# How to interpret marginal willingness to pay (MWTP)

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:

$$\textrm{MWTP}_j = V_j / V_p$$

where:

• MWTPj is the standard marginal willingness to pay of feature j,
• Vj is the value (mean coefficient) of feature j,
• Vp is the value (mean coefficient) of 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.

## Requirements for MWTP

In order for this feature work, a few conditions need to be met:

• There must be a price attribute in your study.
• None of the values of price should be zero (because zero is a special price).
• If there are more than two price levels, they should have approximately equal gaps between them.
• Prices should be either all positive or all negative.
• The relationship between price and preference must be linear (i.e. preference for lower price must be higher, or vice versa) as shown below:

There are many potential reasons of cause as to why price-preference relationship is counter-intuitive (i.e. non-linear or positively linear), including:

• Small sample size: If the number of survey respondents is below the recommended number, this will likely affect the results.
• Price-quality inference: Certain groups of respondents prefer higher prices, resulting in a non-linear relationship between price and preference.

## Market Value of Attribute Improvement (MVAI)

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:

• the amount of money by which you can increase the price of your product,
• upgrading from one feature to another feature,
• but keeping share of customers’ preference for your product constant.

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% Total 100% 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: • If Telstra upgrades from 500MB to 1GB of data inclusion, it can charge up to$17 extra for the plan, keeping its share of preference constant.
• If Telstra includes 300 minutes of international calls, it can charge up to ~$2 extra for the plan (again, while keeping its share of preference constant). • If Telstra switches to unlimited text, it can increase the price by$14 (again, while keeping its share of preference constant).

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).

## MWTP for Brand-Specific Conjoint experiments

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:

1. Export "Partworth utilities for each brand" ("Relative performance of levels") for your experiment:

2. Calculate approximate utility per dollar for each brand by dividing the range of utilities for price by the range of prices.
3. Choose baseline levels for each attribute and set their utilities to be 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.
4. Calculate MWTP using the standard formula above.

To see an illustration of this process, please refer to the example calculation Excel spreadsheet. However, again, we strongly recommend that you should use preference share simulations instead.

Here are also some suggestions for further reading:

Would you like to see more example conjoint reports? to explore example reports.