Fortune 100 powerhouse Honeywell uses Elastic Enterprise Search to power search experiences across its marketing, customer support, and e-commerce platforms. Learn how Honeywell replaced their existing search platform with Elastic to deliver enhanced search across multiple sites and use cases, as well as the improvements and what they learned along the way.
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Forward-Looking Statements
6. Arc Flash PPE
Auxiliary Power Units
Intrusion detection
Face shield
Propulsion engines
Grounding equipment
Silicone Insulator
Electric insulating gloves
Batch process monitoring
Outage protection
Weather radar
Flight navigation
Thermal switches
Voltage detectors
Display electronic units
Diverse Set of Brands & Products
AS/RS Systems
Robotics
Lifeline systems
First aid
Commercial gas detection
Temperature screener
Portables gas detectors
Leak detectors
Body suits
Hard hats, helmets
Ear plugs, ear muffs
First responder gear
Sensors
Industrial automation
Process controls
Warehouse automation
Safety googles
Video surveillance
Building controls
HVAC management
Fire alarms & evacuation
Reagents & solvents
Respiratory protection
Refrigerants
Wheels & braking systems
Air & thermal systems
Satellite communication
Cabin connectivity
Adsorbents & Catalysts
Integrated avionics
7. 50 100 1M
INDUSTRIES
Serving many
industries and sub-
industries
BRANDS
Known & unknown
related to Honeywell
PRODUCTS
Various products
and SKU’s available
for customers
Customers
8. How do we help customers find and buy our products
& services?
• Marketing & e-commerce sites with global & localized searches
• Search on product information, orders / invoices, pricing & availability
How do we help customers find support for our
products & services?
• Portal sites for industries with customized content
• Managing cases created through customer service
• Search on technical publications, case studies, white-papers, and
knowledge articles
10. Use Cases
• Information needed to serve customers is stored across multiple canonical data sources
(Salesforce, SAP, Cloud, Adobe)
• Aggregation of schema fields using meta-engines, document API to index multiple sources, and standardized
communication using JSON & RESTful APIs
11.
12. Use Cases
• Customers don’t know the exact phrase to find something
• Query_suggestion API with relevance tuning applied across multiple fields, synonym API to produce similar search
results using different search terms
13.
14. Use Cases
• Better understand what search results customers actually go to
• Clickthrough & analytics API to capture keyword clicks, clickthrough pages, and tagging
15.
16. Use Cases
• Customized global / local search and search results based on bespoke search criteria
• Search API with customized weightage applied to search criteria, nested filters
17.
18. Use Cases
• Easily browse through different product & content categories
• Search API with facets, hierarchical facets, and grouping
19.
20. Learnings, Benefits & Pitfalls
• Implementation can be very quick
• Technical teams should have good experience using JSON, RESTful APIs, and tools like Postman
• Automate, automate, automate – grow & learn using the Elasticsearch console but move toward
integrating front-end development with App Search APIs
• Increased scale - internal marketing teams can manage their own curation, synonyms, relevance
tuning through console
• Prototype – Reference UI to build filter / facet views
21. “Content is the reason search began in the
first place”
Lee Olden, CEO of TopRank Marketing
22. Results & Next Steps
• Generating revenue of $410B through on-line channels
• Serving 12MM documents through 2.6MM API calls to 170 engines
• Migration of existing Swiftype to Elastic Enterprise Search and monitoring with ELK Stack
• More memory / storage capacity, availability on multiple cloud providers, across multiple regions/
zones