CSAT Surveys & Customer Feedback

Customer satisfaction surveys close the loop on every conversation — asking customers to rate the experience and optionally share feedback. RapiDesq supports CSAT across every channel, rolls up results into per-agent and per-team reporting, and can trigger follow-up workflows when scores are low. This guide covers configuring surveys, choosing rating scales and timing, channel-specific delivery, reporting, and best practices for getting useful, actionable feedback.

Overview

CSAT in RapiDesq is:

• Conversation-triggered. Surveys are sent after a conversation resolves, not at random intervals. Each score is tied to the specific ticket, agent, and interaction so you know what it reflects.
• Channel-appropriate. A chat customer gets an in-widget survey; an email customer gets an email survey; a form-submission customer gets a follow-up email. Same underlying data, different delivery to match the channel.
• Configurable per team. Different teams can use different rating scales, different timing, and different thresholds for what counts as "bad" and triggers follow-up.
• Reportable across every dimension. CSAT rolls up into the CSAT Summary report and is available as a breakdown on most other reports — agent performance, team summary, channel performance, and custom-field breakdowns like region or subscription tier.

Configuring Surveys

Navigate to Admin > CSAT & Feedback to configure surveys. You can create multiple survey configurations and apply them to different teams, channels, or ticket types.

Rating scales

Three rating scales are supported:

• Thumbs up / thumbs down — binary rating. Fast for the customer, clearest signal, hardest to extract nuance from. Good default for chat where speed matters.
• Five-star scale — familiar and expressive. The industry standard for most support surveys. Good balance between customer effort and signal quality.
• Numeric scale (0–10) — the basis for NPS-style questions. More granularity, slightly more customer effort. Useful if you want to track Net Promoter Score alongside transactional CSAT.

You can use different scales for different teams if their contexts warrant it, but the CSAT Summary report normalizes across scales for cross-team comparison.

Comment collection

After the rating, customers can optionally leave a comment. Comment collection can be:

• Always offered — every respondent sees a "tell us more" field after rating.
• Offered on negative ratings only — customers who rated poorly see the field. Useful for understanding what went wrong without asking happy customers to type.
• Required for specific ratings — commenting is mandatory for thumbs-down or one-star ratings. Gives you the "why" automatically on every negative response.

Survey timing

Decide when surveys are delivered relative to the conversation:

• Immediately on resolution — the most common choice for chat. The customer's memory of the interaction is fresh.
• Delayed (configurable minutes or hours) — useful when the "resolution" is something the customer needs to verify before rating (e.g., a configuration change they need to test).
• On close rather than resolution — for tickets that have a meaningful gap between resolved and closed, send the survey at close.

Channel-Specific Delivery

Chat

Chat surveys appear inline within the widget when the conversation ends — the customer sees a rating prompt in the same conversation thread they've been using. This is the highest-response-rate channel because the customer is already engaged.

Email

Email surveys are sent as a separate email after the conversation resolves, with a clickable rating that records the score without requiring the customer to open a web page. Customers who click a rating can optionally add a comment in the web view that loads.

Web forms

Web form submissions receive an email survey after the resulting ticket is resolved, using the email address captured in the form.

Bot-handled conversations

When the AI bot resolves a conversation without human escalation, the survey asks about the bot experience. The score is attributed to the bot, not to any agent. This feeds the bot performance metrics in AI analytics and is a key signal for whether the bot is actually helping or just getting in the way.

Follow-Up Workflows for Negative Feedback

A low CSAT score is a signal that something went wrong. Configure automatic follow-up workflows to catch these cases before they become escalations:

• Notify the team lead or supervisor when a score falls below a threshold, so they can review the conversation and decide whether to reach out.
• Automatically reopen the ticket if a survey score indicates the customer wasn't actually satisfied. The ticket routes back to a team for follow-up.
• Send a personalized apology email using a template that references the specific conversation.
• Tag the ticket for QA review so it surfaces in weekly quality reviews.

Negative-feedback follow-up is optional per survey configuration, but it's one of the highest-leverage uses of CSAT data. Customers who leave negative feedback and then receive a thoughtful follow-up often become more loyal than customers who never had a problem in the first place.

Reporting on CSAT

CSAT data surfaces in several places:

• CSAT Summary report — average score, distribution, trend over time, comment sentiment rollup.
• Agent Performance report — per-agent CSAT alongside response times and volume.
• Team Summary report — per-team CSAT with cross-team comparison.
• AI Analytics — CSAT specifically for bot-handled conversations.
• Custom field breakdowns — CSAT by subscription tier, region, product line, or any other custom field.

For each breakdown you can filter to specific time ranges, survey types, and channels. See Reporting & Dashboards for how to build custom dashboards combining CSAT with other metrics.

Response Rates

Typical response rates in support CSAT vary by channel:

• Chat in-widget surveys: 30–60% response rate
• Email surveys: 5–15% response rate
• Bot-conversation surveys: 40–70% response rate (bot conversations are shorter and customers are already engaged)

Don't obsess over getting every customer to rate; focus on making it easy and quick for those who want to. A representative sample of motivated responses is more useful than forced full-coverage that trains customers to click whatever gets them past the prompt.

Best Practices

• Start simple. A thumbs up/down survey with an optional comment is often all you need. You can add granularity later if the binary signal proves insufficient.
• Make negative feedback actionable. Configure follow-up workflows for low scores from day one. Unacted-on negative CSAT is worse than no CSAT — you collected the signal but did nothing with it.
• Review comments weekly. The rating is the metric; the comments are the diagnostic. Read them, look for patterns, share the ones that teach something with the team.
• Use CSAT trends, not snapshots. Last week's average isn't as informative as the trend over the past quarter. Customers' expectations change; so should your baseline.
• Share positive feedback with agents. CSAT data is often used for accountability, which makes it feel negative. Balance by surfacing the specific praise customers leave for agents who did well. It's the cheapest recognition program you'll ever run.
• Don't gamify. Pressuring agents to hit a specific CSAT score is how you get agents gaming the system — prematurely closing tickets, discouraging survey responses, or pleading with customers for good ratings. Use CSAT as a signal, not a target.

Related Topics

• Reporting & Dashboards — where CSAT data surfaces in reports and custom dashboards.
• AI Bot Configuration — CSAT specifically for bot-handled conversations.
• Ticket Management — how CSAT ties to ticket resolution and reopening workflows.