Feature Placement Matrix Calculator
The Feature Placement Matrix Calculator is a free tool developed by Conjoint.ly that allows you to generate a Feature Placement Matrix effortlessly by uploading your own data set. This will allow you to view the distribution of your software features across the four quadrants - premium, add-ons, every tier, and deprioritise within seconds.
How to use the Feature Placement Matrix Calculator?
Follow these steps to get started:
Step 1: Prepare a data set in .csv format
Step 1a: Start the first column with
If your data set does not contain the
participant_id, make sure you insert it as first column to ensure the calculator works.
Step 1b: Include the MaxDiff data and begin each column with
In this example, the starting MaxDiff column for Software Co is Column B
Feature: Unlimited online support.
Step 1c: Include columns
VW: Cheap and
VW: Expensive as the Van Westerndorp columns
The example shows the inclusion of four Van Westendorp columns, the yellow columns are the must-have columns for the calculator to work.
Optional Step 1d: Adding segmentation to the Feature Placement Matrix Calculator
To add segmentation, just add each segment as binary columns. In the example, Software Co added the student segment in Column P and start the column with
Segment: Student. 1 represents students while 0 for non-students.
Step 2: Upload the data set
Upload the csv file at the Feature Placement Calculator and the Feature Placement Matrix will auto refresh and updated according to your data set.
Step 3: The output is now ready!
If you included segments in your data set, you can easily scroll and switch between segments. Conjoint.ly also allows you to export the raw and plot data as csv file.
How the Feature Placement Matrix is computed
The Feature Placement Matrix is generated with the MaxDiff scores on the y-axis and the willingness to pay from the Van Westendorp (VW) analysis on the x-axis. The MaxDiff scores are relatively intuitive and straightforward. However, there are two approaches available to compute the willingness to pay.
The first approach involves asking respondents VW questions per feature. The willingness to pay for each feature is the difference between the feature’s optimal price and average optimal price. This approach provides detailed information on the feature level and requires a small sample size.
The second approach asks respondents VW questions for the whole product. In this approach, the willingness to pay for each feature is computed as the difference between the feature’s optimal software price and average optimal software price but only take into the account the top 20% of respondents with the highest MaxDiff score per feature. This approach involves a simpler survey and focuses on respondents with a high interest in selected features.
Conjoint.ly computes the Feature Placement Matrix using the second approach due to two realistic assumptions:
- The software value is rarely the total value of all features offered. Thus, the second approach helps learn the overall value of the software for respondents and eliminate guesswork.
- The respondents with high interest in selected features are more likely to use the features and hence should weigh more than those with low interest in selected features.
The pros and cons of each approach are summarised below:
VW per feature
- Small sample size (important for B2B)
- Detailed information on the feature level (psychological willingness to pay more or less per feature)
- Transparent calculation
- Lengthy survey
- No differentiation between high-value users (top 20% MD) and the rest
VW per product
- Simpler survey
- We learn the overall value of the software for users
- Focus on respondents with high interest in selected features
- Complicated calculation
- Need to get a large sample size to get meaningful results for MWTP
Get in touch with us if you would like to test your multi-tier pricing structure and get your pricing page validated.