Seven Recommendation Enginess that increase revenue up to 30%!
Personalization - Intelligence - Innovation!
mobiplus shopping Recommendation platform uses eshops and ERPs shopping Purchase Data and proprietary machine learning algorithms and creates seven (7) eshop specific recommendation engines which connect easily with your e-shop through an API!
They are e-shop specific and have significant accuracy.
The recommendation engines are connected to different carousels that recommend seven products each in the following places in e-shops:
After the user makes few clicks he receives recommended products in a front page carousel with 7 products recommended from the cluster that that the user belongs based on clicks, that is, those products he is now looking . In the first entry of the user will recommend the most popular and then after 2-3 click, will go to User Recommendation architecture.
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This carousel with recommended products is placed under the product that the user is visiting. The recommend products are based on content recommendation architecture with word2vec and technologies from the cluster of these products. They are from the same category or close to the current category to help the user find the product he wants!
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Recommended Products are placed over similar Products in product page for guests and for login users under the product they are viewing. The recommended products have the same style and appearance with up to 60% similarity, using mobiplus image shopping recommender to help the user find a products with the same style and design…i.e. special sofas, dining tables, fixtures, clothing, footwear etc. The Recommender Engine is based in Convolutional Neural Network architecture.
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Using user based recommendation architecture we take into consideration the user’s purchase history to date as well as the clicks he has done on the existing session to better understand his needs.
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Recommended Products with item to item recommendation architecture based on the current shopping cart having found the products that are purchased together for your e-shop..i.e. with shoes recommends socks!
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When the consumer browses in the e-shop we use clicks , put items to basket, or favorites in real time for updating the recommendations real time and for continuous training of recommendation engine.
Additionally, the Recommender Engines are trained every week using data from the new products and purchases that were made on the platform.
Model that takes into account products purchased at this time of the year and corresponding seasons in the past and recommends products taking into account seasonality .This model runs on the top of the previous models.
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Retail Businesses using Mobiplus Shopping Personalization platform have:
- 15 to 45% increase in customer conversion rate
- 20% Higher Order Value
- 30% more products in cart
- Increases revenues 16 to 35%
How it Works
Created with your own shopping and product data, mobiplus uses your existing purchases from cart and product descriptions and creates seven Recommendation engines with significant accuracy for your eshop. Full GDPR compliance
Mobiplus Recommender API
Our API makes simple and easy connection with your eshop.
Mobiplus Application Architecture
Read our guide for understanding technologies used by Mobiplus applications
Full Range Analytics and KPIs
Mobiplus Shopping personalization platform provides an extensive and comprehensive Dashboard where you can monitor the Recommendation engines and derive significant insights and KPIs.
We provide insights like:
- Users- no of people that receive recommendation
- Ranking-listing in the industry your company belongs based on impressions
- New users-no of users that do not belong in the e-shop
- Products- no of products that are recommended
- Impression-no of impressions of recommended products
- Clicks- no of clicks on recommended products
- Conversion rate-impressions/clicks
- Site clicks-no of clicks from product to site
- Site conversion rate-clicks/site conversion
- Buy Conversions-no of purchases of recommended products
- Buy conversions- site clicks/no of purchase
- Revenues-Revenues though recommendations
Download our Free Ebook and Learn how we predict what your customers will buy next week!
It is well known that human behavior, and in particular consumer behavior, can be predicted. At present computers posses significant computing power and gather huge amount of data from our digital transactions.
As a result, banks, financial institutions, retailers, political campaigns, hospitals, judges, companies and organizations, have been able to predict the behavior of people.
These efforts, helped win clients, elections and battles against various diseases.
Subscribe and learn how you can take your company in the Prediction era with A.I. and increase revenue by 30%!
