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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’

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 […]

Gauging what works best for a brand using correspondence analysis and hierarchical clustering

Gauging what works best for a brand using correspondence analysis and hierarchical clustering

Posted by on Nov 3, 2017 in Case Studies | No Comments

OBJECTIVE Marketers have always had to manage goals such as making their brands distinctive, making them central in their category, and understand competitors. The objective of the study was to understand how brands and a list of attributes/features are perceived by consumers. While understanding why a customer chooses a particular brand repeatedly over time, the […]