Modern technologies have severely advanced the retail environment.
At the beginning retailers were suffered
with the intimidating remarks of online opponents without any cost of retail
shops. And also they were in a position to make better powerful target
promotions with more effectively target
promotions with comprehensive customer shopping and desire information. Thereafter
retailers try to develop their own online features, characteristics etc. Now, after
developing those features, retailers want to learn new channel marketing
systems to adjust an online, analytical and much focused procedure with an
award of hands-in experience environment.
Retailers mainly focused on intimidating comments from
online contestants, in addition to being more effectively targeted for
promotions, at retail stores that are in a position to buy detailed consumer
shopping and desires information. At the present time, the retailers raise
their online presence, retailers want complete channel marketing that brings
online systematics, hugely selected approaches in-store intimacy and
experience. A number of few new
technologies such as video analytics, Wi-Fi analytics, beacons, smart glasses, micro
electro mechanical systems (MEMS) chips, LED Lighting, Bluetooth 4.0 and Loyalty
Programs have come out to assist retailers optimize their store experience and
1.1 Problem Background
Make use of mobile
applications, Wi-Fi, Bluetooth and Beacon technology, now retailers can track
the customer’s movements, customer’s location within the store. As an example
For example, it holds a track
of customer movements and sends relevant information in each time a customer
installing a store application and gets into the store and connects to the
Internet. Now retailers use beacons to track customer location and send
notifications via Bluetooth for customers without applications. Some retailers
offer free Wi-Fi to customers and track their locations.
Video tracking and face
recognition technology also uses to learn about customer behavior in spite of privacy
related to in-store. As a better approach, no retailers collect Wi-Fi or GSM
signals from customers’ mobile phones and track customers since this technology
perform with a high accuracy and coverage.
Through this study I wish to
propose a system that that leverage analytics to refine store layouts without
doing any customer disturbance.
1.2 Research Question
How can we develop a system that leverage analytics to refine store
layouts without doing any customer disturbance.
1.3 Research Objectives
Exploring the customer
location tracking technologies, pros and cons of each technology.
Optimize store layouts applying
a mining approach.
2.0 Literature Review
2.1 Existing Systems
work of applicability to this study crosses a wide range: localization, vision-based
sensing, human activity sensing, and physical analytics in retail
Localization and Sensing: We can sensing both environments and users by utilizing
groundwork and the environment. In spite of the many work on Wi-Fi
localization, existing work can achieve high precision only at the price of
high arrangement costs or extra information or adjustments on the Wi-Fi ingress
point. CrowdInside introduces how to build an indoor floor plan using the
movements of users with smartphones. Use dead reckoning along with anchor
points to prevent accumulation of errors. Anchor points are defined by unique
inertial sensor signatures corresponding to elevators, escalators, and stairs.
However, such an inertia-based anchor point may be small or absent in the