2), only 19% view this as a top business pressure to be dealt with. This indicates a potential disconnect between what companies are expecting from investments in predictive capabilities, and the actual business pressures driving the initiatives in the first place. The Maturity Class Framework Aberdeen used three key performance criteria to distinguish Best-in-Class companies: ROMI during the past 12 months. Best-in-Class companies have achieved an average of 3.5-times ROMI during the past 12 months, compared to 1.4-times ROMI by Industry Average companies, and times ROMI by Laggards. "Our growth rates cause the working documents developed to hold up for only the first few months of the year. The issue we generally run into involves major new business being closed that requires new offices, staff, and business systems development. We need to become more adept at predicting this activity in order to be better prepared top respond. ~ Bruce Stone, National Director, Sales & Quality Systems; Cable Tech Inc. Change in the ability to detect and act upon harmful events, before company performance is affected, during the past 12 months. Best-in-Class companies improved their ability to detect risk by an average of 2.5-times during the past 12 months, compared to Industry Average companies that neither improved or decreased their ability to detect and act upon harmful events, and Laggards that experienced a decrease in this ability by 1.1-times during the past 12 months. Percent change in customer retention rate in the past 12 months. Seventy-six percent (76%) of Best-in-Class respondents have

8 Page 8 achieved a customer retention rate of 90% or higher during the past 12 months, compared with 12% of Industry Average companies and just 4% of Laggards. Table 1: Top Performers Earn Best-in-Class Status Definition of Maturity Class Best in Class: Top 20% of aggregate performance scorers Industry Average: Middle 50% of aggregate performance scorers Laggard: Bottom 30% of aggregate performance scorers Mean Class Performance Achieve an average of 3.5-times ROMI during the past 12 months Improved ability to detect risk by an average of 2.5-times during the past 12 months 76% of Best-in-Class respondents have achieved a customer retention rate of 90% or higher during the past 12 months Achieve an average of 1.4-times ROMI during the past 12 months Neither improved or decreased their ability to detect risk during the past 12 months 12% of Industry Average respondents have achieved a customer retention rate of 90% or higher during the past 12 months Suffer an average of -0.9-times ROMI during the past 12 months Decreased ability to detect risk by an average of 1.1-times during the past 12 months 4% of Laggards respondents have achieved a customer retention rate of 90% or higher during the past 12 months The Best-in-Class PACE Model Achieving predictive capabilities requires a combination of strategic actions, organizational capabilities, and enabling technologies. Best-in-Class companies - based on the performance measures defined in Table 1 - have identified the specific approaches they are taking (Table 2). Table 2: The Best-in-Class PACE Framework Pressures Actions Capabilities Enablers Need to elevate customer satisfaction and loyalty Obtain a 360º view of the customer Increase customer crosssell / up-sell opportunities Access to customer behavior data Offers are customized to specific market segments Established KPIs for measurement of customer performance Ability to track and measure KPIs associated with customer performance Ability to apply predictive analysis to existing data Integration of analytic results into existing applications (i.e. customer service next actions ) Data mining technology Management dashboards Data integration and re-fresh of analytic models Business intelligence software suite Web analytics software Enterprise search technologies Real-time reporting and alerting

