Research on Personalized Product Integration Improvement Based on Consumer Maturity

Making personalized product integration improvement based on consumer evaluation behavior plays a vital role in enhancing user satisfaction. However, the difference in consumer maturity leads to various standards of satisfaction decision-making in consumer evaluation behavior. Classical theories and models for describing product evaluation decision cannot granularly depict the well-thought-out decision-making process of consumer evaluation behavior and neglect the nonlinear influence of product attributes and consumer personality. Besides, existing studies usually adopt subjective methods like questionnaires and interviews to analyze consumer evaluation behavior, which have a limited number of data and questionable external validity. In order to fill these research gaps, consumer maturity indicators such as product cognition level, preference characteristics and evaluation characteristics are extracted from online reviews, and a three-phase Planned Behavior Model for Consumer Evaluation (PBMCE) is established based on the theory of planned behavior, including the attribute attitude formation phase, the evaluation intention formation phase and the evaluation result generation phase. Simultaneously, a Genetic Algorithm is developed to find the solution of the nonlinear three-phase PBMCE, and personalized product integration improvement strategies are proposed accordingly to achieve high user satisfaction. Finally, experiment results based on 5,200 users’ online reviews validate the effectiveness of the proposed model, indicating that our model can identify the importance of product attributes and consumer personality and characterize their nonlinear impacts on satisfaction decision.