A Strategic Study on Improving Targeting Accuracy of Marketing Campaigns Using Factor Analysis

Abstract

Abstract Precision marketing emerges as a pivotal mechanism to foster market development, enhancing the market economy and propelling internal economic growth. This study collates data pertinent to marketing activities, standardizes the raw datasets to compute descriptive statistics such as the mean and variance, and constructs a correlation coefficient matrix under specified conditions. Through factor analysis, the structure of this correlation matrix is meticulously examined. Factor loadings are employed to elucidate the relationships between factors and variables, thereby establishing a link between the precision of marketing endeavors and consumer attribute variables for further analytical probing. To assess the impact of implemented marketing strategies on consumer response behaviors, this research develops a precision marketing model using the Uplift algorithm. This model innovatively addresses the challenge of individual causal effects—which are realistically unresolvable—by transforming it into estimating the conditional average causal utility derivable from observed data. The factor analysis feasibility test yields a Kaiser-Meyer-Olkin (KMO) measure of sampling adequacy at 0.792, and Bartlett's test of sphericity attains a significance level of 0.000, indicating robust factorability. Subsequent tests on variance and principal factor extraction reveal that five variables—such as income, gender, and age—are common factors across the datasets analyzed. Application of the Uplift model to the MineThatData and MegaFon datasets further substantiates the efficacy of the proposed marketing model. Particularly, the results from the MegaFon dataset validate the comprehensive applicability of this model, demonstrating its effectiveness in real-world scenarios.