9 Page 9 Best-in-Class Strategies Best-in-Class companies are focusing on the customer. All of the top-five strategies for gaining improved predictive capabilities identified by Best-in- Class companies have to do with customer-focused information requirements (Figure 4). Figure 4: Top Five Best-in-Class Predictive Analytic Strategies 40% 32% 30% 20% 22% 26% 20% 20% 19% 19% 19% 14% 12% 10% 0% Obtain a 360º view of the customer Increase customer cross-sell / up-sell opportunities Capture information from customer interactions Manage customer interactions across all channels Best-in-Class All Best-in-Class companies are far more likely to be actively seeking a 360º view of the customer. This concept pertains to the ability to gain knowledge about customer interactions, perceptions, and behavior from both internal and external data sources, and integrate it to establish a more complete view of the customer across all aspects of the relationship. Bestin-Class companies seem to have already established a greater ability to capture information from customer interactions than all other respondents, and are now more heavily focusing on managing customer interactions across all channels and integrating data relating to customers, processes, and human resources. These two strategies indicate that a Best-in-Class approach includes an understanding of how company processes, and the people who manage them, impact the overall ability to become more predictive. Aberdeen Insights Strategy Predictive analytic capabilities are still in their infancy compared to other technologies within the BI realm (data warehousing, reporting / query, dashboarding, etc.). While the length of time that most companies have endeavored to gain predictive capabilities has not been long, Best-in-Class companies are more likely to have started their journey at least three years ago, in comparison to all other respondents (Figure 5 and Figure 6). continued Integrate data relating to customers, processes, and human resources

10 Page 10 Aberdeen Insights Strategy Figure 5: Best-in-Class Maturity with Predictive Analytics Best-in-Class Maturity not existing/no plans, 20% Budgeted/plann ed for next 12 months, 20% 1 Year or less, 17% More than 3 years, 36% 1 to 3 years, 26% Figure 6: All Other Respondent s Maturity with Predictive Analytics All Others Maturity not existing/no plans, 27% Budgeted/plann ed for next 12 months, 20% 1 Year or less, 17% More than 3 years, 22% 1 to 3 years, 30%

11 Page 11 Chapter Two: Benchmarking Requirements for Success Taking a predictive approach to understanding customer behavior, evaluating market opportunities, managing risk, and uncovering process efficiencies requires a combination of capabilities and mastery of technologies and techniques that have been introduced to the market only within the past decade. Yet many of these capabilities and tools are based on technologies that have been in use for much longer periods of time. The following case study describes the blending of capabilities that a mid-tier online retailer has successfully accomplished to improve company performance through the combination of predicting customer behavior and market opportunities. Case in Point On-line Retailer Outperforms Competition Through Predictive Analytic Capabilities At the beginning of 2004, this on-line retailer of consumer electronics and specialty products was struggling to manage marketing spend and determine which activities and product package offers were driving profitability, and which were bleeding the organization of its diminishing marketing budget. New campaigns were being launched on a daily basis, and during the holiday season, the frequency increased to multiple campaigns per day. The company's Director of Database Marketing began to search for ways to obtain more frequent updates on campaign performance in order to improve predictability and efficiency of resources spent on marketing efforts. "It's not so much about the amount of money we were spending on campaigns. Web-based marketing can actually be fairly inexpensive in terms of out-of-pocket cost. The real issue was the amount of time and human resources we were expending. Without changing our behavior, we were not going to be able to improve performance. Without access to real-time data about campaign performance, our behavior was not going to change," said the company's Director of Database Marketing. That is when the marketing team began to search for real-time campaign management capabilities. What they found was a predictive analytics solution that incorporated a combination of capabilities that the organization identified as being critical for success, including: Access to campaign response information within the same day (or less) from site visits Aggregated view of site activity sorted by campaign Sell-through data for each campaign by product and selling price Automated price / offer adjustment based on incremental step approach and on-hand inventory levels continued "Our most important advantage is the ability to have immediate access to sales and web activity information, and relate it back to the on-line promotional campaigns that are launched on a daily basis. When I came onboard, we were not able to see results and determine any change in our course of action for days or sometimes weeks after a promotional campaign was launched. Now, we can see the results within minutes." ~ Director of Database Marketing, Mid-tier On-line Retail Company

