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Conjoint is a mathematical tool used in industry for market analysis. The technique is used in marketing research to assess the customer's interests and desires so that the firm can best represent him or her. Thus, the instrument is used to determine if the organization's different clients or consumers value such items, 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 since it is used to get into the minds of the customers of an organization to determine their demands about the product, and why they demand these needs be met. The method or technique is used with a combination of goods and services given to the selected respondents. After this, the process is used to find out how the respondents decided to choose specific types of the products and not others. The valuation that is carried out using this method can be employed to come up with market models which can approximate the costs, profits, revenue and market share of an organization (Gustafsson et al. 2013, 212). Data is collected by the use of surveys or in the conjoint analysis. Once the opinions of the respondents are obtained, it is possible to gauge the products produced for selling, and the reasons why they are popular with more respondents or customers.
Several steps are involved in the conduction of the conjoint analysis. The first one is the determination of essential services or products in the current market. The second one is to decide on the data collection methodology which will be employed, as well as the methods of recording data. The third step involves the determination of the conjoint techniques that will be used. The most often used are the choice-based conjoint (CBC). After identifying the method, an experimental design is formulated. The design aids in determining the significant interactions between the various attributes of the products under question (Weihs and Wolfgang 2015, 204). 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 obtained, 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 an excellent way of ensuring the commodities that an organization comes up with are pleasing to the customers and satisfies their needs.
Advantages of Conjoint Analysis
One of the benefits of conjoint analysis is that it can be applied in market segmentation. The technique is among the most effective ways of examining the tastes and preferences of the clients. Therefore, it is an actual measure of the perceptions the consumers have towards the products of an organization or an enterprise. Market segmentation seems to be in the middle of the technique in term of applicability. Thus, an organization understands how valuable its products are too specific customers and then it becomes easy to create marketing programs that can be used to determine the benefits of the products to the consumer (Sharma and Malhotra, 2015). An individual or an organization is also 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. Through their innovativeness after carrying out conjoint analysis, the General Motors company has been able to come up with different automotive designs which are unique for each of the 200 countries where they have customers (Aribarg 2017, 280). Different customers from various countries have varied preferences. With this knowledge, GM has been able to attain position nine in the world’s largest publicly traded companies.
Another benefit is that a customer can arrive at decisions in the market as a result of using conjoint analysis. This situation happens where they are given different alternatives to choose (Dillon et al. 1987, 66). Thus, the people who are used as the respondents can mimic the real-life behavior of consumers to provide the organization with a rough idea of what the customers are likely to prefer and act as a guide to the processes in the company (Netzer 2008, 337). The more the process resembles the real action of customers, the more it becomes accurate in its analysis.
Thirdly, the technique helps in 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 that a certain organization provides to its customers (Donche et al. 2015, 146). 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, 350). 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. Application of the technique at General Electrics has enabled the company to analyze the price sensitivity of their customers as an external factor. Thus, they have been able to maintain a low sensitivity, and this lowers the bargaining power of the customers, and this enhances the customer loyalty.
Another benefit is brand equity where through the appliance of the technique, it is possible to measure the value of a brand name. By using conjoint analysis, 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, 20). The technique archers take years of a survey on the popularity that the product will have among the consumers so that they can maximize the profits. The analysis provides information on the advantages of a brand when compared to the characteristics of its products and its prices. At In Vitro Diagnostic Industry (IVD) for example, before a product is released into the market, the res
Conjoint analysis aids in the decision-making process especially the purchase decisions. The respondents in the analysis can make any decision regarding the commodity preference (Mitchell 2002, 104). There is an option of selecting none of the commodities or a service, meaning the customer has walked away in that case. Thus, 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, 59). The firm can 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 (Gaul et al. 2010, 15). Apple is an organization that produces iPhones, a great competitor of Samsung Galaxy. What has enabled Apple to thrive and become victorious in such a competitive environment is carrying out conjoint analyses to help them in understanding the how different customers value features of a phone like battery length, price, size, operating system, among others (Nikou et al., 2014, 82). Knowing what the customer wants is essential because it helps an organization to sell more and make a higher profit.
One of the principal disadvantages is its complexity. Designing the method is a complicated affair that requires careful planning and massive use of funds. The questions asked in the formulation of the technique have to be accurate to avoid getting unreliable data and information (Meissner and Decker, 2010, 64). Thus, the process needs expertise for it to succeed, and the right amount of resources should also be pumped.
Secondly, there are many issues associated with the technique when it comes to intangible attributes. There are some commodities that whose attributes or image are invisible. Most of the goods or services that fall into this category are luxury commodities (Sekaran et al., 2016, 76). Luxury commodities are made of emotional factors rather than the objective factors, and this makes it difficult to make use of the method when luxury goods are involved.
Thirdly, the method comprises excess characteristics. In real life products, the number of features 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, resulting in the collection of unreliable data that cannot be used in real-life (Wittink and Bergestuen, 2001, 145). An example of a company that can face this problem is HP. There are so many features on a laptop that a consumer can consider before buying a laptop from a certain company. These features include the size of the hard disc, size of the screen, processing speed, weight of the laptop, RAM, number of USB ports, operating system, among many others. Therefore, the respondents may be tired of responding to each of these attributes. Additionally, the respondents end up simplifying the strategies (Low et al. 2013, 54). This leads to invalid responses.
Fifthly, the technique works with the assumption that the respondent will have to choose one of the products from the vast variety that is offered. It only considers a consumer who has many products to choose. The technique does not take care of the consumers who have no selection of any of the organization’s products or services or those who have no interest in one or two of the company’s products. (Green et al. 2001, S60). 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, 60). 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 research data to be analyzed 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 date, the web is used to gather most of the data that is required. This improvement has led to the collection of more information per question in the respondents (Parniangtong 2017, 90). 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, 68). 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.
A major issue that the analysis has been associated with since its inception is the analysis of products or services with multiple features (Smith, 2008). 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, 44). The hybrid method combines the preference data with the self-explicated data derived from the partial profiles.
Suggestions have been placed on how to deal with the large-product dimensionality by coming up with ingenious methods of data collection. Through such innovative plans, it will be possible to solve even more complex problems in future with a higher degree of accuracy (McQuarrie 2015, 88). 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 (Swaim 2011, 100).
Challenges and Issues in Conjoint Analysis
One challenge that comes up in the use of the technique under discussion 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, 122). 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 (Weihs and Wolfgang 2005, 342). By including a price range, the respondent will have to evaluate the attributes first before considering the price.
Conclusively, conjoint analysis is a helpful tool in evaluating the consumer perception of a particular product or service offered by an organization. The primary purpose of the analysis technique is to determine the best combination of characteristics 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 faced with several options. On the other hand, it also has drawbacks like the difficulty involved in drawing up 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.
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