How to Build a Serverless Customer Feedback System with AWS Lambda & DynamoDB (A Step-by-Step Guide)

Learn how to collect, store, and analyze customer feedback in real time using AWS and with zero servers to manage.

Overview

This tutorial walks you through creating a serverless customer feedback app using AWS Lambda, DynamoDB, API Gateway, and Amazon Comprehend. You’ll learn how to:

Accept feedback from customers via a simple form
Store feedback securely in DynamoDB
Perform sentiment analysis automatically
Deploy a scalable, low-cost solution perfect for startups and SMBs

Introduction

Customer feedback is gold – But only if you can capture it easily and analyze it fast enough to act on it. The problem? Many small businesses and startups either rely on clunky Google Forms or expensive survey platforms.

What if you could build your own feedback system that’s fast, cost-efficient, and runs without you having to manage any servers?

That’s exactly what we’re going to do today using AWS Lambda, API Gateway, DynamoDB, and Amazon Comprehend (for optional sentiment analysis). You’ll end up with a serverless system that:

Lets customers submit feedback via a form
Stores feedback securely in DynamoDB
Optionally detects sentiment (positive, neutral, or negative)
Costs next to nothing when idle

Why This Matters in 2025

Customer feedback is a competitive advantage, especially in an AI-first business world. A serverless AWS solution gives you automation, instant insights, and almost zero infrastructure cost, which makes it ideal for businesses that want to move fast.

Real-World Use Cases

1. Restaurants Tracking Diner Reviews in Real Time

Imagine a busy Friday night at your restaurant. Reviews are pouring in from Google, Yelp, TripAdvisor, and even Instagram comments. By the time you manually check them, the unhappy diners have already gone home — and possibly told 10 friends.

With AWS Lambda + DynamoDB + Amazon Comprehend, you can:

Ingest reviews in real time using an API Gateway endpoint that receives data from review site integrations or webhooks.
Analyze sentiment instantly so you can spot negative experiences before the customer leaves the area.
Trigger instant alerts (via Amazon SNS or Slack integration) to your manager if a review is negative, so they can call the customer, offer a voucher, or fix the issue that night.

Why it matters:
Responding within minutes instead of days can turn a 1-star review into a repeat customer, and create a “wow” moment that people talk about online.

2. SaaS Products Analyzing Feature Requests and Bug Reports

If you run a SaaS product, your feedback inbox is probably a mix of bug complaints, feature requests, “how do I” questions, and random praise. Manually sorting these is tedious, inconsistent, and slow.

Using AWS:

Collect feedback from your in-app feedback widget or customer support tickets (via API Gateway + Lambda).
Classify automatically with Amazon Comprehend or a custom SageMaker model into categories like “Bug Report,” “Feature Request,” or “Positive Feedback.”
Prioritize intelligently by assigning scores based on urgency, sentiment, and how many users requested the same feature.
Feed into your product backlog by integrating with Jira, Trello, or Notion via API.

Why it matters:
Your product team gets actionable, categorized insights in real time. No more missing high-impact bugs or delaying popular feature launches.

3. E-Commerce Stores Flagging Negative Delivery Experiences Instantly

In e-commerce, shipping delays and damaged products can erode trust quickly. But if you only see customer complaints during your weekly review, you’ve already lost them.

Here’s how AWS can help:

Capture feedback automatically after delivery using automated post-purchase emails or chatbot prompts.
Run sentiment analysis to detect dissatisfaction in phrases like “late delivery,” “box damaged,” or “item missing.”
Alert your fulfillment team instantly via Amazon SNS so they can call the customer, send a replacement, or issue a refund same-day.
Track patterns in DynamoDB to see if certain couriers or regions cause more problems — and take action.

Why it matters:
Instead of letting a negative delivery experience go viral, you proactively fix it, and possibly turn that customer into a brand advocate.

Now that we understand the importance of customer feedback, let’s move ahead to developing the actual application using AWS.

Step 1: Understand the Architecture

Essentially, the system will have the following architecture:

A customer fills out a simple web form and clicks “Submit.”
The form sends the feedback to an API Gateway endpoint.
AWS Lambda processes the request — saving data in DynamoDB.
(Optional) Lambda calls Amazon Comprehend to analyze sentiment.
You can later view feedback via DynamoDB queries or dashboards.

Here’s how the workflow looks:

[Customer Form] → [API Gateway] → [AWS Lambda] → [DynamoDB]
                                        ↓
                                  [Amazon Comprehend]

Step 2: Set Up DynamoDB Table

We’ll start by creating a table to store feedback.

Open AWS Console → DynamoDB → Create table
Table name: CustomerFeedback
Partition key: feedbackId (String)
Default settings → Create

Step 3: Create AWS Lambda Function (Python)

Next, we’ll create a Lambda function that stores feedback in DynamoDB and analyzes sentiment.

