A New Retail Paradigm: Solving Big Data to Enhance Real-Time Retailing

Transcription

1 June 2012 A New Retail Paradigm: Solving Big Data to Enhance Real-Time Retailing Data from Aberdeen s October 2011 report, Business Intelligence Enhancements in Retail, indicates that for 62% of retailers, escalating big datarelated complexities within their enterprises makes day-to-day decisionmaking and creating a single view of the product and customer an arduous task. The problem is not just data aggregation but also lack of real-time access to customer and business information. This impedes customercentricity and business process continuity. Another roadblock for retailers is also the volume, sources, complexity, and velocity of data. Aberdeen's latest April 2012 survey of 50 retail enterprises shows that 70% of retailers are currently grappling with, on average, at least eight disparate sources of business and customer data (both structured and un-structured) within their organization. Such data variability fluctuates quite a bit due to seasonality, number of Stock Keeping Units (SKUs), and types of customers. The collection and analysis of customer and business data, from its raw form of analytical data to its polished form of predictive Business Intelligence (BI) helps to increase precision and real-time retailing. This includes: product innovation, supply chain, pricing, customer engagement, promotions and marketing, and other value chain areas. The benefits associated with realtime and precision retailing can be realized at every stage of the crosschannel retail lifecycle - from product design stage to customer fulfillment, and loyalty creation. This Analyst Insight addresses the aforementioned complexities and benefits, and identifies a best practices roadmap that enables companies to apply big data initiatives for real-time customer engagement and agile operations. Four main issues are also addressed: Cross-channel impact of big data Consumer pressures and organizational challenges surrounding big data Capabilities and enablers to tame big customer and business data Actionable recommendations for overcoming big data complexities Analyst Insight Aberdeen s Insights provide the analyst's perspective on the research as drawn from an aggregated view of research surveys, interviews, and data analysis Big Data in Retail Defined Big data in retail and consumer markets refers to the overall size or extent of active data an organization stores, as well as the size of the data sets it uses for its business intelligence and analysis. Big data is also used to describe the common difficulties associated with this active data: size or extent (storing and accessing the data), speed (how fast the data must be captured, processed, analyzed and delivered), complexity (the sophistication and level of detail in the data analysis), and types (the number of different formats the data takes). The Cross-Channel Impact of Big Data For today's consumer, who has multi-faceted channel and shopping preferences, retailers need to be prepared at all times to provide one view of the customer and product across all channels. However, this has not been easy for a majority of retailers. The need for addressing big data is a 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.

2 Page 2 cross-channel challenge and a transformation need for retailers. Consider the following trends: The rise in digital retailing. Online (used by two-thirds of retailers) and mobile commerce (used by one-third of retailers) have given consumers increased amounts of product information and ease of access to competitive alternatives. For instance, smartphone-based UPC scanning capabilities, as well as mobile search engine accessibility, has allowed both new and existing customers to closely examine product price and details to make a more immediate and informed decision within and outside the four walls of a store. Retailers are challenged to compete with this reality by offering a more personalized, digital retailing experience or lose out to a competitor. In-store transformation. The proliferation of retail categories in non-traditional retail formats (such as Wal-Mart s in-store banking, optometry, and hair salon offerings) pressure these organizations to further scrutinize their customer base to match established purchase patterns with new purchase patterns. Moreover, multiple store formats appeal to product affinity and preferences of multiple customer segments. Customer segmentation requires re-thinking of existing store models, precision merchandising, and inventory localization requirements. Voice retailing integration. The increased use of voice retailing by a third of retailers provides not just another channel sales avenue but also valuable information about customer experience before, during, and after a sale. This information yields important clues about future purchasing patterns across all channels. A stated focus on electronics, for example, may yield success in the cross-selling of extension cords, batteries, and other accessories online or in the store. An extended supply chain. Two-thirds of retailers are far from creating a unified view of product and customer data across all channels to understand category-level affinity and preferences. A unified view of product, order management, and customer data also aids accurate and timely supply chain planning and logistics to deliver the right product, at the right place, at the right time. Aberdeen's March 2012 Best-in-Class Strategies to Overcome Disconnected Customer Experience report indicates that only a third of retailers overall are sharing customer and product information across all channels to create one view of the product and customer. Upon taking a deeper look, retailers find that creating a customer-centric and localized assortment-mix (71%), shelf-level inventory optimization (65%), and product innovation (60%) are the most affected value chain competencies due to big data issues. This means that while retailers want to be more customer-centric, addressing big data issues is "front and center" in the way of cross-channel customer-centric retailing. "Impact is more from lack of analysis / learning from big data than from data issues themselves." ~Vice-President, Logistics, Large Apparel Retailer, North America

