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Pre/Post-Chat Survey Template

Ready-to-deploy survey copy and logic for the two moments that bookend every live-chat conversation: the pre-chat form that routes and contextualizes, and the post-chat survey that measures satisfaction and effort. Use it to collect just enough up front without adding friction, and to capture CSAT/CES afterward in a way visitors actually complete.

  • The two surveys, two jobs
  • Pre-chat form fields (keep it minimal)
  • Post-chat survey questions
  • Survey design rules that protect response rate
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Spotsaas · 2026
Pre/Post-Chat Survey Template
The two surveys, two jobs
Pre-chat form fields (keep it minimal)
Post-chat survey questions
Survey design rules that protect response rate
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What it is

The Pre/Post-Chat Survey Template is ready-to-deploy copy and logic for the two surveys that bookend every live-chat conversation: the pre-chat form that routes and contextualizes, and the post-chat survey that measures satisfaction and effort. The two have different jobs. The pre-chat survey exists to route and prepare — gather the minimum needed to send the visitor to the right agent and arm that agent with context. The post-chat survey exists to measure — primarily CSAT (satisfaction) and CES (customer effort), plus one open comment for the 'why' behind the score. The template gives you the exact fields, questions, scales, and the design rules that keep both from killing chat-start and response rates.

On the pre-chat side, the template is deliberately minimal: name (used in the greeting), email or phone (so an async follow-up is possible if the chat drops), a 'what can we help with?' dropdown that drives routing, an optional account/order number shown only for billing or account topics, an optional one-line description so the agent opens with context instead of 'how can I help?', and a consent line linking the privacy policy. The guiding rule is brutal — only ask what changes the routing or saves the agent a question, because every extra field is friction that lowers chat-start rates.

On the post-chat side, the template provides five questions with their types and scales: a CSAT rating (1-5 stars or emoji), a CES rating (very hard to very easy), a single-choice resolution question (Yes/Partly/No), an optional transactional NPS (0-10), and an open free-text comment. It pairs these with survey-design rules that protect response rate — show CSAT as a single one-tap rating in the chat window, fire the survey the instant the chat closes, make everything except the first rating optional — and a 'from survey to action' workflow that routes detractors into a recovery queue and feeds results back into QA and coaching.

What it's used for

Chat surveys exist to do two things well without doing either badly: prepare the conversation up front and measure it afterward. The template is used to get the balance right — enough signal, minimal friction. Specifically, teams use it to:

  • Deploy a minimal pre-chat form that routes by topic and arms the agent with name, contact, and a one-line description, without adding friction that lowers chat-start rates.
  • Capture an async follow-up path (email or phone) up front so a dropped chat can still be answered rather than lost.
  • Measure CSAT and CES after every chat with one-tap ratings that fire the instant the conversation closes, while the experience is fresh.
  • Add a self-reported resolution question (Yes/Partly/No) so you can track first-contact resolution alongside satisfaction.
  • Protect response rate with design rules — single one-tap rating in the chat window, everything past the first rating optional, no redirect to an external page.
  • Route low-score chats into a recovery queue within the hour, reading the open comment first so the follow-up isn't generic.
  • Feed survey results into QA and coaching by correlating low CSAT with scorecard results and spotting macros or topics that consistently underperform.

Who uses it

Surveys touch the visitor, the agent, and everyone who acts on the scores afterward, so several roles shape and consume them:

Support / CX managersThey own CSAT and CES as headline metrics, set the survey design rules, and decide how detractors are recovered and how scores feed coaching.
Chat agentsThey benefit from the pre-chat context (topic, description, account number) so they open with continuity, and they're affected by the post-chat scores tied to their chats.
Routing / chat adminsThey wire the pre-chat dropdown to routing rules and configure conditional fields (account number only for billing/account topics) in the platform.
QA and analytics leadsThey slice survey results by agent, queue, and topic, separate abandons from completed chats, and correlate CSAT with the QA scorecard.
Privacy / compliance ownersThey review the consent line and the handling of contact data so the pre-chat form meets privacy requirements without adding a forced checkbox where one isn't needed.

Context & good to know

The two surveys fail in opposite ways, which is why the template treats them separately. A pre-chat form fails by being greedy — every extra field is friction that lowers the chat-start rate, so the discipline is to ask only what changes the routing or saves the agent a question. A post-chat survey fails by being long, late, or mandatory — response rates collapse when it doesn't fire immediately, when it redirects to an external page, or when it blocks the visitor from leaving. Designing each to its own job is what keeps both useful.

