Increasing Retail Productivity


2008 to determine the opportunities and pitfalls of an enterprise-wide BI strategy in retail. ..... Table 1 provides a framework with which companies ...

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Increasing Retail Productivity: Enterprise-Wide Business Intelligence November 2008

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Executive Summary Aberdeen surveyed 150 retail enterprises between October and November 2008 to determine the opportunities and pitfalls of an enterprise-wide BI strategy in retail. Our data indicates that while retailers are currently using BI in silo formats within transactional data environments such as customer management, merchandising, and store operations, the roadmap for BI is gradually leading towards enterprise-wide adoption. Enterprise-wide BI has emerged as a top-two priority in terms of planned deployment.

Best-in-Class Performance

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

Aberdeen used three key performance criteria to distinguish Best-in-Class companies: •

Average increase in return on net assets(year-over-year): 22.5%



Average increase in customer retention (aggregated yearly for all stores): 24.43%



Average gross margin increase (year-over-year): 12.8%

Competitive Maturity Assessment Survey results show that the firms enjoying Best-in-Class performance shared several common characteristics: •

Seventy-four percent (74%) of Best-in-Class companies are creating enterprise-wide BI guidelines when compared to 50% of all other retailers



Best-in-Class retailers are twice as likely as Laggards to collect, analyze, and integrate transactional intelligence from customer data on a nearreal or real-time basis



Only 35% of Best-in-Class retailers report having "end-to-end" BI platforms encompassing a full range of capabilities, from collection, cleansing and integration of transactional data, data warehousing, modeling and application development, to reporting, dashboards, scorecarding and ad hoc analytics

Required Actions

"We have deployed an enterprise-wide strategy. In the first 18 weeks of deployment, sales increased by more than $8 million, 20% of which was attributed to the new data warehouse. Virgin was able to pay back the initial BI investment in the first 15 weeks. The BI tools are ingrained into the fiber of all day to day store operations, making Virgin stores more efficient, productive, and profitable." ~ Robert Fort, VP-Technology & CIO, Virgin Megastores

In addition to the specific recommendations in Chapter Three of this report, to achieve Best-in-Class performance, companies must: •

Map enterprise, store, and channel-productivity with BI needs



Continue to build towards "end-to-end" BI



Identify specific performance metrics for measuring enterprise-wide success

© 2008 Aberdeen Group. www.aberdeen.com

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Table of Contents Executive Summary....................................................................................................... 2 Best-in-Class Performance..................................................................................... 2 Competitive Maturity Assessment....................................................................... 2 Required Actions...................................................................................................... 2 Chapter One: Benchmarking the Best-in-Class ..................................................... 4 Business Context ..................................................................................................... 4 The Maturity Class Framework............................................................................ 6 The Best-in-Class PACE Model ............................................................................ 7 The Changing Nature of BI Strategy ................................................................... 7 Chapter Two: Benchmarking Requirements for Success ..................................11 Competitive Assessment......................................................................................12 Capabilities and Enablers......................................................................................14 Chapter Three: Required Actions .........................................................................21 Laggard Steps to Success......................................................................................21 Industry Average Steps to Success ....................................................................22 Best-in-Class Steps to Success ............................................................................22 Appendix A: Research Methodology.....................................................................26 Appendix B: Related Aberdeen Research............................................................28 Featured Underwriters ...................................Error! Bookmark not defined.

Figures Figure 1: Business Intelligence Use and Maturity in Retail .................................. 4 Figure 2: Pressure to Address the Changing Customer Trends........................ 6 Figure 3: Best-in-Class BI Strategy Pivot's Enterprise-Wide Guidelines .......... 8 Figure 4: BI Impacting Retail Performance ............................................................10 Figure 5: Best-in-Class Process Management Capabilities.................................14 Figure 6: Production of Reports Based on Job Role...........................................15 Figure 7: Best-in-Class Knowledge Management Capabilities ..........................16 Figure 8: Best-in-Class Performance Management Capabilities .......................17 Figure 9: Top Five Best-in-Class Technology Investments................................18 Figure 10: Planned BI Technology Adoption - All Retailers..............................19

Tables Table 1: Top Performers Earn Best-in-Class Status.............................................. 6 Table 2: The Best-in-Class PACE Framework ....................................................... 7 Table 3: The Competitive Framework...................................................................13 Table 4: Tier Retail Enterprise-Wide BI ROI Components and Deployment Needs.............................................................................................................................20 Table 4: The PACE Framework Key ......................................................................27 Table 5: The Competitive Framework Key ..........................................................27 © 2008 Aberdeen Group. www.aberdeen.com

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Chapter One: Benchmarking the Best-in-Class Business Context Aberdeen's January 2008 benchmark report Business Intelligence in Retail: A Best-in-Class Roadmap for Performance Improvement, revealed the difficulties related to data integration (i.e. transactional, shipping, inventory), cleanliness, and alignment of business processes to the metrics that drive effective planning and decision-making at the corporate network, stores, and other channels. This report focuses on the ability of retailers to tap into enterprise-wide Business Intelligence (BI) (definition in appendix section), to improve the 360-degree view of the customer, optimize financial performance, and remove enterprise-wide wasteful spending in the current recessionary global economy. Aberdeen will detail the ways by which enterprise-wide BI tools can be used by retail companies to improve customer knowledge, gain visibility across the enterprise, and understand drivers for financial performance.

