2 Page 2 Executive Summary New Aberdeen research reveals that customer data quality is a sales and marketing leadership issue. In surveying over 400 organizations, Aberdeen found that over 70% of Best-in-Class firms are driven to improve customer data quality by competitive and profitability pressures. This report examines the metrics, processes and technology that Best-in-Class firms use to gain a competitive advantage from their customer data. Best-in-Class Performance The Aberdeen Group used three key performance indicators (KPIs) to distinguish Best-in-Class, Average and Laggard organizations. 94% of Best-in-Class firms reported improvement in customer data integrity (accuracy, completeness, consistency), 95% of Best-in-Class firms reported improvement in usability of customer data, 89% of Best-in-Class firms reported positive performance in the time necessary in preparing customer data for business line use. Leading organizations that deploy, benchmark and refine customer data quality initiatives are four times more likely than Laggard organizations to report gains in customer, organizational and revenue key performance indicators (KPIs). Competitive Maturity Assessment Survey results show that the firms enjoying Best-in-Class performance shared several common characteristics: 100% of Best-in-Class firms invest in data collection, cleansing, and analysis. 94% of Best-in-Class firms invest in data process management. 80% of Best-in-Class firms incorporate assignment of accountability strategies for customer data quality to a data steward or manager. While IT plays a major role in the customer data management process, customer data quality is definitely a sales and marketing issue. Organizations must adopt adequate audit points and training to ensure that the customer data management process is driven by the business side. ~Arun Kumar IT Manager Global Shipping & Logistics LLC Required Actions In addition to specific recommendations outlined in Chapter 3 of this report, companies must take the following steps to achieve Best-in-Class performance: Customer data quality is a learning-by-doing endeavor. Firms must take steps towards comprehensive customer data quality programs by adopting small-scale efforts, such as point solutions, and establishing benchmarks to iteratively refining the process. Best-in-Class organizations experience improvements in customer, organizational and revenue performance. Firms undertaking customer data quality efforts must measure KPIs to determine the ROI from their customer data quality initiatives. The Best-in-Class are more likely to invest in baseline technology enablers, such as data cleansing and analysis, as well as investing in modernized information infrastructures. Firms wishing to achieve Best-in-Class performance must invest, or budget, for these investments now.
3 Page 3 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 Maturity Class Framework...4 Best-in-Class PACE Model...5 Chapter Two: Benchmarking Requirements for Success...7 Competitive Assessment...8 Organizational Capabilities and Technology Enablers...9 Chapter Three: Required Actions Laggard Steps to Success...12 Industry Norm Steps to Success...12 Best-in-Class Steps to Success...13 Appendix A: Research Methodology Appendix B: Related Aberdeen Research Figures Figure 1: Priority of Data Quality Initiatives...4 Figure 2: Implementation of Point Data Quality Solutions...9 Figure 3: Implementation of Pilot Programs Figure 4: Best-in-Class Future Use of Technology Tables Table 1: Top Performance Earn Best-in-Class Status...5 Table 2: Best-in-Class PACE Framework...5 Table 3: Competitive Framework...8 Table 4: PACE Framework Table 5: Maturity Framework Table 6: Relationship between Pace and Competitive Framework... 15
4 Page 4 Chapter One: Benchmarking the Best-in-Class Customer data quality has become a top sales and marketing leadership issue. The Aberdeen Group found that 70% of top performing organizations identified competitive and revenue pressures as the leading drivers for customer data quality programs. Companies at all levels of maturity are concerned about growth and revenue, but the Best-in-Class have realized that data quality is a key source of competitive differentiation. As such, Best-in-Class (80%) organizations are significantly more likely than either the industry norm (52%) or Laggards (48%) to consider data quality initiatives a top priority (Figure 1). Figure 1: Priority of Data Quality Initiatives 80% 52% 49% Fast Facts Best-in-Class firms are: two times more likely to report improvements in customer related metrics (retention, growth, satisfaction) than Laggard firms, five times more likely to report improvements in managing data quality operations (time saved accessing data, time saved through automation), and two times more likely to report improvements in revenue metrics (upselling, cross-selling). BIC AVE LAG Percent Rating Data Quality as a Top Priority Maturity Class Framework Aberdeen used three KPIs to distinguish Best-in-Class, Average and Laggard organizations: 1) customer data integrity (accuracy, completeness and consistency), 2) usability of customer data and 3) time necessary in preparing customer data. Based on this benchmarking method, Best-in-Class, Average and Laggard organizations were segmented as follows (Table 1): Customer data quality pertains to the value of customer data for business-line use via factors such as usability, completeness, consistency, format, relevance, timeliness.
