Support Response Quality Grader
Paste any customer support response and get instant AI-powered feedback on empathy, clarity, professionalism, and more. Free, no signup required.
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Context
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How we score your response
We grade across 6 dimensions, each weighted by how much it actually affects whether a customer walks away satisfied.
💛
Empathy 20% weight
Acknowledges the customer's feelings and situation before jumping to solutions
✨
Clarity 20% weight
Uses clear, jargon-free language that any customer can understand
🎯
Solution Quality 20% weight
Provides a concrete, actionable solution or clear next steps
👔
Professionalism 15% weight
Maintains a professional yet warm tone throughout
🤝
Personalization 15% weight
Tailors the response to the specific customer rather than sounding generic
📋
Completeness 10% weight
Addresses all parts of the customer's question without leaving gaps
How to write better support responses
- Start with empathy, not solutions. Acknowledge what the customer is feeling before you fix anything. "I can see how frustrating this is" does more work than you'd think, especially before diving into the fix.
- Use their name. Two seconds. Immediately less robotic. People genuinely notice.
- Mirror their language, not yours. If they said "my dashboard is broken," don't reply with "the analytics interface is experiencing a rendering anomaly." Nobody talks like that. Use their words.
- Vague timelines kill trust. "I'll look into this" means nothing. "I've escalated this to engineering and you'll hear back within 24 hours" is something they can hold onto.
- Answer every question they asked. If they asked three things and you answered one, they noticed. Even a "great question - let me find out and follow up" is better than silence on the others.
- Cut the internal jargon. "Tier 2 escalation" and "known issue in the backlog" mean nothing to your customer. Everything needs translating into plain language.
- End with an open door. "Let me know if there's anything else" beats "This ticket is now resolved" every single time.
- Read it out loud before sending. If it sounds stiff or cold when you say it, it reads that way too. Your ear catches what your eyes miss.
Frequently asked questions
It takes your customer support response and grades it across 6 dimensions: empathy, clarity, solution quality, professionalism, personalization, and completeness. You'll get a score for each one, an overall letter grade, specific suggestions for what to fix, and an AI-rewritten version that shows what a 90+ response actually looks like.
Each of the 6 dimensions gets scored from 0-100. The overall score is a weighted average - empathy (20%), clarity (20%), solution quality (20%), professionalism (15%), personalization (15%), and completeness (10%). The weights aren't arbitrary: they reflect how much each factor actually moves the needle on customer satisfaction.
Think of it like school grades. 90+ is excellent, 80-89 is strong, 70-79 is decent but improvable, 60-69 is mediocre, and below 60 needs real work. Here's the honest part: most real-world support responses score between 50 and 75. If your team's consistently hitting above 80, that's genuinely good.
Nope. Your response gets sent to the AI for analysis and that's it - nothing's stored, logged, or used for training. The whole thing happens in real-time and the data's gone after you see your results. No account, no signup, no catch.
Different situations call for different standards. A complaint response should be dripping with empathy. A technical troubleshooting reply needs precision and clarity above all else. Setting the context tells the AI what kind of interaction it's looking at so it can grade accordingly. A refund response, for example, gets scored much more heavily on empathy and professionalism than a how-to question would.
Absolutely - that's one of the best uses for it. A lot of support leads grade 10-15 responses from their team, look at where the scores cluster, and use that to build focused training. The rewrite suggestions are great for coaching too, because they show rather than just tell.
Because it actually changes outcomes. Customers who feel heard are more likely to stick around, even when their issue doesn't get fully resolved. A Salesforce study found 73% of customers expect companies to understand their needs - not just their problem, but their needs. Empathy isn't soft. It's what makes everything else land better.
Pretty good, but not perfect. It's calibrated against CX research and support best practices from companies known for doing this well. It's strict - it'll catch tone issues and missing acknowledgments that humans often let slide. What it can't do is verify whether your technical information is actually correct. Use it as a quality lens, not a fact-checker.
Start with whatever dimension scored lowest and work through its suggestions one by one. The fastest wins are usually: open with empathy before anything else, use the customer's name, ditch the jargon, give specific next steps with actual timelines, and make sure you've answered every question they asked. The AI rewrite at the bottom is worth reading - it shows the changes in context.
Yes. A casual, conversational response can score just as high as a formal one - what matters isn't the tone, it's whether you're hitting the quality markers: empathy, clarity, specificity, completeness. The tool won't penalize you for being friendly or brief, as long as the substance is there.
It can analyze responses in other languages, but English is where it's most reliable. In other languages the scoring gets less nuanced - especially around tone and formality, which vary a lot by culture and don't always translate cleanly.
Yes, and honestly you should. Templates usually score low on personalization and empathy because they're built to be generic - that's the whole point of them. But the suggestions will show you where to add name placeholders, empathy hooks, and context-aware phrasing so the template doesn't sound like a template.
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