Clans: The Intersection of AI/Machine Learning with behavioral science

NEW! – Increase Customer Retention, Acquisition & Revenues With Implicit AI/ML Recommendation

Lewis Perdue – Future Drinks Expo Presentation – 051722 – Copyrighted Material

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How Existing Recommenders Throttle Ecommerce

25-to 35% of ecommerce revenues come from ordinary recommendations.

93% of consumers do not click on recommendations because they are irrelevant.

How is Clans Different?

Clans algorithm & code has been tested, and verified from 70% up to 81% accurate
using 250,000 normalized real-world, real people data records.

“Native” Clans data expected to be 90%+.

How is that Possible?

No ratings, no stars or 100-point scales, no reviews.

How then?

Clans captures user perceptions, then makes recommendations based on people
who perceive EXACTLY the same thing, EXACTLY the same way.

Why is this important, #1?

Because individual ratings and reviews are biased — fatally distorted — by genetics,
education, life experience, vocabulary, social pressure,
psychology, environmental settings
and other factors.

Why is this important, #2?

Because existing recommendations cost business billions of dollars and stifle revenue potential: especially for ecommerce.

The Clans perception-based system is vital
for ACCURATE recommendations for wine,music, books,
scents, movies, and every other product “of taste.”

Learn More About Clans

• Click here to download a 47-slide PowerPoint on Clans (Select “read only”).
• Click Here for the 6-article series with deep background on the behavioral, genetic and neurophysiological aspects of the Clans algorithm development
• Click here for more background on the evolution of the Clans system and why current recommenders fail.
• Email Clans inventor and company founder Lewis Perdue.