Equipment Rental Companies Leaders’ Customer Retention Strategies

Equipment rental business leaders that employ inadequate customer retention strategies negatively impact organizations’ performance and profitability. Improved customer retention strategies might improve key customer retention and increase market share. Grounded in the customer retention management theory, the purpose of this qualitative multiple case study was to explore strategies rental leaders use to retain customers. Participants were 10 equipment rental leaders from the Southwest Region of the United States that successfully retained their key customers through effective customer retention strategies. Data were collected using semistructured interviews and a review of materials from organizational websites regarding customer order fulfillment and technology. Through thematic analysis, three themes were identified: (a) customer engagement, (b) customer fulfillment, and (c) technology. A key recommendation for equipment rental business leaders is to identify customer needs and conduct customer surveys. The implications for positive social change include the potential for higher tax revenues for the community

A New Approach for Construction of Geodemographic Segmentation Model and Prediction Analysis

Customer retention is invariably the top priority of all consumer businesses, and certainly it is one of the most critical challenges as well. Identifying and gaining insights into the most probable cause of churn can save from five to ten times in terms of cost for the company compared with finding new customers. Therefore, this study introduces a full-fledged geodemographic segmentation model, assessing it, testing it, and deriving insights from it. A bank dataset consisting 11,000 instances, which consists of 10,000 instances for training and 10,000 instances for testing, with 14 attributes, has been used, and the likelihood of a person staying with the bank or leaving the bank is computed with the help of logistic regression. Base on the proposed model, insights are drawn and recommendations are provided. Stepwise logistic regression methods, namely, backward elimination method, forward selection method, and bidirectional model are constructed and contrasted to choose the best among them. Future forecasting of the models has been done by using cumulative accuracy profile (CAP) curve analysis