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Data Driven Decision Making

We use a set of tools based on statistics to find solutions to market and social problems



Support in diagnosing various aspects of a brand

These are statistical solutions that support in diagnosing various aspects of a brand, they range from brand maps using multidimensional scaling for which enables us to graphically show the different values attached to a range of competing brands, identification of brand drivers using regression analysis, Conjoint analysis for quantifying brand value and many more.


Optimizing Targets

Targeting the mass market may not be highly effective for many brands, similarly, it may be so costly to go for all prospective customers that are out there. In the switchable consumer approach, we derive personal brand-choice probabilities, to understand the customers who have the highest likelihood to switch, stay or at-risk.
There are customer who have an extraordinarily strong liking for competitor brands, but others may prefer a competing brand, when they also like your brand, it may not take a lot of effort to get them to switch, these are the switchable consumers. Do you want to understand what proportion of customer would be likely to switch to you brand before investing in a campaign? This model may be appropriate to you.


Customer-Based Brand Equity

We believe in customer-based brand equity, which is measuring the brand equity at individual level, which allows decision making to parcel out product equity into respective components and measure the relative importance of each component. This allows to also calculate the monetary equivalent value of each of the sub-components of the brand equity. We use conjoint methodology and hierarchical linear bayes modelling to estimate the brand equity.


Knowledge is not always presented in a linear form

A significant amount of world knowledge is not organized in a structured way, a large amount of data is in form of text, pictures of even numbers which are not in a database form. We combine both machine learning approaches (Indictive theme analysis) and human interpretation (topic modeling). This can be applied in both quantitative and qualitative analysis.

Brand Analytics
Switchable Consumer Analytics
Brand Equity Studies
Unstructured Data Analysis
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