5 Page 5 Table 1: Top Performance Earn Best-in-Class Status Maturity Class Definition 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 94% reported improvement in effectiveness of data integrity 95% reported positive performance in usability of data 89% reported positive performance in time necessary to prepare data 37% reported improvement in effectiveness of data integrity 41% reported positive performance in usability of data 33% reported positive performance in time necessary to prepare data 5% reported improvement in effectiveness of data integrity 6% reported positive performance in usability of data 1% reported positive performance in time necessary to prepare data Best-in-Class PACE Model With growth and profitability as the single biggest pressure, companies at all levels of maturity understand that they must begin to automate more of the customer data management process and broaden access to customer intelligence across organizational silos. More than others, the Best-in-Class have begun to link operational, customer and revenue KPI s to data quality initiatives, as well as assign ownership for delivery to a designated role. However, the distinctive differentiator has been the Best-in-Class enabling of technologies. Table 2: Best-in-Class PACE Framework Pressures Actions Capabilities Enablers Growth and Automate more of the customer data management processes Broaden access to customer intelligence across all frontoffice functions Measurement and oversight of operational, customer, revenue KPIs Assign accountability for execution (i.e. a data steward role) Process to build cross functional data quality goals, priorities and workflows Pilot programs and benchmark evaluations Enterprise data and information management solutions Data collection, cleansing, analysis tools Modernized architectures Archiving, storage tools Extraction, transformation and load tools
6 Page 6 Aberdeen Insights Integrating for Quality Customer data management does not start and end with customer data quality. Organizations must also focus on the bigger picture. The research reveals that Best-in-Class companies are also focused on data integration; ninety-two percent of Best-in-Class firms cited customer data quality as a key priority, while 95% indicated customer data integration as a key priority. How Best-in-Class firms address customer data quality and integration also suggests a linkage between the two priorities. Best-in-Class firms plan consistent strategies for addressing customer data quality and integration, citing broadening access to customer information and increasing automation of data management. The prominent customer data management processes that the Best-in-Class use to develop cross-channel work flows and assign accountability for optimized customer data quality are applicable to customer data integration; furthermore, the top technological solutions of enterprise customer data management and data integration hubs are just as relevant to the issue of optimizing customer data quality. There is a strong correlation between customer data quality and business performance. Improved customer data quality draws improved customer experience, making it easier for organizations to leverage upselling/cross-selling with less time and data management costs. ~John Flipse, Quality Manager, Sajan, Inc., River Falls WI.
7 Page 7 Chapter Two: Benchmarking Requirements for Success Best-in-Class firms know that organizational capabilities and technological enablers go hand-in-hand. Approaching customer data quality as a process that requires continual analysis, benchmarking and refinement leads to differentiation in the areas of customer, organizational and revenue performance. Case Study Die-Tech, Inc. Die-Tech is a firm based in York Haven, Pennsylvania that specializes in precision metal stamping. The company serves the aerospace, automotive, electronics, medial, military and telecommunications industries. Die-Tech assigns top priority to its customer data quality efforts for competitive growth. According to Richard Dennis, Senior Manager at Die-Tech: We re a small company. We believe that our growth has been inhibited by our inability to leverage technology enablers to help us utilize our capacity more effectively. There continues to be a need for a better flow of information, higher quality of data, to more effectively meet customer needs and respond to environmental changes. Similar to other organizations, Die-Tech sees customer data quality impacting bottom-line business performance. Dennis detailed to Aberdeen how customer data quality affects a range of business related areas: The reason we collect, analyze, distribute, and improve the quality of customer data is to impact business performance. Up-selling/cross-selling and innovation go hand-in-hand with retention and satisfaction. Being able to do that quickly and effectively impacts time, and therefore revenue. Managing customer data is a difficult and continuing process, where successful organizations learn by doing. Die-Tech plans on rolling-out a pilot program to benchmark, analyze and define the capabilities, enablers and processes important for its customer data quality needs. We ve had much more success in any project we undertake by starting small, using a pilot program or prototype, then using that information collected to refine expectations, metrics, Dennis continues. To address its customer data quality concerns, Die-Tech plans to broaden access to customer information across the organization and increase automation of customer data management. The firm plans to assign accountability for customer data quality while implementing cross-channel workflow processes. Technology solutions Die-Tech plans to leverage include enterprise data management solutions, data archiving and storage tools and data collection-cleansing-analysis tools. Fast Facts Best-in-Class current and planned adoption of organizational capabilities: 80% Accountability for execution (data steward/ manager) 80% Operational KPIs (metrics measurement, oversight) 79% Process to build cross functional consensus about goals, priorities, workflows Best-in-Class current and planned adoption of technological enablers: 88% Enterprise data and information management 84% Data collection, cleansing and analysis tools 75% Data archiving and storage tools
