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Retail Big Data and Analytics
 

Retail Big Data and Analytics

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Find out how Neiman Marcus is using Cloudera to enhance customer experience.

Find out how Neiman Marcus is using Cloudera to enhance customer experience.

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Retail Big Data and Analytics Retail Big Data and Analytics Presentation Transcript

  • March 19, 2014 Retail Big Data and Analytics How Neiman Marcus Is Using Cloudera To Enhance Customer Experience
  • Agenda — Company Overview — Why is Big Data Important to Neiman Marcus? ϒ  Evolving Customer Expectations / Omni Channel Imperative ϒ  “Big Data” Analytics Enables Superior Service at Scale — Evolution of Hadoop at Neiman Marcus ϒ  Background: Enterprise Data Warehouse before Hadoop ϒ  POC and Construction of the Business Case ϒ  Cloudera Decision — Forward Direction ϒ  Enterprise Data Repository & ETL Engine ϒ  Integration with Experience API and EDW 19-­‐Mar-­‐2014   STRICTLY  PRIVATE  AND  CONFIDENTIAL   2  
  • Company Overview —  For more than a century, The Neiman Marcus Group has stayed focused on serving the unique needs of the luxury market. —  Objective is “to be recognized as the premier luxury retailer dedicated to providing our customers with distinctive merchandise and superior service.” —  The Neiman Marcus Group is comprised of the Specialty Retail Stores segment - which includes Neiman Marcus Stores and Bergdorf Goodman - and the Online segment. —  The Company operates forty-two Neiman Marcus Stores across the United States and two Bergdorf Goodman stores in Manhattan. —  The Company also operates thirty-one Last Call clearance centers. —  The Online segment conducts direct to consumer operations under the Neiman Marcus, Horchow, Last Call and Bergdorf Goodman brand names. —  Total revenues for the year most recently ended (FY 2013) were $4.65 billion. 3   19-­‐Mar-­‐2014   STRICTLY  PRIVATE  AND  CONFIDENTIAL  
  • 19-­‐Mar-­‐2014   STRICTLY  PRIVATE  AND  CONFIDENTIAL   4   Why is Big Data Important to Neiman Marcus?
  • Customer Experience Expectations — Customer experience expectations are converging on the retail brand, not channel or device ϒ  Consistent across all channels, brands and devices ϒ  Personalized to reflect preferences and aspirations ϒ  Contextualized to present location and circumstances ϒ  Relevant, in the moment, to her needs and expectations In short, seek to leverage each experience to connect with customers and cultivate deep, long-term relationships How to do this? 19-­‐Mar-­‐2014   STRICTLY  PRIVATE  AND  CONFIDENTIAL   5  
  • 6   19-­‐Mar-­‐2014   STRICTLY  PRIVATE  AND  CONFIDENTIAL   Retail Brand, not Channel, Drives Expectations —  Customer expects that each channel knows her full history: purchases, preferences, and promotions, regardless of channel or device In-Store Web Catalog Mobile Investigate Select Payment Fulfill Advocate §  Harnessing data at very large scale – i.e., every interaction, rather than each transaction — enables creation of a data-driven, robust customer profile that reveals her preferences and aspirations §  Big Data enables all of these customer touch points to be assembled into a consolidated profile “a.k.a. single view of the customer,” which may then be used to identify services opportunities that historically have been managed solely though sales associate relationships
  • Life Before Big Data
  • Life Before Big Data —  Enterprise Data Warehouse (EDW) was launched in 2002, enabling significant new functionality in areas of ad hoc query and reporting for both merchandising and marketing. —  In subsequent years, additional new capabilities were introduced, including consolidated Customer Master Data Management, sales associate CRM, and more robust Campaign Management. —  Over this period, major technology advances were implemented, moving from relational (3NF) DBMS, to MPP, row-cache data appliance and, most recently, to column-store/ shared nothing data store. —  Each advancement delivered significantly greater performance, lower cost and larger scale —  But in spite of these advancements, challenges remained ϒ  Latencies were too great ϒ  Time-to-market for enhancements/features was too long & too expensive ϒ  Data strategy remained reactive and retrospective, rather than proactive and directly actionable ϒ  Investment requirements were too large – insufficient ROI 8   19-­‐Sep-­‐2014   STRICTLY  PRIVATE  AND  CONFIDENTIAL  
