Conjoint analysis is a survey-based statistical method frequently used in market research and the social sciences to understand people’s preferences, and shape products and policies accordingly.
Conjoint analysis can be an invaluable tool in any sector, to ensure maximum success and stakeholder satisfaction. In this article, we will explain what conjoint analysis is and how it works, what the different types of conjoint analysis are, and how 1000minds can help you achieve your goals in uncovering people’s preferences.
What is conjoint analysis?
Conjoint analysis is a statistical survey-based technique used to evaluate people's preferences regarding different attributes or characteristics of a given object of interest. This information is then used to improve products based on consumer preferences, or make decisions involving stakeholders, such as in policy making.
Some examples of conjoint analysis include:
- Analyzing consumer preferences in regards to smartphone features, in order to manufacture the most successful smartphone product.
- Understanding employee preferences regarding work benefits, so that the company can offer the right work benefits and increase employee satisfaction.
- Analyzing citizen preferences regarding the management of freshwater resources, in order to shape public policy.
- Discovering the relative importance of the various attributes of hospital services that people care about: such as ‘quality of care’ vs ‘waiting time’ vs ‘amenities’ vs ‘price’, etc
- ...and so much more!
Conjoint analysis has its own specialised terminology:
- Attributes: Features or characteristics of the product or other alternative of interest, with two or more levels of performance or achievement.
- Alternatives: Combinations of attributes representing particular products or other alternatives of interest. Also called concepts or profiles.
- Utilities: Values (or weights) representing the relative importance of the attributes. Also called part-worths, or part-worth utilities.
How does conjoint analysis work?
Conjoint analysis works by presenting people with a survey where participants have to decide which of different hypothetical alternatives they prefer. Each alternative is made up of a different combination of attributes, so participants are forced to make trade-offs, as they would in real-world decisions.
Through the choices that participants make, the conjoint analysis software can uncover people’s relative preferences and assign utilities to the attributes – numerical values that represent the relative importance of the attributes. These values can be subject to further analyzes (see below), and be used to create an ideal alternative.
Why use 1000minds for conjoint analysis
1000minds is the only conjoint analysis software created specifically to reduce the cognitive burden on survey participants as much as possible, while maximizing reliability, repeatability, and scientific validity of the results.
Owing to our user friendliness and scientific rigor (see our awards), 1000minds has been used by businesses and government agencies across all sectors, as well as for research and teaching at 410+ universities and other research organizations worldwide. See our case studies and publications to see how 1000minds can be used in your field of interest.
We have a wide range of conjoint analysis examples available in our app to help you get started, which are designed to give you a feel for how 1000minds works. Try them out now (you’ll be asked to create a free trial account if you don’t have one already – very easy!).
Our conjoint analysis surveys allow you to include as many participants as you like, potentially 1000s. Participants can self-enroll from a sign-up webpage – e.g. great for ‘convenience’ or ‘snowball’ sampling. Or, if you know people’s email addresses, you can enter them into 1000minds, and they’ll be invited to take part.
A fun demonstration
The following link is to a conjoint analysis survey set up for helping you to choose a breed of cat as a pet! This light-hearted example neatly demonstrates many of 1000minds’ features from a survey participant’s perspective.
1000minds – Choice-based, adaptive conjoint analysis
1000minds developed the award-winning PAPRIKA pairwise comparisons method to determine people’s utilities (weights) by asking questions based on choosing between pairs of alternatives involving trade-offs between the attributes. An example of a question in 1000minds – involving designing a car – appears below. Hence this type of conjoint analysis is referred to as ‘choice-based’.
In addition, 1000minds is a type of ‘adaptive’ conjoint analysis because each time a choice is made, 1000minds adapts by formulating a new question to ask based on all previous choices.
1000minds is fast and scaleable. No extra analysis is needed to derive standard conjoint analysis outputs (see analyzing outputs). Also, potentially 1000s of people can participate in conjoint analysis surveys.
Conjoint Analysis Example
Suppose the conjoint analysis survey is to discover what consumers of ‘flavoured milk drinks’ care about (generalisable to other products or alternatives of interest too).
The survey would usually involve each survey participant answering a series of questions involving trade-offs between attributes associated with flavoured milk drinks – e.g. taste, nutrition, price, shelf life, brand image. Take the flavored milk drinks survey for yourself with this free demo!
From each participant’s answers to the survey questions, ‘utilities’, representing the relative importance of the attributes, are calculated. These utilities are then used to rank different flavoured milk drink ‘alternatives’ (i.e. combinations of the attributes), including choosing the ‘best’ alternative – e.g. for a manufacturer to produce.
These basic conjoint analysis outputs are now presented and analyzed in various useful ways. Though the example here has a marketing-research focus, the ideas illustrated below can be generalised to other conjoint analysis applications too (e.g. with a government policy focus).