12 Page 12 Case in Point On-line Retailer Outperforms Competition Through Predictive Analytic Capabilities Since implementing the solution, and developing the business rule calculations that drive automated actions, the company has seen positive results. We have grown rapidly over the past few years, and have surpassed our competition in net new sales. Our most important advantage is the ability to have immediate access to sales and web activity information, and relate it back to the on-line promotional campaigns. When I came onboard, we were not able to see results and determine any change in our course of action for days or sometimes weeks after a promotional campaign was launched. Now, we can see the results within minutes. This has allowed us to automate some of the changes we can make. Visitors to our site experience these actions as they navigate to our site. This allows us to analyze performance on a much tighter timeline, and optimize promotions as the campaign draws response. Competitive Assessment Aberdeen Group analyzed the aggregated metrics of surveyed companies to determine whether their performance ranked as Best-in-Class, Industry Average, or Laggard. In addition to having common performance levels, each class also shared characteristics in five key categories: (1) process (the approaches they take to execute their daily operations); (2) organization (corporate focus and collaboration among stakeholders); (3) knowledge management (contextualizing data and exposing it to key stakeholders); (4) technology (the selection of appropriate tools and effective deployment of those tools); and (5) performance management (the ability of the organization to measure their results to improve their business). These characteristics (identified in Table 3) serve as a guideline for best practices, and correlate directly with Best-in-Class performance across the key metrics. Table 3: The Competitive Framework Process Organization Knowledge Best-in-Class Average Laggards A regular method for updating predictive KPIs based on business changes 21% 11% 4% Integration of analytic results into existing applications (i.e. customer service next actions ) 29% 13% 7% Formal committee to define predictive KPIs 26% 15% 11% Access to customer behavior data 57% 46% 35% Provide offers customized to specific market segments 49% 36% 33% Fast Facts 64% of respondents deem domain expertise (industry or job function) as having high or critical importance when it comes to selecting a predictive analytics vendor 27% of respondents that have adopted predictive analytic technology have deployed it as an embedded capability within an existing BI software suite

13 Page 13 Technology Performance Capabilities and Enablers Best-in-Class Average Laggards Data mining technology 56% 37% 32% Management dashboards 49% 40% 38% Data integration and re-fresh of analytic models 42% 28% 24% Business Intelligence software suite 36% 31% 27% Establish KPIs for measurement of customer performance 48% 31% 29% Based on the findings of the Competitive Framework and interviews with end users, Aberdeen s analysis of the Best-in-Class demonstrates that successful predictive analytic initiatives depend on a combination of specific capabilities and technology enablers. Aberdeen's research has identified several capabilities that Best-in-Class companies share in order to achieve their predictive analytic goals. Process Aberdeen research has found that, as business dynamics change, so too must the KPIs that are used to measure performance. Best-in-Class companies are almost twice as likely as Industry Average companies and over five-times as likely as Laggards to employ a method for continually updating KPIs related to predictive measures (Figure 7). Figure 7: Best-in-Class Process Capabilities 30% 20% Best-in-Class Average Laggard 21% "We engage in periodic meetings with managers on their departments, usually quarterly, and they specify changes up or down in their departments and the budget is changed accordingly. The reason we make the changes is that we have covenants that we need to maintain with our lenders and we want to make sure we stay within those covenants. Having the latest information available is essential to predicting these movements." ~ David Conradt, Controller, Wyckoff Farms 10% 11% 4% 0% A regular method for updating predictive KPIs based on business changes The process of updating KPIs on a continual basis is extremely important when it comes to predictive analytics. The ability to become more predictive depends on an iterative process where each cycle of data input

