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Recommender System powered by Machine Learning
Fayrix Machine Learning solution features our proprietary recommender engine to create a personalized product offering and customer experience
Business problems solved
Create a customised list of recommended product for each client bases on their interests and profile
Offer additional discount for bundle purchases (cross-selling)
Notify clients about new products (up-selling)
Segment client base and define target audience
Collect client feedback on products and services
Business applications
Displaying and presenting relevant product recommendation on a website, at office, during a call to the contact center (+60% conversion growth)
Customized package (promo) product offering
Selecting optimal client communication time slots
Marketing campaigns optimization
Defining optimal communication channel
Narrowing target audience and decreasing the number of marketing campaigns by 5-10 times
Methods of making product recommendation
Product Hierarchy
You bought a printer, you would probably need an ink cartridge.
Recommendations based on product features
If you like action movies with Clint Eastwood, you will probably like "The Good, the Bad and the Ugly" on Netflix.
Collaborative filtering
- Based on product similarity: searching for products similar to the ones you like. For example, if you like "The Godfather", you will also like "Scarface"
- Based on customer similarity: searching for similar users and recommended products they like. For example, people like you buy diapers with a bottle of beer. So if you buy some beer, you will probably need diapers as well (Target case)
Recommendations based on models
Support vector machine learning, linear discriminant analysis, singular value decomposition for implicit functions.
Social and interest graph
Based on trust and social interactions between people. For example, if your friends like Lagy Gaga, you will probably like her as well. (used by Facebook and LinkedIn)
Hybrid methods
Combining any of the above-mentioned methods
Recommender system
success stories
2/3
of watched movies are recommended
of watched movies are recommended
38%
CTR increase with recommended content
CTR increase with recommended content
35%
of orders contain recommended products
of orders contain recommended products
SOURCE DATA
for the recommender engine
for the recommender engine
Do you want to increase your business revenues with personalized product recommendations?
Contact us now!
Contact us now!