O C T O B E R 1 3 - 1 6 , 2 0 1 6 • A U S T I N , T X

Deep Data at Macys Searching Hierarchical Documents for eCommerce Merchandising

Denis Kamotsky, Macys.com Eugene Steinberg, Grid Dynamics

Peter Gazaryan, Macys.com

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01Introductions

Denis Kamotsky

Eugene Steinberg

Peter Gazaryan

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02The Macys Story v 1858 Entrepreneur R.H. Macy opens R.H. Macy & Company, a small dry

goods store.

v 1902 R.H. Macy & Co. moves uptown to Herald Square and shortens its name to Macy’s.

v 1924 Macy’s employees march from 145th Street to 34th Street to celebrate Thanksgiving, which sparks an annual tradition.

v 1976 Macy’s sponsors the first annual Macy’s Fireworks, now a 4th of July tradition.

v 1994 Federated Department Stores, the largest operator of department stores, acquires Macy’s.

v 1998 Macys.com is launched and operates out of New York and San Francisco.

v 2006 Macy’s expands to over 800 locations across the U.S

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03The Macys.com Story

v 1997 MCOM operates out of San Francisco, California and Brooklyn, New York.

v 1998 MCOM is officially launched.

v 2001 New York offices moves from Brooklyn to 1440 Broadway in Manhattan.

v 2001 Macy’s By Mail catalogue business shuts down, making macys.com the sole provider.

v 2010 We reach one billion dollars in annual sales volume.

v 2013 Macys.com launches Keyword Search running on Apache Solr.

v 2013 We reach two billion dollars in annual sales volume.

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01History of Search Engine at Macys.com

2015  Dec   2012   Aug   Dec   2013   Aug   Dec   2014   Aug   Dec  

Merchandising  Management  Tool  becomes  available  to  the  Users  

3/2013  

Solr-­‐Based  Keyword  Search  Engine  goes  live  on  macys.com  

4/2013  

Solr-­‐Based  Type-­‐Ahead  autocomplete  funcIonality  goes  

live  on  macys.com  9/2013  

Legacy  Keyword  Search  Engine  is  

reIred  

10/2013  

Management  of  the  Category  Browse  funcIonality  becomes  available  in  the  Saturn  Tool  

3/2014  

Category  Browse  is  fully  migrated  to  Solr  on  macys.com  

8/2014  

Intra-­‐day  SKU  availability  updates  go  live  

5/2014  

Dynamic  grouping  and  ungrouping  of  

Product  CollecIons  is  live  on  macys.com  

1/2015  

Solr  Re-­‐PlaQorm  effort  begins  

12/2011  

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01Solr Delivers Result

session conversion increase attributed to migration to new Solr-based Search engine 28%

customers clicking on the ungrouped collection convert higher 6%

macys.com traffic is Solr-Powered Keyword Search and Category Browse queries

8% increased conversion in type-ahead autocomplete sessions

35%

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01Types of Retail

Boutique

Mall

Big Box

Specialty

Department

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01Types of E-Retail

Inventory volume

Cur

atio

n

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01Merchandising

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01Great Expectations

Mary the Shopper Alice the Merchandizer

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01Dreaded “Relevancy Tuning”

One size doesn’t fit all, even if you can stretch it

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01Customer facing features

Quality • Natural relevance • Data quality • Concept search

Refinement • Range, hierarchical facets • Sorting, grouping • Guided navigation

Usability • Input methods • Presentation and productivity • Device form factors

Targeting • Geography, demographics • Personalization • Collaborative shopping

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01Structured Catalog

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01Structured Catalog and the Quest of Precise Filtration: Part 1

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01Structured Catalog and the Quest of Precise Filtration: Part 2

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01Structured Catalog and the Quest of Precise Filtration: Part 3

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01Concept Search and Controlled Precision Reduction

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01Concept Structured Search Under the Hood

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01Merchandiser facing features

Curation •  Rule-driven results

•  Product categorization

•  Date and time-based campaigns

Bias •  Coarse natural scoring •  Metric-driven boosting •  Context-based placement

Comprehensiveness •  Omnichannel data

•  Real-time availability

•  Accurate pricing

Referring •  Cross-sells, up-sells, recommendations •  Predictive search •  “Did you mean”, “do not carry”

suggestions

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01Dynamic Grouping and Ungrouping

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01  Dynamic Grouping and Ungrouping: Under The Hood catalog  =  SELECT  *  FROM  sku  JOIN  product  JOIN  collec=on  

SEARCH  "DKNY"  FROM  catalog      GROUP  BY  collec=on.id        WHEN  ALL(product.brand="DKNY")    

SEARCH  "white  cup"  FROM  catalog      GROUP  BY  collec=on.id        WHEN  COUNT(product.id)>collec=on.threshold    

SEARCH  ”dinnerware  cup"  FROM  catalog      GROUP  BY  collec=on.id        WHEN  NOT  EXISTS(product.id)  

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01Tiered Natural Scoring

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01Rule Driven Query Rewriting

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01Make The Magic of Macys!

Inspiring Story

o  Great traditions o  Versatility of the

business model o  Merchandising as a key

differentiator

Genuine Innovation

o  High Precision Concept Search

o  Controlled Precision Reduction

o  Tiered Natural Scoring o  … and much more

Challenging projects ahead

o  Omnichannel integration o  All flavors of scalability

on omnichannel data

Technical challenges hazard

Thank you!

Q&A