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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.

Market Mix Modelling/ROI Modelling

ROI Modeling

Objective

Identify relationships between sales and various marketing activities to understand how various elements of the marketing mix combine to drive sales in a competitive framework.

Approach

From the client’s transnational database, we have considered the retail audit data like Volume sales, WARP, Weighted Distribution, and Promotion volumes. From media data, both GRP and NGRP for their premium brands on monthly basis were obtained. We observed that most of the brands/SKUs have non-intuitive relationship with variables at the uni-variate level and hence, we created some transformed variables by using domain knowledge such as SOV, Adstock and Price index –etc., and built a Multiplicative model to capture the interactive effect by using an Empirical Bayesian framework to estimate the relationships.

The model was evaluated on the basis of MAPE (Mean Absolute Percentage Error) and we determined the price elasticity and contribution of each activity on the sales.

Equation

Impact
The client was able to assess the impact of self and competitors activities on sales which impacted its ability to allocate marketing budget based on impact – deriving maximum return on investment.

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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