Response rate is a design outcome, not a wish. The template's rules — show CSAT as a single one-tap rating right in the chat window, fire it the moment the chat closes, make everything except the first rating optional, and only open the comment box on low scores — exist because each protects the rate. A realistic post-chat survey response rate is 15-30%, and chasing 100% by making the survey mandatory backfires: it biases the sample toward the annoyed and degrades the very data you wanted.

Measuring satisfaction without measuring effort tells half the story. CSAT captures whether the visitor was happy; CES captures how hard they had to work to get there. A chat can score high on CSAT but high on effort too — the visitor got what they needed but it took too long or too many steps — and that combination still signals a process problem worth fixing. Storing CES and self-reported resolution alongside CSAT, as the template does, gives you the diagnostic depth a single satisfaction number can't.

The survey is only valuable if it drives action, so the template ends with a closed loop. Tag every response with agent, queue, topic, and chat ID so scores can be sliced; separate involuntary closes (the visitor left) from completed chats so abandons don't pollute CSAT; route any 1-2 star or 'No' resolution into a recovery queue within the hour, reading the open comment before reaching out; and correlate low CSAT with QA scorecard results to tell a bad call from a bad day. A fast, personal follow-up on a one-star chat recovers more loyalty than any volume of survey responses.

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FAQ

Questions, answered

What is the difference between a pre-chat and post-chat survey?

A pre-chat survey routes and prepares — it collects the minimum needed to send the visitor to the right agent and give that agent context. A post-chat survey measures — primarily CSAT and CES, plus an optional open comment. They have opposite design priorities: the pre-chat form must stay minimal to avoid friction, while the post-chat survey must fire instantly and be answerable in one tap.

What fields should a pre-chat form include?

Keep it to the essentials: name (for the greeting), email or phone (for async follow-up if the chat drops), and a 'what can we help with?' dropdown that drives routing. Add an optional account/order number shown only for billing or account topics, an optional one-line description, and a consent line. The rule is to ask only what changes routing or saves the agent a question.

What should a post-chat survey measure?

Primarily CSAT (a 1-5 star or emoji satisfaction rating) and CES (how easy it was to get the issue handled), plus a self-reported resolution question (Yes/Partly/No), an optional transactional NPS (0-10), and an open free-text comment for the 'why' behind the score. CSAT and CES together tell you both whether the visitor was happy and how hard they had to work.

What's a realistic chat survey response rate?

About 15-30% for a well-designed post-chat survey. Chasing 100% by making the survey mandatory backfires — it biases your sample toward the annoyed and degrades data quality. Focus instead on response quality and on fast recovery for detractors rather than maximizing raw response count.

How do I keep the survey from hurting response rate?

Show CSAT as a single one-tap rating inside the chat window rather than redirecting to an external page, fire it the instant the chat closes while the experience is fresh, make everything past the first rating optional so one tap counts, and only open the comment box on low scores. Localizing the survey to the visitor's language also keeps scores from skewing by region.

What's the difference between CSAT and CES?

CSAT measures satisfaction — how happy the visitor was with the chat. CES measures effort — how hard they had to work to get their issue handled. A chat can score high on CSAT but also high on effort, which still signals a process problem. Tracking both gives you a fuller picture than satisfaction alone.

What should happen when a chat gets a low score?

Route any 1-2 star CSAT or 'No' resolution into a recovery queue within the hour, read the open comment before reaching back out so the follow-up isn't generic, and log the root cause (agent, product, or policy) so patterns surface. A fast, personal follow-up on a one-star chat recovers more loyalty than any number of survey responses.

Should the consent line be a required checkbox?

Not necessarily. The template uses a consent line — 'By starting a chat you agree to our privacy policy' with a link — as compliance copy rather than a forced checkbox, unless your jurisdiction or policy specifically requires an explicit opt-in. Keeping it lightweight avoids adding friction to the chat-start while still meeting the disclosure requirement.

How do I keep abandoned chats from skewing my CSAT?

Separate involuntary closes — where the visitor simply left — from completed chats so abandons don't pollute the satisfaction data. Tag every response with agent, queue, topic, and chat ID so you can slice scores cleanly, and analyze completed-chat CSAT distinct from drop-offs.

How does survey data feed coaching?

Correlate low CSAT with QA scorecard results to separate a genuinely bad chat from a one-off bad day, spot macros or topics that consistently underperform and rewrite them, and report CSAT, CES, and resolution by agent and by topic weekly so coaching targets the right thing rather than just the loudest complaint.

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