Classify and Cluster Datadog Accounts

Business Context

Datadog wants to improve adoption of a newly launched observability workflow. The growth team has labeled a subset of customer accounts that either adopted the workflow within 30 days or did not, while many newer accounts remain unlabeled. You need to compare a supervised approach for predicting adoption with an unsupervised approach for discovering account segments that may inform targeting.

Dataset

You are given an account-level dataset built from Datadog product usage over the first 14 days after signup.

• Size: 82K accounts, 30 input features
• Labeled subset: 49K accounts with known 30-day adoption outcome
• Unlabeled subset: 33K accounts with no target yet
• Class balance (labeled only): 28% adopted, 72% did not adopt
• Missing data: ~9% missing in support/sales fields, ~4% missing in some usage fields for low-activity accounts

Success Criteria

A strong solution should:

• Build a supervised model with ROC-AUC >= 0.82 and PR-AUC >= 0.58 on a held-out test set
• Produce an unsupervised segmentation with clusters that are stable and interpretable enough for GTM or product teams to act on
• Clearly explain when supervised learning is preferable, when unsupervised learning is preferable, and how the two can complement each other

Constraints

• Batch scoring must finish daily for ~100K accounts
• The growth team needs interpretable outputs, not just black-box scores
• The solution should work even when a large fraction of accounts are temporarily unlabeled

Deliverables

1. Train a supervised model to predict 30-day workflow adoption on labeled accounts
2. Build an unsupervised clustering pipeline on all accounts and profile the resulting segments
3. Compare the two approaches: objective, data requirements, outputs, and evaluation methods
4. Recommend how Datadog should use both models in production
5. Provide metrics, feature importance, and cluster summaries