Feature Placement Matrix Calculator

Try out and generate a Feature Placement Matrix within seconds by uploading your own data set. Let's get started!
FPM
Features and claims
Feature and Pricing Suite for SaaS

Feature Placement Matrix

Place product features into appropriate tiers according to users' perceived importance and willingness to pay.

Feature Placement Matrix Calculator

The Feature Placement Matrix Calculator is a free tool developed by Conjointly 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 participant_id:

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 Feature:

In this example, the starting MaxDiff column for Software Co is Column B Feature: Unlimited online support.

Start the first column with participant_id:
Include MaxDiff columns and start each with Feature:

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.

Include Van Westendorp columns with column VW: Expensive and VW: Cheap
Add segmentation into the Feature Placement Calculator

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.

Upload the 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. Conjointly also allows you to export the raw and plot data as csv file.

Scroll and switch between segments to view how the matrix changes

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.

Conjointly 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

PROs

  • Small sample size (important for B2B)
  • Detailed information on the feature level (psychological willingness to pay more or less per feature)
  • Transparent calculation

CONs

  • Lengthy survey
  • No differentiation between high-value users (top 20% MD) and the rest

Get in touch with us if you would like to test your multi-tier pricing structure and get your pricing page validated.


Written on 12 January 2022 by:
Harrigan Davenport image
Harrigan Davenport
Market Researcher

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