Customer Analytics using Surveillance Video
This work addresses the problem of enhancing marketing strategies for retail businesses by analyzing customer behavior from video data, but it appears incremental as it builds on existing clustering methods without demonstrating broad impact.
The paper tackles the problem of analyzing customer shopping behavior in retail using surveillance video to identify purchase patterns, proposing an extended MCOKE algorithm with weighted k-Means to map customers to garments based on traits like age, gender, time spent, and expressions, with the result aimed at inferring product interests to improve business strategies and increase sales.
The analysis of sales information, is a vital step in designing an effective marketing strategy. This work proposes a novel approach to analyse the shopping behaviour of customers to identify their purchase patterns. An extended version of the Multi-Cluster Overlapping k-Means Extension (MCOKE) algorithm with weighted k-Means algorithm is utilized to map customers to the garments of interest. The age & gender traits of the customer; the time spent and the expressions exhibited while selecting garments for purchase, are utilized to associate a customer or a group of customers to a garments they are interested in. Such study on the customer base of a retail business, may help in inferring the products of interest of their consumers, and enable them in developing effective business strategies, thus ensuring customer satisfaction, loyalty, increased sales and profits.