Fast Facts √ Seventy-four percent (74%) of Best-in-Class companies are creating enterprise-wide BI guidelines compared to 50% of all other retailers √ Eighty percent (80%) of retailers do not possess the ability to provide their user organization with real-time or near-real time business information that relates to customer demand or key customer metrics

Retailers Plan for Enterprise-Wide BI Aberdeen surveyed 150 retail enterprises between October and November 2008 to determine the opportunities and pitfalls of an enterprise-wide BI strategy in retail. Our data indicates that while retailers are currently using BI in silo formats within transactional data environments such as customer management, merchandising, and store operations, the roadmap for BI is gradually leading towards enterprise-wide adoption. Of the BI functional areas in retail, enterprise-wide BI has emerged as a top two priority in terms of planned deployment (Figure 1). Figure 1: Business Intelligence Use and Maturity in Retail Customer Management

30%

Store Operations

23%

Supply Chain

22%

18%

14%

19%

24%

11%

17%

In Place for > 2 years

13%

12%

In Place for < 2 years

14%

Budgeted Enterprise-Wide

23%

14%

15%

21% Planned but not budgeted

Merchandising

28%

0%

20%

14%

40%

60%

12%

80%

12%

100%

Source: Aberdeen Group, November 2008

Leading retailers such as Wal-Mart, Staples, and Best Buy have already adopted an enterprise-wide BI strategy. The reasons for this "silo to big © 2008 Aberdeen Group. www.aberdeen.com

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picture" shift in the BI roadmap are due to the following internal and external factors characterizing the nature of retail operations for the rest of 2008, 2009, and beyond: •

The high cost of goods sold and low consumer confidence is currently impacting 80% of retailers surveyed. This has led to the need for incisive category, department, and channel-level business information that can act as a catalyst for improved integrated retail planning, productivity, and performance for the corporate network, stores, and other sales channels.



Retailers need to monitor and counter promotions, pricing, and new product offers from competitors in the stand-alone, mall-based, online, and catalog environments. Hyper competitiveness at the shelf-level in key retail sub-segments such as discount retail, specialty, consumer electronics, apparel, and grocery has led to the need for better time to information, time to decision, and visibility towards decision-making Key Performance Indicators (KPIs) on an enterprise-wide basis. For 75% of retailers, time to information and decision has not improved over the past two years. Therefore, Best-in-Class companies realize the need for structured enterprise-wide BI that can demystify the business information complexities within the retail value chain.



The shift towards enterprise-wide operational BI requires a series of BI technology and process changes. The IT, process, and BI development requirements will be discussed in Chapter Two.

"In 2007, our company adopted a more mainstream BI strategy from a small group of fringe users of BI tools. We are using BI to optimize customer response, supply chain, inventory investments, and labor hours. Our BI delivery combined with our robust data warehouse ensures that we manage our business fluctuations better." ~ Court Newton, IT Director, FYE Stores

Customer at the Center of BI Pressures Factors such as fluctuating customer demand and regressive market conditions have meant that retailers have to re-think their customer strategies. From the data in Figure 2, it is unequivocally clear that the "lack of focus on the customer" is at the heart of the retailer's anguish of not meeting defined business goals and shareholder expectations in the prevailing economic times. The top pressure facing almost half (47%) of Best-in-Class retailers is the need to read the mind of customers to identify their product and service demands and accurately plan sourcing, procurement, supply chain, assortment, and day-to-day channel operations. The question that often presents a quagmire for retailers is how to accurately measure customer behavior and metrics to provide timely business planning, execution, and performance updates to the retail business leaders, departmental heads, and managers at the retail store front? Currently, 80% of retailers do not possess the ability to provide their user organization with real-time or near real-time business information that relates to customer demand or key customer metrics. This impedes the retailer's ability to gain transactional intelligence to manage agile merchandise planning processes, optimize inventory needs, and capture the most updated buying affinity for executing promoted and complementary products. This is despite the fact that 48% and 42% of all retailers © 2008 Aberdeen Group. www.aberdeen.com

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respectively, claim to possess BI tools for customer management and merchandising. Figure 2: Pressure to Address the Changing Customer Trends Laggard

Average

Best-in-Class 47%

Need to accurately predict customer demand

35% 24%

45%

Need to improve promotion effectiveness

37% 29%

37% 37%

Need to increase customer satisfaction

23% 0%

10%

20%

30%

40%

50%

60%

70%

Source: Aberdeen Group, November 2008

The Maturity Class Framework Aberdeen used three key performance criteria to distinguish the Best-inClass from Industry Average and Laggard organizations. The maturity class has been developed via the weighted average of retail performance within key customer, financial, and operational metrics. These year-over-year KPIs are essential to determine the health and sustainability of any retail business. Table 1 provides a framework with which companies can benchmark themselves and identify the category into which they fall. 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

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Mean Class Performance ƒ Average increase in return on net assets (year-overyear): 22.5% ƒ Average increase in customer retention (aggregated yearly for all stores): 24.43% ƒ Average gross margin increase (year-over-year): 12.8% ƒ Average increase in return on net assets (year-overyear): 8.5% ƒ Average increase in customer retention (aggregated yearly for all stores): 10.5% ƒ Average gross margin increase (year-over-year): 5%

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Definition of Maturity Class

Mean Class Performance

Laggard: Bottom 30% of aggregate performance scorers

ƒ Average increase in return on net assets (year-overyear): .5% ƒ Average increase in customer retention (aggregated yearly for all stores): 2.5% ƒ Average gross margin increase (year-over-year): .8% Source: Aberdeen Group, November 2008

The Best-in-Class PACE Model Table 2 shows a roadmap to the key Pressures, Actions, Capabilities, and Enablers (PACE) prioritized by Best-in-Class companies for the use and application of enterprise-wide BI. This will help identify the key capabilities and enablers that are being considered as part of their multi-channel initiatives. Table 2: The Best-in-Class PACE Framework Pressures ƒ Need to accurately predict customer demand

Actions ƒ Create enterprise-wide customer intelligence guidelines ƒ Create enterprise-wide operational intelligence guidelines

Capabilities

Enablers

ƒ Ability to segment customer based on purchase behavior and affinity ƒ Ability to generate automated reports ƒ Develop reporting process based on job role ƒ Ability to collect, integrate and analyze customer data ƒ Ability to establish performance thresholds ƒ Ability to provide store-level performance data ƒ Ability to provide performance data at the associate-level

The Changing Nature of BI Strategy

ƒ Enterprise data warehouse ƒ Executive dashboards ƒ Operational dashboards ƒ Scorecards ƒ End-to-end BI platform (data access, integration, application / model assembly, and reporting / dashboarding / scorecarding) ƒ Data cleansing software Application ƒ In-store analytics suite ƒ Web analytics suite

Source: Aberdeen Group, November 2008

Last year's BI survey results showed that 56% of Best-in-Class retailer's top BI-related strategy was to identify customer segment buying trends and patterns. While customer segmentation remains a priority of retailers, this year our results demonstrate a move towards the "bigger picture applicability of BI" from an enterprise-wide perspective (Figure 3).