8 Page 8 Competitive Assessment Survey respondents fell into one of three categories Laggard, Industry Average, or Best-in-Class based on their characteristics in five key categories: (1) process (ability to detect and respond to changing customer data quality needs), (2) organization (corporate focus and collaboration among stakeholders), (3) knowledge (contextualizing customer data), (4) technology (selection or appropriate tools and intelligent deployment of those tools) and (5) performance management (ability of organizations to measure the benefits of technology deployment and use the results to improve key processes). Table 3: Competitive Framework Laggards Average Best-in-Class Invest in data collection, cleansing and analysis Process 71% 92% 100% Invest in data process management 64% 88% 98% Organization Strategy for Customer Data Quality Accountability (data steward/ manager) 27% 53% 83% Strategy for Cross functional consensus on goals, priorities, actions 28% 56% 79% Operational KPI Strategy (measurement, oversight of data management) Knowledge 31% 64% 81% Pilot program, benchmark evaluation strategy 26% 48% 79% Technology investment strategies Technology 35% Enterprise data and information 61% Enterprise data and information management 88% Enterprise data and information management management 70% Data collection, 84% Data collection, 32% Data collection, cleansing and analysis tools cleansing and analysis cleansing and analysis 66% Data archiving and tools tools storage tools 75% Data archiving and 42% Data archiving and storage tools storage tools Measurement of customer-related KPIs (measuring growth, retention, satisfaction) Performance 62% 85% 89% Measurement operational KPIs (measuring time saved in data access and automation) 55% 82% 89% Revenue Metrics Measurement (measuring up-selling, cross-selling) 51% 77% 84% Nearly 100% of the Best-in-Class invest in managing the customer data cycle and data collection-cleansing-analysis, an indication of the importance of customer data quality to their businesses. Furthermore, Best-in-Class firms adapt their organizations for managing the customer data quality process by
9 Page 9 integrating accountability and cross-functional workflows into their customer data programs at roughly three times the rate of Laggards. Organizational Capabilities and Technology Enablers The importance that Best-in-Class organizations place on customer data explains the adoption of certain technology enablers. For example, roughly 62% of Best-in-Class firms that leverage customer data for customer centric planning and strategies deploy data collection-cleansing-analysis and data archivingstorage tools (compared to 30% of Average firms and 19% of Laggards). In fact, roughly one-third of Laggard firms do not budget for data collection-cleansinganalysis tools. The majority of Best-in-Class deploy point solutions (80%) to address their data quality challenges, whereas the Average and Laggards are predominately still in the planning phases (Figure 2). Figure 2: Implementation of Point Data Quality Solutions 7% 16% 10% 47% 12% 19% 11% 19% Through pilot/benchmark programs, the Best-in-Class are also significantly more likely to take a measured approach when implementing technological enablers (Figure 3). For the most part, Best-in-Class firms are focusing investments on data collection and analysis tools (94%), enterprise data management solutions (91%) and data archiving and storage tools (90%) (Figure 4). 5% 7% 8% 12% BIC AVE LAG Currently have Planned 7-12 months out Planned 1-6 months out Planned months out Poor quality and incomplete customer data definitely impacts on sales and marketing activities. A company might have to give more time and efforts to communicate with right targeted customer or market- as a result, poor performance of the company. The philosophy and objective to maintain customer data should be business driven. To effectively manage the data should be IT lead. As the company I work for provides IT services to international clients, customer data management is of paramount important to understand the customer's market and business. Poor customer data shall lead design of wrong services processes for customer which can throw customers daily operations out of gear. Customer data quality is more of a process oriented. To some extent data management can be automated something like developing an software application which monitor the data but largely its a process oriented. Vishwanath Rao Project Leader, L&T Infotech
10 Page 10 Figure 3: Implementation of Pilot Programs 7% 16% 10% 47% 12% 19% 11% 19% 5% 7% 8% 12% BIC AVE LAG Currently have Planned 1-6 months out Planned 7-12 months out Planned months out Organizational capabilities must be in place first to take advantage of either under utilized existing technology or to determine the path to obtaining the correct technology. If organizations don't deploy organizational capabilities with technical enablers, they won't be able to leverage the enablers effectively enough to make the desired quality difference and realize the appropriate return. ~Anonymous Figure 4: Best-in-Class Future Use of Technology 100% 90% 91% 90% 4% 94% 9% 80% 70% 60% 50% 71% 7% 12% 5% 14% 14% 14% 13% 9% 17% 13% 77% 13% 9% 14% 74% 13% 13% 40% 16% 30% 20% 47% 59% 54% 55% 41% 32% 10% 0% Extract, transform and load tools Enterprise data management Data archivingstorage tools Data collection, cleansing and analysis tools Software as service solutions Master data management Currently leverage Plan to leverage 1-6 months Plan to leverage 7-12 months Plan to Leverage months
