Generally, marginal willingness to pay (MWTP) is the indicative 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, in addition to the price they are already paying now). 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. Conjoint studies are well-suited to the calculation of MWTP.
Conjoint.ly offers a straight-forward way to estimate MWTP, which can be useful in situations where you want to get a directional estimate and your study does not include competitor brands, SKUs, or pricing tiers. Mathematically, it is defined as marginal rate of substitution of feature for price:
A negative value of MWTP means that the feature is less preferred by the customer than the baseline. Therefore customers need to have a reduction in price to compensate for the downgrade to the inferior feature.
In order for this feature work, a few conditions need to be met:
There are many potential reasons of cause as to why price-preference relationship is counter-intuitive (i.e. non-linear or positively linear), including:
Another way to calculate marginal willingness to pay is Market Value of Attribute Improvement (MVAI). This concept was developed in 2002 by Elie Ofek and V. “Seenu” Srinivasan. It is defined as:
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||Share of preference|
|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 MVAI. The chart below was created with the use of Conjoint.ly for the brand “Telstra”. It suggests, for example, that:
Importantly, MVAI is not necessarily how much a particular feature is worth to the current buyers 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).
At Conjoint.ly, we do not recommend calculating MWTP for Brand-Specific Conjoint because it will lack statistical robustness, and often managerial usefulness. Instead, we suggest using preference share simulations to understand what price you would need to put for your product to take share from competitors.
However, if you have a sufficiently large sample size (say, 50% more than the sample size recommended by the system), then you are able to use the following calculation for MWTP:
Export "Partworth utilities for each brand" ("Relative performance of levels") for your experiment:
0. You can then calculate the utilities of the other levels within each attribute as differences between reported utilities of those other levels and the reported utility of the baseline level.
Here are also some suggestions for further reading:
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