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Big Data

  1. 1. June 2012 A New Retail Paradigm: Solving Big Data to Enhance Real-Time RetailingData from Aberdeen’s October 2011 report, Business Intelligence Analyst InsightEnhancements in Retail, indicates that for 62% of retailers, escalating big data- Aberdeen’s Insights provide therelated complexities within their enterprises makes day-to-day decision- analysts perspective on themaking and creating a single view of the product and customer an arduous research as drawn from antask. The problem is not just data aggregation but also lack of real-time aggregated view of researchaccess to customer and business information. This impedes customer- surveys, interviews, andcentricity and business process continuity. Another roadblock for retailers data analysisis also the volume, sources, complexity, and velocity of data. Aberdeens Big Data in Retail Definedlatest April 2012 survey of 50 retail enterprises shows that 70% of retailers Big data in retail and consumerare currently grappling with, on average, at least eight disparate sources of markets refers to the overallbusiness and customer data (both structured and un-structured) within their size or extent of active data anorganization. Such data variability fluctuates quite a bit due to seasonality, organization stores, as well asnumber of Stock Keeping Units (SKUs), and types of customers. the size of the data sets it uses for its business intelligence andThe collection and analysis of customer and business data, from its raw form analysis. Big data is also used toof analytical data to its polished form of predictive Business Intelligence (BI) describe the commonhelps to increase precision and real-time retailing. This includes: product difficulties associated with thisinnovation, supply chain, pricing, customer engagement, promotions and active data: size or extentmarketing, and other value chain areas. The benefits associated with real- (storing and accessing the data),time and precision retailing can be realized at every stage of the cross- speed (how fast the data mustchannel retail lifecycle - from product design stage to customer fulfillment, be captured, processed,and loyalty creation. This Analyst Insight addresses the aforementioned analyzed and delivered), complexity (the sophisticationcomplexities and benefits, and identifies a best practices roadmap that and level of detail in the dataenables companies to apply big data initiatives for real-time customer analysis), and types (theengagement and agile operations. Four main issues are also addressed: number of different formats the • Cross-channel impact of big data data takes). • Consumer pressures and organizational challenges surrounding big data • Capabilities and enablers to tame big customer and business data • Actionable recommendations for overcoming big data complexitiesThe Cross-Channel Impact of Big DataFor todays consumer, who has multi-faceted channel and shoppingpreferences, retailers need to be prepared at all times to provide one viewof the customer and product across all channels. However, this has notbeen easy for a majority of retailers. The need for addressing big data is aThis document is the result of primary research performed by Aberdeen Group. Aberdeen Groups methodologies provide for objective fact-based research andrepresent 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. 2. A New Retail Paradigm: Solving Big Data to Enhance Real-Time RetailingPage 2cross-channel challenge and a transformation need for retailers. Considerthe 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 "Impact is more from lack of non-traditional retail formats (such as Wal-Mart’s in-store banking, analysis / learning from big data optometry, and hair salon offerings) pressure these organizations to than from data issues further scrutinize their customer base to match established themselves." purchase patterns with new purchase patterns. Moreover, multiple ~Vice-President, Logistics, store formats appeal to product affinity and preferences of multiple Large Apparel Retailer, North customer segments. Customer segmentation requires re-thinking of America 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. Aberdeens 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.© 2012 Aberdeen Group. Telephone: 617 854 Fax: 617 723 7897
  3. 3. A New Retail Paradigm: Solving Big Data to Enhance Real-Time RetailingPage 3Need for Increased Consumer Insights is ParamountAs detailed in the previous section, as customer shopping options andchannels proliferate, 59% of retailers are compelled to respond to the needfor creating granular consumer insights in areas such as; cross-channelbuying behavior, share of wallet, market basket analysis, and segmentationstrategies (Figure 1).Figure 1: Lack of Consumer Insights is a Top Market Pain-Point Need to increase overall consumer 59% insight Need to improve speed of access to 45% relevant business dataNeed to move beyond data integration 28% stage Need to improve data accessibility for 22% customer-facing employees Improve ease-of-use of BI for non- 18% technical employees 0% 10% 20% 30% 40% 50% 60% 70% Percent of Respondents Source: Aberdeen Group, April 2012More