Retail & CPG
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The document discusses new rules and strategies for retailers in an evolving customer relationship landscape. It notes there are now 56 touchpoints between a customer's moment of inspiration and transaction. It then discusses components of digital transformation like customer experience management, cross-channel order orchestration, and building a single customer view. The document outlines how retailers can create customer connections and profiles by leveraging enterprise data. It also discusses the need for customer engagement in stores through technologies like self-scanning and mobile payments. Finally, it discusses how front-end store technologies can empower associates and optimize processes.
Retail & CPG
- 1. New rules… ..Retailers’ New Games
- 4. Customer Relationships are Multi – Dimensional and Non Linear View Product banner ad Search product details online Ask friends on Face Book for opinion View product reviews by experts on You Tube Read blog on product Search for product SHOP ON WEBSITE / MOBILE / STORE Track order status on mobile Like You on Face Book Download the mobile app Share posts on your Face Book page Visit Store for product demo Compare prices with competitors Write reviews about product Follow the Twitter page ….56 touch points between moment of inspiration and moment of transaction
- 5. What we are hearing from our Customers…
- 6. Anatomy of Digital Transformation User Experience Multi-Option Fulfillment Cross Channel Loyalty Single View of Customer Mobility Multichannel Foundation On Demand Service (search, inventory) Deep Personalization Time to Market Components Customer Experience Management as a capability to achieve seamless and intuitive shopping experience Cross Channel Order Orchestration to facilitate profitable cross channel fulfillment supported by real-time Omni- channel analytics Faster Time to Market through frequent updates/ release of functionalities enabling fresh digital experience Foundation of Customer knowledge base to drive deep personalization and targeting
- 7. Creating the customer “connections” Creating a single view Core Attributes Extended Attributes Transactions Interactions Customer information management In store behavior Social Media Data Online Behavior
- 8. Leverage Enterprise-wide data to create 3600 customer profiles Deploy Personalization as a differentiator – Moving away from black box techniques Business goal driven – data driven “Plan” & “Execute” Orchestrate personalization across touch-points Personalization for Customer Engagement
- 9. Customer engagement in the store Self Scanning Identification of customer Self Help mobile App Customer shopping enablement ( Aug Reality) Mobile Payment Associate Empowerment Associate Customer Center of cross channel orchestrations …Process Optimization New Processes. Old Systems Keeping Stores relevant… Simplify…Standardize…Synergize Labor Productivity (receiving, cashiering, RDQ….) Inventory Optimization Space Productivity Back office process optimization
- 10. Front end of tomorrow… empowered associates Location based services Mobile based customer engagement Mobile Payment Self Check Out, Faster Check Out Associate mobility Employee collaboration Real time predictive alerts Front end of tomorrow Empowered Associates 1 Store Technology for agility The New POS Cloud (Near) Real Time integration Cost/ Process Optimization Energy Usage Backroom Efficiency Macro Space Optimization
- 11. …integrating customer into the merchandising process ..NOW ..NEXT Lists, Basket, Trips Consumer decision trees Demand Substitution Event, Sentiments Weather Demand Based Forecasting Sentiment based forecasting Velocity based space and inventory Product Elasticity Demographic based assortment Substitution, CDT based space and Inventory Basket Elasticity Trips based assortment
- 12. New rules… ..Retailers’ New Games Thank You
- 14. June 2013
- 17. Legacy Rides the Elephant Aashish Chandra Divisional VP, Sears Holdings GM / Head, Legacy Modernization, MetaScale
- 18. 18 Legacy Rides The Elephant Hadoop has changed the enterprise big data game. Are you languishing in the past or adopting outdated trends?