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Figure 3: Best-in-Class BI Strategy Pivot's Enterprise-Wide Guidelines Laggard

Average

Best-in-Class 42%

Create enterprisewide customer intelligence guidelines

39% 22%

32%

Create enterprisewide operational intelligence guidelines

24% 19%

0%

10%

20%

30%

40%

50%

60%

70%

Source: Aberdeen Group, November 2008

Seventy-four percent (74%) of Best-in-Class retailers are currently creating enterprise-wide BI guidelines when compared to 50% of all other retailers. On the surface, creating customer and operations-related guidelines on an enterprise-wide basis seems like an academic and rudimentary exercise. However, in actuality it takes several years for all the user organizations in retail to perfect a common charter of guidelines and practices to harmonize the BI foundational elements of forecasting, planning, and budgeting for varied retail departments such as marketing, merchandising, finance, procurement, or supply chain. The IT analytics team in retail has their task cut out as they seek to address unified BI operations in multi-tier user organizations in functional areas such as data access, integration, application / model assembly, and reporting, dashboarding, as well as scorecarding. Global retailers, especially, in the apparel, high-fashion, fast moving consumer goods, and specialty goods segment face unique challenges related to an enterprise-wide BI strategy as they seek to address the need for value chain agility, time to market, and time to decision, as well as local versus global BI guidelines. This business problem is illustrated in the case of a globally-renowned denim brand that recently addressed the issue of unifying BI operations and delivery tools for domestic and foreign markets. The problem was as basic as the lack of common guidelines for data access, coding, cubing, querying, development as well as end-users grappling with performance facts and figures presented via varied disparate formats of BI delivery tools such as spreadsheets. These issues lead to lack of a unified view of the brand, customer, and day-to-day operations. In studying the varied cases of BI implementation in retail organizations, Aberdeen's analysis indicates that retailers need an enterprise-wide BI strategy if they want to see customer and business

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dynamics through the same prism in order to scale, differentiate, and grow in these challenging times. Aberdeen Insights — Impact of BI in Retail Our data shows that 64% of retailers currently use BI within their enterprise in some form, whether in a single department, within multiple departments, or enterprise-wide. Of those surveyed, we conducted a comparative performance analysis to determine whether companies with or without a BI perform were at different levels. We also analyzed whether the number of years or the maturity of BI in a company makes a significant difference on performance. The results showed that while on the first count (companies with or without BI) there were significant differences in customer, financial, and operational performance (Figure 4), on the second count (BI maturity stage of a retailer) there were no significant variances in performance. continued

Aberdeen Insights — Impact of BI in Retail

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Aberdeen Insights — Impact of BI in Retail This means that maturity of BI process does not guarantee performance improvement. However, the use of BI at any stage in the lifecycle of a retailer can make a significant impact on performance. This can be caused by retailers' under-utilization of transactional intelligence regarding pointof-sale, inventory, and shipping data analytics for building significant retail efficiencies. For example, Aberdeen's April 2008, Technology Strategies for Multi-Channel Integration report found that 45% of retailers collect CRM but do not use it in any internal and external program. Our results indicate that retailers using BI applications are 39% more likely to attain a year-over-year increase in RONA, when compared to those retailers that are not current users. In a gloomy retail climate which does not project encouraging signs for same store sales comp (year-over-year increase in sales for stores open for at least one year) or profitability in the upcoming quarters, BI may have a role to play for streamlining operations and customer focus. Figure 4: BI Impacting Retail Performance Year-over-year increase in return on net assets (RONA)

53% 38%

Year-over-year increase in gross margin

50% 42%

Year-over-year increase in customer retention

Retailers currently using BI Retailers currently not using BI

50% 34%

0%

10%

20%

30%

40%

50%

60%

70%

Source: Aberdeen Group, November 2008

In the next chapter, we will see what the top performers are doing to achieve these gains.

© 2008 Aberdeen Group. www.aberdeen.com

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Chapter Two: Benchmarking Requirements for Success An enterprise-wide deployment of BI capabilities enables performance improvements at all levels of the retail organization, from the corporate office, right down to the "front lines" within customer interactions. The business case for investing in an enterprise BI strategy is illustrated within the real-world example below of a large retail organization that has recently learned a great deal about optimizing its store-level operations by taking an enterprise-wide view of day-to-day activity. Case Study — Virgin Megastore’s “Mission Critical” EnterpriseWide BI Strategy In 2004 and 2005, Virgin Megastores saw a need for more accurate reporting that could lead to better and more informed decision-making. At the store level, managers could not generate in-depth performance reports in real or near-real time. They only had access to high level daily reports or weekly performance reports, which did not allow them to run the stores as effectively as they wanted. At the same time, executive management recognized there was too much of a time lag for decisions on inventory turns, product placement, and promotions. The answer was the launch of an enterprise-wide real time data warehouse, and business intelligence delivery tools.

