Machine Learning Applications in Salesforce CPQ Transforming Sales

Abstract

This paper aims to understand the innovative disposals of machine learning applications on Salesforce Configure, Price, Quote (CPQ) applications more precisely how these innovations are revolutionizing software development. Salesforce CPQ is an ideal solution for organizations that want a modern tool to help them with their sales processes, and adding machine learning elements significantly improves the system. For example, the study analyses different cases where and how machine learning algorithm is utilised in the application of the following areas: pricing strategy, product configuration and sales forecasting. Upon collection of historical data, machine learning models are able to make computations of patterns that enables businesses to make quick analysis of the best strategies to use in an offer of various prices to the customers in an effort to enhance customer satisfaction. Furthermore, the application of I/A in the context of CPQ decreases the time engaged in manual configurations, liberating software development teams to work on other priorities essentially. These difficulties and strategies are enumerated in the paper: data quality issues and the impossibility of the Machine learning successful implementation without cooperation with other departments, specifically with Salesforce CPQ teams. In addition, it shares its vision on the tendencies in application of machine learning in CPQ systems with reference to software development paradigms. Finally, the scope of this research is as follows: The study will examine how the utilization of machine learning applications in Salesforce CPQ has changed traditional software development practices in today’s dynamic market environment by increasing efficiency and accuracy effectively, improving competitiveness within the market.