3 Page 3 Need for Increased Consumer Insights is Paramount As detailed in the previous section, as customer shopping options and channels proliferate, 59% of retailers are compelled to respond to the need for creating granular consumer insights in areas such as; cross-channel buying behavior, share of wallet, market basket analysis, and segmentation strategies (Figure 1). Figure 1: Lack of Consumer Insights is a Top Market Pain-Point Need to increase overall consumer insight 59% Need to improve speed of access to relevant business data 45% Need to move beyond data integration stage Need to improve data accessibility for customer-facing employees Improve ease-of-use of BI for nontechnical employees 18% 22% 28% 0% 10% 20% 30% 40% 50% 60% 70% Percent of Respondents Source: Aberdeen Group, April 2012 More often than not, retailers blame disparate data sources and the enormity of active customer data as the primary reason for lack of adequate and timely consumer insights that inhibits new customer acquisition, customer retention, and re-activation. Currently, the total amount of active (non-archive or backup) business data that retailers store is between 1TB and 25 TB for 38% of retailers, and another 21% store significantly higher amounts of business data. One of the most fundamental challenges for retailers is revenue growth despite any economic climate, positive or negative. To accomplish this goal: 81% of retailers are relying on increased customer insight for new customer acquisition 75% are increasing efforts to derive additional value from existing customers - the challenge, however, is how to accomplish this task effectively The enormity of customer data coupled with inadequate guidelines for agile data-driven insights fuels the inability to conduct timely analysis. This inability in turn curtails effective customer-centric merchandising, marketing, promotions, supply chain planning and pricing strategies, among other critical operational competencies. The question that often perplexes retailers is how to accurately analyze customer data and predict customer Variety of Different Data Formats- Big Data in Retail (by % of respondents) Pricing data- 68% Point-of-sale transaction data (in-store, online, call center, and other channels)- 65% Supplier community business-to-business data (e.g. EDI)- 65% Shipping data- 55% Text resulting from business activities- 55% Merchandising data- 45% Other data sources- 43% Social media data- 39% Human resources data- 30%

4 Page 4 behavior in order to provide timely updates for retail business leaders, departmental heads, managers and associates. The second highest business pressure according to 45% of retailers is related to faster access to business information. More than a fourth (28%) of all retailers indicated that there is a lag time of at least "a week" between the time they receive critical actionable operation information and the actual business events. For instance, delayed reporting of inventory activity can severely hinder timely on-the-shelf response to customers, suppliers, or internal stakeholders. This in turn hampers the pace of new retail initiatives, business transformation, and recovery strategies that turnaround a poor sales cycle. Moreover, growing hyper-competitiveness on the shelf, has led to the need for better time-to-information, time-to-decision, and improved enterprise-wide visibility towards Key Performance Indicators (KPIs). Another top pressure is related to the inability to move processes beyond the data integration stage toward departmental and user-level access, analysis, and reporting. This need for on-demand self-service reporting and data visualization is not just required at corporate headquarters but also down to the channel or store-level. Aberdeen's April 2012 retail big data and analytics survey indicates that 66% of retailers are unable to provide uniform self-service reporting and data access capabilities that are otherwise available to the core super user team. For instance, customer-facing employees need readily accessible real-time sales and service performance reporting, customer order history, real-time inventory on-hand data access, product information, cross-selling and up-selling data, among other resources. This information enables store or channel-level employees to assist customers in the best possible way and complete the customer experience process in an effective way. However, only 25% of retailers indicate that they have uniformly executed downstream information access among customer-facing employees. This has hurt in-store customer engagement culture the most. Other channel associates (e.g. online or call center agents) who are not necessarily customer-facing, do have access to at least some web-based product information that store employees often lack at the Point-of-Service (POS). Organizational Challenges Data from the January 2012 Omni-Channel Retail Experience report shows that 48% of retailers store customer and business data in two to five disparate systems. Another 20% of retailers store data in six to 15 distinct systems. Relevant customer and business data resides in operational silos leading to data duplication, batch processing, and delays associated with structured and unstructured data integration with other business systems such as: POS, Customer Relationship Management (CRM), marketing management, promotions, pricing, inventory management, etc. As shown in Figure 2, companies find structured and unstructured data integration with other systems most challenging. These companies are also "Too much unstructured data causes delays in compiling actionable information in needed time frames. This relates to CRM, customer data/view; competitive analysis; social engagement; product line evaluation and sales promotional programs." ~Vice-President, Marketing, Mid-Market Retailer, North America