Fast Facts √ Best-in-Class retailers are twice as likely as Laggards to collect, analyze, and integrate customer data on a near-real or real-time basis √ Only 30% of Industry Average retailers possess the ability review and adjust BI data based on market dynamics √ Only 17% of Laggard retailers ensure production of report based on the needs of specific job roles

The first audience for the data warehouse was the store employee; the data, however, is available company-wide within every department. Prelaunch, Virgin was using 300 labor hours per month to extract data and put the information into spreadsheets for reporting purposes, which did not allow for new, in-depth analysis. This BI initiative immediately eliminated these 300 labor hours per month. Virgin was able to load the system with four years of sales history and immediately begin running sales forecasting and budgeting reports. According to Robert Fort, CIO, Virgin Megastores, “There were three major benefits to the new data warehouse: everyone was on the same page, which eliminated debates on the source or integrity of the data; the system was easy to use across the enterprise; and they had increased flexibility in their ability to run reports. The BI tool identified all key sources of data and sourced it into the warehouse.” In the last two years, Virgin has ensured the data warehouse and BI delivery tools provide reporting in real time, which allows managers and associates at the store level to assess performance and access reports on-demand throughout the day. continued

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Case Study — Virgin Megastore’s “Mission Critical” EnterpriseWide BI Strategy They can look at hot sellers, campaigns, and new releases in real time, and make necessary adjustments to their on-floor stocks at any time. For example, a new release will sell approximately 50% of their total units within the first two weeks. Moreover, out of stocks can be an obstacle for the store to post same store sales comp. The BI tool helps to show real-time stocks, and can keep the product on the shelf. Virgin put the tool in the hands of the people who can drive action from it, in real time. The first BI template was launched at Thanksgiving, 2004 and since then this retailer has updated its BI delivery for all departments towards realtime reporting. The key objective was to streamline the number of KPIs that stores, merchandising, finance, and supply chain teams were evaluated on. As a result of Virgin’s real-time and enterprise-wide BI process, every store increased their KPI performance. Virgin is now measuring the right KPI’s, making it easier to manage performance across the entire retail value chain. In the first 18 weeks of deployment, sales increased by more than $8 million, 20% of which was attributed to the new data warehouse. Virgin was able to pay back the initial BI investment in the first 15 weeks. The BI tools are ingrained the fiber of all day to day store operations, making Virgin stores more efficient, productive, and profitable.

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 are the approaches they take to execute their daily operations 2. Organization is the corporate focus and collaboration among stakeholders 3. Knowledge management is contextualizing data and exposing it to key stakeholders 4. Technology is the selection of appropriate tools and effective deployment of those tools 5. Performance management is 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.

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Table 3: The Competitive Framework Best-in-Class

Average

Laggards

Ability to segment customers based on purchase behavior and affinity 61%

Process

45%

35%

Ability to generate automated reports 76%

Organization

66%

53%

Develop reporting process based on job role 53%

30%

17%

Ability to collect, integrate and analyze customer data

Knowledge

63%

46%

35%

The ability to define KPIs that are relevant to the business 67%

50%

48%

Applications or platforms that can support the use and acceptance of BI in retail:

Technology

ƒ 72% enterprise data warehouse ƒ 61% operational dashboards ƒ 56% scorecards ƒ 46% data cleansing software application ƒ 43% web analytics ƒ 35% end-to- end bi application suite

ƒ 43% enterprise data warehouse ƒ 54% operational dashboards ƒ 43% scorecards ƒ 35% data cleansing software application ƒ 39% web analytics ƒ 26% end-to- end bi application suite

ƒ 36% enterprise data warehouse ƒ 36% operational dashboards ƒ 36% scorecards ƒ 22% data cleansing software application ƒ 21% web analytics ƒ 15% end-to- end bi application suite

Performance management parameters used:

Performance

ƒ 67% provide store-level performance data ƒ 50% establish performance thresholds for business improvements ƒ 50% provide performance data at the associate level

ƒ 54% provide store-level performance data ƒ 39% establish performance thresholds for business improvements ƒ 32% provide performance data at the associate level

ƒ 43% provide store-level performance data ƒ 37% establish performance thresholds for business improvements ƒ 24% provide performance data at the associate level

Source: Aberdeen Group, November 2008

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Capabilities and Enablers Process Management BI is not just a set of technologies, but also encompasses a range of capabilities, processes, disciplines and practices that must be mastered in order to take advantage of the technological features and functions of a BI solution. Particularly on an enterprise scale, the ability to make information more visible to decision makers faster, and therefore more "actionable," is a critical piece of the puzzle. Best-in-Class companies are more likely to currently possess the key process management capabilities that can lead to the realization of value from a BI investment (Figure 5). Figure 5: Best-in-Class Process Management Capabilities 0%

20%

40%

60%

80%

76% Ability to generate automated reports

66% 53%

Ability to segment customers based on purchase behavior and affinity

61% 45% 35%

Best-in-Class Average Laggard

"We have had our enterprisewide BI tool in place for eight or nine years. We use it for budgeting and planning within our finance group, and for category management analysis within our merchandising group. We are looking to replace the tool at this time due to customer demand. The users are unhappy with the tool; it did not have the full functionality that we desired." ~ Director IT, Major Travel Retailer, North America

Source: Aberdeen Group, November 2008

The "ability to generate automated reports" requires that the organization have a method for scheduling and publishing reports based on transactional change-data. The speed at which change-data can be accessed, integrated, analyzed and published to report consumers is directly related to the competitive value that a BI solution can deliver. Automating this process not only delivers information faster, but can also increase the accuracy and speed at which decisions are made, opportunities are addressed, and risks are alleviated. Another process that retailers are keen to perform is the ability to segment customers based on their purchase activity. This involves the ability to tap into POS data, web-based streams of click-through and purchase information, as well as being able to access and manage historical customer purchase data and correlate this to current activity. Best-in-Class companies are far more likely to have established this capability than Industry Average and Laggard respondents. © 2008 Aberdeen Group. www.aberdeen.com

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Organizational Management Not all reports and analysis views produced within a business intelligence system are appropriate for all audiences. In fact, Aberdeen research conducted for the August 2008 Operational KPIs and Performance Management report showed that Best-in-Class companies are far more likely to adapt reporting and analytics output to business roles within the organization. Once again, the Best-in-Class are showing the way (Figure 6). Figure 6: Production of Reports Based on Job Role

60%

53%

40%

30% 17%

20% 0% Best-in-Class

Average

Laggard

Source: Aberdeen Group, November 2008

In a retail environment, the reports that corporate managers see versus the reports meant for store associates (such as the example provided in the case study) are completely different and serve entirely different purposes. Therefore, the ability to not only automate report production, but also automate the tailoring and delivery of reports based on job role is extremely important. Many companies assign "BI roles" to different groups of employees in order to simplify this process. Others take a more individualized approach and allow employees to "subscribe" to reports that are most relevant to their jobs.