11 Page 11 Aberdeen Insights The Importance of Measurement The Best-in-Class does not approach customer data quality with a blind eye. Approximately 80% of these organizations have feedback loops to benchmark and refine their customer data management efforts through pilot programs and operational KPIs. Nearly 90% of Best-in-Class organizations then measure the effectiveness of customer data quality with customer, program and revenue performance. Laggards leverage pilot programs, benchmarking programs and measure operational KPIs at almost 30% the rate of the Best-in-Class. The process management, organizational accommodation and metrics measurement of Best-in-Class firms corresponds with their adoption of technology, allowing the Best-in-Class to leverage these technical solutions more effectively. Top performing organizations were nearly three times more likely to deploy technologies such as data collection-cleansing-analysis tools to automate the customer data process, and these companies were three times more likely to assign accountability for overseeing customer data quality to a designated role. Average firms are good at measuring and investing in the right places but need to incorporate organizational capabilities and technical solutions into customer data quality. Average organizations did not lag substantially behind the Best-in- Class for business metrics measurement, nor investment in the data cycle and process management. However, average firms drop-off in benchmarking mechanisms, adapting their organizations for customer data quality efforts, and adopting technical solutions. Average organizations perform moderately in measuring and investing, but they need to leverage organizational capabilities before technical solutions. Laggard organizations perform moderately in measuring business metrics and investing in data collection-cleansing-analysis and the data management process. However, the rate at which Laggards adapt their organizations to customer data and deploy mechanisms for benchmarking, such as pilot programs, makes their use of technical solutions less effective in optimizing customer data quality. Our customer database has become a key competitive advantage. However, the value of the database does not only depend on the number of records but more on their data quality. There is no magic solution to data quality; it must be put in the center of the company -- part of day-to-day business processes. ~Derlin Mputu Kinsa, Business Intelligence Manager, Teleroute Wolters Kluwer Transport Services
12 Page 12 Chapter Three: Required Actions To improve the effectiveness of their customer data quality programs, firms must decide what mix of organizational capabilities and technological enablers suits their needs. Laggard Steps to Success Deploy small-scale customer data quality programs. Smallerscale programs are the first steps towards divisional and enterprise customer data quality programs. Nearly 80% of Best-in-Class firms deploy, or plan to deploy, a benchmarking program within a year, compared to only 28% of Laggards. Assign accountability for customer data quality oversight. Seventy percent of the Best-in-Class leverage, or plan to leverage, a customer data steward in the next year, with Average firms not far behind at 54%. Only 27% of Average firms currently, or plan to, assign customer data quality accountability in the next year, half the rate of Average firms. Adopt baseline technologies for customer data quality. Data collection-cleansing-analysis tools are the first step in optimizing the data cycle. Fifty-three percent of Laggards have no plans to adopt these enablers in the next two years, compared to only 23% of Best-in-Class and Average firms combined. Laggards must reconsider this lack of investment if they wish to obtain Best-in-Class results. Industry Norm Steps to Success Speed up adoption of small-scale customer data quality programs. In the next 12 months, Average firms will meet current levels at which the Best-in-Class deploys point solution, pilot and business unit customer data quality programs; however, they will continue to lag behind the Best-in-Class unless they increase their rate of adoption. Leverage operational, strategic and technological KPIs. Average firms barely surpass Laggards in current adoption of KPIs. Using KPIs for benchmarks and feedback loops will improve the learning experience and consequently improve the effectiveness of customer data quality efforts. Adopt data collection-cleansing-analysis tools. Average firms are six months behind the Best-in-Class in adoption of data collection-cleansing-analysis tools. This enabler directly ties to Average firms top strategic actions of optimizing customer data collection-cleansing- analysis and automating the customer data process. Fast Facts 48% of Laggards cited optimizing the data cycle (collection, cleansing, analysis) as a strategic action, yet only 16% have adopted data collectioncleansing-analysis tools. Only 13% of Average firms currently leverage a pilot or benchmarking program, which may make it difficult to raise their 16% adoption rate of operational, strategic and technological KPIs. Best-in-Class firms align technology KPIs and technology adoption. 45% of Best-in-Class firms leverage or plan to leverage in six months technological KPIs, roughly similar to their current and planned technology adoption.
