Conjoint analysis is the optimal market research approach for measuring the value that consumers place on features of a product or service. This commonly used approach combines real-life scenarios and statistical techniques with the modelling of actual market decisions.
Menu-Based Conjoint Analysis
Menu-based conjoint analysis is an analysis technique that is fast gaining momentum in the marketing world. One reason is that menu-based conjoint analysis allows each respondent to package their own product or service.
Conjoint can help you determine pricing, product features, product configurations, bundling packages, or all of the above. Conjoint is helpful because it simulates real-world buying situations that ask respondents to trade one option for another.
For example, in a survey, the respondent is shown a list of features with the associated prices. The respondent then chooses what they want in their ideal product while keeping price as a factor in their decision. For the researcher, key information can be acquired by analysing what was selected and what was not. If feature A for RM100 was included in the menu question but feature B for RM100 was not, it can be assumed that this respondent prefers feature A over feature B.
The outcome of menu-based conjoint analysis is that we can identify the trade-offs consumers are willing to make. We can discover trends indicating must-have features versus luxury features.
Add in the fact that menu-based conjoint analysis is a more engaging and interactive process for the survey taker, and one can see why menu-based conjoint analysis is becoming an increasingly popular way to evaluate the utility of features.
KAJIDATA has the capability to produce some of the best conjoint surveys – built with the exact look and feel needed to provide a reliable, easy to understand experience for the respondent. This means better quality data for you.
There are numerous conjoint methodologies available:
- Full-Profile Conjoint Analysis
- Choice-Based/Discrete-Choice Conjoint Analysis
- Adaptive Conjoint Analysis
- Max-Diff Conjoint Analysis
To provide a sense of these options, the following discussion provides an overview of conjoint analysis methods.
Two-Attribute Trade-off Analysis
Perhaps the earliest conjoint data collection method involved presented a series of attribute-by-attribute (two attributes at a time) tradeoff tables where respondents ranked their preferences for the different combinations of the attribute levels. For example, if two attributes each had three levels, the table would have nine cells and the respondents would rank their tradeoff preferences from 1 to 9.
The two-factor-at-a-time approach makes few cognitive demands of the respondent and is simple to follow but it is both time-consuming and tedious. Moreover, respondents often lose their place in the table or develop some stylised pattern just to get the job done. Most importantly, however, the task is unrealistic in that real alternatives do not present themselves for evaluation two attributes at a time.
Full-Profile Conjoint Analysis
Full-profile conjoint analysis takes the approach of displaying a large number of full product descriptions to the respondent. The evaluation of these packages yields large amounts of information for each customer/respondent. Full-profile conjoint analysis has been a popular approach to measure attribute utilities. In the full-profile conjoint task, different product descriptions (or even different actual products) are developed and presented to the respondent for acceptability or preference evaluations.
Each product profile represents a part of a fractional factorial experimental design that evenly matches the occurrence of each attribute with all other attributes. By controlling the attribute pairings, the researcher can correlate attributes with profile preferences and estimate the respondent’s utility for each level of each attribute tested. In the rating task, the respondent gives their preference or likelihood of purchase. While many features and levels may be studied, this type of conjoint is best used where a moderate number of profiles are presented, thereby minimising respondent fatigue. We employ experimental designs to reduce the number of evaluation requests within the survey. The output and analysis accumulated from full-profile conjoint surveys is similar to that of other conjoint models.
Adaptive Conjoint Analysis
Adaptive conjoint analysis varies the choice sets presented to respondents based on their preference. This adaption targets the respondent’s most preferred feature and levels, thereby making the conjoint exercise more efficient, wasting no questions on levels with little or no appeal. Every package shown is more competitive and will yield ‘smarter’ data.
Adaptive conjoint analysis is often more engaging to the survey-taker and thus can produce more relevant data. It reduces the survey length without diminishing the power of the conjoint analysis metrics or simulations. There are multiple ways to adapt the conjoint scenarios to the respondent. Most commonly the design is based on the most important feature levels. As each package is presented for evaluation, the survey accounts for the choice and then makes the next question more efficient. A combination of full profile and feature evaluation methods can be utilised and is referred to as Hybrid Conjoint Analysis.
