An Automated Framework for Unbiased Consumer Segmentation: Balancing Statistical Precision and Business Key Performance Indicators

Registration ID: IJNRD_327271

Published ID: IJNRD2607330

: https://doi.org/10.56975/ijnrd.v11i7.327271

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Anvesh reddy minukuri

Consumer Segmentation, Market Analytics, Bias-Variance Tradeoff, Apache Spark, Distributed Computing, Multi-Dimensional Feature Reduction, Principal Component Analysis (PCA), Tetrachoric Correlation, Polychoric Correlation, Sample-to-Population Reweighting, K-Means++ Clustering, Algorithmic Gating & Threshold Disqualification, Calinski-Harabasz Index, Davies-Bouldin Index, Temporal Stability Analysis, Bootstrap Jaccard Similarity, Explainable AI (XAI), SHAP (SHapley Additive exPlanations), Population Stability Index (PSI), Concept Drift Governance, Financial ROI Modeling, Key Performance Indicators (KPIs), Interactive Dashboards, Streamlit

Consumer segmentation remains a foundational imperative for optimizing go-to-market strategies, product development, and organizational sales alignment. Nevertheless, a persistent structural challenge within enterprise analytics lies in the inherent conflict between unsupervised statistical clustering methodologies—which prioritize intra-cluster variance minimization—and strategic business objectives, which necessitate actionable, unbiased segments strictly aligned with Key Performance Indicators (KPIs). Traditional market segmentation initiatives frequently yield heavily skewed, human-biased models attributable to computational constraints, limited exploratory iterations, and the erroneous application of continuous distance metrics to ordinal survey instruments. This paper introduces a highly scalable, automated consumer segmentation architecture deployed on distributed systems via Apache Spark. The proposed framework autonomously generates, evaluates, and audits over 15,000 distinct segmentation combinations. The technical pipeline integrates a rigorous multi-dimensional data reduction sequence, executing variable clustering and Principal Component Analysis (PCA) across every major domain—including customer demographics, firmographics, census data, internal telemetry, consumption patterns, digital interactions, product usage, and service attributes—to ensure robust variance coverage while mitigating information loss in high-dimensional spaces. Furthermore, survey and customer telemetry are dynamically reweighted to correct for sample-to-population divergences. Beyond standard Silhouette validation, the architecture incorporates Calinski-Harabasz and Davies-Bouldin indices, executes temporal stability assessments via bootstrap Jaccard similarity, incorporates SHAP (SHapley Additive exPlanations) for explainable feature attribution, implements Population Stability Index (PSI) monitoring for concept drift, and provides rigorous financial ROI impact modeling and feature weight sensitivity analyses. Candidate models are filtered via automated business profiling thresholds and statistical delta metrics, with final curated solutions surfaced through an interactive Streamlit dashboard

Published Paper   E-Certificate

Anvesh reddy minukuri (July-2026). An Automated Framework for Unbiased Consumer Segmentation: Balancing Statistical Precision and Business Key Performance Indicators. INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 11(7), d289-d295. https://doi.org/10.56975/ijnrd.v11i7.327271

Volume 11 Issue 7, July-2026

Pages : d289-d295

Paper Reg. ID: IJNRD_327271

Published Paper Id: IJNRD2607330

Research Area: Other area not in list

Author Type: Indian Author

Country: hyderabad/Rangaareddy, telangana, India

Published Paper PDF: https://ijnrd.org/papers/IJNRD2607330.pdf

Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2607330

Crossref DOI: https://doi.org/10.56975/ijnrd.v11i7.327271

Journal Name: INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT(IJNRD)

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