Classify and Segment Retail Customers | Dataford Interview Questions

Business Context

ShopSphere, a mid-sized e-commerce marketplace with 1.2M registered users, wants to improve retention and marketing efficiency. The data team needs both a model to predict which customers will churn and a segmentation approach for customers who have no labeled outcomes.

Dataset

You are given a customer-level dataset extracted from 12 months of activity.

Feature Group	Count	Examples
Behavioral metrics	12	sessions_30d, avg_session_duration, add_to_cart_rate, days_since_last_visit
Transactional metrics	9	orders_90d, avg_order_value, discount_usage_rate, refund_rate
Customer profile	6	country, device_type, acquisition_channel, loyalty_tier
Engagement metrics	5	email_open_rate, push_click_rate, support_tickets_90d
Label	1	churned_60d

Size: 240K customers, 32 input features
Target availability: 180K rows include a churn label; 60K rows are unlabeled due to recent acquisition or tracking gaps
Class balance: 18% churned, 82% retained in the labeled subset
Missing data: ~8% missing in engagement fields, ~3% missing in profile fields

Success Criteria

A strong solution should:

Build a supervised model that predicts 60-day churn with ROC-AUC >= 0.82 and F1 >= 0.55 on a held-out test set
Produce an unsupervised segmentation with clusters that are stable, interpretable, and useful for marketing
Clearly explain the difference between supervised and unsupervised learning using this dataset

Constraints

Predictions must run in a nightly batch job for 1.2M users in under 20 minutes
Marketing needs interpretable outputs, not a black-box-only solution
The same preprocessing logic must work for both labeled and unlabeled customers

Deliverables

Train a supervised model on labeled customers to predict churn
Build an unsupervised clustering pipeline on the full customer base
Compare the two approaches, including when each should be used
Evaluate both approaches with appropriate metrics and interpretation
Recommend how to deploy both outputs in production

Problem

Business Context

Dataset

You are given a customer-level dataset extracted from 12 months of activity.

Feature Group	Count	Examples
Behavioral metrics	12	sessions_30d, avg_session_duration, add_to_cart_rate, days_since_last_visit
Transactional metrics	9	orders_90d, avg_order_value, discount_usage_rate, refund_rate
Customer profile	6	country, device_type, acquisition_channel, loyalty_tier
Engagement metrics	5	email_open_rate, push_click_rate, support_tickets_90d
Label	1	churned_60d

Size: 240K customers, 32 input features
Target availability: 180K rows include a churn label; 60K rows are unlabeled due to recent acquisition or tracking gaps
Class balance: 18% churned, 82% retained in the labeled subset
Missing data: ~8% missing in engagement fields, ~3% missing in profile fields

Success Criteria

A strong solution should:

Build a supervised model that predicts 60-day churn with ROC-AUC >= 0.82 and F1 >= 0.55 on a held-out test set
Produce an unsupervised segmentation with clusters that are stable, interpretable, and useful for marketing
Clearly explain the difference between supervised and unsupervised learning using this dataset

Constraints

Predictions must run in a nightly batch job for 1.2M users in under 20 minutes
Marketing needs interpretable outputs, not a black-box-only solution
The same preprocessing logic must work for both labeled and unlabeled customers

Deliverables

Train a supervised model on labeled customers to predict churn
Build an unsupervised clustering pipeline on the full customer base
Compare the two approaches, including when each should be used
Evaluate both approaches with appropriate metrics and interpretation
Recommend how to deploy both outputs in production

Problem

Business Context

Dataset

You are given a customer-level dataset extracted from 12 months of activity.

Feature Group	Count	Examples
Behavioral metrics	12	sessions_30d, avg_session_duration, add_to_cart_rate, days_since_last_visit
Transactional metrics	9	orders_90d, avg_order_value, discount_usage_rate, refund_rate
Customer profile	6	country, device_type, acquisition_channel, loyalty_tier
Engagement metrics	5	email_open_rate, push_click_rate, support_tickets_90d
Label	1	churned_60d

Size: 240K customers, 32 input features
Target availability: 180K rows include a churn label; 60K rows are unlabeled due to recent acquisition or tracking gaps
Class balance: 18% churned, 82% retained in the labeled subset
Missing data: ~8% missing in engagement fields, ~3% missing in profile fields

Success Criteria

A strong solution should:

Build a supervised model that predicts 60-day churn with ROC-AUC >= 0.82 and F1 >= 0.55 on a held-out test set
Produce an unsupervised segmentation with clusters that are stable, interpretable, and useful for marketing
Clearly explain the difference between supervised and unsupervised learning using this dataset

Constraints

Predictions must run in a nightly batch job for 1.2M users in under 20 minutes
Marketing needs interpretable outputs, not a black-box-only solution
The same preprocessing logic must work for both labeled and unlabeled customers

Deliverables

Train a supervised model on labeled customers to predict churn
Build an unsupervised clustering pipeline on the full customer base
Compare the two approaches, including when each should be used
Evaluate both approaches with appropriate metrics and interpretation
Recommend how to deploy both outputs in production

Problem

Business Context

Dataset

You are given a customer-level dataset extracted from 12 months of activity.

Feature Group	Count	Examples
Behavioral metrics	12	sessions_30d, avg_session_duration, add_to_cart_rate, days_since_last_visit
Transactional metrics	9	orders_90d, avg_order_value, discount_usage_rate, refund_rate
Customer profile	6	country, device_type, acquisition_channel, loyalty_tier
Engagement metrics	5	email_open_rate, push_click_rate, support_tickets_90d
Label	1	churned_60d

Size: 240K customers, 32 input features
Target availability: 180K rows include a churn label; 60K rows are unlabeled due to recent acquisition or tracking gaps
Class balance: 18% churned, 82% retained in the labeled subset
Missing data: ~8% missing in engagement fields, ~3% missing in profile fields

Success Criteria

A strong solution should:

Build a supervised model that predicts 60-day churn with ROC-AUC >= 0.82 and F1 >= 0.55 on a held-out test set
Produce an unsupervised segmentation with clusters that are stable, interpretable, and useful for marketing
Clearly explain the difference between supervised and unsupervised learning using this dataset

Constraints

Predictions must run in a nightly batch job for 1.2M users in under 20 minutes
Marketing needs interpretable outputs, not a black-box-only solution
The same preprocessing logic must work for both labeled and unlabeled customers

Deliverables

Train a supervised model on labeled customers to predict churn
Build an unsupervised clustering pipeline on the full customer base
Compare the two approaches, including when each should be used
Evaluate both approaches with appropriate metrics and interpretation
Recommend how to deploy both outputs in production