audience-segmentation

Automate RFM scoring and customer segmentation by combining behavioral clustering with cohort retention analysis to generate segment profiles for targeted campaigns. The pipeline applies scalable feature engineering to transaction data and outputs interpretable segment mappings for quick marketing decisions.

Identify high-value customers and predict churn risk using K-Means and DBSCAN clustering with deterministic preprocessing and reproducible seeds. The pipeline generates segment-based campaign lookalikes across channels while ensuring consistent, reproducible clustering results for reliable churn prediction.

Yes, pandas and scikit-learn support cohort retention analysis and segment migration tracking by processing transaction data into retention matrices and migration maps. The pipeline leverages numpy for scalable feature engineering to monitor segment evolution over time across customer lifecycles.

Build lookalike audiences from behavioral clustering results by exporting segment profiles with integration points for paid media and email analytics. The pipeline generates deterministic cluster assignments using K-Means and DBSCAN, enabling reliable audience expansion across marketing channels.

Use RFM scoring for interpretable, rule-based segment mappings enabling quick decisioning, and behavioral clustering for discovering data-driven patterns using K-Means and DBSCAN. The pipeline combines both approaches, applying scalable feature engineering to produce comprehensive segment profiles and retention matrices.

Yes, transaction and behavioral data are required to run cohort retention analysis and generate segment migration maps. The pipeline processes this input data through scalable feature engineering and deterministic clustering to output retention matrices, segment profiles, and lifecycle insights for targeted marketing.