Knowledge Management In the process management analysis, we investigated the importance of being able to segment customers based on their behavior. This requires that retailers have the ability to access and collect this information, integrate it with existing customer and purchase transaction data, and analyze it in order to derive new meaning and drive decisions and actions. This is a knowledge management capability that retailers must prioritize in order to achieve Best-in-Class performance. The ability to analyze customer data is not technical. Too many retailers focus on the technological capabilities of software programs, but forget that it is really the capabilities of the business analysts themselves that also must be evaluated as part of the equation. The lack of BI skill sets has long been a top barrier that Aberdeen research has uncovered within many survey results over the past two years. If the © 2008 Aberdeen Group. www.aberdeen.com

"The cornerstone of our success is our Business Intelligence. In 2005, when the company went into a rapid growth stage, opening on average three new franchises a week, we developed our business intelligence to a level so sophisticated that we were able to obtain all the market intelligence for our business in 45 days, something that would take our competition at least two years to create. As a result, we can automatically track every visit, every comment, and every sale made by each of the 1,000 customers in every franchise territory. Using this data we can determine which customers will benefit from marketing efforts and what is the best marketing program for each customer type. Our BI initiatives are enterprisewide, but are customizable at the local store level." ~ John Thys, VP Franchise Reporting and Analytics, 1800-RADIATOR

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analytical skill sets are not in supply, retailers may want to consider outsourcing for these capabilities (Figure 7). Figure 7: Best-in-Class Knowledge Management Capabilities 0%

20%

40%

The ability to define KPIs that are relevant to the business

80% 67%

50% 48%

Ability to collect, integrate, and analyze customer data Ability to review and adjust BI programs based on market dynamics

60%

63% 46% 35% 41% 30% 17%

Best-in-Class Average Laggard

Source: Aberdeen Group, November 2008

In addition, an understanding of the business drivers is critical to a successful BI implementation and deployment. The ability to measure performance starts with a knowledge of the KPIs that drive the business. Best-in-Class companies are well ahead of other respondents when it comes to being able to identify and incorporate KPIs into their BI systems. This approach must also be taken when looking outward to the retail market as a whole. In the current unstable economy, it is critical that retailers maintain a close watch on market dynamics and be able to adjust assumptions and KPIs on-the-fly as conditions change. This leads to the ability to deliver performance management capabilities.

Performance Management As pointed out earlier, visibility to the metrics that drive the business is critical to a successful BI implementation, but moreover, it is also essential that performance metrics be identified, accessed, tracked, and acted upon at the lowest levels of the organization. Best-in-Class companies are far more likely to gain visibility to performance at the store level (i.e. through access and analysis of granular transaction-level data for promoted and complementary products) , and are also more likely to achieve an even greater level of granularity at the store associate level. This enables a company to affect performance improvement among the very people who have the most effect on customer interactions (Figure 8). © 2008 Aberdeen Group. www.aberdeen.com

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Figure 8: Best-in-Class Performance Management Capabilities 0%

20%

40%

60%

80% 67%

The ability provide store-level performance data

54% 43% 50%

The ability to provide performance data at the associate level

24% 32% 50%

The ability to establish performance thresholds for business improvements

37% 39%

Best-in-Class

Average

Laggard

Source: Aberdeen Group, November 2008

No matter what level of granularity is achieved, Best-in-Class companies are also focusing efforts on establishing performance thresholds, or "business rules" that dictate the generation of alerts for relevant managers and decision-makers. This is still a capability that is emerging as Best-in-Class retailers enter into more advanced areas of BI capability.

Technology In addition to the management capabilities described earlier, Best-in-Class companies are also showing leadership when it comes to the adoption of technologies related to business intelligence. This is an important point. Business intelligence is not a single technology, such as an accounting software program or a reporting tool. Rather, it is a collection of tools and software components that can either be purchased as a "stack" of business intelligence software components from a single vendor, or in separate pieces from "best-of-breed" software vendors. In either case, Best-in-Class companies have identified the top technologies that they are investing in to deliver BI capabilities to the enterprise (Figure 9). The foundation of the BI "stack" is the data source from which information is pulled, integrated, analyzed, reported and viewed. Best-inClass companies are far more likely to have developed and deployed an enterprise data warehouse to serve this purpose. A purpose-built data warehouse within a retail environment may also serve as the central point for data integration. Best-in-Class companies are four-times more likely to have implemented an enterprise data warehouse than Laggards.

