In this competitive market, a company cannot capture its market with random and haphazard pricing strategy decisions. The winning firm will be the one which develops and prices its products, especially those with new features, according to market demand after scientifically collecting data from target customers like conjoint analysis in spss.
But the direct survey question "how much would you pay for a particular mobile?" is unreliable and misleading. So instead, we have to ask the consumer's opinion on a series of similar products with differing features over a range of prices.
In order to carry out this survey, we can take the help of spss conjoint function. After the data has been collected from the respondents, we generate orthogonal design, which then use regression analysis to compute mathematical values that explain consumer behaviour - how much value is placed on price, or location, or features, etc. and then correlate this data to demographic, lifestyle, or other consumer profiles.
As a result of conjoint, the current product offerings or price can be tweaked to match consumer behaviour and expectations. Also, vulnerabilities - like weak brand or uncompetitive prices - can be exposed with conjoint analysis.
To start with, we select what attributes of the product we would like to test, and what are the possibilities within each attribute. To demonstrate, let's use the example of a mobile, about which we want to know consumer attitudes about:
Price, memory, camera, weight, SMS, alarm, download, internet, Bluetooth etc.
Once the features have been shortlisted, then various options within each attribute need to be decided.
Then the customers are supported to be asked various indirect questions to know their opinion about various features because direct questions could be misleading. Then the answers are fed in the software which computes a mathematical regression to tell us how important each of the factors is to the individual responding consumer, and to the group of responding consumers as a whole. In addition, each consumer will be asked a number of informational questions to create a demographic profile, so that we can compare the results and analyze them based upon income, age, location, and other variables that may affect consumer behaviour towards a particular product.
The end result of this technique is a quantitative, robust analysis of what consumers really want, with each attribute evaluated in the context of the others, incorporating the trade-offs that ultimately project the greatest influence on consumer behaviour.