Conjoint Research Medium

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Conjoint is a mathematical tool used in industry for market analysis. It is used in business research to assess the customer's expectations and desires so that the firm can best represent him or her. As a result, conjoint analysis is used to determine how different buyers or clients of the company value such goods, as well as the various factors or qualities that cause them to choose the products over alternatives. Card sorting, decision modeling, differential choice, preference-based conjoint, tradeoff matrices, hierarchical modeling, and many other types of conjoint analysis exist. Conjoint analysis is, therefore, a powerful tool that is used to analyze markets. It is used to get into the minds of the customers of an organization to determine their needs and wants and the reasons for those needs and wants. The method or technique is used with a combination of goods and services which are given to the selected respondents. After this, the process is used to find out how the respondents decided to choose specific types of the goods and not others. The valuation that is carried out using this method can be used in coming up with market models that can approximate the costs, profits, revenue and market share of an organization (Gustafsson et al., 2013).

Data is collected through the use of surveys or in the conjoint analysis. Hover, it is also possible to make use of data that has been designed carefully. Once the opinion of the respondents is obtained, it impossible to gauge the products produced for selling, and the reasons why they are popular with more respondents or customers.

There are several steps that are involved in the conduction of the conjoint analysis. The first one is the determination of essential services or products in the current market, and then makes a decision on the data collection methodology that will be applied, and how the data will be recorded. The third step involves the determination of the conjoint methodology that will be used. The most often used are the choice-based conjoint and the one that is based on preference. Later, an experimental design is formulated, and it should be able to determine the main interactions among the various attributes of the products complicated. After this, the real data is collected, and then the utilities are calculated for all the participants in the study. Finally, once the information is collected, the market simulation model is formulated. The importance of the market simulation model is to predict the changes that might occur in future regarding the current product in the market and the new products that might be brought into the market. The conjoint analysis technique is a great technique for ensuring the commodities that an organization comes up with is pleasing to the customers.

Advantages of Conjoint Analysis

Can be applied in market segmentation- Conjoint analysis is the most efficient way of finding out the customer tastes and preferences. Hence, it is an actual measure of the perceptions the consumers have towards the products of an organization or business. Market segmentation seems to be in the middle of the technique in term of applicability. Hence, an individual or an organization understands how valuable his products are too specific customers; then it becomes easy to create marketing programs that can be used to tell the merits of the products (Sharma and Malhotra, 2015). It also enables an individual or an organization to be able to remodel an existing service or product by making use of the benefits obtained so that the service or product can appeal to new or existing customers.

The close resemblance of customer Decisions- the customers can arrive at decisions in the market as a result of using conjoint analysis. This happens where they are given different alternatives to choose from. Hence, the people who are used as the respondents can mimic the real-life behavior of customers to provide the organization with a rough idea of what the customers are likely to prefer (Rao, 2014). The more the process resembles the real action of customers, the more it becomes accurate in its analysis.

Evaluating Price sensitivity- As seen in the text above, it is possible to measure the relationship between price and other attributes of the commodity or service. In the information on the interaction or relationship between the two, it is easy to evaluate the sensitivity of the price which changes depending on the brand of a commodity (Annunziata and Vecchio, 2013). Hence, an organization or an individual can run simulations at different levels of costs to determine the changes in one's process or those of a rival.

Brand Equity –Through the use of conjoint analysis, it is possible to measure the value of a brand name. It is possible to acquire information relating to the strength of the brand or the popularity of the commodity when compared with prices of the product and other features (Hainmueller et al., 2014). The technique provides information on the advantages of a brand and when compared to the characteristics of its products and its prices.

Enables Purchase decisions to be made- It is possible for the respondents in the analysis to make any decision touching on the commodity preference. There is an option of selecting none of the commodities or a service, meaning the customer has walked away in that case. Hence, with all the different kinds of purchase decisions available, the customer preference can be obtained with accuracy as the model is almost a reality (Churchill and Iacobucci, 2006). It is possible to determine the real drivers that motivate the consumers to make the kind of decisions they do when it comes to consuming different products or services. Hence, in the long run, it can help an organization to make more money because the company knows what the customer wants. Knowing what the customer wants is essential because it helps an organization to sell more and make a higher profit.

