Empower Your Business With The Magic Of Machine Learning!
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- Aristotelous 83, Athens 163 43
- contact@mobiplus.co
Empower Your Business With The Magic Of Machine Learning!
Recommender Systems are essential for ecommerce applications and now all modern e-shops have them built into their functionality.
All ecommerce platforms nowadays have a lot to offer. magento, cs–cart , opencart, woocommerce and many others use recommender system to be able to predict what consumers want and recommend them without search.
Customers can thus find products without search and discover products they hadn’t imagined.
Customers in eshop which are not personalized have conversion rate 4% only and this increases dramatically with the use of recommendation engines.
You can see the problem in e-shop without recommendation engines here!
E-shops that are personalized have a 30% increase in revenue and they have:
The mobiplus shopping Recommendation platform uses the Shopping Data already within the e-shop and machine learning algorithms and then creates seven (7) recommendation engines which your technician easily connects to the e-shop.
The recommendation engines are placed and connected in the form of carousels and are:
1.Featured products on the first page for guest.
After the user makes a few clicks it recommends products from the cluster that it finds that the user belongs to based on clicks, i.e. the products that the user is now looking to find. On the first entry of the user it will show the popular ones and then it goes after 2- 3 clicks to User Recommendation engine.
2.Related Products on product page for quest.
Down from the product he sees now. It recommends products based on content recommendation architecture with word2vec and image embeddings from the cluster of these products.They are from the same category or close to the category to help the user find the product he wants!
3.Same Style.
Products above Related Products on the product page for quest and user login below the product you are currently viewing. It makes recommend products , of the same style and appearance up to 60% similarity ,using mobiplus image shopping recommender to help user find product with the same style and design…e.g. special sofas , dining tables , lighting , clothing , footwear etc.
4.Featured Products First page for user login.
Using user based recommendation architecture we take into account the user’s shopping history to date as well as the clicks he has made on the existing website. session to better understand their needs.
5.Buy together!
Featured Products in the shopping cart based on item to item recommendation architecture having found the products bought together for your e-shop…e.g. with shoes bought with socks!
6.Retraining engine.
When the customer during his navigation, makes click, basket, favorite actions, we use these data in real time for real time recommendations and for continuous training of the recommendation engine.
7.Hybrid seasonality model.
Model that takes into account products bought at this time and corresponding seasons in the past and thus suggests seasonal products.
This model is added on top of the previous models.
Statistics on the performance of proposed products, clicks and purchases of these are shown within Dashboard.
T
o learn how the top companies in the world increase revenue up to 30% from existing customers using Recommendation Systems and Personalization read the book below.
https://mobiplus.co/ebooks/customer-rediction/
mobiplus member Elevate Greece
Confirmed Innovation.
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.