5 Page 5 most likely to experience "delayed time-to-information" and "slower timeto-decision" among customer-facing and non-customer-facing employees. Structured data sources in retail relate to POS, supply chain, pricing, shipping data, etc. Unstructured data relates to text resulting from business activities, data from social channels, and other data sources. Figure 2: Top Challenges Lack of structured / unstructured data integration with business systems 35% Legacy processes and systems 32% Little or no expertise related to analyzing large amounts of data 29% Too much unstructured data 29% Lack of data analysis mandate 26% 0% 5% 10% 15% 20% 25% 30% 35% 40% Percentage of Respondents Source: Aberdeen Group, April 2012 Secondly, for 32% of companies, business/customer data management and related intelligence is fraught with legacy system obstacles. Multigenerational and legacy processes and systems hinder the advancement of ancross-channel customer experience. Unless channel data is centralized and shared in real-time, there is little chance of timely coordination between channels. Often, the end result is duplicated efforts, duplicated data, and incremental time and money spent on duplicate customers and processes. The line-of-business and IT executives in retail must seek to address unified big data management in multi-tier, multi-site, and multi-channel user organizations. Multi-generational and legacy technology applications do not allow organizations to remain agile enough to meet the changing needs and desires of their customers. Instead, the users of these legacy technologies are saddled with out-of-date technology capabilities, and as a result, an outof-date and out-of-touch approach to the cross-channel customer experience. A related challenge facing 29% of companies is scant expertise within IT teams to handle large amounts of data. As more and more companies deem IT as a cost center, adequate human resource talent and associated expenditure is a constant headache for executives. This is despite the fact that 88% of retailers expect the fastest big data initiative ROI from agile business forecasting value and agile business "Systems have improved and this has led to better customer information being available. This has helped us sustain a good performance despite the economic and other natural disasters impacting our industry in the last year." ~ Director, Marketing, Large Consumer Electronics Retailer, Asia-Pacific Region

6 Page 6 execution value. According to Aberdeen's analysis, the disconnect in what companies want from data insights and their actions, lies in the fact that nearly half (42%) of big data decisions are still taken by the CIO, the next closest job-role associated with big data-related decision making is the CMO (13%). Somehow, retailers have kept big data and business intelligence-related process and system improvement decisions non-collaborative, where IT and line of business do not see eye-to-eye. However, this process of collective data and BI decision-making needs to be reversed for establishing usage and access equilibrium. Realized and Unrealized Benefits of Big Data Strategies The four leading areas where retailers expect big data initiative ROI include: business execution information; transparent sales forecasting; product and customer service innovation; predictive product innovation and customer service capabilities (see first four rows of Table 1). However, the realized gains have been in the teens and low double-digits at best in the aforementioned areas. In fact, the bottom three areas for expected ROI, namely, performance information, deeper customer segmentation, and one view of product information have seen better comparative realization of actual gains from big data initiatives. Table 1: Expected Benefits vs. Actual Benefits of Big Data Initiative Data Summary Expected Actual Agile business execution value as 90% 23% information is easily available Improved product and service 89% 22% innovation Agile business forecasting value as 87% 19% information is transparent Enhanced predicting capabilities 86% 17% related to product and customer problems Detailed performance information 79% 36% available for rectifying errors Possibilities for deeper customer 77% 42% segmentation Assistance with development of one view of product information 72% 34% Source: Aberdeen Group, April 2012 The reasons are short-term vs. long-term realized gains. Retailers applied better organizational focus when it comes to the easiest and fastest route to big data investment justification. In the last two years, more than a third of

