> For the complete documentation index, see [llms.txt](https://docs.userlane.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.userlane.com/best-practices/nps-how-to-set-up-surveys-that-produce-usable-results.md). # NPS: how to set up surveys that produce usable results How to set up NPS surveys that produce interpretable scores, and how NPS relates to the CSAT survey that powers the Happiness dimension. Userlane supports two types of satisfaction measurement. The Net Promoter Score (NPS) survey is a manually configured survey that asks end users how likely they are to recommend the application. The customer satisfaction (CSAT) survey is an automated quarterly survey that powers the [Happiness](/intelligence/heart/happiness.md) dimension of HEART[^1]. Both are delivered through the Assistant[^2], but they serve different purposes and follow different rules. ## How it works {% stepper %} {% step %} #### Understand the difference between NPS and CSAT The NPS survey and the CSAT survey coexist but do not overlap.
NPS surveyCSAT survey
PurposeMeasure loyalty and willingness to recommendMeasure application satisfaction
Who configures itThe team, in the portalUserlane, automatically
QuestionCustomizable (though changing it is not recommended)Fixed: "How satisfied are you with this application?"
Scale0 to 101 to 5
Feeds HEARTNoYes (Happiness dimension)
FrequencySet by the team (recommended: leave end date open, auto-reappears after six months)Quarterly, automated. No configuration needed.
The NPS survey is useful for tracking recommendation intent over time. The CSAT survey is the input for the Happiness dimension of HEART. Both can run on the same application without conflict. When both are active, the CSAT survey takes priority in the Assistant. {% endstep %} {% step %} #### Keep the NPS question unchanged The NPS method depends on a standard question: "How likely are you to recommend this application?" Changing the question makes the score impossible to compare over time or with NPS figures reported elsewhere. If the team needs to ask a different question (satisfaction with a specific feature, feedback on a recent change), use a [standard survey](/intelligence/surveys.md) instead of the NPS survey. The NPS survey is for the standard recommendation question only. {% endstep %} {% step %} #### Set up timing and reminders **Leave the end date open.** When no end date is set, the NPS survey runs continuously. After a user responds, the survey disappears for that user and reappears automatically after six months. This produces a steady stream of responses without manual reactivation. **Turn on reminders.** Not every user responds the first time. Automatic reminders re-prompt users who have not responded, increasing the response rate without manual follow-up. **Target with audiences.** Exclude users who joined recently and have not had enough experience to form an opinion. A common approach is to create an audience[^3] that excludes users whose first activity was less than 30 days ago. This prevents new users from skewing the score based on first impressions rather than sustained experience. {% endstep %} {% step %} #### Read the results correctly NPS results are only meaningful if the sample is understood. The most common misreadings come from not understanding how Userlane counts users. **Company users vs. application users.** Company users are all users across all applications in the Userlane instance. Application users are users active on a specific application. Calculate the NPS response rate against application users, not company users. Using the wrong denominator makes the response rate look artificially low. **The 90-day active window.** Userlane considers a user active if their last-seen date falls within the past 90 days. Users who have not visited the application in more than 90 days are not counted as active. If the user base is seasonal or cyclical, the active count will fluctuate and the NPS sample size will change with it. **Admin vs. end-user view.** The portal shows data from the administrator's perspective. If the administrator tests the NPS survey, that response counts. Exclude test responses from analysis, or use a test account that can be filtered out. **Sample size matters.** An NPS score based on 5 responses is not meaningful. Before drawing conclusions or reporting the score, check how many responses the score is based on. If the sample is small, extend the survey period rather than acting on the number. {% endstep %} {% endstepper %} ## How to know it is working Check two things: 1. **Response rate.** A healthy NPS survey should collect responses from a meaningful share of active users over each cycle. If the response rate is very low, check whether reminders are enabled, whether the audience excludes too many users, and whether the survey is being surfaced (check the display rules). 2. **Score trend.** A single NPS score is a snapshot. The trend over multiple cycles is what matters. A declining trend after a major application change suggests the change affected user sentiment. A stable score after launching new Userlane content suggests the content is not disrupting the experience, but it may not be improving satisfaction on its own. {% hint style="warning" %} **Caveat.** NPS measures recommendation intent, not satisfaction. A user can be satisfied with the application but unlikely to recommend it, because recommendation is not relevant to their role. The CSAT survey, which feeds the Happiness dimension of HEART, is a more direct measure of satisfaction. {% endhint %} ## Relevant articles {% content-ref url="/pages/eZyCMoDQCbFnb6Z0c7g7" %} [Surveys](/intelligence/surveys.md) {% endcontent-ref %} {% content-ref url="/pages/ILiw0D1KrrpcJPxLRm7X" %} [Happiness](/intelligence/heart/happiness.md) {% endcontent-ref %} {% content-ref url="/pages/TBF4QYHDMWQghy26u8xI" %} [HEART](/intelligence/heart.md) {% endcontent-ref %} {% content-ref url="/pages/TKBRGmMaapWk0xuF3XwG" %} [Audiences](/administration/audiences.md) {% endcontent-ref %} ## FAQ
Do NPS and CSAT surveys conflict if both are active? No. Both can run on the same application. When both have pending prompts for the same user, the CSAT survey takes priority in the Assistant. The NPS survey appears after the CSAT survey is dismissed or answered.
Can the CSAT survey question or frequency be changed? No. The CSAT survey question, scale, frequency, and translations are fixed. This is by design: a fixed survey keeps Happiness results consistent over time.
How often does the NPS survey reappear for the same user? After a user responds, the NPS survey disappears and reappears after six months. This interval is automatic when the end date is left open. If an end date is set and the survey expires before the six months are up, the user will not be surveyed again unless a new NPS survey is created.
Should the NPS score be reported alongside HEART? They measure different things. The Happiness dimension of HEART is powered by CSAT (satisfaction). NPS measures recommendation intent (loyalty). Both are useful. Report HEART for the overall application health view and the NPS score for the loyalty trend. Do not combine them into one number.
[^1]: HEART measures application health across five dimensions: Happiness, Efficiency, Adoption, Risk, and Task Success. [^2]: The Assistant is the end-user-facing panel where guides, search, announcements, and help resources are accessible inside the application. [^3]: An audience is a reusable group of users defined by rules about their attributes, behavior, or tag interactions. Audiences control who sees which content and can also filter analytics. --- # Agent Instructions This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com. ## Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation by asking a question. Perform an HTTP GET request on the following URL with the `ask` and `goal` query parameters: ``` GET https://docs.userlane.com/best-practices/nps-how-to-set-up-surveys-that-produce-usable-results.md?ask=&goal= ``` `ask` is the immediate question: it should be specific, self-contained, and written in natural language. `goal` is what the user is ultimately trying to achieve, the reason they need the answer. Sharing it helps GitBook give you a better, more relevant answer. A goal is most helpful when it describes the outcome the user wants rather than restating the question. For example, with `ask=how do I create an API token`, a goal like `automate deployments from our CI pipeline` lets GitBook tailor the answer to that use case. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.