AI Powered Decision Intelligence Frameworks for Predictive and Prescriptive Business Optimization in Salesforce Enterprise Platforms

Enterprise Customer Relationship Management (CRM) systems are capable of generating immense amounts of customer-focused data but it is still challenging to transform the data to create the best and timely business outcomes. Although predictive analytics are able to approximate the future behavior of the customer, such as the chances of churning, prediction is not enough to take a useful step in management in the context of real world. The paper introduces an AI-Based Decision Intelligence system, which combines predictive machine learning workflows and prescriptive optimization plans to facilitate actionable and cost-effective business decisions in Salesforce-type enterprise systems. In the analogy of churn customer relationship management (CRM), the IBM Telco Customer Churn data is used to compare the support of various machine learning models, such as Logistic Regression, Random Forest, Gradient Boosting, AdaBoost, Extra Trees, Support Vector Machines, k-Nearest Neighbors, and churn prediction. Logistic Regression has the best predictive performance with $\mathbf{R O C}$-AUC $\boldsymbol{=} \mathbf{0. 8 4 2 6}$. The chosen predictive model is then integrated into a budget-constrained Next-Best-Action policy maximally investing retention interventions on customers at risk. The results of the experiment indicate that the proposed prescriptive layer reduces the anticipated loss due to churn by $55,981.48$ in comparison with the no-action baseline. The results prove that the frameworks of Decision Intelligence can effectively address the gap between forecasting and performing action and can provide a measurable business value and assist with the scalability of decision-making automation in enterprise CRM. The code is publicly available at following git-hub repository: https://github.com/mldlprojects/The-AI-Powered-Decision-Intelligence-Frameworks-for-Predictive-.