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Before launching a new product or improving its existing products, companies often are in a state of dilemma when they have several features and components to choose from to have a competitive edge in the market. This type of indecisiveness can be addressed through a Conjoint Analysis. Conjoint Analysis also referred to as a trade-off analysis is a survey-based research technique which organizations use to gauge the interest level of customers when they are presented a product with different permutations and combinations of features. A statistical analysis is performed on the data derived from this survey. Relationship with Design of Experiments: Conjoint analysis crosses over Design of Experiments in many ways and use the same concepts of Runs (Profiles, Cards, Stimuli, & Panels), Experiments (Studies, Investigations), Factors (Attributes, Features), their corresponding Effects (part-worth score) on the response variable. The only difference is that in Conjoint analysis, it is assumed that only main effects are significant and two-way and other higher-order interactions are insignificant, so they use highly fractionated factorial designs. Types of Conjoint Analysis: The two most commonly used Conjoint analysis are as follows: Choice Based Conjoint (CBC) Analysis: - Most commonly used conjoint analysis in which a customer is asked to respond to a combination or levels of features in a product also called as full profile product. This is a preferable method when the number of attributes are up to 7-8. For e.g. the following are the various combinations of features that a smartphone company wants responses to: Attributes/Factors Level 1 Level 2 Level 3 RAM 4 Gb 6 Gb 8Gb Storage 80Gb 128 Gb 256 Gb Battery 5000mAh 4000mAh 4500mAh Display 6.4 inch 6.70-inch 7.0-inch Camera 16-megapixel 32-megapixel 32-megapixel Price Rs. 15000 Rs. 18000 Rs. 20000 Expandable Storage Yes Yes No Adaptive Choice-Based Conjoint Analysis (ACBC): In cases where the attributes are more than 8, adaptive conjoint analysis is performed. In this case the subsequent set of questions in the survey is modified based on the responses to the previous set of questions. This type of analysis helps where responses are required against too many attributes and their corresponding levels. Responses to the previous set of questions provide a guideline for customizing the next half of the questions thereby reducing the number of questions significantly and yet extracting the most valuable insights from the respondents in less amount of time before the respondents gets cognitively tired. This type of conjoint analysis is more interactive and more engaging with the customers. It consists of the following 3 steps: Step 1 – Build Your Own: For a product of interest, against each feature the respondents are asked to select their preferred level. The cost for the selected level auto-populates. Step 2 – Screening: The respondents are presented with a combination of features based on their response to step 1 and asked for their preferred choice. Step 3 –Choice Task: Based on their response to step 2, the respondents are provided with a set of attributes and their corresponding levels and asked for their preferred choice. How are the survey results analyzed? Once the survey results are populated, the data is fed into a statistical software and each level of an attribute is assigned a score based on its weighted preference called as a Part-Worth Utility Score. Higher the score, more the chances of the feature being included in the final product. If we sum up all the part-worth utility scores of a level for each attribute it would give us the Total Utility Score for the entire product. The table below shows an illustration for same. Attributes/Factors Level 1 Part-Worth Utility Score Level 2 Part-Worth Utility Score Level 3 Part-Worth Utility Score RAM 4 Gb 1.7 6 Gb 1.7 8Gb 2 Storage 80Gb 2.0 128 Gb 2.0 256 Gb 2.5 Battery 5000mAh 2.0 4000mAh 3.0 4500mAh 2.0 Display 6.4 inch 1.2 6.70-inch 1.5 7.0-inch 1.7 Camera 16-megapixel 2.1 32-megapixel 2.8 32-megapixel 2.8 Price Rs. 15000 3.5 Rs. 18000 4.5 Rs. 20000 2.5 Expandable Storage Yes 2.2 Yes 2.2 No 1 Total Utility Score 14.7 17.4 14.5 From the above illustration, it is evident that a smartphone with Level 2 Attributes appeals the most to the customers. For Adaptive Choice-Based Conjoint Analysis, a utility score is also calculated for each respondent as well along with the levels of attributes. Conclusion: The Total Utility Score thus arrived at by the above analysis, can be a valuable input to determine the preference market share for the product of interest. It would also provide insights into the most optimal sales offer that the company could go for. The organizations use the results of the analysis to understand the price elasticity of the product, optimal price point, demand forecasting, sensitivity to a brand name and how much a customer is willing to pay for a new service or a new feature. A product thus developed with optimized product features and components drives its value proposition in the market.