- 19. 19 The Classic Enterprise Challenge The Challenge Growing Data Volumes Shortened Processing Windows Escalating Costs Hitting Scalability Ceilings Demanding Business Rqts ETL Complexity Latency in Data Tight IT Budgets Constant pressure to lower costs, deliver faster, migrate to real time and answer more diff questions for business.. • Copy & Use Source once and Re-use • Linear Parallel Processing • Proprietary Open Source • Capital Cloud Expense • Batch Real time • Operating Costs Down
- 20. 20 The Gartner Hype Curve
- 21. 21 Gbytes Tbytes 100's of Tbytes Minutes Hours Days Data Size Seconds Milliseconds ComplexAnalyticalQuery Hadoop Database and high speed appliances with parallel processing Pbytes Expensive Inefficient Cheaper price for performance Why Hadoop?
- 22. 22 Gbytes Tbytes 100's of Tbytes Minutes Hours Days Data Size Seconds Milliseconds ComplexAnalyticalQuery Hadoop Database and high speed appliances with parallel processing Pbytes Expensive Inefficient Cheaper price for performance Why Hadoop?
- 23. 23 Gbytes Tbytes 100's of Tbytes Minutes Hours Days Data Size Seconds Milliseconds ComplexAnalyticalQuery Hadoop Database and high speed appliances with parallel processing Pbytes Gets expensive and inefficient as the data size grows. Will need investments in specialized appliances and will not scale beyond a point Databases are best used for fast response needs on smaller datasets and for specific SQL access Expensive Inefficient Cheaper price for performance Why Hadoop?
- 24. 24 Gbytes Tbytes 100's of Tbytes Minutes Hours Days Data Size Seconds Milliseconds ComplexAnalyticalQuery Hadoop Database and high speed appliances with parallel processing Pbytes Gets expensive and inefficient as the data size grows. Will need investments in specialized appliances and will not scale beyond a point Databases are best used for fast response needs on smaller datasets and for specific SQL access Expensive Inefficient Cheaper price for performance Why Hadoop?
- 25. 25 Gbytes Tbytes 100's of Tbytes Minutes Hours Days Data Size Seconds Milliseconds ComplexAnalyticalQuery Hadoop Database and high speed appliances with parallel processing Pbytes Gets expensive and inefficient as the data size grows. Will need investments in specialized appliances and will not scale beyond a point Databases are best used for fast response needs on smaller datasets and for specific SQL access Hadoop is inefficient for small datasets but is designed to handle big and complex data: Stays efficient as you encounter Big Data and run complex workloads. Will provide a lower price for performance Expensive Inefficient Cheaper price for performance Why Hadoop?
- 26. 26 What Hadoop is Not? Understanding Hadoop’s limitations will help you identify the right use cases: •Hadoop is not a high-speed SQL database •Hadoop is not a particularly simple technology •Hadoop is not easy to connect to legacy systems. You can do it, but the complexity needs to be considered. •Hadoop is not a replacement for traditional data warehouses. It is an adjunctive product to data warehouses. •Normal DBAs will need to learn new skills before they can adopt Hadoop tools. •The architecture around the data - the way you store data, the way you de- normalize data, the way you ingest data, the way you extract data - is different in Hadoop. •Linux and Java skills are critical for making a Hadoop environment a reality.