often than not, retailers blame disparate data sources and the Variety of Different Dataenormity of active customer data as the primary reason for lack of adequate Formats- Big Data in Retail (byand timely consumer insights that inhibits new customer acquisition, % of respondents)customer retention, and re-activation. Currently, the total amount of active(non-archive or backup) business data that retailers store is between 1TB √ Pricing data- 68%and 25 TB for 38% of retailers, and another 21% store significantly higher √ Point-of-sale transaction dataamounts of business data. (in-store, online, call center, and other channels)- 65%One of the most fundamental challenges for retailers is revenue growthdespite any economic climate, positive or negative. To accomplish this goal: √ Supplier community business-to-business data • 81% of retailers are relying on increased customer insight for new (e.g. EDI)- 65% customer acquisition √ Shipping data- 55% • 75% are increasing efforts to derive additional value from existing customers - the challenge, however, is how to accomplish this task √ Text resulting from business effectively activities- 55%The enormity of customer data coupled with inadequate guidelines for agile √ Merchandising data- 45%data-driven insights fuels the inability to conduct timely analysis. This √ Other data sources- 43%inability in turn curtails effective customer-centric merchandising, marketing,promotions, supply chain planning and pricing strategies, among other √ Social media data- 39%critical operational competencies. The question that often perplexes √ Human resources data- 30%retailers is how to accurately analyze customer data and predict customer© 2012 Aberdeen Group. Telephone: 617 854 Fax: 617 723 7897
  4. 4. A New Retail Paradigm: Solving Big Data to Enhance Real-Time RetailingPage 4behavior 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 isrelated 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" betweenthe time they receive critical actionable operation information and theactual business events. For instance, delayed reporting of inventory activitycan severely hinder timely on-the-shelf response to customers, suppliers, orinternal stakeholders. This in turn hampers the pace of new retail initiatives,business transformation, and recovery strategies that turnaround a poorsales cycle. Moreover, growing hyper-competitiveness on the shelf, has ledto the need for better time-to-information, time-to-decision, and improvedenterprise-wide visibility towards Key Performance Indicators (KPIs).Another top pressure is related to the inability to move processes beyondthe data integration stage toward departmental and user-level access,analysis, and reporting. This need for on-demand self-service reporting anddata visualization is not just required at corporate headquarters but alsodown to the channel or store-level. Aberdeens April 2012 retail big dataand analytics survey indicates that 66% of retailers are unable to provideuniform self-service reporting and data access capabilities that are otherwiseavailable to the core super user team. For instance, customer-facingemployees need readily accessible real-time sales and service performancereporting, customer order history, real-time inventory on-hand data access,product information, cross-selling and up-selling data, among otherresources.This information enables store or channel-level employees to assistcustomers in the best possible way and complete the customer experienceprocess in an effective way. However, only 25% of retailers indicate thatthey have uniformly executed downstream information access among "Too much unstructured datacustomer-facing employees. This has hurt in-store customer engagement causes delays in compilingculture the most. Other channel associates (e.g. online or call center agents) actionable information inwho are not necessarily customer-facing, do have access to at least some needed time frames. Thisweb-based product information that store employees often lack at the relates to CRM, customerPoint-of-Service (POS). data/view; competitive analysis; social engagement; product lineOrganizational Challenges evaluation and sales promotional programs."Data from the January 2012 Omni-Channel Retail Experience report showsthat 48% of retailers store customer and business data in two to five ~Vice-President, Marketing,disparate systems. Another 20% of retailers store data in six to 15 distinct Mid-Market Retailer, Northsystems. Relevant customer and business data resides in operational silos Americaleading to data duplication, batch processing, and delays associated withstructured and unstructured data integration with other business systemssuch as: POS, Customer Relationship Management (CRM), marketingmanagement, promotions, pricing, inventory management, etc.As shown in Figure 2, companies find structured and unstructured dataintegration with other systems most challenging. These companies are also© 2012 Aberdeen Group. Telephone: 617 854 Fax: 617 723 7897
