Depth camera based customer behaviour analysis for retail: Master's thesis

Ville Huotari

    Research output: ThesisMaster's thesisTheses

    Abstract

    In 2000s traditional shop-based retailing has had to adapt to competition created by internet-based e-commerce. As a distinction from traditional retail, e-commerce can gather unprecedented amount of information about its customers and their behaviour. To enable behaviour-based analysis in traditional retailing, the customers need to be tracked reliably through the store. One such tracking technology is depth camera people tracking system developed at VTT, Technical Research Centre of Finland Ltd. This study aims to use the aforementioned people tracking system's data to enable e-commerce style behavioural analysis in physical retail locations. This study is done following the design science research paradigm to construct a real-life artefact. The artefact designed and implemented is based on accumulated knowledge from a systematic literature review, application domain analysis and iterative software engineering practices. Systematic literature review is used to understand what kind of performance evaluation is done in retail. These metrics are then analysed in regards to people tracking technologies to propose a conceptual framework for customer tracking in retail. From this the artefact is designed, implemented and evaluated. Evaluation is done by combination of requirement validation, field experiments and three distinct real-life field studies. Literature review found that retailing uses traditionally easily available performance metrics such as sales and profit. It was also clear that movement data, apart from traffic calculation, has been unavailable for retail and thus is not often used as quantifiable performance metric. As a result this study presents one novel way to use customer movement as a store performance metric. The artefact constructed quantifies, visualises and analyses customer tracking data with the provided depth camera system, which is a new approach to people tracking domain. The evaluation with real-life cases concludes that the artefact can indeed find and classify interesting behavioural patterns from customer tracking data.
    Original languageEnglish
    QualificationMaster Degree
    Awarding Institution
    • University of Oulu
    Place of PublicationOulu
    Publisher
    Publication statusPublished - 2015
    MoE publication typeG2 Master's thesis, polytechnic Master's thesis

    Fingerprint

    Cameras
    Software engineering
    Profitability
    Sales
    Internet
    Experiments

    Keywords

    • brick-and-mortar retail
    • people tracking
    • depth sensors
    • behavioural analysis
    • flow analysis
    • traffic calculation
    • point of interest analysis
    • dwell time
    • data visualisation

    Cite this

    Huotari, Ville. / Depth camera based customer behaviour analysis for retail : Master's thesis. Oulu : University of Oulu, 2015. 77 p.
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    abstract = "In 2000s traditional shop-based retailing has had to adapt to competition created by internet-based e-commerce. As a distinction from traditional retail, e-commerce can gather unprecedented amount of information about its customers and their behaviour. To enable behaviour-based analysis in traditional retailing, the customers need to be tracked reliably through the store. One such tracking technology is depth camera people tracking system developed at VTT, Technical Research Centre of Finland Ltd. This study aims to use the aforementioned people tracking system's data to enable e-commerce style behavioural analysis in physical retail locations. This study is done following the design science research paradigm to construct a real-life artefact. The artefact designed and implemented is based on accumulated knowledge from a systematic literature review, application domain analysis and iterative software engineering practices. Systematic literature review is used to understand what kind of performance evaluation is done in retail. These metrics are then analysed in regards to people tracking technologies to propose a conceptual framework for customer tracking in retail. From this the artefact is designed, implemented and evaluated. Evaluation is done by combination of requirement validation, field experiments and three distinct real-life field studies. Literature review found that retailing uses traditionally easily available performance metrics such as sales and profit. It was also clear that movement data, apart from traffic calculation, has been unavailable for retail and thus is not often used as quantifiable performance metric. As a result this study presents one novel way to use customer movement as a store performance metric. The artefact constructed quantifies, visualises and analyses customer tracking data with the provided depth camera system, which is a new approach to people tracking domain. The evaluation with real-life cases concludes that the artefact can indeed find and classify interesting behavioural patterns from customer tracking data.",
    keywords = "brick-and-mortar retail, people tracking, depth sensors, behavioural analysis, flow analysis, traffic calculation, point of interest analysis, dwell time, data visualisation",
    author = "Ville Huotari",
    note = "BA1155 Project code: 85054",
    year = "2015",
    language = "English",
    publisher = "University of Oulu",
    address = "Finland",
    school = "University of Oulu",

    }

    Huotari, V 2015, 'Depth camera based customer behaviour analysis for retail: Master's thesis', Master Degree, University of Oulu, Oulu.

