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Updated: 5 days ago


This Strategic Advisory Services Provider (SASP) approached Scalesology with the objective of creating a targeted data-based marketing strategy for another organization. The organization had the goal of expanding their reach to small businesses across North America. To effectively target those businesses, it was essential to study and understand the underlying buying patterns in their small business data.  


The SASP provided Scalesology with raw customer systems data to detect patterns and specify customer segments. The raw data was further merged with data from multiple external sources to enhance the quality of the customer segmentation exercise. The final data set included:

  • Internal Marketing Data from the organization’s CRM system

  • Data from 3rd Party Sources for Demographic and Firmographic information


The merged data was further cleaned, transformed, normalized, and standardized to prepare it as an input for clustering machine learning (ML) models. These ML models were used to uncover patterns of data and segregate them into different segments for the purpose of targeted marketing and internal business development.    


As per the business requirements, the segmentation exercise was done in three iterations:

  • The First Iteration: With All Customer Data and Engineered Features (K-Prototype)

  • The Second Iteration: With All Customer Data and No Feature Engineering (K-Prototype)

  • Third Iteration: Internal Data only (K-Modes internal only Attributes)


Post- Clustering Analysis:

  • Feature Importance using LGBM Classifier

  • Feature Importance using Shapley Values

  • Cluster Analysis and Visualizations in Power BI


The clustering and feature importance analysis led to the discovery of distinct market segments. The marketing firm created a dedicated marketing strategy targeting each unique market segment for the organization.





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