Date post: | 23-Jan-2017 |
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Technology |
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Unlocking the Power of Ecommerce Product Recommendations to Boost
Conversions23rd September, 2015 | 2 PM EDT
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If I have 2 million customers on the web, I should have 2 million stores on the web.
IN WORDS OF JEFF BEZOS
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THE SUCCES OF ECOMMERCE PRODUCT RECOMMENDATIONS DEPENDS ON
RELEVANCY
TIMELINESS
DESIGN & USABILITY
PRODUCT RECOMMENDATIONS EVOLUTION
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ORIGIN OF CONTENT FILTERING RECOMMENDATION SYSTEM
• FAB – The first Unified recommender system
• Amazon – Proposed Item based collaborative filtering and filed for a patent in 1998
• Pandora (2000) – The Music Genome Project
UNDERSTANDING CUSTOMER JOURNEY & HOW PRODUCT RECOMMENDATIONS FIT
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• Explorers – People who are evaluating the site, they don't have any product in mind, yet.
• Targeted shopper– People who have some idea of what they want to explore and buy may assume the role of a targeted shopper.
• Committed shopper – People assume the role of committed shopper when they have found product are ready to check out.
• Repeat shopper – People assume the role of repeat shopper if they come back again to the site to but more
TYPES OF SHOPPERS
BEST PRACTICES AND EXAMPLES
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PEOPLE ALSO BOUGHT
Do not show different color
variants on recommendations
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COMPLETE THE LOOK
Offer ‘complete the look’ type
recommendations to increase AOV!
Thank You!