The Choice-based conjoint analysis (CBC) (also known as discrete-choice conjoint analysis) is the most common form of conjoint analysis. Choice-based conjoint requires the respondent to choose their most preferred full-profile concept. This choice is made repeatedly from sets of 3–5 full profile concepts.
This choice activity is thought to simulate an actual buying situation, thereby mimicking actual shopping behaviour. The importance and preference for the attribute features and levels can be mathematically deduced from the trade-offs made when selecting one (or none) of the available choices. Choice-based conjoint designs are contingent on the number of features and levels. Often, that number is large and an experimental design is implemented to avoid respondent fatigue. KAJIDATA provides extreme flexibility in utilising experimental designs within the conjoint survey.
The output of a Choice-based conjoint analysis provides excellent estimates of the importance of the features, especially in regards to pricing. Results can estimate the value of each level and the combinations that make up optimal products. Simulators report the preference and value of a selected package and the expected choice share (surrogate for market share).
Self-Explicated Conjoint Analysis
Self-explicated conjoint analysis offers a simple but surprisingly robust approach that is easy to implement and does not require the development of full-profile concepts. Self-explicated conjoint analysis is a hybrid approach that focuses on the evaluation of various attributes of a product. This conjoint analysis model asks explicitly about the preference for each feature level rather than the preference for a bundle of features.
Although the approach is different, the outcome is still the same in that it produces high-quality estimates of preference utilities.
- First, like ACA, factors and levels are presented to respondents for elimination if they are not acceptable in products under any condition
- For each feature, the respondent selects the levels they most and least prefer
- Next, the remaining levels of each feature are rated in relation to the most preferred and least preferred levels
- Finally, we measure how important the overall feature is in their preference. The relative importance of the most preferred level of each attribute is measured using a constant sum scale (allocate 100 points between the most desirable levels of each attribute).
- The attribute level desirability scores are then weighted by the attribute importance to provide utility values for each attribute level.
Self-explicated conjoint analysis does not require the statistical analysis or the heuristic logic required in many other conjoint approaches. This approach has been shown to provide results equal or superior to full-profile approaches, and places fewer demands on the respondent. There are some limitations to self-explicated conjoint analysis, including an inability to trade off price with other attribute bundles. In this situation, the respondent always prefers the lowest price, and other conjoint analysis models are more appropriate.
Max-Diff Conjoint Analysis
Max-Diff conjoint analysis presents an assortment of packages to be selected under best/most preferred and worst/least preferred scenarios. Respondents can quickly indicate the best and worst items in a list, but often struggle to decipher their feelings for the ‘middle ground’. Max-Diff is often an easier task to undertake because consumers are well trained at making comparative judgments.
Max-Diff conjoint analysis is an ideal methodology when the decision task is to evaluate product choice. An experimental design is employed to balance and properly represent the sets of items. There are several approaches that can be taken with analysing Max-Diff studies including: Hierarchical Bayes conjoint modelling to derive utility score estimations, best/worst counting analysis and TURF analysis.
Hierarchical Bayes Analysis (HB)
Hierarchical Bayes Analysis (HB) is similarly used to estimate attribute level utilities from choice data. HB is particularly useful in situations where the data collection task is so large that the respondent cannot reasonably provide preference evaluations for all attribute levels. As part of the procedure to estimate attribute level utilities for each individual, hierarchical Bayes focuses individual respondent measurement on highly variable attributes and uses the sample’s attribute level averages when attribute-level variability is smaller. This approach again allows more attributes and levels to be estimated with smaller amounts of data collected from each individual respondent.
Conjoint is a Highly Effective Analysis Technique
Conjoint analysis methodology has withstood intense scrutiny from both academics and professional researchers for more than 30 years. It is widely used in consumer products, durable goods, pharmaceutical, transportation, and service industries, and ought to be a staple in your research toolkit.