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Figure 9: Top Five Best-in-Class Technology Investments 0%

30%

Enterprise Data Warehouse

52%

Operational Dashboards

Scorecards

60%

18%

37%

29%

31%

Executive Dashboards

22%

Data Cleansing Software Application

21%

11%

15%

90%

13%

13%

21%

10%

12% 5% Best-in-Class

Average

Laggard

Source: Aberdeen Group, November 2008

Storing and managing data within an enterprise warehouse is a "back-end" BI technological capability. The "front-end" data access, viewing, and analysis capabilities are also important, and this is where the concepts of dashboards and scorecards come into play. This is particularly important when discussing how a retail organization is able to take corrective action based on performance metrics. Dashboards and scorecards are driven by KPIs (see knowledge management capabilities earlier), and Best-in-Class retailers are far more likely to have implemented either operational dashboards (i.e. dashboards focused on an operational area of the business such as inventory, web activity, POS activity, human capital management, etc.) and executive dashboards (i.e. dashboards that are focused on strategic KPIs such as profitability, RONA, same store sales, etc.). Retailers are still investigating the merits of adding data cleansing to their BI arsenal, and Best-in-Class companies are nearly twice as likely as all others to take this approach. Clean data yields more accurate information upon which decisions are made. Aberdeen's September 2008 study One Version of the Truth 2.0 details the various management and technology approaches to this issue. Planned adoption of technologies among all respondents shows that there is a great interest in developing more BI capabilities for the enterprise, especially when it comes to building out an end-to-end BI platform for the enterprise (Figure 10).

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Figure 10: Planned BI Technology Adoption - All Retailers 0%

25%

Enterprise Data Warehouse

50%

75%

9%

29%

62%

38%

49%

Operational Dashboards

100%

13%

Scorecards

43%

44%

13%

Executive Dashboards

40%

50%

10%

40%

Data Cleansing Software Application

35%

Web analytics suite

38%

End-to-end BI Platform (data access, integration, application/model assembly, and reporting / Dashboarding In-store analytics suiite Current

24%

23%

46%

54%

46% Planned

25%

16%

22%

31% No Plans

Source: Aberdeen Group, November 2008

Aberdeen Insights — The Business Case for Enterprise-wide BI Any enterprise-wide BI strategy requires a perceptive assessment of the cost-benefits as well as the deployment factors that enable seamless BI operations for all user-organizations in retail. Table 4 shows the specific cost, benefit, and deployment factors pertaining to small, mid-size, and large retailers so that these retailers can create a roadmap for BI integration across all their sales channels as well as the corporate network in an effective manner. The ROI and deployment preferences of these three groups can prove useful in creating the BI solution acquisition, discovery, testing, and deployment plans. While small, mid-size, and large companies see similar benefits from BI applications, they differ somewhat on costs (except for ongoing support costs where all three tiers converge). From a deployment standpoint, aside from the need for customization for BI features and functionality in the small and mid-tiers, as expected all other the pain points and criteria differ across all three tiers. continued © 2008 Aberdeen Group. www.aberdeen.com

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Aberdeen Insights — The Business Case for Enterprise-wide BI Table 4: Tier Retail Enterprise-Wide BI ROI Components and Deployment Needs Retail Classification

Benefits

Cost Factors

Deployment Criteria

Large Retailers (Tier 1)

ƒ Operational Efficiency (56%) ƒ Rapid response to consumer demand (47%)

ƒ Development costs (40%) ƒ On-going support costs (33%)

ƒ Scalability for enterprise-wide adoption (53%) ƒ Configurability with ERP application (35%)

Mid-size Retailers (Tier 2)

ƒ Operational efficiency (63%) ƒ Rapid response to consumer demand (56%)

ƒ Ongoing support costs (44%) ƒ Product acquisition and customization costs (30%)

ƒ Customization of specific features and functional requirements (43%) ƒ Configurability with ERP application (35%)

Small Retailers (Tier 3)

ƒ Rapid response to consumer demand (70%) ƒ Operational efficiency (51%)

ƒ Ongoing support costs (38%) ƒ Product acquisition costs (36%)

ƒ Customization of specific features and functional requirements (45%) ƒ Cost of deployment (42%)

Source: Aberdeen Group, November 2008

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Chapter Three: Required Actions Whether a company is trying to move its 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 •



Map enterprise, store, and channel-productivity with BI needs. On average, a third of Laggard retailers possess the ability to establish thresholds for business performance improvements. Worse still, only 17% possess capabilities to produce and deliver business insights and reporting for specific job roles. This leads to a regressive retail environment where lack of access and connectivity around impactful business performance data, including transactional data generated from POS, can cause skewed retail planning, sales execution, and fulfillment of brand promise. As an industry Laggard, it is imperative to view BI as a "leveler or a unifying force" of enterprise financial, operational, and customer management performance. When considering on-premise or hosted end-to-end BI solutions (from data collection, integration, cleansing, data warehousing, modeling and application development, to reporting, dashboards, scorecarding and ad hoc analytics), it is vital that Laggard retailers create a BI framework that ties the top enterprisewide productivity needs to specific business intelligence processes such as data gathering, aggregation, cubing, reporting, and delivery. Laggards should ensure that BI planning, reporting, and delivery integrates seamlessly into the existing architecture without creating too many data gaps, cost overages and silo creation. Empower corporate, channel, and store teams. Only 36% of Laggards provide granular store-level and associate-level performance data that can impact the day-to-day functioning of a store, web or catalogue channel or even the corporate departments that develop the retail sales and operations plans. Granular retail reporting and real-time delivery of BI data in the areas of sales and margin performance, sales and operations compliance, task management, in-stock percentage, and customer satisfaction can help build agile retail operations. During the ensuing weak consumer spending climate, Laggard retailers need to provide deeper business insights to their employees for improving customer, inventory, and merchandise assortment-related decision making. Real-time or near-real time BI reporting and delivery enables retailers to develop a knowledge-driven culture, one that encourages rapid decision-making during a typical retail sales day, week, quarter, and fiscal year. Laggard retailers must consider developing operational data management collection and aggregation capabilities prioritized by department that can be easily modeled towards actionable scorecards, operational dashboards and ad-hoc analytics.

