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All too often companies have only the vaguest idea about what kind of data they’re holding; because such data is very often hidden deeply away in a variety of databases and fragmented across different departments. We identify this data and bring it to light, making it visible, cohesive, comparable and easy to understand so that it really does support YOU in making the right decisions. And if need be, we can also identify any lacking data and define a concept to fill in the gap.

Segmentation, Targeting and Positioning (STP)

Posted by on Nov 5, 2015 in Case Studies | No Comments

Client: A consultancy firm in South East Asia
Time: January 2014

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Objective

The objective was to develop a segmentation algorithm on the basis of a set of psychographic attributes like basic values, beliefs, attitudes, perceptions, motivations and needs (functional, emotional, social and situational) in order to embark on targeted marketing and to enable positioning strategies.

Approach

An online survey was conducted in six Asia Pacific countries: Singapore, Philippines, Indonesia, Malaysia, Vietnam and Thailand. Responses were captured on 120 psychographic attributes related to society and politics, national sentiments, race, religion, family, consumption and purchasing, savings and investment, health, work/days off, self-expression, “vision of my future” and ecology.

These inter-correlated attributes were condensed to a number of composites using data reduction techniques (factor analysis) and these composites were then used to create clusters. These clusters were profiled and dimensioned.

Customer segments on desire for digital connectivity Vs ability to influence

Customer segments on desire for digital connectivity Vs ability to influence

 

The solution was fine-tuned. Non-essential attributes, those which do not contribute to the classification, were dropped iteratively. Consequently, the algorithm can now be used for future classifications of respondents.

Impact

Using this information, the client was enabled to define positioning strategies for each single product for each of their relevant segments.

Venugopala Rao Manneni

A doctor in statistics from Osmania University. I have been working in the fields of data analysis and research for the last 14 years. My expertise is in data mining and machine learning – in these fields I’ve also published papers. I love to play cricket and badminton.

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