MS 66 IGNOU MBA Solved Assignment -What is conjoint analysis? Explain its application in marketing Research.

What is conjoint analysis? Explain its application in marketing Research.  

Conjoint (trade-off) analysis is one of the most widely-used quantitative methods in Marketing Research. It is used to measure preferences for product features, to learn how changes to price affect demand for products or service, and to forecast the likely acceptance of a product if brought to market.

With 30 years in business and thousands of users worldwide, Sawtooth Software is the consensus leading provider of conjoint analysis software. Companies like Procter & Gamble, Google, General Motors, General Electric, and Microsoft use our software and have presented case studies and success stories at our popular research conferences.

Over the last few decades, conjoint analysis has become the premier market research methodology for studying how buyers value the characteristics (attributes) of products/services and for predicting buyer behavior. Perhaps the most valuable aspect of conjoint analysis is the strategic what-if market simulator.

Conjoint analysis questionnaires ask respondents to evaluate realistic product profiles (described by multiple features) and to choose which they would buy. Such surveys are more realistic than traditional questionnaires that simply ask respondents which features they prefer or regarding the generic importance of attributes.

Common business applications include:

• Designing new products

• Re-designing existing products

• Product line extension research

• Estimating brand equity

• Measuring price sensitivity (elasticity)

• Optimizing employee compensation packages and workplace conditions

• Branding and packaging

• 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 importance of each attribute, these preferences are derived from these relatively realistic tradeoff situations.

• The result is usually a full set of preference scores (often called part-worth utilities) for each attribute level included in the study.

• Sometimes it can be challenging to decide which conjoint method is most appropriate for your particular research situation. We've developed an interactive advisor to help you decide which conjoint method might be best for your specific situation. We’ve also written a document entitledWhich Conjoint Method Should I Use? for further reference.

• 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 (such as Sawtooth Software's Advanced Simulation Module) can 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, reposition existing products, and make more profitable pricing decisions.