Paper Type
Complete
Paper Number
1371
Description
In B2B sales, assessing and selecting leads to pursue have traditionally relied on sales practitioners’ expertise. However, due to inherited limitations and subjectivity, this approach has been widely acknowledged as ineffective and inefficient for lead scoring/qualification. Therefore, scholars have shifted their focus to more objective data-driven approaches that could increase lead conversion rate and, eventually, sales performance. However, there is a lack of studies on lead scoring models that consider the leads’ willingness and purchase intentions. Therefore, this study develops a “smart sales” predictive lead scoring model using a combination of both unsupervised and supervised machine learning methods and incorporates leads’ purchase intentions from their perspective in the phased sales process. The empirical findings of this study claim that data-driven models can improve decision-making in the B2B sales process. Implementation of the proposed solution will help enhance the effectiveness and efficiency of the B2B sales process and its performance.
Recommended Citation
Wu, Migao; Andreev, Pavel; and Benyoucef, Morad, "Smart Sales: Amplifying the Power of Predictive Lead Scoring in B2B Sales" (2024). PACIS 2024 Proceedings. 3.
https://aisel.aisnet.org/pacis2024/track09_digittrans/track09_digittrans/3
Smart Sales: Amplifying the Power of Predictive Lead Scoring in B2B Sales
In B2B sales, assessing and selecting leads to pursue have traditionally relied on sales practitioners’ expertise. However, due to inherited limitations and subjectivity, this approach has been widely acknowledged as ineffective and inefficient for lead scoring/qualification. Therefore, scholars have shifted their focus to more objective data-driven approaches that could increase lead conversion rate and, eventually, sales performance. However, there is a lack of studies on lead scoring models that consider the leads’ willingness and purchase intentions. Therefore, this study develops a “smart sales” predictive lead scoring model using a combination of both unsupervised and supervised machine learning methods and incorporates leads’ purchase intentions from their perspective in the phased sales process. The empirical findings of this study claim that data-driven models can improve decision-making in the B2B sales process. Implementation of the proposed solution will help enhance the effectiveness and efficiency of the B2B sales process and its performance.
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