IoT+Small Data: Transforming In-Store Shopping Analytics and Services
We espouse a vision of small data-based immersive retail analytics, where a combination of sensor data, from personal wearable-devices and store-deployed sensors & IoT devices, is used to create real-time, individualized services for in-store shoppers. Key challenges include (a) appropriate join...
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| Main Authors: | , , , , |
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| Format: | text |
| Language: | English |
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Institutional Knowledge at Singapore Management University
2016
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| Online Access: | https://ink.library.smu.edu.sg/sis_research/3570 https://ink.library.smu.edu.sg/context/sis_research/article/4571/viewcontent/1570228275.pdf |
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| Institution: | Singapore Management University |
| Language: | English |
| Summary: | We espouse a vision of small data-based immersive retail analytics, where a combination of sensor data, from personal wearable-devices and store-deployed sensors & IoT devices, is used to create real-time, individualized services for in-store shoppers. Key challenges include (a) appropriate joint mining of sensor & wearable data to capture a shopper’s product level interactions, and (b) judicious triggering of power-hungry wearable sensors (e.g., camera) to capture only relevant portions of a shopper’s in-store activities. To explore the feasibility of our vision, we conducted experiments with 5 smartwatch-wearing users who interacted with objects placed on cupboard racks in our lab (to crudely mimic corresponding grocery store interactions).Initial results show significant promise: 94% accuracy in identifying an item-picking gesture, 85% accuracy in identifying the shelf-location from where the item was picked and 61% accuracy in identifying the exact item picked (via analysis of the smartwatch camera data). |
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