Abstract :

This Study, Titled Regression Analysis For Predicting Consumer Purchase Decisions, Evaluates Predictor Feature Importance Weights, Model Prediction Accuracy (R² Score), Clickthrough Conversion Expansion, Cart Abandonment Compression, And Financial Feasibility Of Predictive Regression Analytics Engines In Digital Commerce Platforms. E-commerce Platforms Operate In Highly Dynamic Consumer Markets, Where Product Pricing And Discounts Represent 40% And Brand Reputation Accounts For 28% Of Purchase Decision Influence. A Five-year Project Lifecycle (2021-2025) Of An Automated Regression Analytics Platform Is Evaluated Using Capital Budgeting Parameters: Net Present Value (NPV), Internal Rate Of Return (IRR), Payback Period (PBP), And Benefit-Cost Ratio (BCR). Quantitative Analysis Indicates That Non-linear Random Forest Regression Achieves The Highest Predictive Accuracy At R² = 0.94, Compared To R² = 0.58 Under Linear OLS. Scaling Monthly Purchase Conversions To 1.45 Million Drives Average Order Value (AOV) To ₹3,850, Expanding Regression Model Adoption To 94.5% And Compressing Digital Cart Abandonment Rates To 8.5% By 2025. The Financial Model Yields A Positive NPV Of 284.5 Crores And An IRR Of 38.6%, Far Exceeding The 10% Discount Hurdle Rate. The Study Concludes That Deploying Regression Analytics Engines Is Highly Viable, Boosting Digital Sales Conversions, Marketing ROI, And Enterprise Profitability.

Published:

04-9-2026

Issue:

Vol. 26 No. 9 (2026)

Page Nos:

30-38

Section:

Articles

License:

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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