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!