Conjoint analysis is the techniques of survey or market research to estimate the values a customer place on different attributes of the product.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 chord with target buyers.
Why conjoint Analysis?
The traditional conjoint analysis asks the respondent to place value on each attribute which is very difficult for consumers other than the experts on the product values. Conjoint Analysis makes the survey easier for majority of consumer by breaking it into easier choice and ratings.Later these ratings or choices can be used to calculate the relative importance of each attributes
The results of conjoint analysis help in developing market simulation model that can be used well into future. This helps the business managers to create their own market scenario and to study that how market will react to any change in price or any other quality of the product. It gives a thorough understanding of business market and true value of the product. Not only this,Conjoint analysis actually quantifies the value or importance of different attributes in a market.
How Do You Define the Attributes to be used?
Experience, management intuition and qualitative research are needed to develop the list of key attributes for any product or service. It should not be too many as it will increase burden on the respondent and can adversely affect the quality of the survey data. Too few information will restrict the predictive capability of the model in lack of information. So the number of attributes should be decided wisely using all experience, domain knowledge and qualitative research on the product placement and intended audience.
The other most important thing is to decide the attribute levels. It should cover all of the products that exist in the market or expected to exist within the near future. For continuous variables like price, 3 or 4 price levels can cover the market from low price leader to premium product. For discontinuous attributes, 3 to 5 levels are typically specified so somesacrifices may have to be made to eliminate the least desirable or least important options.The critical factor in specifying attributes and attribute levels is that a product cannot be accurately simulated if it cannotbe defined reasonably well using the attribute levels chosen for the conjoint survey.
How to determine the overall values of the product?
Once we have the utilities of each attribute levels then a product’s value is calculated by summing the utilities across all the attributes that define that product. For each attribute, the attribute levelis selected most closely associated with the product and also note it’s utility. This process is repeated for every attribute included in the study. Thenoted utilities are added for each attribute to compute a “total utility” for that product. This is done for all products to compare to create a market simulation. Market share or share of preference for a product isusually calculated as the proportion of that product’sutility to the utility of the total market. Market simulation programs can quickly and easily make all of these calculations and present results in graphicalor tabular form. Simulation programs also allow making any changes to any product to see the effects on buyer preferences.
Which conjoint to use?
The choice of experiment depends of number of attributes as follows:
Number of Attributes: If the number of attributes required to be studied is high then ACA is considered to be more solid approach. In case of fewer attributes CBC is preferred
Mode of interviewing: If it is pen and pencil type of interview then CBC is preferred.
Sample Size: If sample size is small then CBC is avoided. Old rating based approach is preferred like ACA etc.
Pricing research: If studying price then CBC and ACBC is preferred.
How does it work?
Based on the type conjoint survey conducted statistical methods like Ordinary Least Square Regression, weightedLeast Square Regression and Logit Analysis are used to translate the respondent’s answer to importance values and utilities. Conjoint analysis is most popular analytical tool for marketing, brand promotion and any kind of market research for the business managers.
Basic steps to conduct conjoint analysis
- Determine which product/service attributes or features are most important to the market.
- Determine which data collection methodology will be used to recruit respondents and how the Data will be captured (mail, telephone-mail, disk-by-mail, Internet, etc.).
- Determine which conjoint methodology will best fit the research problem. Choice-based Conjoint and preference-based Conjoint are the most common methodologies used today.
- Create an experimental design which will allow the calculation of main effects and key interactions between the attributes being studied. Many conjoint studies only focus on the main effects or direct utilities for each attribute, however, when attributes such as price or brand name are used, potential interactions between attributes should also be considered.
- Collect the data. After pre-testing your attribute list and survey instrument, begin collecting data from the target market.
- Calculate the utilities for each respondent or for groups of respondents.
- Create a market simulation model. This allows to predict the impact of changes in existing products and the introduction of new products on the market.
What are the techniques used for the conjoint analysis?
ACA (Adaptive Conjoint Analysis): In ACA(Adaptive conjoint Analysis) the choice set presented to respondent will vary based on the preferences they express. This adaptation focuses on the respondent’s most preferred features and levels. This process makes the conjoint exercise more efficient, wasting no questions on levels with little or no appeal. Every package shown is more relevant to the respondent and will yield ‘smarter’ data.Adaptive conjoint reduces the survey length without diminishing the power of the conjoint analysis metrics or simulations.
Choice based conjoint analysis (CBC): CBC has become very widely used conjoint techniques these days. CBC interviews tries to mimic the purchase process for products in competitive market.Instead of rating or ranking product concept,respondent are shown to a set of product and asked to indicate which one they would purchase. Though CBC is easy for the respondent to evaluate but it contains less information than rank based conjoint analysis. Unlike the rating system in CBC we could not learn the degree of preference or rejects.
Partial profile CBC: Many researchers who favors choice based conjoint rather than rating based approaches, turned to Partial profile CBC to increase the number of attributes that can be measured effectively using CBC. With partial-profile CBC, each choice question includes a subset of the total number of attributes being studied. These attributes are randomly rotated into the tasks, so across all tasks in the survey each respondent typically considers all attributes and levels.
MaxDiff Conjoint analysis : Respondents are asked to select the best and worst from the combination of statements shown. Typically used for soft attributes that are not quantified.
How ScoreData can help?
ScoreData can help with application of conjoint in the business problem- what type of conjoint is applicable. We also help our clients with complete research design, sample plan and data collection methodology. Once data is collected we can help you to draw meaningful insight from it. This includes the application of advanced statistical techniques to calculate the utility or importance of the attributes.
ScoreData can also help you in the reporting and presentation of the findings. Our focus is totally on converting the data into actionable and impactful results.