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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.

Understanding the factors that make a brand a ‘favourite brand’

Client: A global fast food restaurant chain

Objective

The client intended to find the performance attributes driving the “Favourite brand” of the category where the population is more heterogeneous.

Approach

Since the population was heterogeneous in nature, merely aggregating the data and deriving the set of drivers (factors driving the brand’s success) would not have been effective. Hence, we had to look at drivers within homogeneous groups. Using this technique would have allowed the client draw out different action plans for each segment.

For the analysis, we used the Latent Class Regression approach, in which, we first segmented the population based on various characteristics (demographic, socio-graphic, etc.) and then, determined the drivers that contributed to the brand featuring as the ‘Favourite Brand’.

Impact

The analysis helped the client gauge the drivers that worked best for each segment enabling it to draw out segment-specific strategies.

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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