Objective / Business Problem
To segment a diverse customer base by combining demographic, psychographic (values, beliefs, attitudes, motivations), and behavioural data, enabling precise targeting to optimize marketing efforts and enhance customer engagement.
Approach / Techniques Used:
-
Data Collection: Integration of demographic, psychographic, and behavioral variables.
-
Data Reduction: Factor Analysis to reduce dimensionality and identify key underlying constructs.
-
Segmentation Algorithms:
- K-Means Clustering for grouping customers into homogeneous segments.
- Latent Class Analysis to identify unobserved (latent) segments within the population.
- Discriminant Function Analysis to validate segment distinctiveness and support classification of new customers.
-
Iterative Process: Each step involved iterative refinement with deep business insight and cultural context applied by analysts to ensure meaningful segments.
Outcomes and Impact:
-
Positioning Strategy: Enabled partners to define clear and differentiated positioning strategies tailored to each segment.
-
Market Potential: Assessed segment attractiveness and growth potential, guiding resource allocation.
Market Mix Optimization: Informed development of targeted marketing mix strategies (product, price, place, promotion) per segment.
-
Opportunity Identification: Revealed underserved segments and market gaps, fostering innovation and new product/service development.
-
Enhanced Targeting: Customers were able to deploy specific advertisements aligned with segment profiles, improving conversion rates and customer engagement.
Business Value:
This segmentation framework empowered partners to shift from broad-based marketing to precision marketing, improving ROI on advertising spend and driving competitive advantage through culturally informed, data-driven decision-making.