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Fast Facts √ Only 35% of Best-in-Class retailers report having "end-to-end" BI platforms encompassing a full range of capabilities, from data collection, integration, cleansing, data warehousing, modeling and application development, to reporting, dashboards, scorecarding and ad hoc analytics √ Currently, 50% of Industry Average retailers do not possess the capability to define the appropriate operational, customerrelated, and financial performance metrics to truly integrate sales and operations planning, execution, and result attainment across varied retail departments √ On an average, a third of Laggard retailers possess the ability to establish thresholds for business performance improvements

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Industry Average Steps to Success •

Identify specific performance metrics for measuring enterprise-wide success. Currently, 50% of Industry Average retailers do not possess the capability to establish the appropriate operational, customer-related, and financial performance metrics that can integrate transactional data with sales and operations planning, execution, and result attainment across varied retail departments. Industry Average companies must measure, monitor, and enable their teams to perform towards four to five larger corporate performance objectives based on historical demand data, short-term demand assessment, and shareholder expectations. Too many metrics do not necessarily translate into business success. More often than not, a lack of meaningful metrics can lead to skewed retail growth, de-motivational employee programs, and s lack of competitive differentiation as retailers try to be everything to everyone. For example, an example of specific performance metrics within the store environment is a "unified scorecard for retail store performance" with three to six metrics that are aligned with annual retail triggers such as in-stock, customer satisfaction, and comp sales attainment. This performance measurement and tracking capability can be developed not just in stores but across all retail departments. Focused operational metrics ensure a far more fundamental and unified retail strategy over the long-haul.



Continue to develop focus on the top BI enablers. Our data shows that Industry Average retailers do not demonstrate current and planned Best-in-Class BI technology practices. Figure 9 describes the five critical BI technology investment areas in retail. Industry Average companies indicate lower intended adoption than the Best-in-Class in all five areas including some of the foundational BI components such as an enterprise data warehouse. On average, on four out of five of the enablers, less than a fifth of Industry Average retailers, indicate adoption in solution areas such as enterprise data warehouse, scorecards, executive dashboards, and data cleansing. BI is not one technology, it does involve a series of technology components and processes. It is critical that Industry Average retailers focus on developing a comprehensive BI strategy covering both front-end (scorecards, dashboards) and back-end BI (data warehouse, data cleansing) so that the BI planning to delivery process is as structured and performanceoriented as possible. Half-hearted ad-hoc efforts around some of the BI components will most likely fail in a complex retail end-user environment.

Best-in-Class Steps to Success •

Continue to build towards "end-to-end" BI. The ability to deliver true enterprise-wide access to BI will inevitably require Best-in-Class retailers to obtain a full range of capabilities, from collection, integration and cleansing of transactional data, data warehousing, modeling and application development, to reporting, dashboards, scorecarding and ad

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Increasing Retail Productivity: Enterprise-Wide Business Intelligence Page 23

hoc analytics. Only 35% of Best-in-Class retailers report having "end-toend" BI platforms encompassing these tools and capabilities today. Aberdeen research indicates that this is in line with Industry Average levels of BI platform adoption across all industries. Through the research contained in this report, Best-in-Class companies should be able to identify the components that are currently lacking. •

If the entire platform is too much to contemplate now, focus on data integration and cleansing first. Too many BI projects get mired in added cost and timeframe overruns due to poor data quality. Aberdeen research conducted for the February 2008 report, Managing the Total Cost of Ownership (TCO) of Business Intelligence, showed that data integration and quality were the top two factors that drive up costs during a BI implementation. Only 21% of Best-in-Class retailers currently have invested in a data quality solution. Retailers that address their data quality first will find follow-on steps (data integration, warehousing, modeling, reporting, etc.) to be more efficient. Moreover, the quality of the data relates directly to end-user adoption. Employees in the field will ascertain immediately whether the data they are looking at is "believable" or not. This initial perception will dictate overall sentiment and drive adoption accordingly.



Continue to seek methods for incorporating external market data into existing BI reporting and analytics applications. Most BI initiatives start and build out from internal data, first at a summarized strategic level, and over time, at a day-to-day operational level. To enhance the intelligence gained from analysis of internal data, external data such as customer communications (emails, hot-line CRM notes, call center dialog, etc), and Web 2.0 content (customer sentiment within blogs, product reviews, wikis, etc) can provide added insight into customer sentiment and behavior that was previously unavailable for analysis. Aberdeen Insights —The Role of Technology in Enterprise-Wide Business Intelligence Enterprise-wide BI is not defined as a single reporting or analytics application applied to a specific set of data. It involves the ability to access information affecting the entire business as the data is created. This can involve one or multiple sets of data sources, and can affect one or many sets of decisions, actions, departments and people. Retail organizations that take a strategic approach to enterprise BI, and the access to relevant data when, how, and where people need it, will be better positioned to achieve Best-in-Class success. This requires the ability to dynamically collect and integrate data and make it available to decision makers for improvement of time-to-decision, or to business rules management systems for automation of actions. There is an added degree of difficulty as data volumes and complexity continues to grow in retail. Best-in-Class retailers are far more likely to be meeting this challenge through the adoption and implementation of an enterprise data warehouse. Storing and managing data within an enterprise warehouse is a "back-end" BI technological capability. The "front-end" data access, viewing, and analysis capabilities are also important, and this is where the concepts of dashboards and scorecards come into play.

