1.0 retailers try to develop their own
1.0 Introduction Modern technologieshave severely advanced the retail environment. At the beginning retailers were suffered withthe intimidating remarks of online opponents without any cost of retail shops.
And also they were in a position to make better powerful target promotionswith more effectively targetpromotions with comprehensive customer shopping and desire information. Thereafterretailers try to develop their own online features, characteristics etc. Now, afterdeveloping those features, retailers want to learn new channel marketingsystems to adjust an online, analytical and much focused procedure with anaward of hands-in experience environment. Retailers mainly focusedon intimidating comments from online contestants, in addition to being moreeffectively targeted for promotions, at retail stores that are in a position tobuy detailed consumer shopping and desires information. At the present time, theretailers raise their online presence, retailers want complete channelmarketing that brings online systematics, hugely selected approaches in-store intimacyand experience. A number of few newtechnologies such as video analytics, Wi-Fi analytics, beacons, smart glasses, microelectro mechanical systems (MEMS) chips, LED Lighting, Bluetooth 4.0 and LoyaltyPrograms have come out to assist retailers optimize their store experience andprofitability. 1.
1 ProblemBackgroundMake use of mobile applications, Wi-Fi,Bluetooth and Beacon technology, now retailers can track the customer’smovements, customer’s location within the store. As an example For example, it holds a track ofcustomer movements and sends relevant information in each time a customerinstalling a store application and gets into the store and connects to theInternet. Now retailers use beacons to track customer location and sendnotifications via Bluetooth for customers without applications.
Some retailersoffer free Wi-Fi to customers and track their locations. Video tracking and face recognitiontechnology also uses to learn about customer behavior in spite of privacy relatedto in-store. As a better approach, no retailers collect Wi-Fi or GSM signalsfrom customers’ mobile phones and track customers since this technology performwith a high accuracy and coverage. Through this study I wish to propose asystem that that leverage analytics to refine store layouts without doing anycustomer disturbance. 1.2 ResearchQuestion How can we develop a system thatleverage analytics to refine store layouts without doing any customerdisturbance.
1.3 ResearchObjectives· Exploring the customer location trackingtechnologies, pros and cons of each technology.· Optimize store layouts applying a miningapproach. 2.0 LiteratureReview2.1Existing SystemsPrevious work ofapplicability to this study crosses a wide range: localization, vision-basedsensing, human activity sensing, and physical analytics in retail IndoorLocalization and Sensing: Using the foundation and environment, you can senseboth the environment and the user.
Despite the many work related to Wi-Filocalization, existing work can achieve high accuracy only at the highdeployment cost of Wi-Fi ingress points and at the price of additionalinformation and adjustments. CrowdInside introduced a way to build an indoorfloorplan using a customer’s location on a smartphone. Vision-basedapproaches are usually costly. Especially when 3D model construction ispossible, it is applied to popular landmarks. The interior of the store isgenerally lacking in such a typical landmark, often gathers with people andpositions.Detection ofhuman activity: delicate work Detection ofhuman activity using apparel device such as pedometer,Heart ratemonitor, microphone etc. Analysis ofretailing startup: In modern systems, it is necessary to utilize the basis ofspecific Wi – Fi localization to examine consumer in – store at a retail store.Euclid Analytics purchases an existing in-store Wi-Fi substructure and providesthe same analysis to retailers.
In this approach, refined item levelinformation has not yet been provided. Apple iBeacon communicateslocation-specific messages in the store to nearby smartphones via Bluetooth LowEnergy (BLE). Mondelez needs a retail store that puts the camera on a shelfthat uses face recognition to aware the demographics of grazing certainproducts. 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 withoutdoing any customer disturbances.
3.0 Methodology *Understanding customer flow isessential for enhancing your store layout. *Byanalyzing customer location data (camera data), inventory data, try to find themost effective arrangement of products, shelves and departments. 4.0 Timeline 5.0 References 1 M.
Moody, “Analysis of promising beacontechnology for consumers,” Elon J. Undergrad. Res. Commun., vol. 6, no. 1,2015.2 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 storesusing smart glasses,” 2014, pp. 115–126.6 “wp-spot-analytics.pdf.
” .7 “V544.pdf.” .
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