# Hear what customers keep telling you, theme by theme. Feedback analyst is an AI customer feedback analysis agent. It reads every comment in your feedback table, labels each one positive, neutral or negative, groups them into themes ranked by how often they come up, counts each theme month by month, and posts the themes, quotes and three fixes to Slack. You build it in Arclyx from one sentence. ## Why is customer feedback hard to act on? Because there's too much of it in too many places, and reading it properly takes days. Survey answers, reviews and support comments pile up in exports nobody reads end to end. Teams act on the loudest or latest comment, and miss the theme that has been growing for months. Without it: - Feedback sits in an export nobody reads end to end. - The loudest comment sets the priority. - A growing complaint shows up a quarter late. - Churn signals hide in support threads. With Feedback analyst: - Every comment read, every month. - Themes ranked by how many people raise them. - Monthly counts show what's getting worse. - Churn risks flagged with their evidence. ## What does AI customer feedback analysis find? Four things on every run, all from your own feedback table and counted with queries. ### Themes Comments grouped by what they're about, ranked by how many people raise each one. For example: Onboarding, Billing, Missing features. ### Sentiment Each comment labelled positive, neutral or negative by reading it, not by a keyword score. For example: Positive, Neutral, Negative. ### Trends Month-by-month counts for each theme, so you can see what's improving, getting worse or flat. For example: Improving, Worsening, Flat. ### Churn risks and fixes Churn risks only where there's evidence, two quotes per theme in customers' own words, and three fixes tied to the themes. For example: Cancellations, Switching, Three fixes. ## How does AI customer feedback analysis work? Every run follows the same five steps, then posts one message. Each step uses one tool, and only the last one writes anything. 1. **Finds your feedback** (Tables list, reads): The table of survey answers, reviews or support comments. 2. **Reads the columns** (Tables schema, reads): Comment, date, source and plan, whatever your export has. 3. **Reads every comment** (Tables query, reads): Labels each one positive, neutral or negative, and gives it a theme. 4. **Counts and checks** (Tables query, reads): Theme counts and monthly trends from queries, checked before sending. 5. **Posts the themes** (Slack, posts): Ranked themes, two quotes each, churn risks and three fixes. When it runs: - Schedule: The first of each month at 9:00, before the product review - @mention: @feedback-analyst what are people saying about onboarding? - Webhook: Run when a survey closes and its export is uploaded ## From one sentence to themes in Slack. Describe the job, let Arclyx draft the agent, test it once on your real feedback, then read what lands in #product. Every count comes from a query. Example output in #product, "Feedback analyst · September" (placeholder companies): - Customer A · Onboarding: Top theme: 38 comments, 31 negative. Up from 22 in August, worsening. "Took me a week to work out how to invite my team." Fix: add an invite step to setup. Source: example-a.com/survey - Customer B · Billing: 21 comments, mostly negative. Flat for three months. "Charged twice and support took four days to reply." Fix: show the invoice history in the app. Source: example-b.com/support - Customer C · Reporting: 17 comments, mostly positive. Improving since July. "The new export saves me an hour a week." Nothing to fix; worth telling the team. Source: example-c.com/reviews - Customer D · Churn risk: Says they're moving to Competitor E after a third sync failure. Evidence: a switching mention and repeated complaints. Worth a call this week. Source: example-d.com/survey 412 comments · 6 themes · 2 churn risks · 3 fixes ## One summary, four teams. The same monthly message answers a different question for each team that reads it. - Product: Which themes are growing, with quotes to share. - Support: The complaints that keep coming back, counted. - Customer success: Churn risks with the evidence behind each one. - Founders: One message on what customers said this month. ## How do you analyse customer feedback with AI? Export your feedback into one table with the comment, date and source, then have an AI agent read every row, label sentiment, group comments into themes and count them by month with real queries. It should quote customers and tie each fix to a theme. In Arclyx that's five steps, starting with one sentence. 1. **Describe the job**: One sentence: which feedback, how often, and where the summary goes. 2. **Upload your feedback**: A CSV export of survey answers, reviews or support comments becomes a table. 3. **Connect Slack**: Arclyx asks for the one channel it may post to. 4. **Watch a dry run**: Optional. See one full run on your real feedback before anything is posted. 