Household Segmentation
Vodafone, a large international telecommunications company, sought to implement a strategic segmentation based on household lifecycle stages to better understand their customer base. The marketing department defined specific groups based on demographic rules, such as customer age and family composition. However, this initiative was hindered because the company's databases lacked the necessary information, specifically regarding the presence of children and accurate customer ages,which rendered the proposed rule-based segmentation ineffective and the resulting groups largely empty.
To resolve these data gaps, a comprehensive data processing pipeline was built using the PySpark framework to handle millions of records and complex identifier mappings. The team developed and trained supervised machine learning models to infer the missing demographic attributes, specifically targeting customer age and family structure. These models utilized advanced algorithms, including Gradient Boosting and Random Forest, and were optimized to predict these attributes based on existing usage patterns, effectively filling the missing fields required for the segmentation.
The machine learning solution successfully populated the marketing segments, providing coverage that was previously impossible due to missing data. This approach outperformed previous reference models by generating a validated distribution of household profiles. Consequently, the Vodafone marketing department was able to utilize this enriched dataset within their standard analytical tools to refine their strategies and target customers more effectively.
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