  5. 5. A New Retail Paradigm: Solving Big Data to Enhance Real-Time RetailingPage 5most likely to experience "delayed time-to-information" and "slower time-to-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 businessactivities, data from social channels, and other data sources.Figure 2: Top ChallengesLack of structured / unstructured data integration 35% with business systems Legacy processes and systems 32% Little or no expertise related to analyzing large 29% amounts of data 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 2012Secondly, for 32% of companies, business/customer data management andrelated intelligence is fraught with legacy system obstacles. Multi-generational and legacy processes and systems hinder the advancement of "Systems have improved andancross-channel customer experience. Unless channel data is centralized this has led to better customer information being available. Thisand shared in real-time, there is little chance of timely coordination has helped us sustain a goodbetween channels. Often, the end result is duplicated efforts, duplicated performance despite thedata, and incremental time and money spent on duplicate customers and economic and other naturalprocesses. disasters impacting our industry in the last year."The line-of-business and IT executives in retail must seek to address unifiedbig data management in multi-tier, multi-site, and multi-channel user ~ Director, Marketing, Largeorganizations. Multi-generational and legacy technology applications do not Consumer Electronics Retailer,allow organizations to remain agile enough to meet the changing needs and Asia-Pacific Regiondesires of their customers. Instead, the users of these legacy technologiesare saddled with out-of-date technology capabilities, and as a result, an out-of-date and out-of-touch approach to the cross-channel customerexperience. A related challenge facing 29% of companies is scant expertisewithin IT teams to handle large amounts of data. As more and morecompanies deem IT as a cost center, adequate human resource talent andassociated expenditure is a constant headache for executives.This is despite the fact that 88% of retailers expect the fastest big datainitiative ROI from agile business forecasting value and agile business© 2012 Aberdeen Group. Telephone: 617 854 Fax: 617 723 7897
  6. 6. A New Retail Paradigm: Solving Big Data to Enhance Real-Time RetailingPage 6execution value. According to Aberdeens analysis, the disconnect in whatcompanies want from data insights and their actions, lies in the fact thatnearly half (42%) of big data decisions are still taken by the CIO, the nextclosest job-role associated with big data-related decision making is theCMO (13%).Somehow, retailers have kept big data and business intelligence-relatedprocess and system improvement decisions non-collaborative, where IT andline of business do not see eye-to-eye. However, this process of collectivedata and BI decision-making needs to be reversed for establishing usage andaccess equilibrium.Realized and Unrealized Benefits of Big Data StrategiesThe four leading areas where retailers expect big data initiative ROI include:business execution information; transparent sales forecasting; product andcustomer service innovation; predictive product innovation and customerservice capabilities (see first four rows of Table 1).However, the realized gains have been in the teens and low double-digits atbest in the aforementioned areas. In fact, the bottom three areas forexpected ROI, namely, performance information, deeper customersegmentation, and one view of product information have seen bettercomparative realization of actual gains from big data initiatives.Table 1: Expected Benefits vs. Actual Benefits of Big DataInitiative Data Summary Expected ActualAgile business execution value as 90% 23%information is easily availableImproved product and service 89% 22%innovationAgile business forecasting value as 87% 19%information is transparentEnhanced predicting capabilities 86% 17%related to product and customerproblemsDetailed performance information 79% 36%available for rectifying errorsPossibilities for deeper customer 77% 42%segmentationAssistance with development of one 72% 34%view of product information Source: Aberdeen Group, April 2012The reasons are short-term vs. long-term realized gains. Retailers appliedbetter organizational focus when it comes to the easiest and fastest route tobig data investment justification. In the last two years, more than a third of© 2012 Aberdeen Group. Telephone: 617 854 Fax: 617 723 7897
  7. 7. A New Retail Paradigm: Solving Big Data to Enhance Real-Time RetailingPage 7companies focused on big data initiatives that are geared towards customersegmentation for tactical business objectives, internal employee and externaltrading partner/supplier performance management, and centralized productinformation management due to expansive cross-channel needs. Businessexecution correction, product/service innovation, and predictive capabilitieswere delayed, getting pushed into the category of "long-term aspirationalgains" or "long-term roadmap goals." Retailers show low levels of processmaturity in handling complex and real-time big data models that can begeared towards accurate forecasts and predictive sales and operations. Thevalue of business forecasting and predictive sales and operations isundeniable. For instance, in the area of predictive capabilities, two key "Detailed