Create a new Python file named ‘lambda_function.py’ and paste the following code into it.

import json
import boto3
import uuid

# AWS clients
dynamodb = boto3.resource('dynamodb')
table = dynamodb.Table('CustomerFeedback')
comprehend = boto3.client('comprehend')

def lambda_handler(event, context):
    body = json.loads(event['body'])
    feedback_text = body.get('feedback')
    customer_name = body.get('name', 'Anonymous')

    feedback_id = str(uuid.uuid4())

    sentiment_result = comprehend.detect_sentiment(
        Text=feedback_text,
        LanguageCode='en'
    )
    sentiment = sentiment_result['Sentiment']

    table.put_item(Item={
        'feedbackId': feedback_id,
        'name': customer_name,
        'feedback': feedback_text,
        'sentiment': sentiment
    })

    return {
        'statusCode': 200,
        'body': json.dumps({'message': 'Feedback received', 'sentiment': sentiment})
    }

Code Explanation:

We take the feedback and name from the request body.
Generate a unique ID so every feedback entry is unique.
Use Amazon Comprehend to get sentiment (Positive, Neutral, Negative, or Mixed).
Store everything in DynamoDB.

Step 4: Deploy with API Gateway

We will now create an API Gateway Endpoint.

Create a REST API in API Gateway
Add a POST method for /feedback
Connect to Lambda
Enable CORS

Step 5: HTML Feedback Form

The next step is to create a basic HTML-based feedback form.

<form id="feedbackForm">
    <input type="text" name="name" placeholder="Your Name" />
    <textarea name="feedback" placeholder="Your Feedback"></textarea>
    <button type="submit">Submit</button>
</form>

<script>
document.getElementById('feedbackForm').addEventListener('submit', async (e) => {
    e.preventDefault();
    const formData = new FormData(e.target);
    const res = await fetch('YOUR_API_GATEWAY_ENDPOINT', {
        method: 'POST',
        headers: { 'Content-Type': 'application/json' },
        body: JSON.stringify({
            name: formData.get('name'),
            feedback: formData.get('feedback')
        })
    });
    const result = await res.json();
    alert(`Feedback submitted! Sentiment: ${result.sentiment}`);
});
</script>

Step 6: Test the System

Open your HTML form in a browser.
Submit feedback like “The new website design looks amazing!”
Check DynamoDB to see your stored feedback along with sentiment.

Extra Features That Can Be Added

In addition to the current functionality, we can add some extra features to improve the overall usability of the system. Some of these features include –

Email Notifications via AWS SES when negative feedback comes in.
Analytics Dashboard using Amazon QuickSight.
Multi-language Support with Comprehend’s language detection.
Authentication using Amazon Cognito for secure submissions.

Why This Approach Works

The benefits of using this system are as follows –

Serverless: No servers to maintain. Pay only when used.
Scalable: Handles 1 or 1 million feedback entries without changes.
Insightful: Built-in sentiment analysis gives you actionable insights instantly.

Real-World Action Plan

Here’s how you can deploy this serverless architecture for real-world use –

Build and deploy this system in your AWS account.
Set up alerts for negative sentiment using Amazon SNS.
Integrate QuickSight for visual analytics.
Expand with multi-language feedback detection.

What’s Coming Next in AI-Driven Feedback Analysis

AI-powered feedback analysis is moving beyond just “spotting a bad review”. It is evolving into a continuous, automated customer relationship system. Here’s where things are headed –

Real-time AI summaries of feedback clusters: Right now, you can run sentiment analysis to see if feedback is positive or negative. But next-gen AI tools will group related feedback together in real time and give you instant summaries. With AWS, you can could pair Amazon Comprehend’s custom classification with a Kinesis real-time data stream to automatically detect and summarize these clusters.
Auto-prioritization of urgent issues: Not all negative feedback is created equal. A single “my payment failed” complaint could be more urgent than 50 “dark mode is missing” requests. With AWS, a Lambda function could take feedback sentiment + category data, then run a scoring algorithm and push the top issues straight into Jira with “High Priority” labels. Future AI systems will score and rank issues automatically based on:
- Impact on revenue (e.g., checkout bugs).
- Security risks (e.g., data leak mentions).
- Legal implications (e.g., compliance complaints).
- Virality potential (e.g., complaints from social media influencers).
Generative AI responses to customers based on sentiment: Instead of sending generic “Thank you for your feedback” emails, AI can now craft custom responses in your brand’s tone, tailored to the customer’s emotion. With AWS, you could use Amazon Bedrock to generate responses, pulling customer history from DynamoDB so the reply feels personal.

Conclusion

By combining AWS Lambda, API Gateway, DynamoDB, and Amazon Comprehend, we’ve created a fully serverless customer feedback system that’s affordable, scalable, and intelligent.

This isn’t just about collecting feedback. It’s about understanding your customers and improving based on what they tell you. And since the system costs almost nothing when idle, it’s perfect for startups and small businesses looking to get smarter without having to deal with large and unnecessary expenses.

FAQs

Q: How do I create a serverless customer feedback app with AWS?
A: You can build it with AWS Lambda, API Gateway, DynamoDB, and Amazon Comprehend to process and store feedback without managing servers.

Q: What’s the cheapest way to store customer feedback in AWS?
A: DynamoDB is cost-effective and scales automatically, making it ideal for feedback storage.

Q: Can AWS analyze customer sentiment automatically?
A: Yes, Amazon Comprehend detects Positive, Negative, Neutral, and Mixed sentiments in feedback.

Q: Do I need AWS certification to build this?
A: No. You just need an AWS account and basic understanding of Lambda and DynamoDB.