The outputs below are from a 1000minds conjoint analysis survey, implementing the PAPRIKA pairwise comparisons method. A major strength of the PAPRIKA method is that utilities are generated for each individual participant, in contrast to other methods that produce aggregate results only. Individual-level data enables more in-depth analysis, as illustrated below.
Conjoint Analysis outputs
- Attribute rankings
- Radar chart
- Attribute relative importances
- Rankings of entered alternatives
- Market shares
- Market simulations (“What ifs?”)
- Rankings of all possible alternativets
- Willingness-to-pay (WTP)
- Cluster (market segmentation) analysis
For simplicity, suppose there are just five participants in the survey (of course a real survey would probably involve 100s or 1000s of participants): Consumers ‘X’, ‘Y’, ‘Z’, ‘Paul’ and ‘Alfonse’, as in the tables below.
First, here are the utilities for each participant – in this example relating to attributes associated with flavoured milk drinks – as well as the usual summary statistics (median, mean, standard deviation).
The value for each level on an attribute represents the combined effect of the attribute’s relative importance (weight) and its degree of achievement as reflected by the level (for more information, see interpreting preference (utility) values.
As well as the utilities reported above, here are the ‘normalized’ attribute weights and scores – an alternative, though equivalent, representation of the mean utility values (second-last column above). This equivalence is easily confirmed by multiplying the weights and single attribute scores to reproduce the (mean) utilities above.
Consistent with the utilities data above, here are the rankings of the attributes for each of five participants.
The data in the first chart can be visualized in several ways, including using a ‘radar’ chart.
This chart indicates the strength of preferences for the attribute shown by each of the five participants; each one has a differently colored ‘web’; the further from the centre of the chart, the more important the attribute. The thick black line shows the mean values.
Attribute relative importances
These ratios – sometimes known as ‘marginal rates of substitution’ (MRS) – capture the relative importance of the column attribute for the row attribute (based on the mean utilities).
Rankings of entered alternatives
Although utilities (as above) are interesting, there is also enormous power in applying each individual’s preferences to new product alternatives and also to competitors’ offerings, in order to predict the likely market share or market shift that might occur.
Such analysis is useful for answering questions like, “What would it take to make Product A the market leader (or to, at least, increase its market share)?”
Here are 12 illustrative product alternatives for flavoured milk drinks.
The five participants’ utilities can be easily applied to the 12 alternatives by calculating ‘total utility’ scores for each alternative – simply by summing the values for each level on each attribute for each alternative, and the alternatives are then ranked for each participant by their total scores. (The linearity of the equation means that, by construction, interaction effects between the attributes are ruled out – i.e. the attributes are independent.)
Thus, it can be seen below that 60% of participants in the survey (3 out of 5 participants) would have chosen (i.e. ‘bought’) Product C and 40% (2 out of 5) would have chosen Product G.
By contrast, just 20% of participants (1 participant) would rank Product A as their 3rd most-preferred product (and probably not buy it).
Of course, just five participants is insufficient to represent the market for flavoured milk drinks! More realistically, 500 – or 1000! – survey participants would be necessary, but hopefully you get the idea of how this analysis works. Note that how a sample is selected – e.g. randomly – is more important than just sample size.
Also, look at the table below to see the frequencies of ranks for each of the 12 alternatives – where we can see that 3 of the 5 participants would rank Product A 4th and 1 participant each would rank it 3rd and 6th respectively.
The number in each cell is the number of participants – out of 5 in the survey – who would give the identified alternative the identified rank.
Market simulations (“What ifs?”)
If our objective is to answer a question like, “What would it take to make Product A the market leader?”, we can make predictions, based on the utilities from the survey, as to what would happen if Product A’s attributes were changed. (As mentioned before, bear in mind that just five participants is insufficient to properly simulate a market.)
Below is a comparison of the total utilities for Product A versus Product C (the current market leader) disaggregated across the five attributes (see below for color coding).
Clearly, relative to Product C, Product A is deficient with respect to its brand image and it is more expensive (on the other hand, A is superior with respect to shelf life).
Other attribute fine-tunings are possible too; e.g. if lowering Product A’s price were infeasible, then improving its brand image and its nutrition would be sufficient to overtake Product C. This can be discerned from these Tornado Charts: −/+ 1 level (one-way sensitivity analysis):
As product alternatives are refined – e.g. improving Product A’s brand image and nutrition (as above) – we can see the impact this may have on the market. In this case Product A could be expected to take a 70% market share (based on these 12 alternatives and the five participants’ preferences), at the expense of the market shares of Products C and G.
Rankings of all possible alternatives
In addition to rankings of particular entered alternatives (e.g. 12, as above), it’s possible to see rankings of all theoretical combinations of the attributes – in this example, 3 × 3 × 4 × 3 × 3 = 324 alternatives; here are the first 20:
Based on the outputs above, the following types of analysis are easily performed using Excel or, for the cluster analysis, a statistics package (e.g. SPSS, Stata, MATLAB).