knowledge of howprocess capabilities have emerged as top strategies retailers are focusing on customers perceive ourin the immediate future: products, our services, our promotions, and the brands in • Predict customer purchasing behavior (66% of retailers planning, all channels give us the most 19% current) important facts to decide how • Real-time analysis based on segmentation, affinity, and preference to be closely personal with our (64% of retailers planning, 25% current) customers." ~Director of Marketing, LargeBig Data Capabilities Specialty Retailers, North AmericaSo how can retailers maximize gains from big data initiatives described inthe previous section? The next two sections address key ways in attainingbenefits from big data initiatives.To execute a cross-channel big data strategy within retail, enterprises mustdevelop a solid foundation of business-to-consumer process, organizational,knowledge, and performance management capabilities.The top three currently deployed capabilities relate to setting-up guidelinesfor data gathering, security, and external sharing of data with businesspartners/suppliers (Table 2). Guidelines are required as not all departmentsare alike when it comes to the role of solving big data aggregation, analysis,and access. The capabilities that are critical for laying out commonguidelines include: data access, coding, cubing, querying, security, and job-role based reporting need to be presented via a common set of datapresentation in varied formats of data delivery tools. The disparate analyticspresentation formats (i.e. dashboards vs. spreadsheets) lead to lack of aunified view of the brand, customer, and day-to-day operations.For the above reasons, big data and BI-related processes require adequateIT expertise, and line of business collaboration to solve big data analyses,quantitative / statistical analytics or dashboards and drill downs. Only a thirdof retailers possess the IT and line of business expertise today to addressbig data, however, 55% of retailers plan to adopt these capabilities in theforeseeable future. If internal resources are inadequate or cost prohibitive,then companies can turn towards managed and outsourced services forintegrating structured and un-structured data with customer-facing andback-end systems. This can create a homogenous way of treating the bigdata and lack of consumer/business insights in a cost-effective manner. The© 2012 Aberdeen Group. Telephone: 617 854 Fax: 617 723 7897
  8. 8. A New Retail Paradigm: Solving Big Data to Enhance Real-Time RetailingPage 8April 2012 retail big data and analytics survey indicates that 36% plan to useIT / systems integrator consulting services within two years. In fact, withinthe next 24 months, some of the leading retail data and infrastructure -related planned technology improvements for companies that aspire tobecome Best-in-Class include delivery models such as: managed/outsourcedservices (33%), and cloud services (36%).Table 2: Current and Planned Process and OrganizationCapabilities Data Summary Currently Use Plan to UseEstablished data gathering and assembly 54% 43%guidelinesGuidelines for external data sharing (e.g. 52% 30%EDI) with suppliers and trading partnersGuidelines for data security, privacy, 48% 48%and consumer / client rights protectionAlignment of new product releases with 21% 59%customer preference and affinityJob-role based access to customer 36% 49%behavior and purchase trendsIT expertise to solve Big Data analyses, 31% 55%quantitative / statistical analytics ordashboards and drill downsThe ability to provide performance data 15% 55%at the associate level Source: Aberdeen Group, April 2012In studying the varied cases of big data initiatives in retail organizations,Aberdeens analysis indicates that retailers need an enterprise-wide big datastrategy. These companies must apply an enterprise-wide strategy if theywant to see customer and business dynamics through the same prism inorder to scale, differentiate, and grow in these challenging times.Finally, as seen in Table 3, as companies embark upon an enterprise-wide bigdata complexity solving mission, it is important to take into considerationthe extent of real-time data capture (from varied sources) capabilities thatcompanies currently possess or plan to use in the future. These capabilitiesmost likely impact "time-to-information" and "time-to-decision" goals ascompanies also need to ensure rapid data processing and intelligence so thatall departments and teams have an equal measure of real-time customerneeds, response times, collaborative, and performance improvementrequirements.For instance, retailers not only need to capture POS data in real-time acrosschannels but also drive real-time promotions to customers by analyzing POSand loyalty data so that channels can benefit from real-time offers andcustomer mapping. The real-time nature or velocity of data capture,© 2012 Aberdeen Group. Telephone: 617 854 Fax: 617 723 7897