7 Page 7 companies focused on big data initiatives that are geared towards customer segmentation for tactical business objectives, internal employee and external trading partner/supplier performance management, and centralized product information management due to expansive cross-channel needs. Business execution correction, product/service innovation, and predictive capabilities were delayed, getting pushed into the category of "long-term aspirational gains" or "long-term roadmap goals." Retailers show low levels of process maturity in handling complex and real-time big data models that can be geared towards accurate forecasts and predictive sales and operations. The value of business forecasting and predictive sales and operations is undeniable. For instance, in the area of predictive capabilities, two key process capabilities have emerged as top strategies retailers are focusing on in the immediate future: Predict customer purchasing behavior (66% of retailers planning, 19% current) Real-time analysis based on segmentation, affinity, and preference (64% of retailers planning, 25% current) Big Data Capabilities So how can retailers maximize gains from big data initiatives described in the previous section? The next two sections address key ways in attaining benefits from big data initiatives. "Detailed knowledge of how customers perceive our products, our services, our promotions, and the brands in all channels give us the most important facts to decide how to be closely personal with our customers." ~Director of Marketing, Large Specialty Retailers, North America To execute a cross-channel big data strategy within retail, enterprises must develop a solid foundation of business-to-consumer process, organizational, knowledge, and performance management capabilities. The top three currently deployed capabilities relate to setting-up guidelines for data gathering, security, and external sharing of data with business partners/suppliers (Table 2). Guidelines are required as not all departments are alike when it comes to the role of solving big data aggregation, analysis, and access. The capabilities that are critical for laying out common guidelines include: data access, coding, cubing, querying, security, and jobrole based reporting need to be presented via a common set of data presentation in varied formats of data delivery tools. The disparate analytics presentation formats (i.e. dashboards vs. spreadsheets) lead to lack of a unified view of the brand, customer, and day-to-day operations. For the above reasons, big data and BI-related processes require adequate IT expertise, and line of business collaboration to solve big data analyses, quantitative / statistical analytics or dashboards and drill downs. Only a third of retailers possess the IT and line of business expertise today to address big data, however, 55% of retailers plan to adopt these capabilities in the foreseeable future. If internal resources are inadequate or cost prohibitive, then companies can turn towards managed and outsourced services for integrating structured and un-structured data with customer-facing and back-end systems. This can create a homogenous way of treating the big data and lack of consumer/business insights in a cost-effective manner. The

8 Page 8 April 2012 retail big data and analytics survey indicates that 36% plan to use IT / systems integrator consulting services within two years. In fact, within the next 24 months, some of the leading retail data and infrastructure - related planned technology improvements for companies that aspire to become Best-in-Class include delivery models such as: managed/outsourced services (33%), and cloud services (36%). Table 2: Current and Planned Process and Organization Capabilities Data Summary Currently Use Plan to Use Established data gathering and assembly 54% 43% guidelines Guidelines for external data sharing (e.g. 52% 30% EDI) with suppliers and trading partners Guidelines for data security, privacy, 48% 48% and consumer / client rights protection Alignment of new product releases with 21% 59% customer preference and affinity Job-role based access to customer 36% 49% behavior and purchase trends IT expertise to solve Big Data analyses, 31% 55% quantitative / statistical analytics or dashboards and drill downs The ability to provide performance data at the associate level 15% 55% Source: Aberdeen Group, April 2012 In studying the varied cases of big data initiatives in retail organizations, Aberdeen's analysis indicates that retailers need an enterprise-wide big data strategy. These companies must apply an enterprise-wide strategy if they want to see customer and business dynamics through the same prism in order to scale, differentiate, and grow in these challenging times. Finally, as seen in Table 3, as companies embark upon an enterprise-wide big data complexity solving mission, it is important to take into consideration the extent of real-time data capture (from varied sources) capabilities that companies currently possess or plan to use in the future. These capabilities most likely impact "time-to-information" and "time-to-decision" goals as companies also need to ensure rapid data processing and intelligence so that all departments and teams have an equal measure of real-time customer needs, response times, collaborative, and performance improvement requirements. For instance, retailers not only need to capture POS data in real-time across channels but also drive real-time promotions to customers by analyzing POS and loyalty data so that channels can benefit from real-time offers and customer mapping. The real-time nature or velocity of data capture,