14 Page 14 provides additional information that then allows for more accurate predictive ability. KPIs that are established at the outset may become obsolete very quickly. For example, an on-line retail company that is presenting a new product line on its web site may set KPIs initially based on past performance of similar products. However, after the first few days of activity, results may indicate a new method for presentation or a new offer type to be introduced. The KPIs used to measure performance, therefore, may need to be altered to reflect the new paradigm. Organization In addition to having a process for updating KPIs related to predictive analysis, Best-in-Class companies are also formalizing this process within the construct of a committee tasked with defining the KPIs (Figure 8). Figure 8: Best-in-Class Organizational Management Capabilities 30% Best-in-Class Average Laggard 26% 20% 10% 15% 11% 0% Formal committee to define predictive KPIs A formal committee enables an organization to develop specialized skill sets when it comes to predictive analytics. Often, the KPIs associated with predictive analytics involve complex mathematics and formulas that involve not only the ability to calculate results based on predicted performance, but also the ability to interpret unstructured information, such as customer sentiment collected from text, blogs, customer service notes fields, and other non-traditional data sources. Establishing quantitative measures based on these types of data sources is enhanced by the existence of a formal committee dedicated to understanding this information, and applying the necessary business rules and metrics to create appropriate KPIs. Knowledge Management In order to establish a set of predictive analysis KPIs, access to customer behavior data must be established. This leads to the end-game the ability to segment customers and provide offers that are appropriate for each segment based on predictive analysis. Best-in-Class companies are significantly more likely to have established both of these capabilities (Figure 9).

15 Page 15 Figure 9: Best-in-Class Knowledge Management Capabilities 60% 50% 57% 46% 49% 40% 36% 35% 33% 30% 20% 10% 0% Access to customer behavior data Provide offers customized to specific market segments Best-in-Class Average Laggard Customer behavior data comes in many flavors and is captured and accessed in many ways. Companies are starting to realize the potential for external and unstructured data (both internal and external) as sources for gaining an enhanced understanding of customer behavior. While traditional sources of information about customers (CRM systems, GL transactions, sales force automation systems, and customer service records) have made customer interactions visible to marketing and sales organizations, the ability to gain an understanding of customer sentiment and reaction to product and service delivery has been difficult and conjectural at best. Aberdeen research has found that Best-in-Class companies are far more likely to be currently accessing or planning to integrate unstructured data (from both internal and external applications and sources) to gain a new and deeper understanding of customer attitudes and behaviors in order to become more predictive about customer actions in the future. Among Bestin-Class respondents that are utilizing unstructured data (45%), the data sources currently being tapped, or planned for future access are numerous (Figure 10 and Figure 11).

16 Page 16 Figure 10: Internal and External Data Unstructured Data Integration is a Planned Best-in-Class Capability 100% 40% 54% 55% 75% 50% 60% 46% 45% 25% Internal Structured Data Sources (i.e. ERP, CRM, Financial Apps, etc) Internal Unstructured Data Sources (i.e. , Service Notes, Call Transcripts, etc) External Data Sources (web pages, blogs, wikis, RSS feeds, etc) 0% Current Planned Figure 11: Internal and External Unstructured Data Sources (Currently in Use and Planned) 0% 25% 50% 75% 100% Call Center narrative RSS Feeds Wikis Blogs Field service notes Customer Service system notes Instant Message text 27% 33% 35% 37% 45% 46% 48% 73% 67% 65% 63% 55% 54% 52% text Web pages 68% 71% Current Planned 32% 29% Performance Management As stated in Chapter One, the top business pressure driving companies to become more predictive is the desire to elevate customer satisfaction and loyalty. Best-in-Class organizations are not only establishing predictive KPIs to measure the company s predictive capability improvement over time, but are also engaging in performance management around customer KPIs (Figure 12).

