Choice Based Conjoint Analysis as tool for New Product Development

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Do you want to increase your sources of revenue through new products? The conjoint analysis allows you to identify target market preferences about the products that you are developing. It has the following benefits:
The conjoint analysis simulates the way that the consumer makes decisions in the real life. The test presents all the relevant attributes together to the respondent.
The conjoint analysis estimates the interviewee’s evaluation of the attributes without asking directly each feature importance. This is especially relevant regarding the price because the interviewees will generally reduce the price that they are willing to pay if they are asked directly. Besides, respondents tend to evaluate high all the attributes. If you ask, what is important to you about a product, they are likely to say that everything is important, but the reality is that the products can not have all the benefits at maximum level. Some attributes can have a higher level, but other will have a lower level. A higher quality will be more expensive (lower benefit from price) for example.
Furthermore, the conjoint analysis can uncover hidden drivers that may not be conscious to the respondent themselves. The interviewees just make choices about products. Many times, these choices don’t have a detailed analysis of the information but are a general evaluation of the whole product. However, the analysis of the data will show which attribute is more important.
The choice based conjoint (CBC) is a particular type of conjoint analysis that has been popular for some time. It presents several options of the complete product prototype to the interviewee from which he has to select one or none. This process of evaluation is highly preferred to others because it is very similar to the way that buyers behave in the marketplace and it is a simple task that everyone can understand.
One advantage of the CBC analysis is that it allows considering interactions. They are especially important in pricing studies. For example, you may want to consider the effect of women responses in the estimated price of a product. Another example of interactions is the preference for a smaller size if the price increases.
To execute the CBC, the respondent will be asked to indicate their preferences for different bundles of attributes. For example, a prepared chicken food for sales in the supermarket has the following attributes: weight, style, package, and price. The weight levels of the test options can be 1 Kg, 750 gr, 500 gr, 250 gr. The style levels would be Honey Garlic, Dijon Mustard and Herbs and Teriyaki. There are 2 package designs and the following possible prices: $6, $8, $10, $12, $14, $16 and $18. One choice example would be 750 gr, Honey Garlic, package design 1 at $10. The result of the analysis will tell you that the interviewees prefer a product with 500 gr, Dijon Mustard, package design 2 at $12, for example. This choice will receive an evaluation called utility. The other choices also will have their evaluations, so you can rank the choices. The analysis also will tell you which attribute and level for each attribute are more important for the sample in the utility-scale.
Some CBC Softwares estimate the utilities using the conditional logistic regression. An example of how the logistic regression works can be found in the blog “Analytical model for Lead Qualification”.  The conditional logistic regression is an extension of the logistic regression and it is used because each choice generates several rows of data that should be grouped for the analysis.
The CBC analysis requires experimental design. The number of conditions for the design is equal to the number of levels for attribute 1 x number of levels for attribute 2 x number of levels for attribute 3… The total number of conditions implies too many questions for the respondents. Therefore, some conditions are selected for the test through the Fractional Factorial Design. A conditions’ selection that form an Orthogonal Array meets the experimental design goals. Most of the conjoint programs include the functions needed for the experimental design.
The aggregate utility for each choice let you calculate the share of preference. This indicator is very useful because it allows you to modify the level of the attributes and estimate the share of preference based on the data that you already have collected. Therefore, you can select an attributes’ combination that maximizes the share of preference. Following the example of the chicken package, you have that the preferred product (500 gr, Dijon Mustard, package design 2 at $12) has the higher share of preference. The simulation based on the collected data allows you to increase the weight to maximize the share of preference. The increase of the attributes has restrictions, like the price, which will also increase. Therefore, from a certain level, an increase of the weight will not increase the share of preference of the product.
The choice task of the respondents requires a bit of adaptation and too many questions can tire him. It is recommended to do warm up choices and limit the choices between 10 and 20.
The number of attributes should also be limited to a maximum of 6 in a full profile concept. If there are too many attributes, most probably the respondents will ignore unimportant attributes.
The model for the utility calculation can be validated through the method of Hit Rate. This method calculates the percentage of correct individual predictions of the model compared with the sample size.
As I said, CBC is one type of conjoint analysis. There are also Rating based Conjoint and Hybrid techniques. These basic categories include subdivisions techniques like Adaptive Conjoint Analysis, Hierarchical Bayesian (HB) CBC or HB Rating based Conjoint Analysis. These techniques have the following advantages over CBC Analysis: allow estimating preferences at an individual level, can handle more attributes, and are able to work with smaller samples. The use of them is recommended instead of CBC if the above requirements are necessary.
Summarizing, CBC analysis is a good way of testing new products because it presents a natural way of evaluation. It is especially useful when there are few attributes, there is a high interaction probability between the variables and the “None of the choices” option should be included. Besides, the CBC, as a particular type of conjoint analysis, allows you to evaluate the characteristics of the product simultaneously and can reveal hidden drivers of the respondent.

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