BCG X Customer Churn Analysis

BCG X
Customer Churn Analysis

A client-facing data science simulation for BCG X — EDA, feature engineering, and Random Forest modelling to predict customer churn for XYZ Analytics, delivered with an executive summary.

PlatformForage · BCG X

CompletedOctober 7, 2025

TasksEDA · Feature Engineering · Modelling · Executive Summary

ToolsPython · Pandas · NumPy · Scikit-learn

Working like a BCG data scientist

The BCG X Forage simulation places you inside a real consulting engagement. The client is XYZ Analytics — a company experiencing customer churn. Your job: investigate the data, build a model, and deliver insights to senior leadership in the form they actually want — a concise executive summary, not a Jupyter notebook.

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The consulting context

BCG data scientists don't just build models. They frame business problems, translate technical findings into business language, and recommend specific actions. This simulation tests all three — not just the modelling.

5 tasks, end to end

Background — Frame the Problem

Understood the business context: XYZ Analytics is losing customers. Identified what client data is needed, what questions to investigate, and what success looks like for a churn prediction model.

Business framing · Hypothesis definition

EDA & Data Cleaning

Loaded customer data with Pandas. Ran descriptive statistics, checked for missing values, and visualised distributions using Matplotlib. Identified key behavioural and demographic signals.

Pandas · NumPy · Matplotlib · describe()

Feature Engineering

Created new features from existing columns to improve model signal. Handled categorical variables with encoding. Removed low-signal features.

get_dummies() · Feature creation · Correlation analysis

Random Forest Modelling

Trained a RandomForestClassifier on the engineered dataset. Achieved 50% recall on churn prediction — meaning the model catches 1 in 2 churners, a meaningful improvement over random.

RandomForestClassifier · train_test_split · classification_report

Executive Summary

Wrote a concise summary translating model findings into business language — what drives churn, confidence in the model, and 3 specific actions XYZ Analytics should take.

Business writing · Insight communication

What the model found

Metric	Value	Interpretation
Churn Recall	50%	Model correctly identifies 1 in 2 churners
Model Type	Random Forest	Handles non-linear patterns, ranks feature importance
Class Imbalance	Present	Majority class (retained) dominates — standard churn challenge
Key Features	Usage, tenure, plan type	Behavioural signals outperform demographics

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The executive summary discipline

The most valuable part of this simulation was learning to write an executive summary. Consultants don't present confusion matrices to CEOs — they present a business problem, a confidence level, and a recommendation. That translation is a skill.

What consulting data science actually looks like

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Problem framing comes before coding

BCG spends more time understanding the business question than writing code. A wrong framing produces a technically correct but useless model.

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The summary matters as much as the model

A 50% recall model delivered with a clear business narrative is more valuable than a 75% recall model with no actionable recommendations.

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Churn is inherently class-imbalanced

Real churn datasets always have far more retained customers than churners. The model must be evaluated on churn recall specifically — not overall accuracy.

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Speed and structure over perfection

Consulting simulations mirror real engagements: tight timelines, structured deliverables, and a senior audience who wants the bottom line first.

BCG X
Customer Churn Analysis

Working like a BCG data scientist

5 tasks, end to end

What the model found

What consulting data science actually looks like

BCG XCustomer Churn Analysis

Working like a BCG data scientist

5 tasks, end to end

What the model found

What consulting data science actually looks like

BCG X
Customer Churn Analysis