How to Analyze Customer Feedback at Scale with AI

Process hundreds of customer feedback entries to identify themes, sentiment, and actionable insights.

What This Integration Does

Customer feedback lives in too many places to read manually: Front conversations, NPS responses, support email, app store reviews, form submissions. This workflow pulls all of it into one pipeline, runs each entry through an AI step that returns sentiment, themes, and an optional action item, and stores the structured result so you can chart it over time. The output is a weekly Slack digest plus a queryable feedback table.

The workflow runs on a schedule (e.g. every Monday at 08:00) and is incremental - it only processes entries created since the last run. Re-running is safe because each entry is upserted by source ID.

Prerequisites

A front connection (or another source: form submissions via webhook, support emails via the Email Trigger).
A mongodb or mysql connection for the structured results store.
A slack connection for the weekly digest.
A workspace LLM provider configured for the AI analysis step.

Step 1: Schedule Trigger

Drop a Trigger node and set the type to Schedule. Weekly at Monday 08:00 is a sensible default for a digest workflow; for live dashboards, run every hour. The trigger exposes the previous run timestamp so you can fetch only new feedback each pass.

Step 2: Collect Feedback from Each Source

Use a Parallel node to fan out to your sources concurrently. Each branch is a Connector node:

front / list-conversations filtered to the last 7 days.
mongodb / find-documents against your form_submissions collection if you collect feedback via web form.
http / http-get if you pull reviews from a third-party API.

Add a Transform node after the parallel block to merge the branches into a single array with a common shape: { id, source, text, createdAt, customerEmail }.

Step 3: Loop and Analyze Each Entry

Drop a Loop node (ForEach mode) over the merged array. Inside the loop, add a Connector node in Agent Mode (or a tool-augmented LLM call without tools, if no lookups are needed). Use Structured Output with this schema:

{
  "type": "object",
  "properties": {
    "sentiment":  { "type": "string", "enum": ["positive", "neutral", "negative"] },
    "themes":     { "type": "array", "items": { "type": "string" } },
    "actionItem": { "type": ["string", "null"] },
    "urgency":    { "type": "string", "enum": ["low", "medium", "high"] }
  },
  "required": ["sentiment", "themes", "urgency"]
}

The prompt should tell the model to choose themes from a fixed vocabulary ("pricing", "onboarding", "performance", "bugs", "billing") so you can chart them cleanly later. Free-form themes lead to long-tail noise.

Step 4: Persist the Structured Result

Still inside the loop, add a Connector node on mongodb / update-documents (or mysql / insert-rows if you prefer SQL). Upsert by source ID so re-runs are idempotent:

filter:  { "sourceId": "{{ item.id }}", "source": "{{ item.source }}" }
update:  { "$set": {
    "text":       "{{ item.text }}",
    "sentiment":  "{{ aiResult.sentiment }}",
    "themes":     {{ aiResult.themes }},
    "actionItem": "{{ aiResult.actionItem }}",
    "urgency":    "{{ aiResult.urgency }}",
    "analyzedAt": "{{ now }}"
} }
upsert:  true

Step 5: Route High-Urgency Items Immediately

Inside the loop, add a Condition node checking aiResult.urgency == "high". On the true branch, call slack / send-message to your customer-success channel with the original text and the action item. This catches angry customers within minutes instead of waiting for the weekly digest.

Step 6: Aggregate and Post the Weekly Digest

After the loop, add a Connector node on mongodb / aggregate (or execute-query for SQL) to compute counts per sentiment and per theme over the window. Pass the aggregate result to a final tool-augmented LLM call to write a short narrative summary, then post both the summary and the chart-ready numbers to Slack via slack / send-message.

Tips

Fix your theme vocabulary. Free-form theme tags give you a long tail of synonyms ("speed", "slow", "performance"). Constrain them in the schema.
Batch within the loop. If your feedback volume is high, group entries (e.g. 5 per call) and analyze them together to cut model calls.
Use a cheaper model. Sentiment + theme classification works fine on smaller models. Reserve the top-tier model for the digest narrative.

Common Pitfalls

Re-analyzing the same feedback. Always scope the source pulls to the sync window; otherwise you'll re-charge yourself for old entries.
Mixed languages. If your customer base spans languages, include the source language in the schema or your theme counts will be dominated by English.
Skewed sample. Front conversations include support replies, not just customer messages. Filter to inbound messages only or sentiment will look artificially neutral.
PII in logs. The execution log captures the full feedback text. Mask emails and phone numbers before the AI step if compliance requires it.

Testing

Run the workflow manually with a 2-day window and 20-50 feedback entries. Spot-check 5 random entries in your store - does the sentiment match what a human would say, and are the themes from the fixed vocabulary? Adjust the prompt or vocabulary, then re-run before turning the schedule on.