Brandeis X Demandware
The Next Generation of In Store Shopping
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Features

Location-Based Notifications

Push real-time recommendations and sales on products based on customers' previous online and in-store shopping records and their current location in the store.

Interactive Map

Search products and have them overlay on the map to allow easy navigation in the store. With customers and associates on the map, the interface also allows easy interactions between them.

Customer-Associate Interaction

Chat and communicate with associates about sales, products, prices, recommendations and even ask for a different size while in fitting room.

Mobile Checkout

Pay with the finger tip and choose to have the items shipped or picked up in-store.

Story

How Mary gets her perfect dress

  • 11:00am @ Home

    Online Store

    Mary goes online and searches for a red dress of KateSpace as a party is coming up.

  • 4:00pm @ Boylston St

    Location-Based Notifications

    Mary is on her way to pick up her boys and walks by a Kate Spade store. She gets a notification on her phone. The red dress just popped up!

  • 4:01pm @ The Store

    Interactive Map

    Mary clicks on the notification and a map shows up telling her where she can find the dress that she was looking for this morning.

  • 4:15pm @ Fitting room

    Customer-Associate Interaction

    Mary definitely likes the color, but the dress is a bit too large on the sides. She messages Alison, an available assistant, asking for a smaller size.

  • 4:30pm @ Leaving

    Mobile Checkout

    Alison adds the dress to Mary’s digital shopping cart. Mary pays with her fingerprint and leaves with the red dress.

Technology

Beacon

  Beacons are small wireless sensors that store owners can attach to any location or object in their store. These beacons broadcast tiny radio signals which customers' smartphone can receive and interpret, unlocking micro-location and contextual awareness.
  With beacons, we can connect customers' online information with their in-store shopping experince and push recommendation based on their current in-store location and online shopping record. Moreover, we are able to track customers' shopping behavior and provide a better both in-store and online service by analyzing these data.

We use a set of great beacons from Estimote in this project and we build an Andriod App to demo the beacon notification and location-based recommendation system.

Interactive Map

  We designed an interactive map interface on iOS platform where customers and assistants can see each other on an interactive store map and initiate communication between each other by blending an interactive map with the location coordinate we get from beacons.
   Customer can ask for help, chat with an assistant, or get a differnt size of item in the fitting room via this interface. In the same time, assistant can answer questions, initiate in-app checkout and send recommendations/sales information tailored for customer.
   Moreover, this interactive map can show location and give direction for items customers are searching for or interested in, which will ease their pain in looking for item and provide a more pleasant shopping experinece.

This is a proof-of-concept iOS App we build to demo on integrating Google Indoor Map and in-door location we get from beacons.

Swift In-Store Checkout

  With Apple Pay, customers can checkout in-store without pulling out their credit card or even go to the counter.
   Shop assistant can initiate an in-app checkout session and customers can simply pay the items with their finger print via Appla Pay. Moreover, with bar code scanner intergrated, customers can even check out the items themselves without a fuss.
   As in-app payment is also coming soon for Android Pay, this function will be supported on the major smart phone platforms and work for everyone.

Design   

Product

Design:
proto.io Interface
Prototype

Proof of Concept:
Beacon & Personalized Notification
Andriod Application

Proof of Concept:
Beacon Location & Google Map
iOS Application

Our Brandeis Team

Wesley

Computer Science & Neuroscience
2017'

Shimon

Computer Science & Economics
2016'

Brian

Computer Science & Politics
2016'

Jing

Computer Science MS.
2016'

Special thanks to our point of contact, Mia Stern, and her team at Demandware

&

Our mentor, Professor Pito Salas, here at Brandeis University