Optimization of pharmacy membership management system based on big data: Sleeping member activation and awakening methods using ANN modeling.

In the retail industry, effective management of memberships is crucial, particularly within the pharmaceutical sector, as it fosters customer loyalty and drives sales growth. However, pharmacies often face challenges related to membership attrition and inactive members, which restrict the full potential of their membership programs. This research aims to address these challenges by optimizing pharmacy membership management systems through the utilization of big data technology. By leveraging the power of big data and employing machine learning algorithms, this study examines member data from multiple prominent pharmacy chains. The findings demonstrate the effectiveness of this approach in significantly increasing the level of activity among inactive memberships. Furthermore, this research unveils significant behavioral patterns among pharmacy members, shedding light on their preferences, purchasing habits, and interaction patterns. In this study, an artificial neural network (ANN) is employed to predict reactivation success rates, membership activity, and sales revenue based on website/app usage and member engagement. Two input factors, namely the frequency of website/app usage and member engagement score, are evaluated alongside three output factors: reactivation success rate, increase in membership activity levels, and increase in overall sales and revenue. Tailoring strategies based on member profiles and preferences enables pharmacies to re-engage customers and cultivate renewed loyalty. Importantly, these efforts yield positive impacts beyond membership activity, influencing overall sales and revenue generation for pharmacies. The ANN analysis reveals significant correlations and acceptable prediction errors. The insights gained from this study offer valuable information for enhancing membership management strategies and adjusting marketing efforts to cater to the specific needs and expectations of diverse customer segments. The practical, data-driven approach presented in this study equips pharmacies with the means to activate and re-engage dormant members. By harnessing the potential of big data technology and leveraging machine learning algorithms, pharmacies can optimize their membership management systems, enhance customer engagement, and improve their overall retail operations. This research underscores the significance of leveraging data-driven insights in the retail industry and showcases the transformative capabilities of big data technology in enhancing customer relationship management practices.