  9. 9. A New Retail Paradigm: Solving Big Data to Enhance Real-Time RetailingPage 9processing, analysis, and reporting depends on several factors such asdatabase 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 UseReal-time customer data capture at the 55% 29%point of service (POS)Real-time customer data capture at the 44% 30%call centerReal-time customer data capture at the 44% 50%websiteReal-time customer data capture at the 37% 45%headquartersReal-time customer data capture within 27% 54%online communities Source: Aberdeen Group, April 2012Technology EnablersThere are four broad categories of big data complexity-solving enablers sub-divided in four broad groups: size or extent (storing and accessing the data);speed (how fast the data must be captured, processed, analyzed anddelivered); complexity (the sophistication and level of detail in the dataanalysis), and types (the number of different formats the data takes).For addressing data size or extent needs, on average a third ofretailers indicate usage of distributed databases, data integration tools,enterprise data warehouses, distributed file systems, cloud computing datacenter tools, among other solutions that support data aggregation andassembly.From a data speed and complexity standpoint, retailers currentlyindicate affinity towards real-time enterprise-level data processing andintelligence tools such as in-memory computing processes/analytics, cloudcomputing data delivery models, and Massively Parallel Processing (MPP)databases. At least a third of retailers plan to invest in these tools in thenear future.As shown in Table 4, retail databases initiatives for real-time customerengagement and agile operations can be supported through the use of in-memory computing processes. These tools help support real-time dataprocessing and delivery of intelligence as in-memory computing removes thelatency factor of storing and accessing from multiple disks, on multiplecomputers that are installed across multiple retail store, channel orheadquarter locations. In-memory processes help move data and intelligencefaster than other processes as in-memory processes move data fromdifferent computers to the central memory location.© 2012 Aberdeen Group. Telephone: 617 854 Fax: 617 723 7897
  10. 10. A New Retail Paradigm: Solving Big Data to Enhance Real-Time RetailingPage 10Data from Aberdeens April 2012 retail big data and analytics surveyindicated that companies that have adopted in-memory computingprocesses are two-times more likely to experience real-time operationalinformation availability, and as a result, faster decision making compared toretailers that do not use in-memory computing. Even in the area of retaildata processing and intelligence-related complexity, our data shows that in-memory computing processes/analytics and MPP support close to actualbusiness activity availability of information.The real-time multi-location data processing capability of in-memorycomputing can be of immense value as at least 50% of retailers are stillexecuting overnight or delayed polling of POS data for various types ofcustomer and business analyses. In fact, in-memory computing can enablefaster and more real-time access to customer and business information inthe 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 collaborationTable 4: Enablers Data Summary Currently Use Plan to Use "Our greatest big data complexity is difficulty inIn-memory computing 35% 36% matching strategy to actionsprocesses/analytics and outcomes. It is very difficultData cleansing tools 24% 55% to set the right KPIs and even more difficult to measureCustomer segmentation application 32% 52% them." Source: Aberdeen Group, April 2012 ~ Senior Executive, SMB Retailer, Asia-Pacific RegionFinally, in terms of types or formats (the number of differentformats the data processing and intelligence takes), departmentaland store-level data access, viewing, and analysis capabilities are alsoimportant, and this is where the concepts of dashboards and scorecardscome into play. Data from the April 2012 retail big data and analytics surveyindicates that at least half of the companies plan to use dashboards formultiple departments and functions. Real-time data processing via in-memory computing can help support faster data uploads to the enterprisedashboards and scorecards.© 2012 Aberdeen Group. Telephone: 617 854 Fax: 617 723 7897
  11. 11. A New Retail Paradigm: Solving Big Data to Enhance Real-Time RetailingPage 11ConclusionThe enormity of data coupled with lack of adequate guidelines for agile data- Big Data Demographicsdriven insights fuels the inability to conduct timely analysis. This inability in Of the responding retailturn curtails effective retail planning and execution within: customer-centric organizations, demographicsmerchandising, marketing, promotions, supply chain planning and pricing include the following:strategies, among other critical customer value chain areas. √ Job title: Senior ManagementFew retailers would argue that a difficult economic recovery requires new (23%); EVP / SVP / VP (11%);and creative ways of reaching customers to offer products and services. Director (11%); ManagerMost of these creative ways depend on a closer, more intimate (26%); Consultant (20%);understanding of consumer activity at all touch points to personalize the Other (9%)shopping interaction. This is for the benefit of the retailer in the form of √ Department / function: Salesincreased cross-sells, up-sells and consumer loyalty. It is