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Conjoint Analysis is a technique used to assess the different contributions aspects of a product or service make to individuals in the purchasing decision. For example, if a consumer decides to buy a mobile there are a couple of distinct variables involved:
A conjoint study usually involves showing respondents product profiles and asking them to indicate (in a variety of ways) how much they like or prefer these alternative product profiles. Statistics are then used to work out the contribution that each product attribute is making to the overall like ability.
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Conjoint Analysis is one of the most effective models in extracting consumer behavior into an empirical or quantitative measurement. It evaluates products/services in a way no other method can. Traditional ratings surveys and analysis do not have the ability to place the "importance" or "value" on the different attributes, a particular product or service is composed of. Conjoint Analysis guides the end user into extrapolating his or her preference to a quantitative measurement. One of the most important strengths of Conjoint Analysis is the ability to develop market simulation models that can predict consumer behavior to product changes. With Conjoint Analysis, changes in markets or products can be incorporated into the simulation, to predict how consumers would react to changes.
Choice based or Discrete Choice Conjoint is by far the most preferred model for a conjoint questionnaire. This is primarily because it models after consumer behavior in real-life. Most purchases that consumers make today are basically trade-off based. Will you buy a $150 ticket with 2 flight stops and No miles or a 200$ ticket with no stops and 4000 miles?
Any product or service can be modeled as an entity with a set of attributes. For example an airline ticket between Seattle and Miami may have the following attributes:-
StopsEach of the attributes may have one or more levels. A level can be defined as any value the attribute can take. In the examples above, the attributes can have the following levels:-
Here are some simple steps to assimilate the information before beginning your online conjoint survey.
Attributes: Define the attributes for your market segment. For most studies, try to keep the number of attributes below five. If you have a large number of attributes, try aggregating and combining these attributes into meaningful composite attributes.
Levels: Define at least two levels for each of the attributes. Try to stick to a maximum of four or five levels per attribute.
Minimal Respondent Base: Try to figure out if your respondent base is homogeneous. Are you interested in interaction of the conjoint data between different demographics? For example are you trying to figure out if Males place a higher value for miles than females in buying an airline ticket? In such a case your Minimal Respondent Base is � of all the participants (assuming equal distribution of males and females.)
Minimal Choice Count for statistical validity: Try to come up with a minimum number of times a Level should be shown to the respondents to make a statistically valid sampling. For most conjoint studies a Minimal Choice Count of 100 to 150 should give good results. What this number represents is that, all attribute levels should be presented at least 100 to 150 times to make the results of the study statistically significant.
You do not need to come up with both the Minimal Respondent Base and Minimal Choice Count. If you have one, the Concept Simulator can determine the other. More details about the Concept Simulator are provided below.
The goal of any conjoint survey is to assign specific values to the range of options buyers consider when making a purchase decision. Armed with this knowledge, marketers can focus on the most important features of products or services and design messages most likely to strike a cord with target buyers.