Empower Your Business With The Magic Of Machine Learning!
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Empower Your Business With The Magic Of Machine Learning!
How karaiskostools.gr a leading e-commerce tools platform in Greece uses mobiplus shopping recommendation platform to offer personalized experiences to customers.
Customers discover products they love and are delighted from the prediction capabilities of the recommender system.
Customers in eshops which are not personalized have conversion rate 4% only and this increases dramatically with the use of recommendation engines.
karaiskostools.gr offers personalized experience to users and has :
Recommender Systems are essential for ecommerce applications and now all modern e-shops have them built into their functionality.
Customers can thus find products without search and discover products they hadn’t imagined.
Customers in eshops which are not personalized have conversion rate 4% only and this increases dramatically with the use of recommendation engines.
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 connect to the e-shop.
So is your entire eshop is personalized.
The recommendation engines are placed and connected in the form of carousels and are:
1. Recommended 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 see products that sale now and then after 2- 3 clicks goes to User Recommendation engine.
2. Recommended 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.
Then we predict what products will buy next and recommended it using collaborative filtering architecture.
3. Related Products on product page for quest.
Below 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!
4. Buy together!
Recommended 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!
5. Retraining engine.
When new products uploaded we use these data and we train the recommendation engines in real time so we can recommend new products immediately to customers.
6. Hybrid seasonality model.
Model that takes into account products bought at this time and corresponding seasons in the past and thus recommends seasonal products.
This model is added on top of the previous models.
Meet George, a DIY enthusiast living in Greece who is looking to buy an electrical screwdriver. George visits KaraiskosTools, a leading e-commerce platform for tools in Greece, renowned for its vast selection and personalized shopping experience powered by the Mobiplus recommendation system.
George logs into KaraiskosTools and enters “electrical screwdriver” in the search bar. The platform quickly displays a range of electrical screwdrivers.
Personalized Recommendations:
As George browses, Mobiplus begins analyzing his past purchases and browsing behavior. It notes that George has previously bought mid-range power tools and has shown interest in brands known for durability and reliability.
Tailored Suggestions:
Leveraging this information, Mobiplus prioritizes electrical screwdrivers that fit George’s preferences. Featured prominently in the search results are mid-range, durable models from brands he likes. Mobiplus also highlights screwdrivers that are popular among other DIY enthusiasts with similar tastes.
Product Details and Comparisons
George clicks on one of the recommended screwdrivers. The product page offers comprehensive details, including specifications, customer reviews, and high-resolution images. Mobiplus further enhances his experience by suggesting related products, such as drill bits or toolkits, that complement his selection.
Smart Filters and Sorting:
To narrow down his options, George uses filters for price range, brand, and user ratings. Mobiplus adapts to these criteria, fine-tuning the recommendations. George identifies a few models that meet his needs.
Purchase Decision:
Satisfied with his research, George adds a recommended electrical screwdriver to his cart. During checkout, Mobiplus suggests additional items like a carrying case or a set of screws, enhancing his overall purchase.
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By integrating Mobiplus, KaraiskosTools ensures that George’s shopping experience is personalized, efficient, and enjoyable. Mobiplus’s advanced recommendation engine helps George find the ideal electrical screwdriver without extensive searching, making his shopping journey smooth and satisfying.
Read our book and learn why instore personalization and shopping data is the name of the game.
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.
