Understanding customer behavior using indoor location analysis and visualization

Yaeli, Avi; Bak, Peter; Feigenblat, Guy; Nadler, Sima; Roitman, Haggai; Saadoun, Gilad; Ship, Harold J.; Cohen, Doron; Fuchs, Omri; Ofek-Koifman, Shila; Sandbank, Tommy

doi:10.1147/jrd.2014.2337552

Cited by 53 publications

(34 citation statements)

References 11 publications

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“…Customer Behavior Analysis. Most marketing scholars suggest that the goal of the "smart store" is to understand customer behaviors in brick-and-mortar stores [5]. Prior marketing studies on retail business emphasize the importance of the smart store technologies and advanced analytics as they turn in-store customer behavior data into actionable insight [6,7].…”

Section: Goals Of Smart Store Frommentioning

confidence: 99%

“…One of the most important tasks of retail firms is to understand and apply various touch points at the moment-of-truth based on the customers' journey in the store. In relation to this, many studies from various fields have emphasized the importance of indoor location analysis [5,[9][10][11][12][13][14][15][16]. Indoor location analysis aims to provide proximity marketing because it makes it possible for retail firms to collect data of customer behaviors and in-store movements in brick-and-mortar stores, which is similar to the online web-browsing analysis in e-commerce sites.…”

Section: Goals Of Smart Store Frommentioning

confidence: 99%

See 1 more Smart Citation

Use of the Smart Store for Persuasive Marketing and Immersive Customer Experiences: A Case Study of Korean Apparel Enterprise

Hwangbo

Kim

Jin

2017

Mobile Information Systems

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Information technology’s introduction of online retail has deeply influenced methods of doing business. However, offline retail has not changed as radically in comparison to online retailing. Recently, studies in computer science have suggested new technology that can support offline retailers, including sensors, indoor positioning, augmented reality, vision, and interactive systems. Retailers have recently shown interest in these technologies and rapidly adopted them in order to improve operational efficiency and customer experience in their retail shops. Marketing studies also address immersive marketing that employs these technologies in order to change ways of doing offline retail business. Even though there is much discussion concerning new trends, technologies, and marketing concepts, there is, as of yet, no investigation that comprehensively explains how they can be combined together seamlessly in the real world retail environment. This paper employs the term “smart store” to indicate retail stores equipped with these new technologies and modern marketing concepts. This paper aims to summarize discussions related to smart stores and their possible applications in a real business environment. Furthermore, we present a case study of a business that applies the smart store concept to its fashion retail shops in Korea.

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Section: Goals Of Smart Store Frommentioning

confidence: 99%

Section: Goals Of Smart Store Frommentioning

confidence: 99%

Use of the Smart Store for Persuasive Marketing and Immersive Customer Experiences: A Case Study of Korean Apparel Enterprise

Hwangbo

Kim

Jin

2017

Mobile Information Systems

View full text Add to dashboard Cite

show abstract

“…On the basic of previous researches, multi-sensors have already been employed for traffic speed and travel time detecting [4][5][6]. Indoor positioning is another such field, with recent publications revealing the capability in healthcare monitoring [7,8], surveillance [9,10], and target group pattern generation [11,12]. Multi-sensing systems outperform traditional tracking techniques in managing the flow of signal and coordinating sensor actions [13].…”

Section: Introductionmentioning

confidence: 99%

Multi-sensing based target tracking by using decision-making strategy with spatial and temporal properties

Liu

Xiu-ju

Jin

et al. 2019

J Wireless Com Network

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Multi-sensing system for target tracking has been addressed by many researchers from different fields. In this work, a multi-sensing system within the time, space, and frequency domains is first described. The system development is based on sensor spatial and temporal characteristics, and therefore is reliable and stable. The frequency outcome of sensors is employed to segment the detection scope into different domains, and is simple to implement. For resolving the challenge of decision-making strategy, this work combines the spatial and temporal properties with the determination mechanism as well as optimizes the system devising-making it a very promising basis for the realization of tracking accuracy improvement. With this strategy, target detection results are obtained and evaluated by probability-based parameters. The proposed decision-making scheme receives a high detection accuracy as well as a good working performance according to statistical analysis, allowing a straightforward methodology for the configuration of multi-sensing system.

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“…Indoor positioning and tracking have attracted an extensive research effort because of their usefulness for a broad range of applications such as audience pattern generation [1], costumer analysis in retail [2], surveillance [3], business related activities [4], healthcare behavioral monitoring [5] among others [6], [7].…”

Section: Introductionmentioning

confidence: 99%

“…Recent research works face this problem by implementing wireless sensors in combination with processing algorithms that combine data gathered from multiple nodes [2], [6], [7].…”

Section: Introductionmentioning

confidence: 99%

Adaptive Fingerprinting in Multi-Sensor Fusion for Accurate Indoor Tracking

et al. 2017

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Abstract-Indoor Localization and Tracking have become an attractive research topic because of the wide range of potential applications. These applications are highly demanding in terms of estimation accuracy and rise a challenge due to the complexity of the scenarios modeled. Approaches for these topics are mainly based on either deterministic or probabilistic methods such as Kalman or Particles Filter. These techniques are improved by fusing information from different sources such as wireless or optical sensors. In this paper, a novel MUlti-sensor Fusion using Adaptive Fingerprint (MUFAF) Algorithm is presented and compared with several multi-sensor indoor localization and tracking methods. MUFAF is mainly divided in four phases: first, a Target Position Estimation (TPE) process is performed by every sensor; second, a Target Tracking Process (TTP) stage; third, a Multi-Sensor fusion (MMF) combines the sensor information and finally, an Adaptive Fingerprint Update (AFU) is applied. For TPE, a complete environment characterization in combination with a Kernel Density Estimation (KDE) technique are employed to obtain object position. A Modified Kalman Filter (MKF) is applied to TPE output in order to smooth target routes and avoid outliers effect. Moreover, two fusion methods are described in this work: Track-To-Track Fusion (TTTF) and Kalman Sensor Group Fusion (KSGF). Finally, AFU will endow the algorithm with responsiveness to environment changes by using Kriging interpolation to update the scenario fingerprint. MUFAF is implemented and compared in a testbed showing that it provides a significant improvement in estimation accuracy and long-term adaptivity to condition changes.

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Understanding customer behavior using indoor location analysis and visualization

Cited by 53 publications

References 11 publications

Use of the Smart Store for Persuasive Marketing and Immersive Customer Experiences: A Case Study of Korean Apparel Enterprise

Use of the Smart Store for Persuasive Marketing and Immersive Customer Experiences: A Case Study of Korean Apparel Enterprise

Multi-sensing based target tracking by using decision-making strategy with spatial and temporal properties

Adaptive Fingerprinting in Multi-Sensor Fusion for Accurate Indoor Tracking

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