Abstract
Mining approaches based on video data can serve in identifying stores’ performance by gaining insight into what needs to be proceeded to further enhance customers’ experience, leading to increased business profits. To this end, this paper proposes an association rule mining approach, depending on video analytic techniques, for detecting store-items that are likely to be out of demand. Our approach is developed upon motion-tracking and facial emotion expression methods. We used a motion-tracking technique to record information related to customers’ regions of interest inside the store and customers’ interactions with the on-shelf products. Besides, we have implemented an emotion classification model, trained on recorded video data, to identify customers’ emotions towards items. Results of our conducted experiments yielded several scenarios representing customer behavior towards out-of-demand stores’ items.
Highlights
The advancement of Internet retailers and online stores for over two decades has empowered and facilitated consumer experiences
We propose an association rule-mining approach comped with video analytic techniques for predicting the sales of items in physical stores, including out-of-demand items
We report on seven different scenarios representing customer behavior towards items that are out-of-demand
Summary
The advancement of Internet retailers and online stores for over two decades has empowered and facilitated consumer experiences. Marketing specialists and store owners are perpetually attempting to find any intelligent solution that could enhance the customer’s shopping experience, using, e.g. sensors equipped with computer vision technologies [1] Such technologies can aid retail stores in staying competitive and offer the best desirable services [2]. Data mining approaches can serve in identifying a store’s performance by gaining insight into what needs to be carried out to further enhance customers’ experience, leading to increased business profits [2]. We propose an association rule mining approach, depending on video analytic techniques, for detecting items that are likely to be out of demand. We propose an association rule-mining approach comped with video analytic techniques for predicting the sales of items in physical stores, including out-of-demand items.
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