In the thirty years from the time Shunu Sen posed the marketing-mix problems, I have been busy with marketing research. I tried modeling most of the studies and discovered that market research data alone is not amenable to statistical predictive modeling. Take for example, imagery. Is there a correlation between Image parameters and Purchase Intention scores? There should be. But rarely does one get more than a 0.35 correlation coefficient. Try and link awareness, imagery, intention to buy, product knowledge, brand equity, etc. to the performance of the brand in the market place and one discovers land mines, unanswered questions and inactionability.
This is where ANN steps in.
Technically ANN (Artificial Neural Networks) offers a number of advantages that statistical models do not. I will list a few of them.
- Non-linear models are a tremendous advantage to a modeler. The real world is non-linear and any linear model is a huge approximation.
- In a statistical model, the model gives the error and one can do precious little to decrease the error. In ANN one can specify the error tolerance. For example we can fit a model for 85, 90, 95 or 99% error. It requires some expertise to figure out whether there is an over fit and what is the optimum error one can accept.
- Statistical models make assumptions on distributions that are not real in the real world. ANNs make no distribution assumptions.
- Most ANN software available today do not identify the functions that are fitted. We, on the other hand, have been able to identify the functions that are fitted and how to extract the weights and build them into an algorithm.
How do we bring the differentiation?
Our biggest strength is in data integration that combines market research and economic data with transaction data into a single file. This is tricky and requires some ingenuity. We use Monte Carlo techniques to build these files and then use ANN for building the Simulation models. Optimization then becomes clear and straight forward since we do not use statistical models. Optimization using statistical modeling, which most modelers use, is a nightmare. Most of the large IT vendors and even analytics companies continue to use statistical modeling for Optimization. And therein lays the problem. Neither are these companies aware of the possibilities that ANN can provide. Most modeling is done using aggregate data, whereas we handle the data at the respondent level. The conventional modeling is macro data oriented whereas we are micro data oriented. Hence the possibilities that we can generate with micro data for modeling is huge, compared to macro data.
We have crossed the stage of theories. There are many projects that we have executed successfully that have gone on to become a must-have analytical marketing input mechanism.
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