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Retail Big Data and Analytics
Neiman Marcus is using Cloudera's Hadoop platform to enhance its customer experience through big data analytics. It evolved from an enterprise data warehouse to implement a Hadoop proof-of-concept in 2011 and went live with Cloudera in 2014. This allows Neiman Marcus to gain real-time insights from customer data across all channels to personalize the customer experience and gain a single view of each customer. The legacy system could not meet modern demands for real-time, granular analytics and actionable insights.
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Retail Big Data and Analytics
- 1. March 19, 2014 RetailBig Data and Analytics How Neiman Marcus Is Using Cloudera To Enhance Customer Experience
- 2. Agenda — Company Overview — Why isBig 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
- 3. Company Overview — Formore 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
- 4. 19-‐Mar-‐2014 STRICTLY PRIVATE AND CONFIDENTIAL 4 Why is Big Data Important to Neiman Marcus?
- 5. Customer Experience Expectations — Customerexperience 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. 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
- 7. Life Before BigData
- 8. Life Before BigData — 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. 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
- 10. Evolution of Hadoopat 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
- 11. Why Cloudera? — As thetimeline 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
- 12. Forward Direction andRoadmap
- 13. Platform for theFuture 19-‐Mar-‐2012 STRICTLY PRIVATE AND CONFIDENTIAL 13
- 14. Parting Thoughts — Revolutionarynew 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
- 15. Let’s Continue theConversation — twitter: @jc_humphries — LinkedIn: http://www.linkedin.com/in/jchumphries — E-Mail: cameron_humphries@neimanmarcus.com 19-‐Mar-‐2014 STRICTLY PRIVATE AND CONFIDENTIAL 15