Disadvantages of Conjoint Analysis

Complexity- The process of designing conjoint analysis is a complicated affair that requires careful planning and massive use of funds. The questions that are asked in the formulation of the technique have to be accurate to avoid getting unreliable data and information (Meissner and Decker, 2010). Hence, the process needs expertise for it to succeed, and the right amount of resources should also be pumped.

Problems when it comes to intangible attributes- It is possible to have some commodities that whose attributes or image are intangible. Most of the goods or services that fall into this category are luxury commodities. Luxury commodities are mostly characterized by the emotional factors rather than the rational factors. This makes it difficult to make use of the method when luxury goods are involved.

A large number of attributes- In real life products, the number of attributes involved is high. When the attributes to be considered are more than 10, it becomes hard to derive the product description. As a result, the respondents are exhausted when they are giving their responses. This results in the collection of unreliable data that cannot be used in real-life. Additionally, due to a large number of attributes, the respondents end up simplifying the strategies. This leads to invalid responses.

Problems with the technique- The technique assumes that the respondent will have to choose one of the products from the vast variety that is offered. It only considers the consumer who has many products to choose. Hence, it does not put into consideration the consumer who dislikes all the products or the consumer who likes more than one product and is willing to purchase two or more of the products (Green et al., 2001). It is possible to formulate adjustment to the technique so that the limitation of a large number of attributes can be solved, but still, it is not possible to exhaust all the weaknesses through the formulation of hybrid models (Kim et al, 2016). The technique does not also include the number of products that are purchased at once. This means that it can end up giving an inaccurate view of the market share of a particular organization or product.

Trends in using Conjoint Analysis

In the traditional practices, the data for preference measurement was collected using pencils and questionnaires, or sometimes through the use of telephone calls or one-on-one communication. However, from the early 1990s to the current time, the web is used to gather most of the data that is required. This has led to the collection of more information per question in the respondents. The new adaptive methods have facilitated the collection of more information. Some of the adaptive techniques include available adaptive conjoint analysis. However, the increase in the accessibility and data collected from the respondents has also come with its disadvantages. One of the drawbacks is the lower levels of patience and attentiveness in the respondents (Vidal et al., 2013). Therefore, it is necessary to ensure that the respondents maintain their focus on the task at hand. One of the modifications that have been made to ensure the respondents keep their focus is the user design approach. In this case, the respondent is permitted to design his perfect product on a virtual basis. With the information on the ideal virtual products, the organization can move forward in developing new products or re-designing the existing products to meet the requirements of the customers.

One of the most significant problems that have been facing the technique is when it comes to the products that have a large number of attributes and commodities. In the future modifications that have been proposed to counter this problem, the traditional self-explicated approach is highly applicable. The limitations brought by this issue have been solved by the hybrid estimation method (Wilson et al., 2015). The hybrid method combines the preference data with the self-explicated data derived from the partial profiles.

Proposes have been brought suggested on how to deal with the large-product dimensionality through coming up with ingenious data collection methods. An example is the Conjoint Adaptive Ranking Database System (CARDS) which was formulated by Dahan in the year 2007. Through innovative plans like this, it will be possible to solve more complex problems in future with a higher degree of accuracy.

Consequently, the new methods used are combing several types of data. The new auxiliary data is used to supplement the traditional preference data to increase the chances of accuracy in the techniques.

Example of the Use of Conjoint Analysis

An example of the use of conjoint analysis is shown in a food company that wants to come up with protein shake. For the company to produce the best and most marketable shake, they have to evaluate the features that are most desired by different consumers of the protein shake. Some of the features that the company should review include the quantity of protein in the shake, the number of carbohydrates, the preferred favor, deciding whether to produce an organic or inorganic protein shake. All these features are measured against the price of the protein shake. This can be achieved by combing various attributes and the expected cost, and then asking the respondent to choose their favorite combination of attributes. Once the best mixture is obtained, then a slightly different set of combinations is provided for the respondents. This is repeated for a couple of times until a final formula is obtained. One thing that should be avoided is bringing too many attributes to the table. If the attributes are too many, the process will become too complex.

