Consumer behaviour modification has always been targeted using different forms of data analytics. Originally it started with subliminal messaging in advertisements which was then banned. Ever since retail as a business was established, consumers have been influenced.
The influence was either direct through a discount offer or indirect through a free sample or suggestions from the shopkeeper. Here the analytics was observational and limited in area. One can also call it personalized.
As technology developed and storage of transaction information became cheap, it was easier to deploy analytics on a population wide basis across the country as a whole. This helped identify regional patterns that helped companies control production based on retail demand. Consumer behaviour modification or influencing consumer’s choice is now a target for companies making products as well as retail companies.
There is a fair amount of research that is available freely to help retail firms and product based companies to impact consumer behaviour. This is now an open playing field where consumers too can understand the techniques and regulate their behaviour. Consumers can choose what to reveal such that they can take a informed and independent choice.
Today, we are now at a point where the data collected is too huge to make any real sense without a data audit. Analytics to effect consumer behaviour modification has to help us streamline the collection and storage process. It has to lead us to a point where unwanted data points are not captured at the very first instance.
The challenge is to go from big data analytics to relevant data capture and analytics. This makes life easy for product and retail companies and also addresses consumer privacy concerns. The other benefit is that data theft in such an environment becomes a cost negative and helps protect the reputation of the companies.