13 Page 13 Best-in-Class Steps to Success Increase adoption of cross-channel workflow processes. Only 39% of Best-in-Class firms currently deploy cross-channel data management processes, which is directly relevant to Best-in-Class firms use of enterprise data management tools and strategic action of broadening front-office access to customer intelligence. Measure technological KPIs. Thirty-eight percent of Best-in-Class firms have no plans to adopt technological KPIs or plan to do so outside of one year. As Best-in-Class firms strive for their goal of increasing automation of customer data, technological KPIs must accompany new adoption of technological solutions. Technologies will only be useful as technological KPIs benchmark their bottom-line value to organizations customer data quality. Aberdeen Insights Summary Organizational capabilities such as KPIs and technological enablers go hand-inhand. Comparing adoption of KPIs and technologies such as enterprise data management solutions, organizations across all business segments appear to understand the importance of putting the two side-by-side. However, a deterrent to organizations implementing the right KPIs relates to pilot and benchmarking programs. Current and planned adoption of these programs for Best-in-Class, Average and Laggards stands at 79%, 49%, 25% respectively. Only Best-in-Class firms pilot and benchmarking program adoption is greater than their planned leverage of KPIs. 87% of Best-in-Class firms currently adopt or will adopt in the next year enterprise data management solutions, while an average 77% of these firms currently or will adopt within one year operational, strategic and technological KPIs. 61% of Average firms currently adopt or will adopt in the next year enterprise data management solutions, while an average 54% of these firms currently or will adopt operational, strategic and technological KPIs in that time. 36% of Laggards currently or will within one year adopt enterprise data management technologies, while an average 29% currently or will adopt operational, strategic and technological KPIs.
14 Page 14 Appendix A: Research Methodology In May 2007, Aberdeen Group examined the customer data quality practices of over 400 enterprises. Respondents completed an online survey that included questions on: Primary drivers behind customer data quality initiatives How organizations responded to the need for improved customer data quality Organizational capabilities adopted for successful customer data quality programs Technological enablers leveraged by successful customer data quality programs Differentiation between customer data quality programs that resulted in superior customer, organizational and revenue performance. Aberdeen supplemented this online survey effort with telephone interviews with select survey respondents, gathering additional information on customer data quality strategies, experiences and results. The study aimed to identify emerging best practices for data quality, as well as provide a framework by which readers could assess their own management capabilities. Responding enterprises included the following: Job title/function: The research sample included respondents with the following job titles: 23% C-level and upper executive, 26% vice president or director, 37% manager or consultant. Industry: The research sample included respondents who competed in the following industries: 31% high technology, 14% finance related, 12% computers and peripherals, 10% education, 10% telecommunications, 9% transportation, 8% healthcare services, 8% public sector, 8% media/publishing, 7% automotive, 7% insurance/ real-estate/ legal services, 7% retail, 6% construction/ architecture, 6% engineering, 6% industrial manufacturing, 5% distribution, 5% pharmaceuticals, 5% utilities, 4% consumer durable goods, 4% consumer electronics. Geography: Respondents represented the major regions of the world, including 62% North America, 20% Europe, 13% Asia-Pacific, 3% Middle East and Africa, and 2% South America. Company size: Organizations represented included 45% small firms (under $50 million annual revenue), 32% mid-sized firms (between $50 million and $1 billion annual revenue) and 22% large enterprises (over $1 billion annual revenue).
15 Page 15 Table 4: PACE Framework PACE Key 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: 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: Maturity Framework Maturity Framework Key The Aberdeen Maturity Framework defines enterprises as falling into one of the following three levels of practices and performance: Best-in-Class (20%) -- Customer data quality practices that are the best currently being employed and significantly superior to the industry norm, and result in the top industry performance. Industry norm (50%) -- Customer data quality practices that represent the average or norm, and result in average industry performance. Laggards (30%) -- Customer data quality 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 customer data quality process management? What is the efficiency and effectiveness of this process? Organization -- How do organizations adapt to optimize customer data quality? Knowledge -- What kind of measurements and information do organizations leverage in customer data quality programs? Technology -- What level of automation have you used to support this process? How is this automation integrated and aligned? Performance -- What is the bottom line for customer, organizational and revenue performance? Table 6: Relationship between Pace and Competitive Framework PACE and Competitive Framework How They Interact Aberdeen research indicates that companies that identify the most significant pressures and take the most transformational and effective actions are most likely to achieve superior performance. The level of competitive performance a company achieves is strongly determined by the PACE choices they make and how well they execute.
16 Page 16 Appendix B: Related Aberdeen Research Related Aberdeen research that forms a companion or reference to this report include: Master Data Management in Data Migration (Apr 2007) Customer Data Management: How Leaders Attain Tangible ROI (Jun 2006) Customer Data Management: Selecting the Right Approach (May 2006) Customer Intelligence Management: Converting Data to Profits (Dec 2005) Information on these and any other Aberdeen publications can be found at Author: Noel Le, Research Analyst, Customer Relationship Management Founded in 1988, Aberdeen Group is the technology- driven research destination of choice for the global business executive. Aberdeen Group has over 100,000 research members in over 36 countries around the world that both participate in and direct the most comprehensive technology-driven value chain research in the market. Through its continued factbased research, benchmarking, and actionable analysis, Aberdeen Group offers global business and technology executives a unique mix of actionable research, KPIs, tools, and services. This document is the result of research performed by Aberdeen Group. Aberdeen Group believes its findings are objective 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, stored in a retrieval system, or transmitted in any form or by any means without prior written consent by Aberdeen Group, Inc.
