Measuring customer satisfaction with questionnaires

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Best practices for measuring Customer Satisfaction Index / This is the approved revision of this page, as well as being the most recent.
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Measuring customer satisfaction with questionnaires

Description How to use questionnaires to measure and track customer satisfaction
Version as of 8.6
Application Pega Platform
Capability/Industry Area Case Management



Best practices for measuring Customer Satisfaction Index
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Pega questionnaires

In Pega Platform version 8.6 and higher, you can create and manage questionnaires in a low-code way and user-friendly way in App Studio. In previous releases, Pega Platform referred to questionnaires as surveys. You can use the questionnaires capability to enable customer feedback on your first Minimum Lovable Product (MLP).

Using questionnaires to track customer satisfaction

Implementing the first release of your application - the MLP - can be a daunting process as it involves multiple stakeholders, competing priorities, and the team is still discovering how to make the release more lovable. In this process, you may ask yourself the following questions:

  • Was the MLP really loveable?
  • How do you measure how loveable the MLP was?
  • Is the MLP improving with time?
  • Which of your applications need extra attention?

This article describes three key ways in which you can use questionnaires to track and gather quantifiable metrics on a release and set up a framework for gathering insights and suggestions from customers about features that they would like to have in the future MLPs.

Before you begin

  1. Create and configure a questionnaire. For more information, see Designing questionnaires.
  2. Adopt the MLP mindset. Avoid asking too many questions. Keep the questionnaire simple. Plan the first increment efficiently so that it does not divert development time from MLP1.

Image displaying the configuration of questionnaires and the run time experience for a user

Running a questionnaire in a case

The options for running a questionnaire in a case are split into different triggering mechanisms, based on their complexity. Select the option that is best-suited to your use case and that allows you to get as many responses from customers as possible without spamming them.

Triggering option Description Complexity
On-demand Display a link to customers to provide feedback at a time that suits them. The link opens a questionnaire case directly. Low
Case-based Insert a questionnaire into your cases. This option is can be configured for more than one case type, and it can be configured to prompt a questionnaire in the beginning, middle, or end of a case. This option is best suited for low-volume and/or long processes. Low-Medium
Interval-based Similar to the case-level triggering option but with safeguards to avoid spamming customers with feedback requests. This option is best suited for short high-volume processes. This option allows further configuration for enhanced tracking and conditions. However, for the first MLP, it is recommended to keep the design simple to focus development effort on the MLP. Medium

On-demand triggering (option 1)

This options allows the user to select the Create navigation menu item with your questionnaire or click the create case action on a button:


Case-based triggering (option 2)

Add a questionnaire to the parent case lifecycle. In this scenario, the system runs the questionnaire at a selected point of the case lifecycle depending on when you want to collate the information (for example, prompting after case creation or after case resolution). This option has two variants: you can either make questionnaire completion mandatory to resolve the parent case that includes the Questionnaire step, or spin off a new, questionnaire case, which you can relate to the parent case and enforce completion before the main process is resolved.



Interval-based triggering (option 3)

  1. Create a sub-process to conditionally run a questionnaire based on a decision shape. For more information, see Branching the action flow with the Decision shape and/or Adding decisions to processes. When you use a sub-process, you do not have to reconfigure the case type if you move or delete the trigger point.
  2. Create a tracking mechanism that helps determine when to run a questionnaire. For more information, see Creating a data page and Report definition rule form. The simplest way of configuring such a mechanism is to use a report definition that checks whether:
    • Another questionnaire has been created by the current user in the past.
    • Another questionnaire has been resolved by the current user in the past.
  3. Configure the decision shape from step 1 to use the tracking mechanism from step 2. For more information, see Creating a When rule and Defining conditions for a When rule. The results of the tracking mechanism should be 0 before the process runs the questionnaire.



Best practices for questionnaire questions

  • Keep the questionnaire questions and trigger mechanisms simple for MLP 1. Avoid complexity to concentrate your team's development efforts on must-have features.
  • Ask short and simple questions.
  • Avoid lengthy explanations of what is required.
  • Use rating scales (for example, by using the slider question type) as they facilitate giving responses by customers and also enable metrics tracking.
  • Ask only a few questions to maximize responses.

Providing a richer UI using Questionnaires

Questionnaires can be extended to provide more options than a standard slide or radio button control. To learn how to extend your questionnaire with more features, see the Using advanced questionnaire features in Dev Studio article.

Note: The procedure in the referenced article is applicable to applications that you build with the section-based UI. For more information, see Feature limitations in Cosmos React.

Related information

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