Empower Your Business With The Magic Of Machine Learning!

Shopping Personalization in e-commerce, In store, In house, anywhere!

Mobiplus recommendation platform enables retailers  to have personalized e-shops and stores, for each customer, by predicting what the customer will buy next, using Machine Learning and the Purchase Data they possess.

Recommend to each customer individually the products they want to buy , in real time , automatically and offer  memorable experiences where customers discover new products effortlessly!  

 The Personalization  recommendation engine we have developed,  is company –specific  with a high degree of accuracy and it falls into two major categories:

  1. Seven mobiplus Recommendation Engines for e-shops
  2. In-Store Personalization with AI recommender & assistant
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THE ERA OF DIGITAL
IN STORE SHOPPING!

The future of shopping is consumer defined, and brands that
offer relevancy, convenience, and seamless experiences will
come out on top. Consumers expect their online and in-store shopping to offer
a similar experience. One that is personalized and catered to them. That is the gap that Mobiplus aims to bridge.

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mobiplus Shopping
Recommendation Platform

Our technology uses e-shops and ERPs shopping Purchase Data and proprietary machine learning algorithms and creates seven eshop specific recommendation engines which connect easily with your e-shop through an API!

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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.

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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:

  1. 15 to 45% increase in customer conversion rate
  2. 20% Higher Order Value
  3. 30% more products in cart
  4. Increases revenues 16 to 35%
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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.

Annie - Intelligent In Store Shopping Assistant

Annie is an Intelligent digital instore Shopping Assistant which offers personal, delightful experience to consumers, helping them find the products they desire and collects rich shopping data that helps you increase your revenue.

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Make instore Shopping Personalized

Annie Intelligent in store shopping assistant utilizes artificial intelligence and machine learning to provide customers with a personalized shopping experience.

It uses data on customer behavior and preferences to make product recommendations and answer questions in real-time.

It connects with mobiplus shopping recommendation platform for predicting and analyzing the shopping data.

Harness the Power of shopping data from stores!

It displays product recommendations in digital display in store and shoppers Smartphone and collects the customer's contact and shipping data, making your store digital! 

 

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Benefits for the Retailers!

  1. Increases the conversion rate and revenues of the store through automated digital consumer experience by 30% using all products available in store  and e-shop.
  2. Increases  revenues in  store from the additional shopping  Data to better meet the needs of in store  and e-shop customers on subsequent visits.
  3. Increase revenues by acquiring new customers from the store obtaining email from QR code interaction.
  4. Reduce sales cost in store for sales qualification.
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Interaction through Voice, Text, and Touch on the Screen

Customers can interact with Annie through voice, text, and touch on the screen, making it easy to use and accessible for all customers.

Annie provides a unique and innovative solution for retailers looking to personalize the shopping experience for their customers. By utilizing artificial intelligence and machine learning algorithms, the platform provides relevant and personalized product recommendations, answers to customer questions, and an easy-to-use interface, leading to increased customer satisfaction and loyalty.

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Download our Free Ebook and Learn 

Read our book and learn why instore personalization and shopping data is the name of the game. The book covers:

  • What Ipsos survey for consumer expectations say.
  • Why physical store is becoming the most significant customer channel.
  • The significance of shopping preferences data collected from store.
  • How ecommerce and instore shopping data increases your revenue by 30%.

Learn how to take your Company to the Personalized Era

Read articles on how to take your company into the new era with shopping recommendations and artificial intelligence in Retail.

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How  Fylliana.gr a  leading e-commerce furniture platform in Greece offers personalized experience to customers!

fylliana.gr   predicts what house, garden, office products the customer will buy. Fylliana  is a leading online furniture e-commerce platform with more...

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Start Free for up to 20k euros in revenue from Recommendations!

 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.

Recommendation System

These are some of the top companies that use machine learning technology

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Subscribe now and learn how Artificial Intelligence and Machine Learning can help your company increase its revenue by 30%. 

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