also for the benefit and Marketing (30%); ITof the customer in the form of a more direct, informed, and relevant (7%); Business Managementexperience to decrease the time needed for product searches and overall (19%); Operations (6%);interaction steps. Logistics (15%); Procurement (11%); OtherIn order to realize these benefits, however, retailers must rely on solving big (12%)data issues to help guide this personalized selling experience goal into √ Segment: Consumer marketsfruition. This can start with data collection processes at, for example, the (25%); Retail/Apparel (15%);POS, continue into a predictive analytical model, and end with increased Software (17%); Automotivebusiness intelligence for a dynamic, macro and micro view of customer and (6%); Food and Beveragebusiness operations at all levels in the retail enterprise. In a challenging (6%); Other (31%)economy, such insight can be a competitive differentiator for a moresatisfied and profitable existing and new customer base. √ Geography: North America (67%); APAC region (14%)The end use of big data is not defined as mere reporting or analytics-related and EMEA (19%)capabilities but what companies actually do with big data initiatives, i.e. √ Company size: Largefinding solutions for filling business gaps and addressing customer process enterprises (annual revenuescomplexities. This involves the ability to access information affecting the above US $1 billion)- 40%;entire business as the data is created from multiple sources. This can involve midsize enterprises (annualone or multiple sets of data sources, and can affect one or many sets of revenues between $50decisions, actions, departments and people. Retail organizations that take a million and $1 billion)- 17%;strategic approach to enterprise big data complexities and the access to and small businesses (annualrelevant data - when, how, and where people need it - will be better revenues of $50 million orpositioned to achieve organizational success. One of the ways to alleviate less)- 43%data and intelligence latency is via in-memory computing that helps removethe latency factor of storing and accessing from multiple disks, on multiplecomputers, across multiple locations, which is very common in retail. In-memory processes help move data and intelligence faster from multiplelocations than other processes as in-memory processes move data fromdifferent computers to the central memory location.Key TakeawaysThe following are some recommendations that can be applied by end-usersto 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© 2012 Aberdeen Group. Telephone: 617 854 Fax: 617 723 7897
  12. 12. A New Retail Paradigm: Solving Big Data to Enhance Real-Time RetailingPage 12 maximize the ROI from big data solutions, retailers should be able to trace the need for increased customer insights to a retailers 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 real- time data processing, intelligence, and delivery options in a resource-constrained IT environment. • Consider in-memory computing processes that 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, across multiple locations, which is very common in retail.For more information on this or other research topics, please© 2012 Aberdeen Group. Telephone: 617 854 Fax: 617 723 7897
  13. 13. A New Retail Paradigm: Solving Big Data to Enhance Real-Time RetailingPage 13 Related Research Best-in-Class Strategies to Overcome State of Multi-Channel Retail Marketing: Disconnected Customer Experience; A Paradigm Shift for Reaching New March 2012 Customers; June 2011 Mobile and Tablet Shopping Demystified: State of Customer-Centric Retailing: A Adoption and the ROI Business Case; Best Practices Guide for Higher Sales, September 2011 Customer Retention, and Satisfaction; Early Consumer Insight Delivers Revenue May 2010 Growth Opportunities for Retailers; July 2011 Author: Sahir Anand, VP/Research Group Director, ( more than two decades, Aberdeens 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 provideorganizations with the facts that matter — the facts that enable companies to get ahead and drive results. Thats whyour research is relied on by more than 2.5 million readers in over 40 countries, 90% of the Fortune 1,000, and 93% ofthe Technology 500.As a Harte-Hanks Company, Aberdeen’s research provides insight and analysis to the Harte-Hanks community oflocal, regional, national and international marketing executives. Combined, we help our customers leverage the powerof insight to deliver innovative multichannel marketing programs that drive business-changing results. For additionalinformation, visit Aberdeen or call (617) 854-5200, or to learn more about Harte-Hanks, call(800) 456-9748 or go to document is the result of primary research performed by Aberdeen Group. Aberdeen Groups methodologiesprovide for objective fact-based research and represent the best analysis available at the time of publication. Unlessotherwise noted, the entire contents of this publication are copyrighted by Aberdeen Group, Inc. and may not bereproduced, distributed, archived, or transmitted in any form or by any means without prior written consent byAberdeen Group, Inc. (2012a)© 2012 Aberdeen Group. Telephone: 617 854 Fax: 617 723 7897