Introduction to conjoint analysis marketing assignment

Consequently, fractional factorial design is commonly used to reduce the number of profiles that have to be evaluated, while ensuring enough data are available for statistical analysis, resulting in a carefully controlled set of “ profiles” for the respondent to consider [edit] Types of conjoint analysis The earliest forms of conjoint analysis were what are known as Full Profile studies, in which a small set of attributes (typically 4 to 5) are used to create profiles that are shown to respondents, often on individual cards.

Respondents then rank or rate these profiles. Using relatively simple dummy variable regression analysis the implicit utilities for the levels can be calculated. Two drawbacks were seen in these early designs. Firstly, the number of attributes in use was heavily restricted. With large numbers of attributes, the consideration task for respondents becomes too large and even with fractional factorial designs the number of profiles for evaluation can increase rapidly. In order to use more attributes (up to 30), hybrid conjoint techniques were developed.

The software creates templates for 16 (or 18 – depending upon the number of variables) of these, and we portray the description of the proposed product visually, on a “ card”, as shown to the right. “ Cards” can describe the product using words only – but can also use logos, pictures, or even smells or sounds. In any case, respondents will be asked to read each of the 16 “ cards”, and then assign a ranking of some kind (using numbers I-x, or using adjectives like favorable, unfavorable, ideal, etc. )Perhaps Card #1 is a factory-produced low-fat cheap vanilla cone. Maybe #2 is a homemade non-low-fat chocolate cup at a medium price point. The process goes on with 16 mathematically designed cards hat offer all the relevant combinations of choices.

I Click for an online example of this actual conjoint analysis survey I I Conjoint Resulting the consumers’ ratings of all 16 diverse combinations, the software package computes a mathematical regression to tell us how important each of the five factors is to the individual responding consumer, and to the group of responding consumers as a whole. According to the results shown to the left (actual output from the online survey), we’d know that consumer X bases 47% of his decision on price, 23% on the flavor, 19% on the freshness, and is less concerned about the container or litheness. We also learn get a relative ranking of the different flavors, as shown in the lower graph. Len addition, each consumer will be asked a number of informational questions to create a demographic profile, so that we can compare the results and analyze them based upon income, age, location, and other variables that may affect consumer behavior towards a particular product.

This, however you can go down to 100 completed surveys if your target market is relatively small. 4) Most use cases of conjoint focus on consumer electronics/durable goods. Is there a case for using conjoint in the FMC/CAP industry? AS . There is an example of a packaged goods study- Trail Mix: http://cryptanalysis. Com/t/ Advantages Crescendos I As you can see in the results Dry Fruit had the highest relative importance compared to other ingredients whereas Nuts Type 1 (sesame seed and sunflower seeds) did not sake an impact on choice. 5) As attributes and levels are important in conjoint what should be appropriate . No. On attribute levels? AS . It would depend if the feature is something you may want to add or not.

For example, if you wanted Trail Mix with/ without Crackers you would set up the following: Features: Crackers ;; Level: Yes, No 6) Did you ask the “ why” questions such as frequency and power questions in a study after the conjoint study? ADS/AS . It has been investigated in other research and will be tested again further. 7) How is conjoint used in the launch of a service may be price/MO. , etc. You must identify attributes and levels similar to a product. AS . A fun example is a hair salon. What kinds of services will you offer to your clients and at what price do you think they would pay for it? As Dorian said you must identify attributes and levels similar to a product. 8 ) With 6 attributes and multiple levels, how long was the [example] survey? I assume that you used experimental design to shorten the length of the survey? AS .

Yes, that is correct. 16) Don. T we need any intelligence in the tool when designing the conjoint study? The tool may generate a profile which has worst features but its price is highest. ADS . This is true, but this is part of a conjoint analysis to understand what your customers deem which attributes and levels are the worst. I don. T think you intelligence into our conjoint tool such as the prohibited pairs tool to ensure certain combinations that are not possible will ever show up. We must be careful in using this tool because the idea is not to limit the profiles based on what the client will not do, but to find out what resonates higher with your audience.