- 27. 27 A super-powerful environment that can transform your understanding of data: • Store vast amounts of data. • Run queries on huge data sets. • Transform traditional ETL • Archive data on Hadoop and still analyze it • Ingest data at incredible speeds and analyze it and report on it in near real-time • Hadoop massively reduces the latency of data • Hadoop allows you to ask questions that were previously impossible to answer Capabilities of Hadoop
- 28. Big Data The Sears Holdings Journey
- 29. 29 Where did we start? • Issues with meeting production schedules • Multiple copies of data, no single version of truth • ETL complexity, cost of software and cost to manage • Time taken to setup ETL data sources for projects • Latency in data, up to weeks in some cases • Enterprise Data Warehouses unable to handle load • Mainframe workload over consuming capacity • IT Budgets not growing BUT data volumes escalating
- 30. 30 The Sears Holdings Approach Implement a Hadoop- centric reference architecture Move enterprise batch processing to Hadoop Make Hadoop the single point of truth Massively reduce ETL by transforming within Hadoop Move results and aggregates back to legacy systems for consumption Retain, within Hadoop, source files at the finest granularity for re-use 1 2 3 4 5 6 Key to our Approach: 1) allowing users to continue to use familiar consumption interfaces 2) providing inherent HA 3) enabling businesses to unlock previously unusable data
- 31. 31 The Journey In 3 years, we are in a much different place.. •From Legacy (>1000 lines) to Ruby / MapReduce (400 lines) • COBOL is cryptic, difficult to support, difficult to train PiG is simple, short and easy to maintain •We tried HIVE (~400 lines, SQL-like abstraction) • Easy to Use, easy to experiment and test with • Poor performance, difficult to implement business logic •We evolved to PiG with Java UDF Extensions • Compressed, very efficient, easy to code / read (~200 lines) • Demonstrated success in transforming mainframe developers to PiG developers in under 2 weeks •As we progressed, our business partners requested more and more data from the cluster –which required developer time • We are now using Datameer as a business-user reporting and query front- end to the cluster
- 32. 32 Re-Think.. • The way you capture data • The way you store data • The structure of your data • The way you analyze data • The costs of data storage • The size of your data • What you can analyze • The speed of analysis • The skills of your team
- 33. 33 Mainframe Migration Batch Processing - JOB FLOW JCL1 - APPLICATION 1 Mainframe Batch Processing Flow User Interface Data Sources Batch Processing External Systems/ Datawarehouse Input Resultant Data Resultant Data SORT Input SPLIT Input SORT Input COBOL Input FILTER Input FORMAT JCL2 - APPLICATION 1 JCL3 - APPLICATION 2 LOAD TO DATABASE COPY Input COBOL Input FORMAT Input Input
- 34. 34 Mainframe Migration Batch Processing - JOB FLOW JCL1 - APPLICATION 1 Mainframe Batch Processing Flow User Interface Data Sources Batch Processing External Systems/ Datawarehouse Input Resultant Data Resultant Data SORT Input SPLIT Input SORT Input COBOL Input FILTER Input FORMAT JCL2 - APPLICATION 1 JCL3 - APPLICATION 2 LOAD TO DATABASE COPY Input COBOL Input FORMAT Input Input Commodity Hardware Based Software Framework Batch Processing - JOB FLOW Batch Process - APPLICATION 1 Batch Processing - JOB FLOW - Legacy Platform Invention - Migration methodology for Legacy Applications to Commodity Hardware User Interface Data Sources External Systems/ Datawarehouse Batch Processing Input Resultant Data PIG/MR Input PIG/MR Input PIG/MR Input PIG/MR Input PIG/MR Input PIG/MR JCL2 - APPLICATION 1 JCL3 - APPLICATION 2 LOAD TO DATABASE COPY Input COBOL Input FORMAT Input Input Resultant Data Seamless migration of high MIPS processing jobs with no application alteration
- 35. 35 Mainframe Migration Mysql Hbase Hadoop price LEGACY - TERADATA/DB2 product SOLR S ales C ustom er Enterprise Systems JQUERY/AJAX Quart z JAXB REST API JDBC/IBATIS JBOSSJ2EE/JBOSS/SPRING Batch Processing HIVE RUBY/MAPREDUCE JBOSSHADOOP/PIG DB2 Oracle UDB price Teradata product MySQL S ales C ustom er Enterprise Systems JQUERY/AJAX Quart z JAXB REST API JDBC/IBATIS JBOSSJ2EE/WebSphere Mainframe Batch Processing VSAM JBOSSCOBOL/JCL MetaScale
- 36. 36 Enterprise Data Hub & ETL Replacement Experience evolved to move into ETL Replacement and architecting Enterprise Data Hub • A major system effort in our Marketing department was heavily reliant on traditional ETL • As data volumes increased the system began to have performance issues as the ETL platform degraded • Re-work CPU-intensive portions in Hadoop • Now run those workloads 20-50 times faster in Hadoop • Run-times do not grow as data volumes grow • Enterprise Data Hub • Source Data once, Re-use multiple times • ETL gives way to ELTTTTTT
- 37. 37 The Sears Holdings Architecture
- 38. 38 Current Focus • ETL Complexity is no longer needed – Data Hub • Source Once, Re-Use many times • ETL changes to ELTTTTTTTTT • Data Latency is the thing of the past • Analysis is routinely possible within minutes of data creation • Long Running Workload • Can be eliminated and executed at any time • Run times are a fraction of the original clock time • Batch Processing on Mainframes or other conventional Batch • Run 10, 50, even 100 times Faster • Intelligent Archive • Put your archives/ large data on Hadoop and make it intelligent • Archive with the ability to run analytics or join it with other data • Modernize Legacy • Mainframe MIPS Reductions has very attractive ROI • Move Data Warehouse workload – Reduce Cost – Go Faster
- 39. 39 Simple & Maintainable Complexity creates fog; Simplicity clears it.
