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CrowdDelegate: An MCS-Based Approach for Improving Retail Labor Cost-Efficiency

Following the revolutionary changes the Internet of Things (IoT) has introduced to sensor networks, the Mobile Crowd Sensing (MCS) paradigm aims to utilize people and their smartphones as an extended instrument to sense. However, the benefit of MCS is limited when it comes to microeconomic entities rather than macroeconomic entities. In this paper, we propose CrowdDelegate (CD), an extension of MCS that aims to delegate employee tasks of a consumer hypermarket to customer-workers, utilizing store's loyalty programs interface to recruit participants and reduce operational and logistical costs. This is done by assigning CD tasks to customer-workers present around the store requesting their engagement in a gamified loyalty membership. Customers are rewarded points for the execution of CD activities, allowing the retail business to channel a portion of the loyalty program budget towards the reduction of labor costs. The benefits are two-fold as this approach increases cost-efficiency as well as customer retention. A restricted optimal transport is proposed over the topology of the store to recruit customer-workers based on task costs. This paper sheds light on an unexplored potential of human-centric sensing, extending it to benefit businesses and to engage participants in "doing" instead of only sensing.