Abstract

The concept of a sales pipeline has gone through major changes due to the use of machine learning (ML) in managing sales pipelines. Analyzing intelligent sales pipelines that utilize ML algorithms for maximizing the deal closure in Salesforce environments constitutes the focus of this paper. The following discussion extends our prior work by specifically considering how the present approach of predictive modeling, NLP, and clustering contribute to more accurate predictions, better customer understanding, and enhanced targeting. To assess the performance of the proposed approach we consider history data analysis, technological background of Salesforce platforms and proper integration of machine learning tools. This we illustrate with hypothetical business cases where the net deals closure rates have been achieved with an improvement in customer satisfaction by 30%. It also provides challenges like data quality, algorithms bias and scalability that have been discussed in this paper as well. Inferences made lay the foundation for organizations to employ intelligent pipelines to support credible sales development.