Nordstrom Rack | Hautelook curates and serves customers a wide selection of on-trend apparel, accessories, and shoes at an everyday savings of up to 75 percent off regular prices. With over a million visitors shopping across different platforms every day, and a realization that customers have become accustomed to robust and personalized search interactions, Nordstrom Rack | Hautelook launched an initiative over a year ago to provide data science-driven digital experiences to their customers. In this session, we’ll discuss Nordstrom Rack | Hautelook’s journey of operationalizing a hefty strategy, optimizing a fickle infrastructure, and rallying troops around a single vision of building an expansible machine-learning driven product discovery engine. The audience will learn about: -The key technical challenges and outcomes that come with onboarding a solution -The lessons learned of creating and executing operational design -The use of Lucidworks Fusion to plug custom data science models into search and browse applications to understand user intent and deliver personalized experiences

1. 11
Where Search Meets Science
and Style Meets Savings:
Nordstrom Rack's Journey to
Data Science-Driven Digital Experience
Pankaj Andhale
Senior Data Scientist
Nordstrom Rack | Hautelook
Peter Curran
General Manger, Digital Commerce
Lucidworks

2. 2
Outline
• Challenges
• Road to Relevance
• Fusion
• Results
• Q & A

3. 3
Search Lenses
Relevance ExperiencePerformance

4. 4
Search Quality
Relevance
Experience
Performance
Search
Quality

5. 5
Challenge #1
Bad Customer Experience
• Un-tuned index
• Customer complaints
• No dedicated search team

6. 6
Challenge #2
Data Driven Culture
• No clear KPIs
• No definition of success
• No A/B testing of algorithms
• No dashboards
Search
Visitors
Non
Search
Visitors
1x
2x
to
3x
Conversion Rates
(Industry)
1.67x

7. 7
Challenge #3
Speed & Flexibility
• No synergy between Search and Merchandising
• Simple tweaks required code changes and deployments
• Slow to fix simple issues

8. 8
Challenge #4
Data Management & Processing at Scale
• Processing customer behavior & leveraging in near real time
• Infrastructure did not support

9. 99
Addressing
Challenges

10. 10
• 1 Product Manager
• 1 Engineering Manager
• 1 Architect / Principal Engineer
• 1 Solr Engineer
• 1 QA
• 1 Data Scientist
Forming a Team
Road to Relevance

11. 11
Fusion Platform
Road to Relevance
• Rapid prototyping
• Testing
• Deployment
• Easy AB testing
+
• Signal aggregation
• Versioning
• Managed rules
• Machine learning

12. 12
Measuring Quality
Road to Relevance
● 42 KPIs and supporting metrics defined,
audited, and tracked
● Unlocked power of Google Analytics
● New Search Dashboard automated time-
consuming analysis
● Democratized search domain analysis
● AB testing on iOS

13. 13
Cross-Functional Feedback
• 30+ Merch Team issues fixed in two weeks
• Reciprocal sharing of knowledge led to quick
understanding
• Most new issues can now be resolved in the
same day
• Giant leap in business agility
MerchandisersRoad to Relevance

14. 14
Signal Aggregation
Road to Relevance
• Query : red dress
• Query : red valentino shoes

15. 15
Noteworthy Example of
Relevance Tuning
Road To Relevance
• Deep dive into the query logs
• Understanding the customer intent
• Significant increase in the search count

16. 16
Conversion Rate
Search vs. Non-Search Sessions
Results
1.7
x

17. 17
Conversion Rate
Search vs. Non-Search Sessions
Results
1.7
x
2.9
x

18. 18
Search Quality
https://opensourceconnections.com/blog/2018/11/19/an-introduction-to-search-quality/
Relevance
Experience
Performance
Search
Quality

19. 19
THANK YOU

20. 2020
Questions