  • 9   19-­‐Mar-­‐2014   STRICTLY  PRIVATE  AND  CONFIDENTIAL   Traditional EDW doesn’t meet new expectations —  Legacy EDW was designed for a specific set of capabilities and modern demands of customer relationship management have expanded that scope substantially: — Batch or otherwise Highly Latent — Proactive, Analytical, Impersonal — Decision Support — Summarized — Real-time or Immediate — Reactive, Operational and Personal – 
 “Segment of One” — Actionable — Granular/Detailed Legacy Modern —  The legacy requirements continue to exist and may be satisfied through incremental enhancements to the existing platform, but the new challenges cannot be met without a fundamental and transformational reconstruction of the environment —  Combination of Big Data, NoSQL, In-Memory and APIs enable the realization of this real-time, large scale, customer engagement vision
  • Evolution of Hadoop at Neiman Marcus — Hadoop was identified as a potentially significant and disruptive technology in 2011 ϒ  Many Neiman Marcus developers were experimenting with it and attending user groups ϒ  Hadoop was generating a lot of hype and buzz among both IT and marketing management teams — Timeline ϒ  November 2011 - Structured POC in a sanctioned lab environment ϒ  July 2012 – Added to the systems roadmap ϒ  February 2013 – Approved project ϒ  March 2014 – Production Deployment 19-­‐Mar-­‐2014   STRICTLY  PRIVATE  AND  CONFIDENTIAL   10  
  • Why Cloudera? — As the timeline reflects, the selection of Cloudera evolved over several years and was validated through a number of project checkpoints in which a few key criteria were measured: — Community ϒ  In our experience, the largest community of developers and administrators has coalesced around Cloudera and its CDH distribution ϒ  As a client, we benefit from a larger talent pool from which to select partners, contractors and employees — Support ϒ  Expertise in Hadoop isn’t synonymous with expertise in supporting Hadoop ϒ  Need the assistance of a partner who’s core competency is supporting other organizations in the configuration, administration and operation of Hadoop — Ecosystem & Vision ϒ  Hadoop’s strengths are a product of its open source heritage but there is room for commercial leadership ϒ  CDH tends to be consistently supported by third parties, not all distributions enjoy such broad support 19-­‐Mar-­‐2014   STRICTLY  PRIVATE  AND  CONFIDENTIAL   11  
  • Forward Direction and Roadmap
  • Platform for the Future 19-­‐Mar-­‐2012   STRICTLY  PRIVATE  AND  CONFIDENTIAL   13  
  • Parting Thoughts —  Revolutionary new capabilities don’t require – or benefit – from “Big Bang” implementations or “Bet The Farm” investments ϒ  Many/most of the new system architectures are designed to scale linearly, so you can start small and scale as demand and value warrant ϒ  Big Data works well with Agile and Test Driven Development methodologies, which favor shorter and more frequent iteration cycles ϒ  Since many of these capabilities are net-new, they may be deployed on a pilot basis while maintaining legacy analytics in parallel —  While promising, Big Data and next-generation analytics are evolving rapidly: these are not “been there, done that” technologies ϒ  Risk profiles need to be calibrated accordingly ϒ  Mitigation strategies need to be calculated —  “Technology Enabled, Business Led” ϒ  Next generation customer analytics may be prototyped and incubated in an IT lab, but this is a business transformation effort ϒ  Significant attention and effort needs to be devoted to ensuring that the technology efforts are aligned with business strategy ϒ  Business management needs to absorb and leverage these capabilities, change management efforts that need to precede technical deployment 14   19-­‐Mar-­‐2014   STRICTLY  PRIVATE  AND  CONFIDENTIAL  
  • Let’s Continue the Conversation — twitter: @jc_humphries — LinkedIn: http://www.linkedin.com/in/jchumphries — E-Mail: cameron_humphries@neimanmarcus.com 19-­‐Mar-­‐2014   STRICTLY  PRIVATE  AND  CONFIDENTIAL   15