9 Page 9 processing, analysis, and reporting depends on several factors such as database processing, data mining grids, in-memory computing processes, etc. We will explore some of these technology enablers in the next section. Table 3: Knowledge Capabilities Data Summary Currently Use Plan to Use Real-time customer data capture at the point of service (POS) Real-time customer data capture at the call center Real-time customer data capture at the website Real-time customer data capture at the headquarters Real-time customer data capture within online communities 55% 29% 44% 30% 44% 50% 37% 45% 27% 54% Source: Aberdeen Group, April 2012 Technology Enablers There are four broad categories of big data complexity-solving enablers subdivided in four broad groups: size or extent (storing and accessing the data); speed (how fast the data must be captured, processed, analyzed and delivered); complexity (the sophistication and level of detail in the data analysis), and types (the number of different formats the data takes). For addressing data size or extent needs, on average a third of retailers indicate usage of distributed databases, data integration tools, enterprise data warehouses, distributed file systems, cloud computing data center tools, among other solutions that support data aggregation and assembly. From a data speed and complexity standpoint, retailers currently indicate affinity towards real-time enterprise-level data processing and intelligence tools such as in-memory computing processes/analytics, cloud computing data delivery models, and Massively Parallel Processing (MPP) databases. At least a third of retailers plan to invest in these tools in the near future. As shown in Table 4, retail databases initiatives for real-time customer engagement and agile operations can be supported through the use of inmemory computing processes. These tools help support real-time data processing and delivery of intelligence as in-memory computing removes the latency factor of storing and accessing from multiple disks, on multiple computers that are installed across multiple retail store, channel or headquarter locations. In-memory processes help move data and intelligence faster than other processes as in-memory processes move data from different computers to the central memory location.

10 Page 10 Data from Aberdeen's April 2012 retail big data and analytics survey indicated that companies that have adopted in-memory computing processes are two-times more likely to experience real-time operational information availability, and as a result, faster decision making compared to retailers that do not use in-memory computing. Even in the area of retail data processing and intelligence-related complexity, our data shows that inmemory computing processes/analytics and MPP support close to actual business activity availability of information. The real-time multi-location data processing capability of in-memory computing can be of immense value as at least 50% of retailers are still executing overnight or delayed polling of POS data for various types of customer and business analyses. In fact, in-memory computing can enable faster and more real-time access to customer and business information in the following areas: 1. One view of the customer through segmented customer purchase behavior, affinity, and preferences-related insights for optimized assortments, real-time pricing management and promotions management 2. Easier mining and granular shelf-level insights provide deeper merchandising insights for category optimization, in-stock, and store/channel product sell-through strategies 3. Creating one view of product, inventory, and order management data-from design stage to customer fulfillment/delivery 4. Solve retail supply chain big data with improved product visibility, data exchange, and supplier collaboration Table 4: Enablers Data Summary Currently Use Plan to Use In-memory computing 35% 36% processes/analytics Data cleansing tools 24% 55% Customer segmentation application 32% 52% Source: Aberdeen Group, April 2012 Finally, in terms of types or formats (the number of different formats the data processing and intelligence takes), departmental and store-level data access, viewing, and analysis capabilities are also important, and this is where the concepts of dashboards and scorecards come into play. Data from the April 2012 retail big data and analytics survey indicates that at least half of the companies plan to use dashboards for multiple departments and functions. Real-time data processing via inmemory computing can help support faster data uploads to the enterprise dashboards and scorecards. "Our greatest big data complexity is difficulty in matching strategy to actions and outcomes. It is very difficult to set the right KPIs and even more difficult to measure them." ~ Senior Executive, SMB Retailer, Asia-Pacific Region

11 Page 11 Conclusion The enormity of data coupled with lack of adequate guidelines for agile datadriven insights fuels the inability to conduct timely analysis. This inability in turn curtails effective retail planning and execution within: customer-centric merchandising, marketing, promotions, supply chain planning and pricing strategies, among other critical customer value chain areas. Few retailers would argue that a difficult economic recovery requires new and creative ways of reaching customers to offer products and services. Most of these creative ways depend on a closer, more intimate understanding of consumer activity at all touch points to personalize the shopping interaction. This is for the benefit of the retailer in the form of increased cross-sells, up-sells and consumer loyalty. It is also for the benefit of the customer in the form of a more direct, informed, and relevant experience to decrease the time needed for product searches and overall interaction steps. In order to realize these benefits, however, retailers must rely on solving big data issues to help guide this personalized selling experience goal into fruition. This can start with data collection processes at, for example, the POS, continue into a predictive analytical model, and end with increased business intelligence for a dynamic, macro and micro view of customer and business operations at all levels in the retail enterprise. In a challenging economy, such insight can be a competitive differentiator for a more satisfied and profitable existing and new customer base. The end use of big data is not defined as mere reporting or analytics-related capabilities but what companies actually do with big data initiatives, i.e. finding solutions for filling business gaps and addressing customer process complexities. This involves the ability to access information affecting the entire business as the data is created from multiple sources. 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 big data complexities and the access to relevant data - when, how, and where people need it - will be better positioned to achieve organizational success. One of the ways to alleviate data and intelligence latency is via in-memory computing that helps remove the latency factor of storing and accessing from multiple disks, on multiple computers, across multiple locations, which is very common in retail. Inmemory processes help move data and intelligence faster from multiple locations than other processes as in-memory processes move data from different computers to the central memory location. Big Data Demographics Of the responding retail organizations, demographics include the following: Job title: Senior Management (23%); EVP / SVP / VP (11%); Director (11%); Manager (26%); Consultant (20%); Other (9%) Department / function: Sales and Marketing (30%); IT (7%); Business Management (19%); Operations (6%); Logistics (15%); Procurement (11%); Other (12%) Segment: Consumer markets (25%); Retail/Apparel (15%); Software (17%); Automotive (6%); Food and Beverage (6%); Other (31%) Geography: North America (67%); APAC region (14%) and EMEA (19%) Company size: Large enterprises (annual revenues above US $1 billion)- 40%; midsize enterprises (annual revenues between $50 million and $1 billion)- 17%; and small businesses (annual revenues of $50 million or less)- 43% Key Takeaways The following are some recommendations that can be applied by end-users to help alleviate big data and BI-related complexities: Develop a robust relationship between line of business needs for customer analytics and IT to increase operational visibility. To

