Pipeline Case Studies — built to order, independently verified | MCP Analytics
The pipeline's portfolio

Pipeline Case Studies

Commissioned by our own team on public datasets, built and verified exactly like customer work. Every study below started as a plain-English question; the pipeline scoped the method, wrote the R, checked the result, and delivered the report.

Prebuilt analyses live in the library.

1
The question
“Which employee attributes actually predict attrition?” — asked in plain English against the public IBM HR dataset.
2
The build
The pipeline chose logistic regression for interpretable odds ratios plus XGBoost with SHAP for non-linear importance — then an independent pass checked the numbers and the narrative.
3
The delivered report
Retention levers ranked by role and department, per-employee risk, diagnostics shown. Interactive, citable, yours to keep.
Open the full report →
report sectionreport section

How the pipeline works →

IBM HR Employee Attrition Drivers — delivered report

Retail & E-commerce

Avocado Price Elasticity Analysis

Estimates price elasticity of demand for avocados using log-log regression of log(total_volume) on log(average_price), controlling for type (conventio

Avocado Price Trends — Deck

Regional avocado price and volume trends across US markets, built as a multi-card deck from a plain-language question.

deck tier

Avocado Price Trends — Snapshot

The same avocado price question answered as an instant snapshot report.

snapshot tier

Avocado Price-Volume Regression

OLS linear regression of weekly avocado average retail price on total sales volume, reporting slope estimate, R-squared, confidence bands, and residua

Customer RFM Segmentation

Segment e-commerce customers into behavioral groups based on purchase recency, frequency, and monetary value using K-Means clustering for targeted mar

Customer RFM Segmentation

Segment ecommerce customers into behavioral groups using Recency, Frequency, and Monetary (RFM) analysis with K-Means clustering. Identify high-value,

Delivery Time vs. Customer Satisfaction

Quantifies how delivery speed affects customer review scores by joining orders and reviews on order_id, computing delivery_days, and running OLS regre

E-Commerce Customer Churn Prediction

Predict which customers are at risk of churning using behavioral engagement, transaction history, satisfaction, and demographic features. Identifies h

E-commerce / SaaS Churn Drivers

Identifies which customer attributes drive churn using logistic regression for interpretable odds ratios and random forest for non-linear importance c

Price Elasticity of Demand Analysis

Estimate price elasticity of demand across product categories and regions using multiple linear regression on historical pricing and volume data. Iden

WHERE NEXT
the buildInside the build pipeline →The stages that wrote, checked, and delivered these studies.price itPricing →What each tier costs and what it delivers.talk to usBook a demo →Walk through the platform with us before you commit.