by Conjoint analysis is a statistical technique that helps in forming subsets of all the possible combinations of the features present in the target product. These features used determine the purchasing decision of the product. Conjoint analysis works on the belief that the relative values of the attributes when studied together are calculated in a better manner than in segregation.
The process provides information about the consumer’s perception about certain characteristics of brands or brand profiles, and they evaluate such characteristics by assigning certain levels to each characteristic. A questionnaire form called the stimuli is presented to the researcher, and this consists of a set of questions that reflects different characteristics of a brand as options that the consumers select as they answer the questionnaires in conjoint analysis.
The stimuli play an important role in conjoint analysis. It is the stimuli that give information to the researchers about the consumer’s preference. With the help of the stimuli, the researchers can perform this method. The researcher should, however, check that the responses are true because the interpretation of the conjoint analysis is dependent on that.
The process of conjoint analysis has found its applications in various disciplines. Such disciplines include branding consumer goods and branding industrial goods, etc. The procedure provides the researcher a flexible opportunity to address certain issues instead of conducting the testing of hypothesis. The researcher should also note that the theory is quite simple and flexible for the researcher to understand, even if he is a non statistical person. The model that is used by the researcher during the procedure is the utility function model. This model is based on the evaluation of conjoint analysis, and is basically a mathematical model. This mathematical model is used by the researcher to express the fundamental relationships between the attributes and the utilities of the attributes that the consumer attaches to it. The dependent variable usually consists of the consumer’s preference or intention to buy a particular brand of product.
There are several procedures for assessing the reliability and validity of conjoint analysis. A reliability test, called test retest reliability in conjoint analysis, can be used to obtain duplicated judgments that are sometimes involved in data collection. If an aggregate level of conjoint analysis has been done, then the estimation sample can be split into several samples and conjoint analysis is again conducted on each sub-sample. This can assure the researcher that the conjoint analysis being conducted is reliable and valid.
It is important for the researcher to know that conjoint analysis and multidimensional scaling (MDS) are complementary. Both rely on the respondent’s subjective evaluations. The difference between them is that of the stimuli. In conjoint analysis, the stimuli are the combinations of attribute levels, whereas in MDS, the stimuli are the products or brands of the products.
The steps involved while conducting conjoint analysis are the following:
- The first and one of the most obvious steps is the formulation of the problem.
- The next step is to prepare the stimuli.
- The third step is to decide upon the form of data to be input.
- The fourth step involves the selection of the procedure.
- The next step is to interpret the results obtained.
- And the last step is to assess the reliability and validity.
Uses of Conjoint Analysis
Conjoint analyses can break-down large number of attributes into smaller bundles for evaluations and comparison. There attributes can also be compared in pairs: Respondents can be asked to indicate preferences between sets of two or more attributes. In this case, one set of attribute appear on left and another on the right on the questionnaire. This method is simpler as compared to evaluating 15 or 25 attributes simultaneously.
A conjoint analysis is able to breakdown utility to consumer at individual level as well as aggregate of all the responses. Numerous new techniques have been recently developed which help companies determine individual level utilities for choice-based conjoint, which provide companies with useful insight which is invaluable to the decision making when it comes to marketing, pricing and product placement.
The technique offers straightforward methods for experimentation with varying factors such as price, attributes, price etc. Before a product is launched the technique helps create a product profile, which can be altered to generate additional profiles for varying attributes. Consequently this helps businesses find the balance keeping in view the relative desirability of each alternative in a choice set and uses each attribute level uniformly throughout the survey. Conjoint analysis provides information that forms the basis of market segmentation, whereby a large homogenous market is divided into smaller groups bases on demographics, preference, age group, etc. Market segmentation enables businesses to target each homogenous group more effectively since their needs and preferences are recognized, and decisions are taken according.
Conjoint analysis can also be employed to exclusive focus on product features and attributes irrespective of price or brand name, hence enabling calculation of utility on individual basis in regard to the aforementioned specific features the companies seeks to evaluate. Moreover, the technique is widely used to measure the value of brand names in comparison to competing brands. Information can be obtained as to how strong a particular brand is in comparison to specific product and price. It helps businesses make decision based on their brand value in the market, since having a popular brand may not be enough as changes in price and features could impact demand.
Conjoint analysis is an important tool which helps in evaluating brand equity and estimate how market share is impact owing to various tradeoffs between brands, prices and some specific features. Conjoint analysis can be used to determine resource allocation, since businesses have already established which attributes are valued more by the consumers through the analysis. Consequently, companies can allocate the scare resources accordingly. They can choose to eliminate or remove features which are superfluous to the customers or not valued by the consumers. This will save costs for the business, and as already established, will not impact sales, since these particular products are not valued by the customers. Reduced cost would lead to more profitability for the businesses.
Conjoint analysis could be a great help when companies are in the process of re-modeling or revamping their products and services. When new version of a product is to be launched, the questionnaire could include all possible features or attributed a company could possible add. Through conjoint analysis it can be determined which features should be included given the important respondents attach to certain attributes. This will also help ascertaining the impact it would have on costs and revenues and overall profitability.
