IRIS: Tapping wearable sensing to capture in-store retail insights on shoppers

• Main Authors: RADHAKRISHNAN, Meera, ESWARAN, Sharanya, MISRA, Archan, CHANDER, Deepthi, DASGUPTA, Koustuv
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2016
• Subjects: IRIS; variable-order HMM-based sequence prediction; change point detection; feature-based landmarking combination; atomic item-level interactions; retail store; smartwatch; smartphone; Sales and Merchandising; Software Engineering
• Online Access: https://ink.library.smu.edu.sg/sis_research/3238; https://ink.library.smu.edu.sg/context/sis_research/article/4240/viewcontent/1476359.pdf
• Institution: Singapore Management University

We investigate the possibility of using a combination of a smartphone and a smartwatch, carried by a shopper, to get insights into the shopper’s behavior inside a retail store. The proposed IRIS framework uses standard locomotive and gestural micro-activities as building blocks to define novel composite features that help classify different facets of a shopper’s interaction/experience with individual items, as well as attributes of the overall shopping episode or the store. Besides defining such novel features, IRIS builds a novel segmentation algorithm, which partitions the duration of an entire shopping episode into atomic item-level interactions, by using a combination of feature-based landmarking, change point detection and variable-order HMMbasedsequence prediction. Experiments with 50 real-life grocery shopping episodes, collected from 25 shoppers, we show that IRIS can demarcate item-level interactions with an accuracy of approx. 91%, and subsequently characterize item-and-episode level shopper behavior with accuracies of over 90%.