How to estimate willingness to pay (WTP)
First we’ll look at the generic steps to calculate WTP for a new feature (i.e. an attribute level vs another attribute level) and then we’ll look at a specific case from an actual survey of the UHT milk (i.e. long life milk) market. Finally we look at some of the issues with WTP.
Generic steps to calculate WTP
Let’s say you want to add a new feature and you want WTP for that feature. You run the simulation to get baseline share. You then introduce the feature in the simulator and run again. You note the new share. You note down current price, and then adjust price incrementally, each time running the simulation again until your share is approximately equal.
The difference between the new price and the current price reveals WTP. It is important to remember, that you should be accounting for external effects (such as distribution / awareness). Preferably use the relevant set indicator approach, or alternatively calibration. Both options are available in Metrisim.
Two WTP case studies from the UHT milk market
Calculating WTP for a new UHT milk bioplastic cap
Kinder Green wants to know what the WTP for a bioplastic lid instead of a normal plastic lid. It aims to introduce with normal UHT cow milk.
Starting with a current price of 12, the the share is 6.08%. Introducing the bioplastic, the share rises to 6.93. After some trial and error, it is found that the price that returns share to 6.08% is 13.7. WTP is calculated by subtracting 12 from 13.7, yielding a WTP of 1.7 currency units.
Calculate WTP for cow-free milk
Kinder Green wants to know what the WTP for cow-free milk (genetically altered flora produce milk in a bioreactor) will be.
As before, starting with a current price of 12, the the share is 6.08%. Introducing the cow-free (flora) in place of normal cow milk, the share actually drops to 4.98, indicating that consumer’s would - on average - want a discount. After some trial and error, it was found that the nearest we can get to the original share of 6.08% is 6.20% at a price of around 10.71. So the WTP is calculated by subtractting 12 from 10.71, yielding a WTP of -1.29.
Relevant set indicator important when running WTP
It is worth keeping in mind that the WTP is just an average, since each consumer is different and reacted in the simulation in a different way. The new consumers attracted by the new feature at a higher price might be different consumers than the ones you already had, at least in part. They may belong to another segment.
Therefore it is important to have the right products included in each consumer’s relevant set, since there may be correlations between awareness and attribute level preference (as reflected by the partworths). A relevant set indicator is set to 1 only for products the consumer is aware of and that are available to them otherwise 0. Read more about this in the setup files help article in relation to the set.csv file.
In the milk example, the indicators for Kinder Green are randomly assigned to meet at target percentage of 38%, since it does not exist on the market and is a new concept. Ideally though, you would want to collect this from consumers during a survey, and of course this is possible for existing products.
Besides relevant set indicators, the none percent should also be calibrated via the targets.csv to improve accuracy.