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Deep Data At Macys v1.0

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Denis Kamotsky presents an expanded version of the talk that was given at Revolution 2015 by Macys.com for the Downtown San Francisco Meetup group.

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Deep Data At Macys v1.0

  1. 1. O C T O B E R 1 3 - 1 6 , 2 0 1 6 • A U S T I N , T X
  2. 2. Deep Data at Macys Searching Hierarchical Documents for eCommerce Merchandising Denis Kamotsky, Macys.com Eugene Steinberg, Grid Dynamics Peter Gazaryan, Macys.com
  3. 3. 3 01 Introductions Denis Kamotsky Eugene Steinberg Peter Gazaryan
  4. 4. 4 02 The Macys Story 1858 Entrepreneur R.H. Macy opens R.H. Macy & Company, a small dry goods store. 1902 R.H. Macy & Co. moves uptown to Herald Square and shortens its name to Macy’s. 1924 Macy’s employees march from 145th Street to 34th Street to celebrate Thanksgiving, which sparks an annual tradition. 1976 Macy’s sponsors the first annual Macy’s Fireworks, now a 4th of July tradition. 1994 Federated Department Stores, the largest operator of department stores, acquires Macy’s. 1998 Macys.com is launched and operates out of New York and San Francisco. 2006 Macy’s expands to over 800 locations across the U.S
  5. 5. 5 03 The Macys.com Story 1997 MCOM operates out of San Francisco, California and Brooklyn, New York. 1998 MCOM is officially launched. 2001 New York offices moves from Brooklyn to 1440 Broadway in Manhattan. 2001 Macy’s By Mail catalogue business shuts down, making macys.com the sole provider. 2010 We reach one billion dollars in annual sales volume. 2013 Macys.com launches Keyword Search running on Apache Solr. 2013 We reach two billion dollars in annual sales volume.
  6. 6. 6 01 History of Search Engine at Macys.com 2015Dec 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 functionality goes live on macys.com 9/2013 Legacy Keyword Search Engine is retired 10/2013 Management of the Category Browse functionality 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 Collections is live on macys.com 1/2015 Solr Re-Platform effort begins 12/2011
  7. 7. 7 01 Solr Delivers Results session conversion increase attributed to migration to new Solr-based Search engine28% customers clicking on the ungrouped collection convert higher6% macys.com traffic is Solr-Powered Keyword Search and Category Browse queries 8% increased conversion in type-ahead autocomplete sessions 35%
  8. 8. 8 01 Types of Retail Boutique Mall Big Box Specialty Department
  9. 9. 9 01 Types of E-Retail Inventory volume Curation
  10. 10. 10 01 Merchandising
  11. 11. 11 01 Great Expectations Mary the Shopper Alice the Merchandizer
  12. 12. 12 01 Dreaded “Relevancy Tuning” One size doesn’t fit all, even if you can stretch it
  13. 13. 13 01 Customer 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
  14. 14. 14 01 Structured Catalog
  15. 15. 15 01 Structured Catalog and the Quest of Precise Filtration: Part 1
  16. 16. 16 01 Structured Catalog and the Quest of Precise Filtration: Part 2
  17. 17. 17 01 Structured Catalog and the Quest of Precise Filtration: Part 3
  18. 18. Hybrid Approach Multi-Paradigmatic Information Retrieval
  19. 19. 19 01 Concept Search and Controlled Precision Reduction
  20. 20. 20 01 Concept Structured Search Under the Hood
  21. 21. 21 01 Merchandiser 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
  22. 22. 22 01 Dynamic Grouping and Ungrouping
  23. 23. 23 01 Dynamic Grouping and Ungrouping: Under The Hood catalog = SELECT * FROM sku JOIN product JOIN collection SEARCH "DKNY" FROM catalog GROUP BY collection.id WHEN ALL(product.brand="DKNY") SEARCH "white cup" FROM catalog GROUP BY collection.id WHEN COUNT(product.id)>collection.threshold SEARCH ”dinnerware" FROM catalog GROUP BY collection.id WHEN NOT EXISTS(product.id)
  24. 24. 24 01 Tiered Natural Scoring
  25. 25. 25 01 Rule Driven Query Rewriting
  26. 26. 26 01 Make 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 and scalability o Natural language comprehension o Merchandising automation o Personalization Technical challenges hazard
  27. 27. Thank you! Q&A

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