Challenges and Issues in Conjoint Analysis

One challenge that comes up in the application of the conjoint analysis is the propensity of the respondents to concentrate on the price of the commodity rather than evaluate all the other attributes before looking at the price (Kucukusta and Guillet, 2014). The respondents do not have a clue of the attributes chosen in the study, meaning that the company wasted time. Hence, it becomes difficult to determine the price sensitivity. One clever technique of addressing this issue is by putting a price range rather than a single price. By including a price range, the respondent will have to evaluate the attributes first before considering the price.


Conjoint analysis is a helpful technique in market research where it is used to evaluate how the people in a market think about particular attributes of a commodity or service. The primary purpose of the analysis technique is to determine the best combination of attributes according to the tastes and preferences of the respondents, who represents the consumer or customer of the commodity. The most preferred combination of attributes is selected as the product to be produced. A professor Paul Green developed the technique, and currently, it is used for different applications in the field of applied sciences and social sciences in sectors such as marketing, operations research, and management of products. Some of the advantages of the technique include its ability to make use of physical objects, its evaluation of the preferences at the level of the single consumer, and its ability to determine the trade-offs the consumers make when they are faced with several options. On the other hand, it also has disadvantages such as the complexity involved in designing the analysis, over-fixation on the price of the commodity rather than the attributes. The technique also lacks applicability in luxuries and other emotional goods, and its lack of consideration of consumers who are willing to buy more than one type of the commodity under evaluation. The conjoint analysis technique is an excellent technique for ensuring the products that an organization comes up with is pleasing to the customers.


Annunziata, A. and Vecchio, R., 2013. Consumer perception of functional foods: A conjoint analysis with probiotics. Food Quality and Preference, 28(1), pp.348-355.

Churchill, G.A. and Iacobucci, D., 2006. Marketing research: methodological foundations. New York: Dryden Press.

Green, P.E., Krieger, A.M. and Wind, Y., 2001. Thirty years of conjoint analysis: Reflections and prospects. Interfaces, 31(3_supplement), pp.S56-S73.

Gustafsson, A., Herrmann, A. and Huber, F. eds., 2013. Conjoint measurement: Methods and applications. Springer Science & Business Media.

Hainmueller, J., Hopkins, D. and Yamamoto, T., 2014. Cjoint: Causal Inference in Conjoint Analysis: Understanding Multi-Dimensional Choices via Stated Preference Experiments (R Package Version 2.0. 4).

Kim, D.S., Bailey, R.A., Hardt, N. and Allenby, G.M., 2016. Benefit-based conjoint analysis. Marketing Science, 36(1), pp.54-69.

Kucukusta, D. and Guillet, B.D., 2014. Measuring spa-goers’ preferences: A conjoint analysis approach. International Journal of Hospitality Management, 41, pp.115-124.

Meissner, M. and Decker, R., 2010. Eye-tracking information processing in choice-based conjoint analysis. International Journal of Market Research, 52(5).

Rao, V.R., 2014. Applied conjoint analysis. Springer Science & Business Media.

Sharma, S. and Malhotra, N.K., 2015. Examining Social Setting and Product Reference via Conjoint Analysis An Empirical Study of Restaurant Patronage. In The 1980’s: A Decade of Marketing Challenges (pp. 59-63). Springer, Cham.

Vidal, L., Antúnez, L., Sapolinski, A., Giménez, A., Maiche, A. and Ares, G., 2013. Can eye‐tracking techniques overcome a limitation of conjoint analysis? Case study on healthfulness perception of yogurt labels. Journal of Sensory Studies, 28(5), pp.370-380.

Wilson, L.S., Loucks, A., Gipson, G., Zhong, L., Bui, C., Miller, E., Owen, M., Pelletier, D., Goodin, D., Waubant, E. and McCulloch, C.E., 2015. Patient preferences for attributes of multiple sclerosis disease-modifying therapies: development and results of a ratings-based conjoint analysis. International journal of MS care, 17(2), pp.74-82.

December 15, 2022

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