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Monal Patel
SVP & CBO, UnbxdTiffani Frey
Director of E-commerce Marketing, U.S.
Patriot Tactical
GUEST SPEAKER PRESENTER
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Site Search drives 20-40% of revenue for most
eCommerce sites → Mission Critical – Unbxd Analytics
Over 30% of eCommerce users prefer to discover
products through Site Search - eConsultancy
80% Visitors WILL Abandon A Site After A Poor Search
Experience
- Jupiter Research
WHY ARE WE TALKING ABOUT SEARCH?
ABOUT UNBXDLEADING MACHINE LEARNING PLATFORM FOR E-COMMERCE SEARCH & DISCOVERY
1200+ Global Websites Over 1.5 Billion Interactions/Month
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FASHION & APPAREL
The world’s largest off-post retailer of apparel, boots and gear for active duty military and
law enforcement personnel
23 brick and mortar stores plus online presence
Founded in 2000, U.S. Patriot is veteran owned and managed
3 consecutive years on the Internet Retailer Second 500 list – the fastest growing
segment of e-commerce retailers
ABOUT U.S. PATRIOT TACTICAL
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1. Exclusive products
2. Fast shipping, free returns
3. SERP rankings
4. Large catalog
5. On-site experience
U.S. PATRIOT’S COMPETITIVE LEVERS
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“For us, being nimble is survival.” – Paul Yoo, President & COO, U.S. Patriot Tactical
Query Type Example Contribution
Product “gloves” 63%
Feature ”waterproof boots” 14%
Exact Match “Princeton tactical headlamp” 6%
Natural Language “Shoes below $100” 4%
Thematic Search “dress shoes” 3%
Non Product ”return policy” 2%
Others“durable boots”, “pouches for the Steiner
X30 binoculars”, “air force jackets” etc.7%
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Understanding the shopper’s intent
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Shoppers don’t want to see Air Force
uniforms or Army boots
Search technology needs to understand the
semantics of the search term (and map it to
the most relevant products and desired
attributes)
The goal is to precisely understand the
shopper’s intentAir force = desired attribute (branch)
BOOT
SAGE
AIR FORCE
Boot = product type
Sage = desired attribute (color)
Understanding the shopper’s intent
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GREEN PANTS
OLIVE PANTS
SAGE PANTS
TDU GREEN PANTS
OD GREEN PANTS
2. SHORTEN THE PATH TO PURCHASE
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Direct shoppers at the search box - the first
point of interaction
Shortening the path to purchase
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Help shoppers narrow down their
queries and reach relevant sub-
categories
Leverage wisdom of the
crowd to directly point
shoppers to the most
relevant products
3. LEVERAGE MERCHANDISERS’
UNIQUE INSIGHTS
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We know our business best. Focus on
merchandising effectively at scale
Data-driven, nimble and self-serve merchandising
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Being nimble with merchandising is crucial
Important to have analytics and
merchandising capabilities without IT
involvement
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Promote products - for advertising partnerships or
exclusive deals
Float products to the top of the search results through
rules based Boosting or Pinning tools
Apply promotional banners on search results pages
Create landing pages or category redirects when
intent is knownCategory
Redirects
Pinning Boosting
Banners
Data-driven, nimble and self-serve merchandising
3. USE TECHNOLOGY TO STRUCTURE
THE CATALOG
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Catalog data cleanliness is critical for effective
search and navigation
Structuring the catalog for search
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Our e-commerce catalog data was spread
across many different systems
Parts of the catalog on DataFeedWatch, and
the rest through BigCommerce API
Reviews and Ratings from Yotpo.
Unbxd Search structures catalog data and easily works off our
existing platforms
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For example, our Category, Type and Origin
data are mixed up, and come from the same
catalog field
Unbxd Search dynamically segments them
and creates logical, custom filters
RESULTS: SIGNIFICANTLY HIGHER CONVERSIONS AND
REVENUE FROM SEARCH
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33% INCREASE IN
REVENUE
CONTRIBUTION
FROM SITE SEARCH
21% INCREASE IN
REVENUE PER
SEARCH
SESSION
22% INCREASE IN
SEARCH
CONVERSION
RATE
* 14 months post-Unbxd Search implementation vs. the same
time period pre-Unbxd
Industry
Conversion Rate Search Revenue
% Change (↑) % Change (↑)
Furniture & Home
Décor35% 18%
Fashion 28% 31%
B2B 23% 42%
Mass Merchant 41% 53%
Catalog 29% 40%
BUSINESS IMPACT OF UNBXD SITE SEARCH
Customers see a Search
revenue increase in 90 days
Revenue increase comes
mainly from Search
conversion increase
We frequently see an
increase in Search % of
traffic as well as AOV.
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MACHINE LEARNING ALGORITHMS MATCH SEARCH QUERY TO
SHOPPER INTENT
Shopper searches for
“Logitech Black Wireless Keyboard”
Stage 1: Query gets split into
Logitech ‘Black’ ‘wireless’ ‘Keyboard’
Logitech ‘Black’ ‘wireless’ ‘Keyboard’
Stage 2: Processing & Data Enrichment
Keyboard : Product Type
Black : Color Attribute
Wireless : Product Attribute
Logitech : Product Brand
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UNDERSTAND SHOPPER INTENT BY LEVERAGING DIFFERENT
QUERY PARAMETERS
Query Spectrum
Exact Search
Product Type
Symptom
Non Product Search
Query Qualifier
Feature
Thematic
Relational
Compatibility
Subjective
Query Structure
Slang, Abbreviation & Symbol Search – ‘MTB’ for “Mountain bikes”
Implicit – Search for “brown tops” when in “women’s” category
Natural Language – “Women’s Dresses for Office”
– “Kitchenaid mixer”
– “Women’s Tshirt”
– “Diabetes”
– “Free shipping”
– “Black dresses”
– “Party tops”
– “Michael Kors Miranda collection”
– “Moto X case”
– “High quality speakers”
Technology Capabilities
Relevance Tuning
Merchandising (Campaign Mgmt.)
Geolocation-based Search
Personalization
Natural Language Processing
REST API
SEO Management
Multilingual
GARTNER’S TECHNOLOGY FRAMEWORK FOR
ADVANCED COMMERCE SEARCH
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