What is the right price for an existing or a new product, its components or a service, is a question that can be answered only through scientific measurement. The approaches may be primary research or transactional data analysis driven or an amalgamation of the two, but the subject of analysis remain the consumer or the competitors of the enterprise. There are significant opportunities to study the consumer and competitor thresholds to optimize pricing to a level that maximizes the throughput at product portfolio level as well as product sub component level.
There are varying levels of risks posed by the heterogeneous sets of customers and if the pricing of the product or service is not in line with the associated risk there is incidence of disproportionate profits or losses over a period of time. Risk based pricing explodes the number of price points but allows better segmentation and targeting of the customers and the eventual cost arbitrage is more realistic. It requires measurement of current and future risk arising from customers and designing of product offers that compensate for the risk and provide a desirable margin.
Different customer segments respond differently to the pricing changes. Any price reduction or increase proposal should estimate the resultant impact on consumption through classical or neo classical methods of elasticity determination. The impact on key customer segments is most critical determinant of an elasticity measurement exercise. There are methods that can distinguish between the base price and promotion price elasticity and provide more accurate measures of elasticity rather than average elasticity.
A new product offering has two basic areas of interrogation - the product features to be offered and the pricing of the product. While the product features are researched and optimized over a period of time, each feature combination should be priced in an acceptable band. Historical data along with primary research data using advanced conjoints provides not only the optimal pricing range for a new product but also a likely impact on the market share distribution across players.
Consumers use certain Key SKUs/ product feature combinations to compare prices across product or service providers. It is important for a service provider to discover the product features/ SKUs that are most recollected and benchmarked, so that their prices can be managed to reflect a certain imagery of the store. This is an elaborate exercise as it involves interpolation of primary data with transactional data and provides fairly stable results that can be actioned upon for a long time.