Conjoint analysis is a survey-based statistical technique used in market research that helps in determining how people value different attributes that make up an individual product or service.
Conjoint analysis is the optimal market research approach for measuring the value that consumers place in features of a product or service. It is used to determine the value consumers associate with different features of a product. In the real world, we make a lot of buying decisions and choose out of hundreds of varieties of a product available. These buying decisions are guided by the value we associate with various features of that product. Conjoint analysis involves conducting surveys and recording their responses. These surveys should include various characteristics of a particular product. It may also involve associating numeric values or rankings to various features of the product. The analysis of the responses gives us the ability to peek into the mind of the target audience and see what they value the most. The information gathered acts as a decision-maker. For example, if one go to buy a phone there are many factors one would like to consider. For simplicity let’s consider only 6 factors- price, screen size, looks, storage, battery and camera, which would impact our decision. Now we survey 100 consumers about what would they prefer the most among these 6 features. The following are the results:
Now using this data, a particular company can manufacture products keeping in mind the consumer preferences. However, conjoint analysis is a complex phenomenon it also involves series of permutations and combinations to determine the results. Using the above results, we can see that Battery and Screen size are the least looked at aspects while buying a phone and camera and price the most noticed aspect. Therefore, the company can save cost using average quality and specifications of battery and screen size and invest them in a good quality camera and reduce the price.
Types of Conjoint Analysis
There are three main types of conjoint analysis: Choice-based Conjoint (CBC) Analysis and Adaptive Conjoint Analysis (ACA) and Maxdiff Conjoint Analysis
Choice-based Conjoint (CBC) Analysis:
This type of conjoint analysis is the most popular because it asks consumers to imitate the real market’s purchasing behavior - which products they might choose, given specific criteria on price and features. For example, each product or service has a specific set of defining characters. Some of these characters might be almost like one another or will differ. For example, you might present the respondents with the following choice:
The devices are almost similar, but device 2 has triple cameras with better configuration, and Device 1 has a higher battery power than Device 2. This helps in knowing the vital trade-off between the number of cameras and battery capacity based on analysis of received responses.
Adaptive conjoint analysis (ACA):
This type of conjoint analysis is often used in scenarios where the number of attributes/features exceeds what can be done in a choice-based scenario. ACA is suitable for product design and segmentation research, but not for determining the ideal price. The adaptive conjoint analysis is a graded-pair comparison task, where the respondents are asked to assess their relative preferences between groups of attributes. Each pair is evaluated thereafter on a predefined scale.
For example, a respondent might be asked to choose between the following two concepts:
The answers are used to determine the respondent’s part-worths of each of the attribute levels. Once part-worths have been determined, the respondent’s overall preference for a given product can be estimated by summing the part-worths of each attribute level that describes that product.
Max-Diff Conjoint Analysis:
The max-Diff conjoint analysis shows a variety of packages to be selected under best/most preferred and worst/least preferred scenarios. Respondents can quickly indicate the best and worst items in a list, but struggle to decipher their feelings for the ‘middle ground’. Max-Diff is an easier task to undertake when consumers are well trained at making comparative judgments. Max-Diff conjoint analysis is an ideal method when the decision task is to evaluate product choice. An experimental design is used to balance and properly represent the sets of items. Several methods can be taken with analyzing Max-Diff studies, including Hierarchical Bayes conjoint analysis to derive utility score estimates, best/worst counting analysis, and TURF analysis.
Below is an example, involving a set of four attributes where the respondents can be asked to indicate the attributes that are the most/ least important to them:
In conclusion, Conjoint Analysis helps in discovering the relative importance of the attributes of a product to the consumers. It is a marketing tool that is gaining momentum and is being used by product developers all over the world.
Hopefully, this blog has enlightened about Conjoint Analysis and it’s different types.
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