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
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.
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.