This document proposes an app called iShopSmart that aims to bridge online and in-store shopping. It summarizes: 1) The pain points of online and in-store shopping and proposes allowing users to research items online and buy them in stores. 2) The solution would allow real-time access to store inventories, search by attributes, and location-based searches. 3) This would benefit consumers through easier finding of items, saving time and money, and the ability to try on clothes. Retailers would see increased traffic, sales, and low-cost advertising.

1. iShopSmart
Nawar Fdhal
B.Eng, M.A.Sc.

2. The Pain in Apparel Shopping
• Online Shopping • In-store Shopping
– Cannot Try clothes on – Time consuming
– Wait for shipping – Miss good deals
– Pay extra shipping cost – Hard to find desired items
• Research Online & Buy Offline (ROBO)
– Complicated
– Store based search
– Unguaranteed Product’s Availability

3. The Solution
• Seamlessly bridge the gap
• Real-time access to stores inventories
• Search based on color, style, size and price
• Location based

4. Value Proposition
• Consumer
– Find items easier
– Save time
– Save money
– Can feel and try clothes on
– Instant gratification
• Retailers
– Drive more traffic
– Increase sales
– New customers
– Inexpensive way to advertise
– Save shipping cost

5. Research and Statistics
• In 2009, 45% of Canadian used the internet to
search for Clothing and accessories without
placing an order online.
• In 2010, 66% of apparel shopping happened
inside retailer stores.
• In 2011, Online clothing sales is 0.5 billion
compared to 25 billion spend on clothing offline.

6. Business Model
• Subscription fee
– $10.00 per month
– First month free
• Consumer
– Free to use

7. The Team
• Nawar Fdhal
– received his M.A.Sc. and B.Eng. degree in Electrical and Computer Engineering from
Ryerson University in May 2011 and May 2008 respectively. His M.A.Sc. thesis was
heavily based on automated color evaluation of printed materials in a viewing booth
for the printing industry. He has developed a new technology of balancing the light in
the viewing booth (patent pending). He has a strong foundation in search
optimization algorithms and machine learning.
• Yiming Qian
– graduated with a B.Eng from the Electrical and Computer Engineering department of
at Ryerson University in May2012. His specialty is database back- end programming
and web development. He has experience with MySQL, PHP, Javascript, CSS,
HTML, Java, .Net, and C++. He worked as a research assistant in Ryerson University
from November 2011 to March 2012 on a project to develop a distributed framework
for corporate color management in print manufacturing. In 2010-2011, He worked as
an engineering intern in Siemens Canada, and developed many custom AutoCAD
and MS Office plug-ins to improve the engineering efficiency.

8. Our Advisor
• Dr. Matthew Kyan
– is an Assistant Professor at Ryerson University, in the Department of Electrical
and Computer Engineering. With a background in both computer science, and
electrical engineering, Dr. Kyan’s primary expertise is in machine learning
algorithms and multimedia signal processing. Dr. Kyan’s research group
specialises in the R&D of technologies that bridge the gap between the mining,
extraction analysis and visualisation of information from within multimedia based
data repositories, and has played a key role in establishing Ryerson’s Centre for
Interactive Multimedia Information Mining (CIM2). Dr. Kyan will oversee the
project management, and will provide technical direction in the development of
the front and backend web and database design.

9. Thank You