CX Score measures how customers feel about their support experience - across both Fin and human interactions. It gives support leaders and teams full visibility into customer sentiment, without relying on surveys.

In this article, you'll learn about the challenges the CX Score aims to solve, how to access and interpret your scores, and best practices to maximize its value.

Note: The CX Score is currently available to all Fin customers who meet the following criteria:

Have imported conversations from your source platform (e.g. Zendesk or Salesforce) which you can toggle on and off.
Have deployed Fin.

What challenges does the CX Score help solve?

The CX Score is helping support leaders and teams address the following issues:

Surveyed CSAT is incomplete and biased - It captures feedback from a small, self-selecting group, often over representing extreme opinions.
Customers score AI and human agents differently - Customers tend to score AI support more harshly, making it hard to assess AI fairly.
Disengaged customers go unmeasured - Customers who don’t respond to surveys - including those who had a neutral or frustrating experience - are often left out of satisfaction metrics.
There’s no scalable way to measure satisfaction across all AI conversations - Without full coverage, teams struggle to track trends, identify friction points, and improve overall support quality.
Survey design can skew results - Small changes, like survey timing or phrasing, impact scores without support quality having changed.

The CX Score addresses these gaps by scoring every meaningful interaction—without relying on surveys—so support teams can get a fuller, more consistent view of customer sentiment across both Fin and teammate conversations.

Note: Fin needs to be set live to see your CX Score across all conversations.

How CX Score works

The CX Score uses machine learning to score meaningful conversations based on specific factors called CX Score Reasons.

The model analyzes the conversation to identify clear signals—positive or negative—across the following categories:

Answer quality (Fin AI Agent): Measures how clear and accurate the Fin AI Agent’s response was, and if it resolved the issue without contradiction.
Answer quality (Teammate): Measures how clear and accurate a human teammate's response was, and if it resolved the issue without contradiction.
Customer effort: Measures how much work the customer had to do to get help (e.g., repeating themselves or multiple handovers).
Strong emotion: Detects if the customer expressed strong positive or negative feelings, such as joy, gratitude, frustration, or anger.
Product/Service feedback: Captures praise or criticism of the product (features, bugs) or the service provided (delivery, reliability).
Policy feedback: Highlights positive or negative responses to company policies, such as refunds or account rules.

Each reason is evaluated independently. If a clear signal is found, the model assigns a value (positive, negative, high, or low). If there is no strong indication, the attribute is considered neutral.

There is no hidden formula or weighting where one reason influences the score more than another. Instead, the model looks at the combination of non-neutral reasons to derive a holistic CX Score rating from 1 to 5.

Specific criteria for CX Score

A conversation will receive a CX rating once the following conditions are met:

The conversation has been closed.
There is enough conclusive information in the conversation to determine a rating.
The conversation is over messenger or email (not phone).

Note: We recently expanded our criteria to include broader coverage. Short or low-context conversations that previously went unscored may now receive a rating, provided there is a clear signal to evaluate.

To keep scores accurate, the model automatically filters out spam.

How to access your CX Score

Performance report

To see your CX Score, go to Analyze > Performance and you will see the CX Score chart toward the top of the report.

You'll see the percentage of positive customer ratings out of all ratings received for conversations handled exclusively by Fin, without any human agent involvement.

This score reflects how satisfied customers are with Fin's service. Click Drill-in to view the conversations behind Fin’s CX Score, along with a summary that highlights key moments and explains why each score was given.

Topics Explorer

From Analyze > Topics Explorer you can track performance by topic. Each topic includes CX Score so you can see which issues are handled well and which need attention.

Focus where it matters most by identifying high-volume, poor CX Score topics and click on them to see the tree map and line charts broken down by subtopics. This enables you to make targeted improvements to the most impactful subtopics by addressing the root cause of the volume and negative CX.

Note: You must have enough eligible conversations to start seeing topics with CX Scores. Learn more about AI-generated topics and subtopics.

Getting the most from your CX Score

To maximize the value of your CX Score, we recommend the following:

Action	What to check
Track trends and patterns	Are scores improving or declining after changes in support content, workflows, or product updates? Do certain categories (e.g., AI Agent vs. Teammate) perform better over time?
Investigate low-scoring conversations	Are common themes emerging in low-rated interactions? Are customers dissatisfied with specific responses or resolution times?
Explore AI-generated explanations	What specific CX Score reasons (e.g., Answer Quality, Strong Emotion) influenced the rating? Are there common friction points affecting satisfaction?

FAQs

How is the CX Score different from CSAT?

Traditional CSAT surveys offer a narrow and often biased view of customer satisfaction. Response rates are low, feedback tends to reflect only extreme experiences, and AI support is often rated more harshly than human-led conversations.

I don’t agree with some of the CX Scores. What can I do?

Just like with CSAT, if you believe a CX Score isn’t accurate or shouldn’t be counted, you can tag the conversation (e.g., with “exclude_from_cx”). Then, use that tag to filter out those conversations in your reports.

Why can’t I filter by CX rating on some charts in the Custom Report?

"CX Score rating" and "CX Score rating explanation" attributes are only available on metrics that are derived from Conversations dataset. We have also added a new attribute called "CX Score Reasons" which allows you to filter or segment charts to see which reasons are influencing CX Scores.

Understand customer experience at scale with the CX Score