Retail shops crowd and profit margin depends on the customer crowd in their shops. Waiting in long queues for billing increases customer dissatisfaction and reduces crowd in shops. This paper discusses a novel learning-based algorithm for such shops to reduce the customer waiting time in queues which in turn would lead to increased profit margin. The algorithm is based on supervised learning which uses slot-based approach on data from shops, learning it, finding the crowd factor and also possible error. It then predicts the upcoming crowd at the shop for coming days, slots required to keep the crowd in control and directs the retailer accordingly to bring in the needed changes in the number counters. The results of the proposed method show that this customer friendly tool can help reducing queuing time for customers at crowded time for coming days leading to customer satisfaction and increased productivity at shops.
Key words: Crowd factor, data prediction, error factor, queuing time, slot-based approach, supervised learning.
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