1.0 micro electro mechanical systems (MEMS) chips, LED

1.0 Introduction

 

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
profitability.

 

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

Previous
work of applicability to this study crosses a wide range: localization, vision-based
sensing, human activity sensing, and physical analytics in retail

 

 

Indoor
Localization and Sensing: Using the foundation and environment, you can sense
both the environment and the user. Despite the many work related to Wi-Fi
localization, existing work can achieve high accuracy only at the high
deployment cost of Wi-Fi ingress points and at the price of additional
information and adjustments. CrowdInside introduced a way to build an indoor
floorplan using a customer’s location on a smartphone.

 

Vision-based
approaches are usually costly. Especially when 3D model construction is
possible, it is applied to popular landmarks. The interior of the store is
generally lacking in such a typical landmark, often gathers with people and
positions.

Detection
of human activity: delicate work

 

Detection
of human activity using apparel device such as pedometer,

Heart
rate monitor, microphone etc.

 

Analysis
of retailing startup: In modern systems, it is necessary to utilize the basis
of specific Wi – Fi localization to examine consumer in – store at a retail
store. Euclid Analytics purchases an existing in-store Wi-Fi substructure and
provides the same analysis to retailers. In this approach, refined item level
information has not yet been provided. Apple iBeacon communicates
location-specific messages in the store to nearby smartphones via Bluetooth Low
Energy (BLE). Mondelez needs a retail store that puts the camera on a shelf
that uses face recognition to aware the demographics of grazing certain
products.

 

2.2  Drawbacks of existing systems

 

Since
these methods used smartphones eg: Wi-Fi, Bluetooth etc. I wish to proposed a
new store layout optimizer without doing any customer disturbances.

 

3.0 Methodology

3.1 Introduction

          *Understanding customer flow is essential for enhancing
your store layout.

*By analyzing customer location data (camera data), inventory data, try
to find the most effective arrangement of products, shelves and departments.