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    Presentation about the Mesh App, a personal shopping assistant
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Project Mesh App

Mesh is an app that is going to transform customers experiences in stores, making it personalized and unique for each customer. 

The development of the app was based on several researches on mobile usage for shopping. According to a study performed by Google Shopper Marketing Agency Council, 84% of smartphone shoppers use their mobile devices to assist them while shopping in a store; these shoppers who use mobile, buy more; customers believe that it saves them time and money, and that it facilitates their lives. Apptentive has also made a research on the subject, in which they list the main reasons consumers use apps in-store, which include redeeming in-store discounts, comparing prices, viewing product ratings, finding products, and to earn reward points. They have also gathered that retail apps drive revenue and loyalty, and that retailers who invest in high-quality apps will benefit on several levels. 

Therefore, using Watson cognitive services, we will create an app that will be able to identify a product through a picture taken by users when shopping and provide information about it, such as its price, other sizes and colors available, and suggest similar products. As customers use the app, we will be able to understand their shopping behavior and suggest products available on the store based on their preferences, as well as make them personalized offers. Users will also be able to perform payment on the app, only needing to go to a counter to retrieve the acquired products. We will make use of cognitive technologies to create natural conversation between users and the app, answering any questions consumers might have concerning the products, the store itself, methods of payment, and more, without having to consult an attendant. 

The main benefits for clients using the app in their stores include:
More engagement with costumers, offering a personalized experience for each of them;
Increased number of sells per costumer, as they will easily find more items they are interested indecreased size of checkout lines, as users will perform payment on the app
Less cost with employees, as costumers will be empowered to find information by themselves 
Ability to develop creative marketing strategies, as stores will have rich insights on costumers behavior in-store.



Design Flávio T. Schirmer 
Developer Cristian Schonmeier
Developer Thayse Onofrio
Advisor Anderson Benhossi

@ Porto Alegre, Brazil
Personality Insights

We use social media information to offer a true personalized experiencie. Using the IBM Watson's API personality insights.

*this featured is not fully implemented by the time our project is being officially reviewed.

Conversation

Have some questions about the store or the products you're interacting in real life? Ask to our chat-bot that we developed using Watson's Conversation API

Visual Recognition

The true gem in our app. Just take a photo of the product that you like, and buy it in the most easy way we could think of!
The brain behind all of that is the Watson's Visual Recognition API.
Design Concept Video

The video below demonstrate the navigation and design concept that we are proposing for IBM's Inside Track '17.
Real Demo Video

The video you're about to see it's the demo version that our team submitted to IBM's Inside Track '17.