Customer Purchase Intention Identification Model Based on Telesales Voice Data

China's automotive industry is undergoing a significant transformation towards electrification and intelligent mobility,emphasizing the need for effective marketing strategies to enhance brand perception and navigate the challenges in traditional sales channels.Within this evolving landscape,telesales play a crucial role in screening customer intentions,a process vital for boosting sales efficiency and reducing costs.However,the reliance on the subjective judgment of sales representatives in this multi-step process often leads to inefficiencies,including missed opportunities and resource wastage on low-intent customers,underscoring the need for a more precise approach in aligning marketing efforts with customer intentions in the automotive sector's dynamic environment.

Our research introduces an innovative model aimed at identifying customers' purchase intentions through the analysis of voice data from telesales conversations.Utilizing advanced voice analysis technology,this model scrutinizes various aspects of customer responses,including emotional,attitudinal,and cognitive dimensions,employing logistic regression techniques for accurate classification.The validation of this model involved an extensive dataset of 542 authentic customer voice recordings,which unveiled a significant link between factors such as emotional arousal,patience,and the likelihood of purchase intentions.Impressively,the model's precision,determined by AUC metrics in tests beyond the initial sample,surpassed the 92% mark.This level of accuracy underscores the model's effectiveness in pinpointing true purchasing intentions,setting it apart in the realm of customer intention analysis.The outcomes of our study underscore the model's practical value,especially in the context of telesales where distinguishing genuine buyers from a vast pool of contacts is crucial.By homing in on pivotal indicators like emotional arousal and patience,our model adeptly filters through the noise to identify those customers most inclined towards making a purchase.This capability not only streamlines marketing efforts but also significantly boosts sales efficiency by allocating resources more judiciously and increasing the focus on high-potential leads.Moreover,the real-world applicability of our model was further evidenced by its remarkable performance metrics,achieving a 90% success rate in capturing positive examples at a coverage rate of 23.1%.Such compelling results highlight the model's robustness and its suitability for broad implementation across the telesales operations within the automotive sector.The research not only paves the way for a more focused and effective marketing strategy but also heralds a new era in customer relationship management,where understanding and meeting the nuanced needs of potential buyers through sophisticated voice analysis becomes a cornerstone of success.

The automotive industry's evolution towards electrification,digitization,and sustainability marks a pivotal era demanding innovative marketing strategies.This model represents a significant advancement in utilizing voice analysis for customer intention identification,offering a more objective,scientific approach to understanding and catering to potential buyers' needs.Through this model,automotive companies can refine their telesales strategies,prioritizing high-intent customers,and thereby maximizing the efficiency of their marketing efforts.