contribution-analysisv0.1.0

A SaaS company collects quarterly NPS scores and dozens of usage metrics across feature adoption, support interactions, and performance indicators. They use contribution analysis to determine which factor categories (e.g., onboarding, reliability, feature depth) explain the most variance in satisfaction, guiding roadmap priorities.

A hospital analytics team wants to understand which groups of factors—lifestyle, medication adherence, socio-economic status, and clinical biomarkers—most influence readmission rates. By decomposing R² across factor scores, they identify the dominant category and target interventions accordingly.

An e-commerce retailer measures spend across search, social, email, and display ads, along with seasonality and pricing variables. Contribution analysis reveals how much each channel group explains sales variance, helping marketers reallocate budget to the most impactful channels.

An HR department surveys employees on engagement and collects data on compensation, work-life balance, management support, and career growth. Using R² decomposition, they quantify which factor category drives engagement the most, informing retention strategies.

A fintech lender analyzes borrower data including credit history, income stability, debt ratios, and alternative data. Contribution analysis identifies which factor groups contribute most to default risk variance, improving underwriting models and regulatory reporting.

Offer expert contribution analysis as a consulting engagement, where clients provide their data and receive a detailed report with R² decomposition, factor interpretations, and actionable recommendations. This model suits agencies or independent consultants targeting mid-to-large enterprises.

Integrate the contribution analysis functionality into an existing BI platform or develop a standalone SaaS tool that automatically runs PCA and R² decomposition on uploaded datasets. Users get visual dashboards showing factor contributions and dominant drivers.

Package the methodology into a self-paced course that teaches users how to apply R² decomposition using Python, along with ready-to-use code templates and datasets. Ideal for analysts and data scientists wanting to upskill without hiring consultants.