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

Abstract

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.

Keywords: Big data technology; Customer loyalty; Dormant members; Membership management; Pharmacy sector; Retail operations.

Fig. 1

RFM three-dimensional scatter plot.

Fig. 2

Feature importance bar chart.

Fig. 3

Awakening strategy repurchase rate comparison histogram.

Fig. 4

Sleeping member engagement optimization using big data.

Fig. 5

Key components and aspects of big data technology.

Fig. 6

Additional insights on customer loyalty.

Fig. 7

Key components of retail operations.

Fig. 8

Schematic of the ANN with one hidden layer comprising 5 neurons and 2 inputs, website/application usage frequency, and member participation.

Fig. 9

The results obtained from the ANN for predicting the reactivation success rate (0–1) tested.

Fig. 10

The results obtained from the ANN for predicting the increase in membership activity level tested.

Fig. 11

The results obtained from the ANN in order to predict the increase in sales and total revenue (US dollars) tested in this study.

Fig. 12

Linear regression charts examining the error of the ANN developed in this study for the a) reactivation success rate (0–1), b) increase in membership activity level, and c) increase in sales and total revenue (in US dollars).