- 40. 40 Faster & Resilient Data Analytics driving the speed of business.. Secured and Resilient..
- 41. 41 Summary of Benefits • Readily available resources & commodity skills • Access to latest technologies • IT Operational Efficiencies • Moved 7000 lines of COBOL code to under 150 lines in PiG • Ancient systems no longer bottleneck for business • Faster time to Market • Mission critical “Item Master” application in COBOL/JCL being converted by our tool in Java (JOBOL) • Modernized COBOL, JCL, DB2, VSAM, IMS & so on • Reduced batch processing in COBOL/JCL from over 6 hrs to less than 10 min in PiG Latin on Hadoop • Simpler, and easily maintainable code • Massively Parallel Processing • Significant reduction in ISV costs & mainframe software licenses fees • Open Source platform • Saved ~ $2MM annually within 13 weeks by MIPS Optimization efforts • Reduced 1500+ MIPS by moving batch processing to Hadoop Cost Savings Transform I.T. Skills & Resources Business Agility
- 42. 42 The Learning • Big Data is here and ready – Avoid the hype • An Enterprise Data Architecture model is essential • Hadoop can revolutionize Enterprise workload • Can reduce strain on legacy platforms • Can reduce cost • Can bring new business opportunities • The Solution must be an Eco-system • Must be part of an overall enterprise data strategy • Not to be underestimated
- 43. 43 Formoreinformation,visit: www.metascale.com Follow us on Twitter @LegacyModernizationMadeEasy Join us on LinkedIn: www.linkedin.com/company/metascale-llc Legacy Modernization Made Easy!
- 46. This presentation, including any supporting materials, is owned by Gartner, Inc. and/or its affiliates and is for the sole use of the intended Gartner audience or other authorized recipients. This presentation may contain information that is confidential, proprietary or otherwise legally protected, and it may not be further copied, distributed or publicly displayed without the express written permission of Gartner, Inc. or its affiliates. © 2013 Gartner, Inc. and/or its affiliates. All rights reserved. Jeff Roster Jeff.Roster@Gartner.com Twitter: @JeffPR Instagram: @JeffPR Retail 2013…and Beyond
- 47. Nexus of Forces: Social, Mobile, Cloud & Information
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- 53. Nexus Impacts Differ Across Industries Manufacturing Government Professional Services Media Retail Manufacturing Communications Banking Healthcare Manufacturing Wholesale Distribution "Process Value Targets" "Business Model Redesign" "Business as Usual" "Technology Platform Refresh"
- 54. 54 Major Market Opportunity for Software 45.3% 41.5% 38.7% 36.8% 32.1% 29.2% 23.6% 17.0% 12.3% 5.7% 2.8% Retiring legacy systems Developing applications to satisfy empowered consumers Application integration Managing big data Optimizing stores as a major channel Consumer smart devices in the enterprise Upgrading store-level bandwidth and infrastructure PCI compliance Mobile security Fighting intrusions and Web attacks Fighting against inflation Over the next 3 years what “pains’ will you devote significant resources to solving?