17 Page 17 Figure 12: Best-in-Class Performance Management Capabilities 60% 50% 40% 30% 20% 10% 0% 48% 31% 38% 29% 27% 28% Establish KPIs for measurement of customer performance Ability to track and measure KPIs associated with customer performance Best-in-Class Average Laggard "We re not too sophisticated in terms of the technology applied to developing and tracking our KPIs. We use Excel spreadsheets on a shared drive to collect and deliver KPI information. There are positives and negatives associated with this, but the main benefit is that people are held accountable for their own KPI data." ~ Mary Kay Gilbert, Vice President, Operations, Compbenefits, Inc. It is not enough to merely establish the KPIs necessary to measure customer performance (i.e. customer satisfaction, retention, return visits, number of complaints, and resolutions of complaints). Best-in-Class companies are also more likely to establish a method for continually tracking and monitoring these KPIs. Conversations with respondents revealed that this often involves business intelligence practices and technology tools such as dashboards, scorecards, and automated reporting. The effectiveness of KPI management is dependent upon the visibility to line-of-business management of the measures, and the ability to alert staff when performance is not meeting company objectives. (For more information, see Aberdeen Group s September 2007 report, Smart Decisions: The Role of Key Performance Indicators). Technology Predictive analytic capabilities and technology enablers allow organizations to combine a variety of data elements in order to gain a clearer picture of events that affect the organization, and possible changes in actions that will yield improved results. While this is a powerful capability in and of itself, it is made significantly more potent when the results can be incorporated back into existing end-user applications. Best-in-Class companies are more than twice as likely as Industry Average companies, and over four-times as likely as Laggards to take this step, allowing end-users to take action based on predictive analysis results (Figure 13).

18 Page 18 Figure 13: Best-in-Class Technology Management Capabilities 40% Best-in-Class Average Laggard 30% 29% 20% 13% 10% 7% 0% Integration of analytic results into existing applications (i.e. customer service 'next actions') The technology tools and solutions that Best-in-Class companies are utilizing to gain the capabilities are numerous (Figure 14). Figure 14: The Top Best-in-Class Current and Planned Technology Investments for Predictive Analytics 0% 25% 50% 75% 100% Data mining technology 56% 23% 14% Data integration and re-fresh of analytic models 42% 25% 22% Web analytics software 36% 19% 24% Enterprise search technologies 29% 19% 34% Complex event processing (CEP) software Web ontology language technology 9% 10% 12% 14% 31% 22% Current Planned-12 months Future Plans Predictive analytic capability involves several technology disciplines that span the entire range of information management and data flow within an organization, including: Data management technologies that allow for the integration of disparate data from multiple internal and external sources The building of complex data models that incorporate business rules

19 Page 19 Complex event processing in order to assess the full impact of changes to data Web ontology capabilities that allow data collected from various unstructured web-based sources to be understood in the context of the business Predictive algorithms that combine complex sets of data to produce measures and indicators Search and semantic language capabilities that allow non-technical endusers to construct queries based on the language of the business Survey respondents have clearly prioritized the selection criteria they deem as critical when choosing a solution provider for predictive analytics. At the top of the list is ease of use for non-technical end-users. This indicates that the use of predictive analytics is moving out of the high-powered database analyst s realm, and into the hands of line-of-business and business analyst roles. Compatibility with existing systems and integration of data from multiple applications is also rated highly, as it is critical that decisions are based on predictive analysis of as much information as possible. Associated costs, both direct and indirect, do not rate as highly, nor does scalability, as the use of predictive analytics within the organization is still reserved to relatively few people (Figure 15). Figure 15: Predictive Analytics Solution Selection Criteria 0% 20% 40% 60% Ease of use for non-technical end-users Integration with other applications Compatibility with existing IT infrastructure Implementation consulting costs Ongoing support costs Software license cost Ease of use for developers/admin Scalability to more data sources Scalability to more end users Project implementation timeframe 45% 41% 40% 38% 37% 36% 31% 31% 30% 27%

20 Page 20 Aberdeen Insights Technology Understanding the customer, and applying that knowledge to marketing, sales, and customer service activity is not a new concept, but the technologies available today to perform these tasks within a predictive environment are increasingly effective. This is evidenced by the current and planned investments that Best-in-Class companies are making, and the performance results they have achieved. Predictive analytics represents the opportunity to gain knowledge of marketing performance as it unfolds, and the ability to make adjustments and changes to activity earlier in the process. This is a departure from the traditional "let's see what happened" approach to historical performance data analysis. The underlying technologies involved are complex and often involve the rapid movement and manipulation of massive amounts of data. Careful consideration of technology infrastructure and processing power will become part of most predictive analysis scenarios.