Small to Midsize Enterprises Profiting from Innovation March 2007 Executive Summary S mall to midsize enterprises (SMEs) are actively pursuing product development improvements to deliver more innovative
Benchmarking VoIP Performance Management March 2008 Page 2 Executive Summary Aberdeen surveyed 159 organizations to identify best practices for managing Voice over Internet Protocol (VoIP). This report
thyu Engineering Change Management 2.0: Better Business Decisions from Intelligent Change Management September 2007 Page 2 Executive Summary Managing engineering change has always been hard, and is a regular
Onboarding 2013 A New Look at New Hires April 2013 Madeline Laurano Page 2 Executive Summary The first impression an organization makes is often the most critical not only with customers and key stakeholders
Human Capital Management Trends 2013 It s a Brave New World January 2013 Mollie Lombardi and Madeline Laurano Page 2 Executive Summary Human capital management is a key business initiative. Without insight
Lead Prioritization and Scoring The Path to Higher Conversion May 2008 Page 2 Executive Summary This report identifies best practices in lead scoring and prioritization by analyzing the processes, capabilities,
Customer Analytics Segmentation Beyond Demographics August 2008 Ian Michiels Page 2 Executive Summary This report isolates best practices in customer analytics and customer segmentation. The report articulates
ITSM IT Transforms Itself into a August 2007 Page 2 Executive Summary This report is a roadmap for IT organizations that desire to shift from a focus on technology and internal needs to how it services
Measuring Marketing Performance: The BI Roadmap to Information Nirvana October 2007 ~ Underwritten, in Part, by ~ Page 2 Executive Summary This report is a roadmap for marketing and business executives
Sales Effectiveness: Getting Sales Back July 2007 Page 2 Executive Summary The pressures of longer sales cycles and declining sales productivity are forcing Best-in-Class companies to streamline and automate
Page 2 Executive Summary Talent acquisition has evolved from a tactical, back-office process to a strategic endeavor that directly impacts organizational growth. Organizations struggling to identify and
Real Estate and Facilities Lifecycle Management Page 2 Executive Summary A business environment that is unyielding in its pursuit of bottom-line savings and process efficiency has driven enterprises to
To ERP or Not to ERP: It Isn't Even a Question Enterprise Resource Planning (ERP) software is designed to be the system of record for operating and managing a business. Growing up out of the Manufacturing
The Next Generation of Manufacturing Systems January 2008 Page 2 Executive Summary The Manufacturing Execution System (MES) has outlived its original definition. Faced with dynamic market changes and competitive
Web Analytics: The Crystal Ball of Customer Behavior April 2007 Page 2 Executive Summary Companies maintaining web sites today have an incredible wealth of customer information available to them in the
Application Performance in Complex and Hybrid Environments January 2012 Jim Rapoza ~ Underwritten, in Part, by ~ Page 2 Executive Summary Companies that do not rise to the challenges of ensuring performance
March 2011 Kevin Prouty ~ Underwritten, in Part, by ~ Page 2 Executive Summary Complex manufacturers face both similar and at the same time different challenges compared to other manufacturers. Complex
Avaya Users Deploy Best-in-Class Practices to Improve Contact Center Between March and July of 2012, Aberdeen surveyed 478 customer care executives regarding their contact center activities. Findings from
The Product Portfolio Management Benchmark Report Achieving Maximum Product Value August, 2006 The Product Portfolio Management Benchmark Report Executive Summary Issue at Hand AberdeenGroup benchmarks
Project Management in Software Development Taking the Complexity Out of June 2012 Nick Castellina, Nuris Ismail Project Management in Software Development: Taking the Complexity Out of In a survey conducted
Disaster Avoidance and Disaster Recovery: May 2010 Dick Csaplar Page 2 Executive Summary In May of 2010 Aberdeen surveyed over 100 organizations that had a formal Disaster Recovery (DR) program to learn
A View into the Best-in-Class Strategic Meetings Management Program June 2011 Christopher J. Dwyer Page 2 Executive Summary The modern organization s planning, execution and management of strategic meetings
Assessing Your Business Analytics Initiatives Eight Metrics That Matter WHITE PAPER SAS White Paper Table of Contents Introduction.... 1 The Metrics... 1 Business Analytics Benchmark Study.... 3 Overall
July, 2007 The Total Cost of Ownership Total Cost of Ownership (TCO) remains a significant factor that influences Enterprise Resource Planning () strategies and decisions. While total costs can and should
Global Manufacturing Operations Management August 2008 Page 2 Page 3 Executive Summary Executives face numerous challenges in managing global manufacturing operations and successfully collaborating across
Performance Management in the Midmarket January 2010 David Hatch, Max Gladstone Page 2 Executive Summary Executives and line-of-business management in mid-sized businesses are increasingly feeling pressure
