Generally, marginal willingness to pay (MWTP) is the amount of money your customers are willing to pay for a particular feature of your product (i.e., how much your customers are ready to pay for an upgrade from feature A to feature B). Conjoint studies are well-suited to the calculation of MWTP.
There are multiple ways to conceptualise and calculate MWTP. Conjoint.ly uses the idea called “Market Value of Attribute Improvement” (MVAI), which was developed in 2002 by Elie Ofek and V. “Seenu” Srinivasan. Market Value of Attribute Improvement is defined as:
The word ‘marginal’ refers to the fact that MWTP is always relative to a baseline, which is your baseline product (with various baseline features specified) placed in a market with other competitors.
Let’s use an example of mobile phone plans. We will consider three attributes (mobile data, international minutes and SMS), each with a different number of levels (in addition to the price attribute, which is required for MWTP to work). First, we need to consider the various offerings that are present on the market. The table below presents a hypothetical set of competitors.
|Brand||Monthly fee||Mobile data inclusion||International calls inclusion||SMS inclusion||Predicted market share|
|Telstra||$49.00||500MB||0 min||300 messages||30%|
|Vodafone||$39.00||10GB||90 min||Unlimited text||20%|
|Optus||$45.00||Unlimited||300 min||Unlimited text||25%|
|None of the above||25%|
Once we know who the competitors are, we can analyse MWTP. The chart below was automatically generated by Conjoint.ly for the brand “Telstra”. It suggests, for example, that:
Importantly, MWTP is not necessarily how much a particular feature is worth to the current customers of the brand, but rather how much is it worth to the whole market (because the brand may lose some current customers but gain others who might be more willing to pay for the feature).
In order for this feature work, a few conditions need to be met:
Please note that MWTP is currently included in generic experiments. If you require to calculate it in a brand-specific experiment, please contact us for a quote.