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

1. March 19, 2014
Retail Big Data and
Analytics
How Neiman Marcus Is Using Cloudera To
Enhance Customer Experience

2. 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
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3. 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.
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Why is Big Data
Important to Neiman
Marcus?

5. 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?
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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 Big Data

8. 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
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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 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
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11. 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
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12. Forward Direction and Roadmap

13. Platform for the Future
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14. 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
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15. Let’s Continue the Conversation
— twitter: @jc_humphries
— LinkedIn: http://www.linkedin.com/in/jchumphries
— E-Mail: cameron_humphries@neimanmarcus.com
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