5. **Schedule it**: The first of each month, or after each survey closes. The sentence that built it (Schedule, 1st of the month, 9:00): > On the first of each month, read every row in our feedback table, group the comments into themes with sentiment and month-by-month counts, flag churn risks with evidence, and post the top themes and three fixes to #product. ### Or start from one of these sentences - After each survey: "When a new survey export is uploaded, read every answer, group them into themes with sentiment, and post the top five to #product." - Support complaints: "Every Friday, read this week's rows in our support comments table, count complaints by theme, and tell #support which are growing." - Churn reasons: "Every Monday, read our cancellation survey table and post each churn reason with counts, quotes and the plan the customer was on." - Low-rating reviews: "On the first of each month, read our app review table and post the themes behind ratings of 2 stars or less, with two quotes each." ## AI customer feedback analysis vs other tools. Reading by hand, a spreadsheet and a survey tool's built-in text analysis each cover part of the job. Here is what each one does, and doesn't, do. | What you need | Reading it by hand | Spreadsheet | Survey tool's text analysis | Feedback analyst | | --- | --- | --- | --- | --- | | Labels sentiment | Depends who reads | Only if you tag each row | Yes: positive, neutral or negative | Yes, by reading each comment | | Groups comments into themes | Slowly, by hand | Only if you tag each row | Yes, with thematic analysis | Yes, ranked by number of comments | | Feedback it covers | Whatever you collect | Whatever you paste in | Open-ended survey answers | Any table: surveys, reviews, support | | What you get | Notes in a doc | Pivot tables and charts | Labels, themes and a summary | Themes, trends, quotes and fixes in Slack | | Setup | Time set aside each month | Tags and formulas per export | Some features on certain plans | One sentence and a CSV | Source: [SurveyMonkey: text analysis features](https://www.surveymonkey.com/product/features/text-analysis/), checked 17 October 2026. Source: [SurveyMonkey Help: sentiment analysis](https://help.surveymonkey.com/en/surveymonkey/analyze/sentiment-analysis/), checked 17 October 2026. ## Is it safe to give an AI agent customer feedback? It reads your feedback table with read-only queries, writes one message, to Slack or your inbox, and records everything it does. It never changes your data, and every run has a spend ceiling. ### Every run is recorded - Every step, in order: Each run keeps a trace: which table it read, the queries it ran, and what it decided. - Every count, checkable: The queries behind each number are kept in the run, so anyone can check them. - What each run cost: Tokens and cost are recorded per run and per agent, next to your plan's limits. - A ceiling on every run: A run that would pass its dollar ceiling stops and tells you, rather than carrying on. ### It can - See the tables you share with it - Run read-only queries on them - Read each table's columns - Post to one Slack channel you name, or email you ### It can't - Change or delete any row - Contact your customers - Browse the web - Email anyone but you - Post anywhere else ## AI customer feedback analysis, answered. ### What is AI customer feedback analysis? Using AI to read customer comments, from surveys, reviews or support, and turn them into themes, sentiment and trends. This agent reads every row of your feedback table, labels each comment, groups and counts the themes, and posts what's changing and what to fix. ### Which feedback can it analyse? Anything in a table: survey answers, review exports, support comments. Upload a CSV export to Documents and it becomes a table the agent can query. It only reads the table; it never changes it. ### How does it decide sentiment? By reading each comment and labelling it positive, neutral or negative. There's no made-up scoring formula. The message states the method and any caveats, such as a month with very few comments. ### Are the counts reliable? Theme and monthly counts come from queries on your table, not from a rough reading, and they're checked before the message is sent. Anyone can open the run and see the queries behind each number. ### Can it spot churn risk? Yes, when there's evidence: a cancellation, a plan to switch, a competitor mention, repeated complaints or a very low rating. Each churn risk in the message says which evidence it rests on. ### How do you analyse customer feedback in Excel? Put each comment in a row with its date and source, tag each one with a theme and a sentiment, then count with a pivot table by theme and month. Feedback analyst does the tagging and counting for you, from the same export. ### Can I get the themes by email instead of Slack? Yes. Arclyx can send the themes to your own inbox instead of Slack, or as well as it. The email only ever goes to you, the person the agent runs for: it has no way to email anyone else. ### Do I need to write code? No. Describe the job in one sentence and upload your feedback export. Arclyx builds the agent and can show you a dry run before you schedule it. ## Agents that work well with it - [KPI reporter](https://arclyx.ai/agents/kpi-reporting): This month's metrics against last month - [Competitor watch](https://arclyx.ai/agents/competitor-monitoring): What competitors changed this week - [Blog drafter](https://arclyx.ai/agents/content-creation): A cited blog draft from your brief ## Early access Build your feedback analyst. One sentence in, a working agent out. Join the beta and we'll set you up. Join at https://arclyx.ai/#join or write to hello@arclyx.ai. Last updated 17 October 2026.