Application of Business Analytics in Pricing

It is estimated that 30% of annual pricing decisions go wrong and do not generate the best price to maximize revenues and/or profits. The traditional methods of cost accounting can be used to arrive at the cost and a profit margin can be added to create a price. But the sheer vast number of products that a company may sell makes it an extremely complex task to calculate price based on cost inputs, price of alternatives, etc. The variables affecting price may also change continuously and it’s a daunting task to track them all. Traditional methods of pricing may therefore ignore potential for higher revenues or a need to cut costs to match buyer sentiment and willingness.

This is where Business Analytics has a role to play. By mining such ‘Big Data’, pricing predictions and prescriptions can be automated and based on much granular information. Such predictions and prescriptions regarding price will be made basis continuous interpretation of data and will be more reflective of the dynamic market conditions in which the prices are set. Revenues can thus be maximized and/or cost can be better managed.

Eg: Fortune.com carried an article on how Big Oil is turning to Big Data in times of volatile gas prices. Data tools such as algorithms, sensor networks etc are helping to lower costs and to improve efficiency in oil infrastructure.

This application of business analytics in pricing, one of the most important 4Ps of Marketing is interesting because price impacts or directs so many other functions in marketing. Price often determines packaging, the promotion message and strategy and perhaps also what quality of raw material is used (eg - a company may try to match market price at the cost of an unsustainable reduction in profit margin). If pricing goes wrong, all efforts by the company including those by the marketing