Predictive Analytics The Right Tool for Tough Times February 2010 David White Page 2 Executive Summary Enterprises are under pressure to predict the future behavior of customers and potential customers,
Smart Machines Lead to Smarter Service: Remote Intelligence Signals Profitable Resolution The emergence of machine-to-machine (M2M) enabled equipment is driving a large growth of Field Service-based data
PRACTICES REPORT BEST PRACTICES SURVEY: AGGREGATE FINDINGS REPORT Business Intelligence and Analytics: Leveraging Information for Value Creation and Competitive Advantage April 2007 Table of Contents Program
Accenture Life Sciences Rethink Reshape Restructure for better patient outcomes The Rising Opportunity for CMO-CIO Collaboration in the Pharmaceutical Industry Demographics Life Sciences Pharma/Biotech:
PLM Software as a Service : Enterprise Strategies Report: Improving Product Development Performance for Smaller Manufacturers Page 2 of 7 Overview Achieving product profitability in the current product
Travel & Entertainment Technologies and Services Overcoming Obstacles Through Data Insight May 2007 ~ Underwritten in part by ~ Page 2 Executive Summary As reported in previous Aberdeen reports, Travel
A Guide for Implementing Best-in-Class Time and Attendance Strategies In July and August, Aberdeen Group surveyed more than 300 organizations in order to determine Best-in-Class practices in managing core
Employee Engagement Drives Client Satisfaction and Employee Success in In professional services, business success is achieved through employee success. Organizations that prioritize top talent gain competitive
Engineering Change: The Key to Cost Reduction for Competitive The automotive industry has seen significant change over the last couple of decades, but looking to the future, there will be even more significant
Why Printed Circuit Board Design Matters to the Executive: How PCBs Are a February 2010 Michelle Boucher Page 2 Executive Summary In today's competitive market place, companies are looking for new ways
Workforce Management in the Contact Center Optimizing Agent Scheduling and Productivity to Improve Customer Experience Results June 2012 Omer Minkara Workforce Management in the Contact Center: Optimizing
Predictive Analytics The Right Tool for Tough Times February 2010 David White Page 2 Executive Summary Enterprises are under pressure to predict the future behavior of customers and potential customers,
Maximizing Return on Assets and Emerging Trends June 2008 ~ Underwritten, in Part, by ~ Page 2 Executive Summary Aberdeen Group's latest research in Enterprise Asset Management (EAM) reveals that Best-in-Class
Operational KPIs and Performance Management Are Your Daily Decisions Based on Fact? August 2008 Page 2 Executive Summary Businesses thrive or fail based on their ability to identify, define, track, and
2010 Project Management Report Standardized Best Practices and Technology Adoption in the AEC Industry January 2010 Cindy Jutras Page 2 Executive Summary A difficult economy and global competition leave
The Challenge of Multi-Site Warehouse and Order Management Meeting Customer Requirements While Lowering Logistics Costs April 2007 Executive Summary A s supply chains become increasingly more global in
Information Governance Workshop David Zanotta, Ph.D. Vice President, Global Data Management & Governance - PMO Recognition of Information Governance in Industry Research firms have begun to recognize the
December, 2011 SaaS and Cloud ERP Trends, Observations, and Performance 2011 Over the past five years, Aberdeen has been measuring the willingness of organizations to consider Software as a Service (SaaS)
A Custom Technology Adoption Profile Commissioned by Trillium Software November, 2011 Introduction Enterprise organizations indicate that they place significant importance on data quality and make a strong
Spend Analysis: The Nexus of Spend Management November 2011 Constantine G. Limberakis Page 2 Executive Summary Due in part to the number of solutions that contain spend information in organizations today,
The Convergence of Field Service and Asset Bridging the Gap June 2007 Page 2 Executive Summary A berdeen s research shows that 72% of field service organizations and 67% of and Repair (MRO) functions view
Workforce Management: Controlling Costs, Delivering Results Organizations today must balance the need to run an efficient and costeffective operation while remaining agile and flexible to meet both customer
Mobile Sales Force Effectiveness Strategies Beyond Mobility Utilization March 2007 Executive Summary Sales teams are under constant pressure to meet customer expectations, while bringing in revenue for
Fixing First-Time Fix: Repairing Field Service Efficiency to Enhance Customer First-time fix is one of the most vital metrics in gauging field service performance. While workforce utilization, productivity,
Is it in the Cards? December 2007 Page 2 Executive Summary This research benchmark provides insight and recommendations for all organizations that are looking to integrate their logical security infrastructure
AberdeenGroup Procurement in New Product Development: Ensuring Profit from Innovation Business Value Research Series March 2006 Executive Summary P roduct innovation is a team sport. Product strategists
Lead Nurturing The Secret to Successful Lead Generation November 2008 Ian Michiels Page 2 Executive Summary This report outlines the essential role lead nurturing will play in weathering the economic downturn.
