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# Conjoint Analysis

Conjoint Analysis is a statistical tool used in market research to understand how customers make decisions with respect to different products or services (like cost, features, quality etc.). Customers are provided with alternatives which they rank giving insights to researchers regarding customer preferences.

An application-oriented question on the topic along with responses can be seen below. The best answer was provided by Pradeep Kandpal on 11th Mar 2023.

Applause for all the respondents - Pradeep Kandpal, Anshul Vaidya, Suresh Kumar Gupta, Kirpa Shanker Tiwari.

## Question

Q 547. What is Conjoint Analysis and how is it used in market research to determine consumer preferences and make strategic business decisions?

Note for website visitors - Two questions are asked every week on this platform. One on Tuesday and the other on Friday.

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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.

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Conjoint Analysis refers to pre-launch survey activity with prospective buyers, that helps gauze demand and price for the product. Conjoint Analysis provides insights into trade-off between price and features, in customer purchase behaviour. The customer opinion is evaluated to determine the impact of demand and competitive market forces. Conjoint Analysis was first developed by Prof. Paul E. Green at the Wharton School of the University of Pennsylvania.

The attributes are pre-screened, and re-grouped into different combinations of levels. Each level has demand and price specification targets with pre-specified measures of the product or service offering. These combination or levels of specification are tested to determine customer preferences, by probing customer about the potential product concepts and product alternatives. The recorded customer preferences are registered as “preference score” or “partworth utility” or “utility score”, where these scores represent the influence of each attribute and attribute levels on respondent choices, represented in percentage score for such responses. The levels with same type of attributes may be used to compare “preference score” or “partworth utility” or “utility score however comparision should not be done for different attributes. The data generated is statistically analysed, to forecast demand and analyse price sensitivity for new product features and new service offering.

The conjoint analysis method can be implemented in following steps:

1.      Identify problem set at hand for market researcher

2.      Design questionnaire to cover customer opinion on desired product feature and service offering.

3.      Survey Methodology is selected.

4.      Data is selected and curated.

5.      Data is analysed and predicted solution is shared as presentation.

Conjoint study may be shared as:

Ranking-based conjoint (Adaptive Conjoint Analysis ACA): Different product features are listed in survey and respondents are asked relative preferences between a set of attributes. The method can be readily applied to conjoint studies on product design and segmentation research, however may not be applied to price sensitivity related studies

Rating-based conjoint (Metrics based Conjoint Analysis): Customer is asked to rate set of similar products on a possible rating scale of 0 to 100. The importance of product features is then analysed through rating shared by customer for the different product and product features.

Choice-based conjoint (Discrete Choice based Conjoint Analysis): Customer is probed about purchasing behaviour, hypothesizing about real market purchase behaviour, with query shared about product, given specific criteria on price and features. The vital importance of character or feature for customer is scrutinized through responses gathered in conjoint study survey.

Conjoint analysis is utilized to decide about feature to be held prime in marketing campaigns, deciding about price sensitivity in pricing research, in new product design studies, and deciding about product packaging. Ideally, Conjoint analysis should have two features at each level and with more features inclusion effective combination increases which may render study dysfunctional or ineffective. The inability to convert customer perception into new product or service basket may lead conjoint study non-utilitarian in purpose. The under valuation or over valuation of a product or service feature in conjoint study, can spoil brand capital and marketing presence for a brand.

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Conjoint analysis is a statistical technique used in market research to measure how customers value different attributes (or features) of a product or service. It is based on the idea that consumers evaluate a product or service as a whole rather than on individual features. By understanding how customers weigh different attributes, businesses can make informed decisions about product design, pricing, and marketing strategies.

Conjoint analysis works by presenting customers with a series of hypothetical product profiles that vary in their attributes, such as price, quality, size, or color. Customers are then asked to rank or rate these profiles based on their preferences. The results of these surveys are analyzed using statistical techniques to estimate the relative importance of each attribute and the ideal combination of attributes that would maximize customer satisfaction.

Conjoint analysis can provide valuable insights into customer preferences and help businesses make strategic decisions, such as:

1. Product Design: By understanding which features are most important to customers, businesses can design products that meet their needs and preferences.

2. Pricing: Conjoint analysis can help businesses determine the optimal price for their products by analyzing how customers value different price points.

3. Market Segmentation: By segmenting customers based on their preferences, businesses can tailor their marketing strategies and product offerings to specific customer groups.

4. Branding: Conjoint analysis can help businesses understand how customers perceive their brand and identify areas for improvement.

Overall, conjoint analysis is a powerful tool for businesses to gain insights into customer preferences and make informed decisions about product design, pricing, and marketing strategies.

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Conjoint analysis is a form of statistical analysis where data generated using customer surveys that companies use in market research to know how customers rate different attributes of their products or services. for example : A car manufacturer want to know about the car from customers based on few attributes like mileage, Strength, looks, comfort, cost, space etc. Here first manufacturer need to define attributes and design survey questions and collect data and analyse survey results.

Here we need to calculate relative importance of each attribute with respect to total utility attributes. This can be done by calculating difference of each attribute w.r.t. total utility of a product. Difference is called the range in the attribute’s utility values. This analysis sometime help to find out hidden drivers which is not easy to look and also it can measure preferences at customer’s level.

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Pradeep's answer has been selected as the best answer for the examples that are quoted. Well done!

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