21 Page 21 Chapter Three: Required Actions Whether a company is trying to move its predictive analytic performance from Laggard to Industry Average, or Industry Average to Best-in-Class, the following actions will help spur the necessary performance improvements: Laggard Steps to Success Define predictive analytic KPIs. Only 11% of Laggard companies have any predictive KPIs established. KPIs can include measures such as: o Number of customer incidents resolved in xx-time o Number of adverse events discovered o Number of out-of-stock items likely within next xx-time o Percent increase / decrease in web site visitors expected o Incremental process step completion improvement timeframes There are many other potential predictive KPIs, and the nature of the measures is typically unique to the organization. The important thing is that the key measures be defined. This leads to the next step Establish a formal method for updating the KPIs based on business changes. No KPI remains a valuable measure forever. Business dynamics are changing rapidly, particularly in industries where a heavy flow of transactions with customers is the norm. To become more predictive about customer behavior and elevate customer loyalty and satisfaction, KPIs must be continually reviewed to ensure applicability to current customer demand and incidents. Only 4% of Laggard companies have established this capability today, versus 21% of Best-in-Class companies. Establish performance KPIs that are related to the predictive KPIs. It is not enough to merely establish and update predictive KPIs. The predictive measures must inevitably be directly related to the overall performance effect on the business. For example, the number of adverse events discovered must relate to actions that will be taken to mitigate the adverse events (i.e. patient drug interactions within a hospital environment measurement of the hospital s outcome performance must be tied to the reduction in adverse drug events). Only 29% of Laggard companies currently take this step, compared to 48% of Best-in-Class companies. Fast Facts 81% of Best-in-Class respondents include financial transactions as part of their customer predictive analytics data, compared to 74% of Industry Average companies 46% of Best-in-Class companies integrate competitive data (such as pricing and offers) into their customer predictive analytics data, compared to 34% of Industry Average companies Industry Average Steps to Success Integrate predictive analytic results into existing business applications. While the act of becoming more aware of predictive

22 Page 22 analysis is important, it remains an indirect benefit if the results are not incorporated into outward-facing business applications. Best-in-Class companies are more than twice as likely to perform this step in comparison to Industry Average companies. For example, if data about expected out-of-stock inventory is not integrated with the inventory management system, the ability to take action on the new information is hampered by the creation of a new process outside of the existing system. Company performance is enhanced when data resulting from predictive analytics can be seamlessly integrated and even more so when it can be used to trigger actions within the system. Establish customer performance metrics. Less than one-third of Industry Average respondents have established customer performance metrics. Typical simple metrics include: o Customer satisfaction score o Customer issue resolution speed / accuracy o Customer profitability o Customer cross-sell / up-sell occurrences and value While there are many other customer metrics, there may only be a few that are critical to measuring performance improvement. Start with customer metrics that can be measured with available data, and expand to additional metrics as more data becomes available from external and unstructured data sources. Inevitably, this will lead to the ability to gain a 360-degree view of the customer. Advanced metrics include measures such as: o Customer sentiment rating (based on customer statements captured from web pages, blogs, bodies, notes fields, etc.) o Customer attitude rating (based on customer feedback collected from call center dialog text analytics and voice inflection measures) Best-in-Class Steps to Success Investigate technology enablers that allow for non-technical end-users to access predictive information. New technologies are emerging that provide a semantic layer to complex predictive data models. The ability to search for information using natural language, or accepted business terms enables non-technical end-users to access previously untapped data sources, such as customer attitude data gleaned from the bodies of or other documents. Semantic capabilities allow the often complex and arcane query languages to be made transparent for the business end-user. This can also often involve the use of text analytics in combination with enterprise search capability

