A Mobile application for smart-shopping for retail stores (M-kiosk)
Thuku, James Gichuhi
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Majority of retail stores in Kenya commonly referred to as kiosks are yet to automate their operations. The mode of operation for majority of the kiosks is manual. This brings about several challenges to both the store owner as well as the customers. Some of the challenges faced by the retailer include lack of instant information regarding products and customers, human error during audit of transactions and tracking of stock is very slow. For the customer, manual operations are prone to long queues, slow to response on product related queries, and no direct interface to interact with the retail store at their comfort. With increase in competition, there is need to get innovative on the use of customer information to enhance customer satisfaction. There also exists no mechanism to reward customers who are loyal to the shop. Availability, performance, productivity, cost reduction and reliability are good reasons for considering an automated solution. This research proposes a model for automating operations and analysis of customer data in a retail store setting to enable the business to run efficiently. A mobile and web based prototype was designed, developed and tested to assist in automating operations, analyzing customer purchases to increase efficiency. Products are assigned a unique bar code which is scanned during purchase, allowing the shopkeeper to easily track stock, and group customers into different categories based on similarity of purchases. These categories will be used to customize promotions to specific customers. The study will identify relationships in customer purchasing habits with an objective of deriving usable values through increasing sales and enhancing customer loyalty and satisfaction. The store owner will use his mobile phone to scan the product as well as the customer card where applicable, and tally the total amount to be paid for the products. The customers are able to interact with the system, and view past transactions as well as get notifications regarding promotions using a mobile based application on their phone. The design of promotions will be done from the backend, and will be based on frequently purchased goods, hence achieving customized promotions. The study has adopted concepts from Social Network Analysis (SNA) , and applied the same to enable retail stores experience Business Intelligence (BI) through studying human behaviour with an aim of creating more opportunities for them to reap more benefits by using the data previously thought to be of no help.