A 1000minds conjoint analysis survey – involving potentially 1000s of participants – lets you capture each individual’s preferences with respect to a particular product.
This page discusses the wide range of outputs available from 1000minds directly or with a little further analysis – via the simple example of flavoured milk drinks (generalisable to most other goods and services too):
- Part-worth utilities
- Attribute rankings
- Radar chart
- Attribute relative importances
- Rankings of entered concepts
- Market shares
- Market simulations (“What ifs?”)
- Rankings of all possible concepts
- Willingness-to-pay (WTP)
- Cluster (market segmentation) analysis
Here are the part-worth utilities for each of five exemplar participants in the above-mentioned illustrative conjoint analysis survey, relating to the attributes of flavoured milk drinks, as well as summary statistics (median, mean, standard deviation).
If you’d like to, you can experience this survey yourself at www.1000minds.com/go/CAdemo.
As well as the part-worth utilities reported above, here are the normalised attribute weights – an alternative, though equivalent, representation of the mean utility values. This equivalence is easily confirmed by multiplying the weights and single attribute scores to reproduce the part-worth utilities.
Consistent with the data above, here are the rankings of the attributes for each of five participants.
This chart – also known as a ‘star’ or ‘spider web’ 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 concepts
Although part-worth utilities (as above) are interesting, there is also enormous power in applying each individual’s preferences to new product concepts and also to competitors’ offerings to predict the likely market share or market shift that might occur. This can help answer 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 concepts for flavoured milk drinks.
By applying the five participants’ utilities, it can be seen 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. In 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 concepts – 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 concept 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 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 concepts 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 concepts and the five participants’ preferences), at the expense of the market shares of Products C and G.
Rankings of all possible concepts
In addition to rankings of particular entered concepts (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 concepts; here are the first 20:
Based on the 1000minds outputs above, the following analyzes 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 part-worth 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 (part-worth 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 part-worth 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
A major strength of 1000minds is that part-worth 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 part-worth 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 part-worth 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 part-worth 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 behaviours, in order to define targetable market segments.
Other methods are available – for a discussion of cluster analysis, see https://en.wikipedia.org/wiki/Cluster_analysis.
In conclusion, hopefully, the examples above have helped to illuminate the richness of the outputs from a 1000minds conjoint analysis survey.
If you already have a 1000minds user account, the following detailed guide might be useful.