Session 19 & 20_Group A_Conjoint analysis

Session 19 & 20_Group A_Conjoint analysis

Conjoint (trade-off) analysis has become one of the most widely-used quantitative methods in Research. It is used to measure the perceived values of specific product features, to learn how demand for a particular product or service is related to price, and to forecast what the likely acceptance of a product would be if brought to market.

Rather than directly ask survey respondents what they prefer in a product, or what attributes they find most important, conjoint analysis employs the more realistic context of respondents evaluating potential product profiles. Each profile includes multiple conjoined product features (hence, conjoint analysis)

There are different ways to show product profiles. The original version of conjoint analysis developed in the early 1970s showed products one-at-a-time.

Respondents usually complete between 12 to 30 conjoint questions. The questions are designed carefully, using experimental design principles of independence and balance of the features. By independently varying the features that are shown to the respondents and observing the responses to the product profiles, the analyst can statistically deduce what product features are most desired and which attributes have the most impact on choice. In contrast to simpler survey research methods that directly ask respondents what they prefer or the important of each attribute, these preferences are derived from these relatively realistic  trade-off situations.

Sometimes it can be challenging to decide which conjoint method is most appropriate for your particular research situation.

Conjoint market simulators let the researcher define specific competitive contexts (specific products in competition with another) and project the share of choices (shares of preference), given respondent’s estimated part-worth scores. These simulators let researchers and managers test a variety of what-if scenarios.

Market simulators can be taken one step further. Rather than using them to answer the question: "How good is this product?" they can be used to discover "Which product is best?".Computer search routines an efficiently find optimal products, based on the criterion of utility, share, revenue or profit. Profit optimization is probably the most actionable and managerially useful application of conjoint analysis data, but requires that the costs of the different components of a product be known with reasonable accuracy.

Today, thousands of conjoint studies are conducted each year, over the internet, via computers not connected to the internet, using person-to-person interviews, or mailed paper surveys. Leading organizations are saving a great deal of money on research and development costs, successfully using the results to design new products or line extensions, re-position existing products, and make more profitable pricing decisions.

Submitted by
Suman Boro
14169
Operations 
MBA 2011-2013
SIBM Bangalore