Integrating Financial Planning and Payments Data Fusion for Essbase SAP BW Cohort Profitability LTV CAC Variance Analysis

Abstract

The growing complexity of financial planning and analysis (FP&A) requires the integration of diverse data sources to deliver accurate insights for decision-making. Traditional FP&A systems such as Essbase and SAP BW have long supported planning, budgeting, and forecasting, yet they often operate in silos, creating reconciliation challenges when analyzing cohort profitability, customer lifetime value (LTV), customer acquisition cost (CAC), and variance decomposition. At the same time, the increasing availability of payments data has introduced new opportunities to enrich financial analytics by linking transaction-level activity with profitability and forecasting frameworks. This review explores the convergence of FP&A and payments data through integrated data fusion approaches, with a particular focus on Essbase– SAP BW reconciliation.
The paper highlights three core areas: cohort-based profitability, where data fusion strengthens the accuracy of P&L analysis; LTV/CAC attribution, where integrated datasets reduce attribution bias across channels; and budget–forecast variance analysis, where reconciliation improves root-cause identification. By synthesizing insights from existing literature, the review underscores the transformative potential of data fusion in enhancing FP&A practices. It concludes by outlining the implications for strategic decision-making and future research directions, emphasizing pathways for industry adoption, standardization, and the incorporation of advanced technologies such as AI and machine learning.