- 55. 55 Mobile POS Breaks into the Mainstream 50.0% 43.4% 42.5% 42.5% 41.5% 40.6% 40.6% 39.6% 39.6% 36.8% Campaign analysis and forecasting Forecasting and planning Mobile POS Predictive analytics In-store pickup or returns of web goods Multi-channel planning and forecasting Campaign management Allocation Assortment planning POS peripherals Top 10 Technologies for 2013
- 56. 56 IT Spend Continues to Climb IT Budgets as a Percent of Total Revenue Change in Year-Over-Year IT Budget 11.3% 30.2% 10.4% 6.6% 1.9% 7.5% Less than 1% 1% to< 2% 2% to < 3% 3% to < 4% 4% to l< 5% 5% or more 4.7% 3.8% 4.7% 28.3% 26.4% 16.0% 16.0% Decrease 10% or more Decrease between 5% to < 10% Decrease between 1% to < 5% No change Increase between 1% to < 5% Increase between 5% to < 10% Increase 10% or more
- 57. 57 Has SaaS Finally Hit the Mainstream 56.6% 50.9% 38.7% 31.1% 35.8% We seek best of breed software We seek integrated solutions suites We seek software-as-service models We use in-house IT resources to develop software We use third party services to help develop software Your general point of view in how you want to acquire software going forward.
- 58. 58 Status of Organization's Customer Facing Current Mobile Channel Development? 17.9% 44.3% 28.3% 9.4% Not planning any activity Planning under way Pilots in progress Fully functioning mobile commerce strategy in place
- 59. 59 An Industry in Full Transformation Mode 13.2% 32.1% 35.8% 18.9% Basic IT infrastructure and systems with critical limitations Mostly basic IT infrastructure and systems but some advanced upgrades Mostly advanced IT infrastructure and systems but lack of comprehensive integration Advanced IT infrastructure and systems with deep integration 14.2% 7.5% 28.3% 15.1% 7.5% 27.4% We don't have an e-commerce platform Platform needs up dating, but no plan to upgrade Currently upgrading platform now Plan to upgrade within 12 months Plan to upgrade within 24 months Re-platformed within 2 years, no need to upgrade Maturity of IT Architecture Status of E-Commerce Platform
- 60. 60 Has POS Hardware Commoditized? 39% 39% 6% 47% 8% 15% 4% 19% 17% 18% 14% 15% 6% 24% 6% 10% 20% 18% 28% 15% 14% 18% 5% 17% 11% 14% 18% 8% 5% 13% 14% 19% POS peripherals POS software Mobile POS POS terminals (traditional, fixed) Self checkout terminals In-store pickup or returns of web goods Item level RFID Returns management Up-to-date tech in place Started but not finished major tech upgrade Will start major tech upgrade in next 12 months Will start major tech upgrade in next 12-24 months Status of In Store Technology
- 61. 61 Mobile POS Looks to be Additive Yes, already investing 29.2% Yes, planning to invest during 2013 22.6% No 35.8% Don't know 12.3% Yes, plan to decrease fixed POS 25.5% No plans to decrease fixed POS 63.6% Don't know 10.9% Does your organization plan to invest in mobile point of sale (POS) in 2013 Does your organization plan to decrease the number of fixed POS devices in stores during 2013 as a result of its mobile POS investments?
