1.0 retailers try to develop their own

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.

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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

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


How can we develop a system that
leverage analytics to refine store layouts without doing any customer


1.3    Research

Exploring the customer location tracking
technologies, pros and cons of each technology.

Optimize store layouts applying a mining


2.0    Literature

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



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.


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

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


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

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

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


4.0   Timeline



5.0  References


1     M. Moody, “Analysis of promising beacon
technology for consumers,” Elon J. Undergrad. Res. Commun., vol. 6, no. 1,

2     R. Max, “12 Technologies to Track People,”
Behavior Analytics in Retail, 01-Jun-2017. .

3     “Forrester_Analyze_This_Web_Style_Analytics_Enters_the_Retail_Web
_Store_White_Paper.pdf.” .

4     “Forrester_Beacons_Report.pdf.” .

5     S. Rallapalli, A. Ganesan, K. Chintalapudi,
V. N. Padmanabhan, and L. Qiu, “Enabling physical analytics in retail stores
using smart glasses,” 2014, pp. 115–126.

6     “wp-spot-analytics.pdf.” .

7     “V544.pdf.” .

8     “WALMART_PRIVACY_.pdf.” .