Integrating Artificial Intelligence into Product Life Cycle Value and Activity Value Management: A Case Study of P Channel Agent

Abstract

Enterprises generate profits by providing products and services, aiming to maximize profits through effective marketing strategies. However, how long can a product survive, and how much profit can it generate throughout its entire life cycle? Related studies remain rare. As this area has not been thoroughly explored by scholars, this study aims to develop a method for measuring Product Life Cycle Value (PLCV). By integrating with Activity Value Management (AVM), this research utilizes profit information produced through AVM as the foundation for estimating PLCV. This study also incorporates Artificial Intelligence techniques to construct and validate predictive models for PLCV, thereby enhancing forecasting accuracy and practical applicability. We adopt the field-based empirical approach, with a well-known domestic channel agent serving as the research subject. From the perspective of the channel agent, the study investigates the life cycle duration and value of distributed products. Furthermore, for products with greater PLCV, the study combines demographic variables from the corresponding channel regions to identify the key population characteristics that influence profitability, thereby improving the effectiveness of resource allocation decisions.