Should I buy Samsung Galaxy II or HTC Desire HD? Samsung Galaxy looks a bit expensive but it’s lighter than HTC. So how to trade off the possibility that choosing Samsung is better because it is lighter than HTC or choosing HTC is better because Samsung is expensive. In this kind of situation Conjoint (trade-off) analysis has become one of the most widely-used quantitative methods in Marketing Research.
Conjoint Analysis is concerned with understanding how people make choices between products or services or a combination of product and service, so that businesses can design new products or services that better meet customers’ underlying needs.
Rather than directly ask survey respondents what they prefer in a product, or what attributes they find most important, conjoint analysis employs the more realistic context of respondents evaluating potential product profiles. Each profile includes multiple conjoined product features (hence, conjoint analysis).
Conjoint analysis takes attribute and level descriptions of product/services and uses them in interviews by asking people to make a number of choices between different products.
For instance would you choose phone A or phone B?
Phone A Phone B
Weight 200g 120g
Battery life 21 hours 10 hours
Price $70 $90
In practice you can see how difficult some of the choices can be. By asking for enough choices (and with good design to minimise the number of choices you need to ask), the researcher can work out numerically how valuable each of the levels is relative to the others around it – this value is known as the utility of the level.
At the end of the conjoint exercise we can plot the utility for each of the levels on a graph.
In this instance we can see that for this customer, the optimum weight is 80g. 40g is too light and more than 80g is too heavy. In designing a mobile phone for this customer therefore, we can see that there is no benefit in spending development money to bring the weight of the phone below 80g.
However, we can also compare across attributes to see which attributes make have the greatest impact in making a choice. We can therefore say which the most important attribute is and measure importance by taking the relative impact of one attribute compared to the others. For example:
In this dummy example, getting the weight right is more than twice as important as looking at the battery life.
Knowing the values of the utility values of each of the levels, but what we really want to know is how our product/service compares to our competitors and how we can best optimise the value we give to the customer.
To do this we can total up the utility value our product is giving the customer and compare it to the value for the competition (in practice we do this via modelling as we typically look at the choices of 100s of customers at a time).
In the example below, utility values are in brackets. Notice that a lower price has a higher utility (we typically prefer cheaper goods)
Weight 200g (15) 120g (35)
Battery life 21 hrs (15) 10 hrs (10)
Price $70 (25) $90 (15)
Total utility 55 60
In this example we are 5 utility points behind the competition. If we reduced the weight of the phone to 160g we would gain 10 utility points which would mean we would expect to be chosen over the competition. Alternatively, we could to reduce the price a little to have the same impact.
For a business making the choice of what to change or improve thus comes down to understanding the cost impact of making the change balanced against the extra value to the customer. Would you get a better return spending more on development to bring the weight of the phone down, or would it be worth bringing the price down despite the lost profit margin (usually the former!)?