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Case in Point – Large U.S. Travel Center Operator This large U.S.-based retail operator of travel centers provides nationally recognized fast food restaurants that remain open 24 hours, convenience stores, and many travel conveniences. The company expects to sell billions of gallons of petroleum this year, and combined retail operations that will produce annual revenues of $16 billion in 2008. Over the past 7 years, the company has experienced rapid growth driven by merger and acquisition activity, and a solid business plan that has included a business intelligence strategy designed to provide both strategic intelligence to corporate executives, as well as store-level intelligence for the travel center managers and line-level cashiers. The ongoing growth has resulted in the company managing over 1,200 reports within a disparate environment that included a tool for operations reporting, financial planning and budgeting, and a web-based portal for report publication and distribution. The company’s supervisor of Business Intelligence and his team started searching for alternative approaches which included several solution providers. In the end, the business requirements made it obvious that the two top criteria were speed and reliability. The organization’s corporate controller realized that an opportunity existed to deliver increased reporting and analysis capability down to the store level, while simultaneously improving corporate visibility into performance drivers. “Every hour of store level management time equates to $1M to the bottom line in terms of manual vs. automated reporting and analysis”, said the controller. “Our under-stock and over-stock issues were a top priority for improved analytical capabilities. While gas and diesel fuel are low margin items, the convenience store items represent the highest margin, but ordering the products appropriately was a challenge. Excess inventory was ordered due to a misinterpretation of line-item ordering metrics as being a goal. Leveraging our BI investment, we implemented a new ordering system that allows us to analyze 100,000 UPCs to optimize the ordering process. As a result, the time it takes to order has been reduced, and the accuracy of our ordering has improved. This represents a double-effect win. Sales have increased while inventory has reduced.” The company also began to analyze store-level data to determine what processes and activities were driving the highest customer cross-sell and up-sell performance. The BI supervisor explains the power of “truth in the data” that was revealed as a result of this project. “We asked our travel center managers to tell us who their top performing cashiers were so we could do some analysis on what they were doing differently and begin to develop some standard practices for training and on-boarding new employees. One of our managers was emphatic about the performance of a particular cashier. continued

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Case in Point – Large U.S. Travel Center Operator The cashier was well-regarded due to the ability to quickly scan and cash out a high number of customers while not really taking the time to engage the customer. In reality, when we looked at the data, we found that this was not the case. In fact, during the busiest times of day, the chatty cashier was performing at the top of the scale, as the cashier was engaging the customer and presenting them with up-sell opportunities. It turned out that the data debunked the popular myth. As a result the manager has started scheduling those cashiers that have the highest upsell scores during the busiest times in the day so they can have the greatest effect on the business.” This performance is also being made visible directly to the employees. Performance data is handed out with the cashier’s paycheck stubs. It allows the staff to see where they stand vs. their peers and it also enables managers to better schedule and set-up mentoring programs among top performers and laggard cashiers. Store-level managers are now able to dive-down into the data and see their performance. This is broken out into “day-part” analysis to identify the heavy-flow periods of the day and to optimize staffing in the restaurants and stores to handle the flow most efficiently. Same-store sales and performance analysis is also delivered so managers can see how they compare to other travel centers. The BI Supervisor concludes, “We have been measuring the ROI of the BI investment, and it is currently at 116%. We have increased profitability by $3.4M in the first year and expect to do so every year going forward, while seeing a reduction in back room inventories by $2M.”

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Appendix A: Research Methodology Aberdeen surveyed 150 retail enterprises between October and November 2008 to determine opportunities and pitfalls of an enterprise-wide BI strategy in retail. Enterprise-wide BI is not defined as a single reporting or analytics application applied to a specific set of data. It involves the ability to access information affecting the entire retail business, often as the data is created. This can involve one or multiple sets of data sources, and can affect one or many sets of decisions, actions, and people. Aberdeen supplemented this online survey effort with interviews with select survey respondents, gathering additional information on multi-channel technology and integration strategies, experiences, and results. Responding enterprises included the following: •

Job title / function: The research sample included respondents with the following job titles: C-level (14%); information technology (14%); business process management (6%); sales and marketing (27%); store operations (20%); finance (4%); customer service and others (8%).



Industry: The research sample included respondents exclusively from retail industries, including consumer electronics (3%); specialty (33%); general merchandise and apparel (16%); supermarket and grocery (11%); department, convenience, petro and drug stores (8%); furniture and hardware (3%); fast food and hospitality (2%); wholesale (6%); and others (8%).



Geography: The majority of respondents (57%) were from Americas. Remaining respondents were from the Asia-Pacific region (23%) and EMEA (26%).



Company size: Twenty-eight percent (28%) of respondents were from large enterprises (annual revenues above US $1 billion); 35% were from midsize enterprises (annual revenues between $50 million and $1 billion); and 37% of respondents were from small businesses (annual revenues of $50 million or less).



Headcount: Twenty-three percent (23%) of respondents were from small enterprises (headcount between 1 and 99 employees); 32% were from midsize enterprises (headcount between 100 and 999 employees); and 45% of respondents were from large businesses (headcount greater than 1,000 employees).

Study Focus Responding executives completed an online survey that included questions designed to determine the following: √ The degree to which Business Intelligence initiatives are deployed in their retail operations √ The structure and effectiveness of existing Business Intelligence implementations √ Current and planned use of data management capabilities √ The knowledge and performance management benefits, if any, that have been derived from Business Intelligence The study aimed to identify emerging best practices for the likely use and adoption of Business Intelligence in retail, and to provide a framework by which readers could assess their own management capabilities.

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.

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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) Source: Aberdeen Group, November 2008

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? Source: Aberdeen Group, November 2008

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. Source: Aberdeen Group, November 2008

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Appendix B: Related Aberdeen Research Related Aberdeen research that forms a companion or reference to this report include: •

Real-Time Loss Prevention; December 2007



Business Intelligence in Retail; January 2008



Multi-Channel Integration; April 2008



Predictive Analytics; May 2008



Business Intelligence in Healthcare; June 2008



Operational KPI's and Performance Management; August 2008



One Version of Truth 2.0; September 2008



Precision Merchandising; November 2008

Information on these and any other Aberdeen publications can be found at www.Aberdeen.com.

Author: Sahir Anand, Senior Analyst, Retail, [email protected] Dave Hatch, Vice President and Principal Analyst, Business Intelligence, [email protected] 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 HarteHanks (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 http://www.aberdeen.com or call (617) 723-7890, or to learn more about Harte-Hanks, call (800) 456-9748 or go to http://www.harte-hanks.com 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.

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