ENSURING TIMELY AND ACCURATE FINANCIAL PLANS, BUDGETS, AND FORECASTS THROUGH AUTOMATION April, 2015 Nick Castellina, Research Director, Business Planning and Execution Report Highlights p3 p5 p7 p8 Best-in-Class
Fortune 500 Medical Devices Company Addresses Unique Device Identification New FDA regulation was driver for new data governance and technology strategies that could be leveraged for enterprise-wide benefit
e AberdeenGroup New Product Development: Profiting from Innovation Business Value Research Series December 2005 Executive Summary D eveloping a new technology or solution that fills a need for a customer
Ball Corporation Ball Corporation strengthens global account management by using CRM in the cloud For more than 130 years, Ball Corporation has led its industry by providing innovative, valuable packaging
Data Governance Primer A PPDM Workshop March 2015 Agenda - SETTING THE STAGE - DATA GOVERNANCE BASICS - METHODOLOGY - KEYS TO SUCCESS Copyright 2015 Noah Consulting LLC. All Rights Reserved. Industry Drivers
Microsoft Dynamics Customer Solution Case Study Ball Corporation Strengthens Global Account Management by Using CRM in the Cloud Overview Country or region: United States and Europe Industry: Manufacturing
B2B Integration and Collaboration: Strategies for Building a ROI Business Aberdeen conducted a survey of over 191 supply chain executives (Chief Supply Chain Officer Survey, January 2011) where only 14%
October 2009 Ian Michiels Page 2 Executive Summary This report explores how organizations are using data to impact marketing performance and marketing effectiveness. CEO s and the Board of Directors at
Creating a Business Intelligence Competency Center to Accelerate Healthcare Performance Improvement Bruce Eckert, National Practice Director, Advisory Group Ramesh Sakiri, Executive Consultant, Healthcare
Streamlining Sales and Use Tax Management June 2011 William Jan Page 2 Executive Summary Being exposed to government tax audits and having to deal with fines and penalties are issues that companies fear.
Embedded BI Boosting Analytical Adoption and Engagement March 2012 Michael Lock Embedded BI: Boosting Analytical Adoption and Engagement In today's business climate, the challenge of effective decision-making
HRO - Early Findings and Best-in-Class Talent Management Metrics - Kevin Martin Vice President and Principal Analyst, Human Capital Management Aberdeen Group October 23, 2008 AberdeenGroup 2007 Human Capital
ERP in Wholesale and Distribution Extending the Enterprise to Extend Profits October 2012 Nick Castellina ERP in Wholesale and Distribution: Extending the Enterprise to Extend Profits Enterprise Resource
THE FUTURE OF CONTENT MARKETING: THE AGE OF CONTENT SCIENCE October, 2015 è Jessie Coan, Senior Director of Content, Marketing Effectiveness and Strategy Report Highlights p2 p4 p5 p8 The Best-in-Class
Excellence: Synchronizing August 2007 Page 2 Executive Summary Gunning for improved customer satisfaction, loyalty and reduced service costs, service organizations are taking steps to tightly align service
Chapter 3: Strategic CRM Overview Topics discussed: CRM perspectives The components of strategic CRM Steps in developing a CRM strategy Case Study: CRM implementation at International Business Machines,
Manufacturing October 2, 2006 Key Facts Total Cost of Ownership (TCO) has long been recognized as a significant factor in ERP strategies and decisions. Yet while both end users and ERP solution providers
Agile Master Data Management TM : Data Governance in Action A whitepaper by First San Francisco Partners First San Francisco Partners Whitepaper Executive Summary What do data management, master data management,
ERP Selection Finding the Right Fit October 2012 Nick Castellina, Peter Krensky Finding a needle in a haystack is hard, but the task pales in comparison to finding a specific needle in a pile of needles.
Migrating to Customer-Centric Point of Service February 2008 Page 2 Executive Summary This report focuses on the Best-in-Class retail methodology of upgrading legacy Point of Sale (POS) or Point of Service
July 2007 ~ Underwritten in part, by~ Page 2 Executive Summary Aberdeen s research shows that field service organizations must deal with increasing customer demand for faster service resolution and escalating
Customer Experience Management Using the Power of Analytics to Optimize January 2012 Omer Minkara ~ Underwritten, in Part, by ~ Page 2 Executive Summary Faced with economic turmoil and increasing competition,
Your consent to our cookies if you continue to use this website.