PERANCANGAN STRATEGI PENGELOLAAN PELANGGAN DI REVOLT SPACE

• Main Author: Fachrul Muarif, Muh.
• Format: Final Project
• Language: Indonesia
• Online Access: https://digilib.itb.ac.id/gdl/view/85618
• Institution: Institut Teknologi Bandung

Revolt Space is a fitness club established in 2022, offering a variety of exercise class programs, including gym class classes. From August 2023 to April 2024, the revenue growth has been fluctuating and has experienced a decline that has exceeded the tolerance limit of target achievements. This instability is due to a decrease in the number of active customers renewing their subscriptions, highlighted by a monthly churn rate significantly above the optimal 4% benchmark for subscription-based businesses. The issue stems from an ineffective retention strategy, exacerbated by inadequate use of data analytics to understand customer behavior and needs. Consequently, Revolt Space must adopt a customer management strategy aligned with its current condition. This study employs a case study method with a qualitative approach, gathering data through observations, interviews, and questionnaires. The data collected will be analyzed to guide the development of a customer management strategy. The process includes identifying the organization's growth stage using the ASTRO ITB model, focusing on marketing strategies, and evaluating the customer management system with the CRM audit framework. According to the findings, Revolt Space is in the expansion phase (3rd stage) and its marketing strategy predominantly focuses on acquisition. The audit reveals a need to concentrate on the information management process. Thus, the customer management strategy for Revolt Space is designed to emphasize information management to enhance the company's retention marketing strategy, aligning with the conceptual marketing strategy model for each organizational growth stage. This research leads to the creation of a Google Spreadsheet-based data management system, integrated with Google Forms responses. This system can summarize customer transactions data and utilize the RFM model to cluster customers, enabling targeted and personalized retention marketing strategies.