Building an Intelligent Pricing Engine with Salesforce

The integration of Salesforce's Configure, Price, Quote (CPQ), Tableau analytics, and Einstein AI capabilities has revolutionized pricing optimization strategies across industries. This transformation enables organizations to shift from traditional static pricing models to dynamic, data-driven frameworks that respond to market changes in real-time. Through automated systems and machine learning algorithms, businesses have enhanced their ability to capture market demand fluctuations, improve quote generation accuracy, and optimize revenue streams. The implementation of these technologies has demonstrated significant improvements in operational efficiency, market responsiveness, and profitability across various sectors including manufacturing, retail, and financial services. Advanced pricing intelligence systems have proven particularly effective in complex business environments, enabling organizations to maintain competitive advantages while ensuring pricing consistency across multiple channels. The combination of robust data collection, AI-powered analysis, and automated execution has created a comprehensive ecosystem that addresses fundamental pricing challenges while delivering sustainable business value through improved decision-making capabilities and enhanced customer satisfaction.

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Publication Details

Journal
International Journal of Computing and Engineering
Published
2025-07-24
DOI
https://doi.org/10.47941/ijce.3015
Primary Topic
Consumer Market Behavior and Pricing
Type
article
Field-Weighted Citation Impact
0.00
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article

Building an Intelligent Pricing Engine with Salesforce

Deepika Bandella
International Journal of Computing and Engineering
Consumer Market Behavior and Pricing
article

Building an Intelligent Pricing Engine with Salesforce

Deepika Bandella
article en

Abstract

The integration of Salesforce's Configure, Price, Quote (CPQ), Tableau analytics, and Einstein AI capabilities has revolutionized pricing optimization strategies across industries. This transformation enables organizations to shift from traditional static pricing models to dynamic, data-driven frameworks that respond to market changes in real-time. Through automated systems and machine learning algorithms, businesses have enhanced their ability to capture market demand fluctuations, improve quote generation accuracy, and optimize revenue streams. The implementation of these technologies has demonstrated significant improvements in operational efficiency, market responsiveness, and profitability across various sectors including manufacturing, retail, and financial services. Advanced pricing intelligence systems have proven particularly effective in complex business environments, enabling organizations to maintain competitive advantages while ensuring pricing consistency across multiple channels. The combination of robust data collection, AI-powered analysis, and automated execution has created a comprehensive ecosystem that addresses fundamental pricing challenges while delivering sustainable business value through improved decision-making capabilities and enhanced customer satisfaction.

International Journal of Computing and EngineeringVol. 7(16)
Fairleigh Dickinson University (US)
Openalex Percentile: Top 77%
Consumer Market Behavior and Pricing
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