12 Page 12 maximize the ROI from big data solutions, retailers should be able to trace the need for increased customer insights to a retailer's number one reason for existence: selling a product and increasing revenue. From a customer-centric retailing standpoint, companies need to invest in providing access to real-time customer purchase affinity, preferences, and segmentation data across; procurement, finance, marketing, merchandising, pricing, promotions, supply chain, and other departments. Enterprise-wide consumer insights have the potential to transform the assortment-mix towards a level of precision that can increase customer recency and frequency in increasingly competitive retail environment. Create a roadmap for addressing complex unstructured and structured data integration with business systems so that enterprise-wide data processing and intelligence can be streamlined. Take into account all unstructured data streams including new customer interaction channels (such as social networking data). Provide deeper business insights to employees for improving customer, inventory, and merchandise assortment-related decision making. Real-time customer/business data intelligence 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. Predicting customer purchasing behavior speaks to the very essence of increased cross-selling and up-selling for retailers, no matter the channel. If a retailer can understand what type of purchase a consumer is likely to make, they can not only tailor marketing efforts to ensure a timely purchase is made, but they can also offer similar companion products to increase order size at the same time. When considering on-premise or hosted end-to-end big data initiatives, it is vital that retailers create a framework that ties the top enterprise-wide productivity needs to specific data processing and intelligence processes such as data gathering, aggregation, cubing, reporting, and delivery. If on-premise deployment is deemed difficult to implement, consider managed services/outsourced services and/or private cloud computing models that address realtime data processing, intelligence, and delivery options in a resource-constrained IT environment. Consider in-memory computing processes that help support realtime data processing and delivery of intelligence as in-memory computing removes the latency factor of storing and accessing from multiple disks, on multiple computers, across multiple locations, which is very common in retail. For more information on this or other research topics, please visit

13 Page 13 Best-in-Class Strategies to Overcome Disconnected Customer Experience; March 2012 Mobile and Tablet Shopping Demystified: Adoption and the ROI Business Case; September 2011 Early Consumer Insight Delivers Revenue Growth Opportunities for Retailers; July 2011 Related Research Author: Sahir Anand, VP/Research Group Director, State of Multi-Channel Retail Marketing: A Paradigm Shift for Reaching New Customers; June 2011 State of Customer-Centric Retailing: A Best Practices Guide for Higher Sales, Customer Retention, and Satisfaction; May 2010 For more than two decades, 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 2.5 million readers in over 40 countries, 90% of the Fortune 1,000, and 93% of the Technology 500. As a Harte-Hanks Company, Aberdeen s research provides insight and analysis to the Harte-Hanks community of local, regional, national and international marketing executives. Combined, we help our customers leverage the power of insight to deliver innovative multichannel marketing programs that drive business-changing results. For additional information, visit Aberdeen or call (617) , or to learn more about Harte-Hanks, call (800) or go to This document is the result of primary research performed by Aberdeen Group. Aberdeen Group's methodologies provide for objective fact-based research and represent the best analysis available at the time of publication. Unless otherwise noted, the entire contents of this publication are copyrighted by Aberdeen Group, Inc. and may not be reproduced, distributed, archived, or transmitted in any form or by any means without prior written consent by Aberdeen Group, Inc. (2012a)