The usual way of calculating WTP is to calculate the number of currency units (e.g. dollars) that each utility unit – often referred to as a ‘utile’ – is worth. And then it’s easy to convert all the non-monetary attributes – valued in terms of utiles (utilities) – into monetary equivalents, which can be interpreted as WTP.
Thus, for example, using the mean utilities (as reproduced below), a price fall from $6 to $3 (i.e. $3) corresponds to a utility gain of 20.7 – 0.0 = 20.7 utiles. Therefore, 1 utile is worth $3/20.6 = 14.5 cents. Applying this ‘price’ of 14.5 cents per utile allows us to convert the utilities associated with the non-monetary attributes into WTPs.
|Non-fattening, but not nutritious||6.5%||$0.94|
|Non-fattening, and nutritious (e.g. calcium rich)||19.1%||$2.76|
|Price (per 500 ml bottle)|
|Short shelf life||0%|
|Medium shelf life||7.0%||$1.02|
|Long shelf life||15.3%||$2.22|
|Dull (a bit embarrassing)||0%|
|OK (but not cool)||13.4%||$1.94|
Cluster (market segmentation) analysis
As mentioned earlier, a major strength of 1000minds is that utilities are generated for each individual decision-maker, in contrast to other methods that only produce aggregate data from the group of decision-makers.
Individual-level data enables cluster analysis to be performed (i.e. after exporting to Excel and then using a statistics package) in order to identify ’clusters’ – or ‘market segments’ – of participants with similar preferences (as represented by their utilities).
The schematic below illustrates the main idea behind the ‘k-means clustering method’, which may be briefly explained as follows.
- Imagine there are just 2 attributes, as represented by the x and y axes in the panels below, and each point in the space corresponds to a participant’s utilities (on x and y attributes).
- The k-means clustering algorithm starts by asking the analyst to set the number of potential clusters (referred to as “k-means” – k signifying the number of clusters); in the schematic there are 3 (i.e. k = 3).
- A starting point (x,y co-ordinates) is randomly chosen for each of the 3 yet-to-be-discovered clusters; see Panel A.
- Next, all the individuals in the space are clustered to whichever of the 3 individuals (x,y co-ordinates) they are closest to; see Panel B.
- Then a new representative centre – i.e. mean value – for each of the nascent clusters is calculated for each of the clusters; see Panel C.
- And the process repeats: All the individuals are clustered to whichever of the 3 individuals (x,y co-ordinates) they are closest to, and this keeps repeating until no further changes are possible; see Panel D.
Finally, having identified clusters of utilities (e.g. 3 clusters, as above) , the usual next step is to test the extent to which each cluster is associated with observable socio-demographic characteristics (e.g. age, gender, etc) or other consumer behaviors, in order to define targetable market segments.
Other methods are available – for a discussion of cluster analysis, read the Wikipedia article.
Finding Survey Participants
To be able to explain the results of your survey, you need to be able to explain who answered it. Here are some different ways you can get your survey in front of the right people.
If you know the people already
If you are surveying a group people you already know, e.g. your customers or colleagues, you can load their email addresses into 1000minds to send from there, or you can create a link (URL) for the survey within 1000minds and send the link from your own email system. The latter is recommended as you are likely to get more engagement from an email sent by someone trusted – you!
If you know where they hang out
If you know what your audience looks at, e.g. a Facebook page, Reddit, your customer portal, a train station or a milk carton, you can share a link to your survey there.
If your survey is interesting enough, you might be able to share your survey with a small group of people, and at the end of the survey ask them to share it on social media, by email etc, essentially creating a snowball effect (read the Wikipedia article).
You can use Facebook advertising, Google AdWords etc to create targeted advertisements for your survey. This can be useful if your survey is interesting enough, or if your advertisement offers a reward.
You can procure a sample of participants from a survey panel provider or market research company. This is particularly useful if you need a sample to be demographically representative, for example. Such panels often have information from their panelists that let you target your survey according to their demographics or interests.
One of our preferred panel suppliers is Cint. Cint boasts over 100+ million panellists across 150 countries, providing an online dashboard so that you can price, automate and report on the survey process.
Seminal articles about Conjoint Analysis in the marketing literature include:
- P Green, A Krieger & Y Wind (2001), “Thirty years of conjoint analysis: Reflections and prospects”, Interfaces 31, S56-S73.
- P Green & V Srinivasan (1990), “Conjoint analysis in marketing: New developments with implications for research and practice”, Journal of Marketing 54, 3-19.
- P Green & V Srinivasan (1978), “Conjoint analysis in consumer research: Issues and outlooks”, Journal of Consumer Research 5, 103-23.