    Depth camera based customer behaviour analysis for retail : Master's thesis. / Huotari, Ville.

    Oulu : University of Oulu, 2015. 77 p.

    Research output: ThesisMaster's thesisTheses

    TY - THES

    T1 - Depth camera based customer behaviour analysis for retail

    T2 - Master's thesis

    AU - Huotari, Ville

    N1 - BA1155 Project code: 85054

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    Y1 - 2015

    N2 - In 2000s traditional shop-based retailing has had to adapt to competition created by internet-based e-commerce. As a distinction from traditional retail, e-commerce can gather unprecedented amount of information about its customers and their behaviour. To enable behaviour-based analysis in traditional retailing, the customers need to be tracked reliably through the store. One such tracking technology is depth camera people tracking system developed at VTT, Technical Research Centre of Finland Ltd. This study aims to use the aforementioned people tracking system's data to enable e-commerce style behavioural analysis in physical retail locations. This study is done following the design science research paradigm to construct a real-life artefact. The artefact designed and implemented is based on accumulated knowledge from a systematic literature review, application domain analysis and iterative software engineering practices. Systematic literature review is used to understand what kind of performance evaluation is done in retail. These metrics are then analysed in regards to people tracking technologies to propose a conceptual framework for customer tracking in retail. From this the artefact is designed, implemented and evaluated. Evaluation is done by combination of requirement validation, field experiments and three distinct real-life field studies. Literature review found that retailing uses traditionally easily available performance metrics such as sales and profit. It was also clear that movement data, apart from traffic calculation, has been unavailable for retail and thus is not often used as quantifiable performance metric. As a result this study presents one novel way to use customer movement as a store performance metric. The artefact constructed quantifies, visualises and analyses customer tracking data with the provided depth camera system, which is a new approach to people tracking domain. The evaluation with real-life cases concludes that the artefact can indeed find and classify interesting behavioural patterns from customer tracking data.

    AB - In 2000s traditional shop-based retailing has had to adapt to competition created by internet-based e-commerce. As a distinction from traditional retail, e-commerce can gather unprecedented amount of information about its customers and their behaviour. To enable behaviour-based analysis in traditional retailing, the customers need to be tracked reliably through the store. One such tracking technology is depth camera people tracking system developed at VTT, Technical Research Centre of Finland Ltd. This study aims to use the aforementioned people tracking system's data to enable e-commerce style behavioural analysis in physical retail locations. This study is done following the design science research paradigm to construct a real-life artefact. The artefact designed and implemented is based on accumulated knowledge from a systematic literature review, application domain analysis and iterative software engineering practices. Systematic literature review is used to understand what kind of performance evaluation is done in retail. These metrics are then analysed in regards to people tracking technologies to propose a conceptual framework for customer tracking in retail. From this the artefact is designed, implemented and evaluated. Evaluation is done by combination of requirement validation, field experiments and three distinct real-life field studies. Literature review found that retailing uses traditionally easily available performance metrics such as sales and profit. It was also clear that movement data, apart from traffic calculation, has been unavailable for retail and thus is not often used as quantifiable performance metric. As a result this study presents one novel way to use customer movement as a store performance metric. The artefact constructed quantifies, visualises and analyses customer tracking data with the provided depth camera system, which is a new approach to people tracking domain. The evaluation with real-life cases concludes that the artefact can indeed find and classify interesting behavioural patterns from customer tracking data.

    KW - brick-and-mortar retail

    KW - people tracking

    KW - depth sensors

    KW - behavioural analysis

    KW - flow analysis

    KW - traffic calculation

    KW - point of interest analysis

    KW - dwell time

    KW - data visualisation

    M3 - Master's thesis

    PB - University of Oulu

    CY - Oulu

    ER -

    Huotari V. Depth camera based customer behaviour analysis for retail: Master's thesis. Oulu: University of Oulu, 2015. 77 p.