Leveraging ML for Customer Segmentation in BI Strategies

Customer segmentation via ML refines BI strategies, enabling hyper-targeted campaigns that lift engagement by 25%. This article unpacks techniques, tools, and metrics for BI pros.

ML Techniques for Segmentation

RFM (Recency, Frequency, Monetary) analysis evolves with ML’s hierarchical clustering, revealing micro-segments like “loyal tech enthusiasts.”

Tools: Orange for visual ML, integrated with Qlik Sense.

Step-by-Step Implementation

1. Data Ingestion: From CRM to BI warehouse.
2. Modeling: Train with XGBoost for feature importance.
3. BI Output: Dynamic segments in Looker dashboards.
4. Iteration: A/B test segments quarterly.

Challenges: Data sparsity—use imputation algorithms.

Retail Example: Precision Targeting

A fashion brand used ML segmentation in Domo BI, increasing email open rates by 32%.

Endnote: ML segmentation turns BI into a growth engine. Experiment and evolve.

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