23 Page 23 to yield a holistic view of the customer s interactions with the business, and externally with peers and other social networking activity. Extend the data sources available for analysis. While Best-in- Class companies are already more likely to tap into uncharted data sources (such as RSS feeds, wikis, and blogs) a majority of companies approximately two-thirds are still not taking advantage of new sources for gaining a 360-degree view of the customer. In order to alleviate the top business pressure elevating customer satisfaction and loyalty companies must continually strive to capture customer sentiment and respond to customer demands that may not always be expressed in typical customer interactions. Aberdeen Insights Summary Mastery of predictive analysis is an elusive goal fraught with a complex mix of organizational capabilities and technology management and utilization. Best-in-Class companies have started to invest in building the capabilities necessary to become more predictive in several areas of the business. The emphasis of efforts so far has been on customer-focused activity. Interviews with respondents revealed that customer interactions are an area that companies feel they can gain the most benefit with the least amount of change to their existing business processes. Still, many process oriented environments are also being addressed with predictive analysis, particularly manufacturing and supply chain environments where small tweaks to processes can yield major cost and resource savings over time. Finally, risk identification and mitigation is also high on the lists of respondents expected areas for improvement, yet the level of activity and investment toward meeting these goals does not seem to be at the same level as customer and process focuses. This represents an opportunity for organizations to further investigate the applicability to predictive capability in order to identify and take action to reduce harmful events before they affect the business.

24 Page 24 Appendix A: Research Methodology Between April and May 2008, Aberdeen examined the experiences, intentions, and performance of 251 enterprises involved in predictive analytics initiatives in a diverse set of enterprises. Aberdeen supplemented this online survey effort with interviews with select survey respondents, gathering additional information on strategies, experiences, and results. Responding enterprises included the following: Job title / function: Sales, marketing and customer service management, and staff made up the largest portion of respondents at 33%. Other job roles included IT manager or staff (27%); senior management (19%); business process management (6%); finance management (5%); logistics / supply chain / manufacturing (4%); and other (6%). Industry: The research sample included respondents from several industries. Manufacturing was the highest represented industry with 17% of response, followed by heavy industry / utilities (12%); finance / banking / insurance (11%); high technology (13%); retail / wholesale (12%); healthcare / pharmaceuticals (4%); public sector / education (6%); consumer goods (6%); telecommunications (8%); publishing / media (5%); and transportation / travel / hospitality (7%). Geography: The majority of respondents (61%) were from North America. Remaining respondents were from EMEA (23%), the Asia- Pacific region (11%), and the rest of world (5%). Company size: Forty-eight percent (48%) of respondents were from small enterprises (annual revenues below US $50 million); 31% were from midsize enterprises (annual revenues between $50 million and $1 billion); and 21% of respondents were from large businesses (annual revenues of US $1 billion or more). Headcount: Thirty-three percent (33%) of respondents were from small enterprises (headcount between 1 and 100 employees); 30% were from midsize enterprises (headcount between 101 and 1,000 employees); and 37% of respondents were from large businesses (headcount greater than 1,000 employees). Solution providers recognized as sponsors were solicited after the fact and had no substantive influence on the direction of this report. Their sponsorship has made it possible for Aberdeen Group to make these findings available to readers at no charge. Study Focus Responding executives completed an online survey that included questions designed to determine the following: The business drivers causing companies to focus resources on predictive analytics The actions and capabilities that organizations are developing in order to improve predictive capability Current and planned use of various technologies, data sources, and services and the degree to which each assists users in achieving performance improvement The study aimed to identify emerging best practices for predictive analysis capability, and to provide a framework by which readers could assess their own management capabilities.

