Empower Your Business With The Magic Of Machine Learning!
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Empower Your Business With The Magic Of Machine Learning!
The second step is to learn from the habits of your registered users, those who have left emails or addresses or phones or something else that can be linked to him.
Based on the data of your users’ actions to date, it can predict what they will buy afterwards.
Record the following data:
The technique used is Collaborative filtering where you choose between your users and the markets that have made the similarities and from there you predict the next market
It predicts someone who is in the middle of the trip what will happen afterwards! And recommends it to him in his email, their mobile, his chat or somewhere else in the future.
For your User based recommender to learn you need users id and products they bought in the past and you need morethan 20.000 transaction.
The User based recommender is usally placed in the first page and recommends products when customers logging.
All the page and what they see is personalized.
Also these recommendations are shown to quest users.When a quser users enters you e-shop the recommender shows the most popular sold in the period.
But after some clicks the recommender switches to user based mode ,recommending products from the cluster of similar users based on the products he shown interest in.
After the extra step he uses both models to make recommendations
Mobiplus shopping recommendation platform contains both architectures Item based and user based recommenders as well as image based recommender.
It also contains item to item recommender that predicts what product will this customer like to purchase additionally to the one he put on the cart.
Item to Item recommenders find all the pairs of products that are bought together from your shopping data.
Recommender systems are build on your existing shopping data but also use data from now on to continuously learn the shopping habits of your customers and make them and you happy.
Your e-shop and store sends to recommender every click , buy , search, favourite , cart for every session and user from now on and your recommender keeps learning the shopping preferences of your users .
The system sees the actions that the user takes with the recommendations from your site from your email, from his Smartphone and other communication tools and also uses these elements to calibrate the recommendation mechanism.
It uses likes , clicks, buys, and is improving its understanding of each user.
The system sees the actions that the user takes with the recommendations, it is from your site, it is from your email, it is from the application and other communication tools and it also uses this data to visualize the recommendation mechanism.
It uses data such as clicks, purchases, pieces, purchases, likes, etc. and thus improving its knowledge of each user.
For more see here https://mobiplus.co/product-recommendations-nea-epoxi-artificial-intelligence-recommendation-engine-e-commerce2-2/
To learn how the top companies in the world increase revenues up to 30% from existing customers read the following book.
https://mobiplus.co/e-books/e-book-proevlepse-ti-tha-agorasoun-oi-pelates/
Then recommends the products to your customer in your e-shop, in his email and on his mobile phone.
mobiplus member Elevate Greece
Confirmed Innovation.
Contact us now to see how the mobiplus shopping recommendation platform can increase your revenue by 30%!
Test out the Recommendation Engine for up to 20.000 euros in revenue from recommendations. No credit card required. Just enter your company information and we’ll contact you with all the details.