- 62. 62 Digital Signage on the Rise 29% 25% 19% 19% 17% 10% 5% 3% 21% 14% 8% 16% 11% 13% 11% 6% 12% 8% 10% 11% 12% 9% 7% 3% 19% 16% 13% 19% 21% 14% 20% 14% Frequent shopper or loyalty program Store level Loss prevention Kiosks Shopper tracking capability Store level task management Digital signage displays NFC (Near Field Communication) payments Electronic shelf labels Up-to-date tech in place Started but not finished major tech upgrade Will start major tech upgrade in next 12 months Will start major tech upgrade in next 12-24 months Status of In Store Technology
- 63. 63 Multichannel Key Even in Supply Chain 32% 24% 24% 22% 20% 16% 8% 2% 16% 13% 14% 13% 14% 16% 12% 7% 11% 8% 8% 10% 19% 18% 17% 8% 8% 18% 13% 11% 17% 22% 10% 9% Warehouse management systems Distributed order management systems Transportation management systems Sourcing Real time inventory visibility (SCIV) Multichannel fulfillment Trade promotion management Radio frequency identification (RFID) Case/Pallet Up-to-date tech in place Started but not finished major tech upgrade Will start major tech upgrade in next 12 months Will start major tech upgrade in next 12-24 months Status of Supply Chain Technology
- 64. 64 Optimization Finds its Ways to retail 37% 25% 22% 14% 13% 14% 15% 18% 14% 11% 16% 11% Time and attendance Labor scheduling and optimization Task management Up-to-date tech in place Started but not finished major tech upgrade Will start major tech upgrade in next 12 months Will start major tech upgrade in next 12-24 months 27% 25% 23% 20% 5% 10% 13% 11% 13% 13% 21% 15% 18% 13% 15% 11% 14% 12% 12% 24% Human Resources and benefits Education and training Recruitment and on boarding Recruiting via social media (e.g. Facebook, LinkedIn) Mobile-enabled workforce and/or HR applications Up-to-date tech in place Started but not finished major tech upgrade Will start major tech upgrade in next 12 months Will start major tech upgrade in next 12-24 months Status of Workforce Mgmt. Technology Status of Human Resources Technology
- 65. 65 Merchandise Technology 33% 25% 24% 24% 21% 21% 19% 19% 17% 15% 12% 9% 8% 21% 18% 25% 13% 21% 17% 23% 15% 14% 25% 19% 21% 16% 11% 17% 19% 14% 19% 15% 17% 16% 15% 25% 14% 20% 25% 10% 8% 13% 8% 7% 11% 12% 15% 13% 16% 8% 12% 22% Replenishment Item management Forecasting and planning New product or private label development Allocation Category management Assortment planning Price and markdown optimization Product lifecycle management Campaign analysis and forecasting Shelf and space planning Campaign management Multi-channel planning and forecasting Up-to-date tech in place Started but not finished major tech upgrade Will start major tech upgrade in next 12 months Will start major tech upgrade in next 12-24 months Status of Merchandise Technology
- 66. 66 BI/Analytics Continues to Show Strength 25% 21% 19% 10% 9% 21% 22% 16% 18% 18% 16% 15% 16% 25% 17% 13% 20% 17% 21% 26% Market basket analysis Shopper Tracking Margin optimization Predictive analytics Social media analytics Up-to-date tech in place Started but not finished major tech upgrade Will start major tech upgrade in next 12 months Will start major tech upgrade in next 12-24 months Status of BI/Analytics solutions
- 67. Recommendations Be assured the rate of innovation in retail will accelerate dramatically over the next 3 years - Meaning your competitors are transforming their businesses….what about you? Understand your organizations (really senior Mgt.s) readiness to absorb new technologies. You are the change agent. Embrace the job!
Editor's Notes
- #3: Manifestation of TCS’ Thought Leadership in the Game of Retail
- #14: Customer Insightsby Naveen Krishna, VP-Online & Mobile Tech., Home Depot
- #17: Customer Insights by Aashish Chandra,Divisional VP, Application Modernization, Sears
- #28: With some new tools such as Storm, Cassandra and Mongo we can do truly real-time analysis. Those tools are starting to change the “batch mentality” in traditional companies from overnight processing to real-time processing.Allows you to ask questions and get answers that were impossible before - This is where the value from data can be derived, by using these tools to ask the previously impossible questions.Latency is the time from when the data is created and the time you can use it. With traditional ETL techniques it can take hours or even days between the time the data is created and the time you can use it. With modern big data tools we can bring latency to minutes and seconds, and transform the way you think about ETL.
- #32: Developer for Hadoop, runs efficiently, flexible spreadsheet interface with dashboards.
- #70: Prasad Raju,VP, Retail, US, TCS