25 Page 25 Table 4: The PACE Framework Key Overview Aberdeen applies a methodology to benchmark research that evaluates the business pressures, actions, capabilities, and enablers (PACE) that indicate corporate behavior in specific business processes. These terms are defined as follows: Pressures external forces that impact an organization s market position, competitiveness, or business operations (e.g., economic, political and regulatory, technology, changing customer preferences, competitive) Actions the strategic approaches that an organization takes in response to industry pressures (e.g., align the corporate business model to leverage industry opportunities, such as product / service strategy, target markets, financial strategy, go-to-market, and sales strategy) Capabilities the business process competencies required to execute corporate strategy (e.g., skilled people, brand, market positioning, viable products / services, ecosystem partners, financing) Enablers the key functionality of technology solutions required to support the organization s enabling business practices (e.g., development platform, applications, network connectivity, user interface, training and support, partner interfaces, data cleansing, and management) Table 5: The Competitive Framework Key Overview The Aberdeen Competitive Framework defines enterprises as falling into one of the following three levels of practices and performance: Best-in-Class (20%) Practices that are the best currently being employed and are significantly superior to the Industry Average, and result in the top industry performance. Industry Average (50%) Practices that represent the average or norm, and result in average industry performance. Laggards (30%) Practices that are significantly behind the average of the industry, and result in below average performance. In the following categories: Process What is the scope of process standardization? What is the efficiency and effectiveness of this process? Organization How is your company currently organized to manage and optimize this particular process? Knowledge What visibility do you have into key data and intelligence required to manage this process? Technology What level of automation have you used to support this process? How is this automation integrated and aligned? Performance What do you measure? How frequently? What s your actual performance? Table 6: The Relationship Between PACE and the Competitive Framework PACE and the Competitive Framework How They Interact Aberdeen research indicates that companies that identify the most influential pressures and take the most transformational and effective actions are most likely to achieve superior performance. The level of competitive performance that a company achieves is strongly determined by the PACE choices that they make and how well they execute those decisions.

26 Page 26 Appendix B: Related Aberdeen Research Related Aberdeen research that forms a companion or reference to this report includes: Data Management 2.0: Making Sense of Unstructured Data July 2007 Delivering Actionable Information to the Enterprise: Does On-Demand Solve the Skill Set Shortage? July 2007 On-Demand BI: Not Just for SMB August 2007 Serving the Underserved: Is On-Demand BI the Answer? August 2007 Enterprise BI: Comparing the BI Giants September 2007 Smart Decisions: The Role of Key Performance Indicators September, 2007 Measuring Marketing Performance: The BI Roadmap to Information Nirvana October 2007 Operational BI: Getting Real-Time About Performance December 2007 The Expansion and Contraction of Business Intelligence January 2008 Managing the TCO of Business Intelligence February 2008 Data Management for Business Intelligence March 2008 Business Intelligence Deployment Strategies April 2008 Financial Planning and Budgeting April 2008 Information on these and any other Aberdeen publications can be found at Author: David Hatch, Research Director, Business Intelligence, david.hatch@aberdeen.com Since 1988, Aberdeen's research has been helping corporations worldwide become Best-in-Class. Having benchmarked the performance of more than 644,000 companies, Aberdeen is uniquely positioned to provide organizations with the facts that matter the facts that enable companies to get ahead and drive results. That's why our research is relied on by more than 2.2 million readers in over 40 countries, 90% of the Fortune 1,000, and 93% of the Technology 500. As a Harte-Hanks Company, Aberdeen plays a key role of putting content in context for the global direct and targeted marketing company. Aberdeen's analytical and independent view of the "customer optimization" process of Harte- Hanks (Information Opportunity Insight Engagement Interaction) extends the client value and accentuates the strategic role Harte-Hanks brings to the market. For additional information, visit Aberdeen or call (617) , or to learn more about Harte-Hanks, call (800) or go to This document is the result of primary research performed by Aberdeen Group. Aberdeen Group's methodologies provide for objective fact-based research and represent the best analysis available at the time of publication. Unless otherwise noted, the entire contents of this publication are copyrighted by Aberdeen Group, Inc. and may not be reproduced, distributed, archived, or transmitted in any form or by any means without prior written consent by Aberdeen Group, Inc.