Auto-Scrape Competitor Shopify Reviews Daily and Generate Outreach Drafts

Input: competitor Shopify app review URLs → Process: Apify scrape, Google Sheets deduplication, Serper website lookup, Hunter contact enrichment, GPT-4o email drafting → Output: Gmail draft + Slack alert

Who

Sales or marketing teams who want to monitor competitor Shopify app reviews and proactively reach out to dissatisfied merchants

You get

Automatically discover new low-star reviews, find the merchant's website and contact, generate a personalized Gmail outreach draft, and notify the team via Slack

You need

n8n · google-sheets · gmail · slack · apify · hunter · serper

Steps

1. Configure schedule trigger and target URL list

   Open the template in n8n. Set the Schedule Trigger node to your preferred daily run time. Edit the Set Target App URLs (Code node), replacing the example URLs with the competitor Shopify app review pages you want to monitor. Each URL will be passed as a separate item downstream.

2. Configure Apify scraping, deduplication and filtering

   Connect your Apify API credential. The Scrape Shopify Reviews node uses the powerai/shopify-app-reviews-scraper actor, receiving URLs from the previous step; adjust maxItems and other inputs as needed. Then the Remove Duplicate Reviews node deduplicates by the id field, and the Filter 4 Stars or Less node keeps only items where reviewer is not empty and rating ≤ 4.

3. Check existing reviews and log new ones

   Process Each Review (Split In Batches) sends one review at a time. Check Existing Reviews searches Google Sheets by Review ID. If Review is New branches: if Review ID is empty, the review is new and proceeds; otherwise it is skipped. New reviews are appended via Log New Review, and Alert New Review Detected sends a Slack block message with brand, rating, review text, etc.

4. Search merchant website and extract clean domain

   After the new review alert, Search Google for Website queries Serper with 'reviewer name + official website'. Extract Clean Domain (Code node) takes organic[0].link, strips https://, http://, www., and path via regex, and writes the clean domain to item.json.domain.

   Execution prompt · Source text。jsCode field of the Extract Clean Domain node

   Copy prompt

   // Grab the #1 organic link from our Google Search
   let rawUrl = $input.item.json.organic[0].link;
   
   // Use regex to strip http://, https://, www. and any sub-pages
   let cleanDomain = rawUrl.replace(/^(?:https?:\/\/)?(?:www\.)?/i, "").split('/')[0];
   
   // Add the clean domain to your existing data so you don't lose the review info!
   $input.item.json.domain = cleanDomain;
   
   return $input.item;

5. Find contact email with Hunter

   Hunter: Find Contact Info receives the domain from the previous step, with limit set to 1, and searches for available email addresses on that domain. Configure your Hunter API credential.

6. Generate personalized outreach draft

   Generate Outreach Draft (Basic LLM Chain) connects to the OpenAI Chat Model (gpt-4o-mini). The node defines a prompt that generates a brief cold email under 100 words, acknowledging the competitor pain point from the review and pitching SATHI, using the contact name and domain from previous nodes.

   Execution prompt · Source text。text field of the Generate Outreach Draft node (promptType=define)

   Copy prompt

   =Write a highly personalized, brief cold email to {{ $('Hunter: Find Contact Info').item.json.first_name || 'the team' }} at {{ $('Extract Clean Domain').item.json.domain }}. 
   
   They just left this review on a competitor's app: "{{ $('Process Each Review').item.json.review }}"
   
   Acknowledge their exact frustration with the competitor and pitch SATHI as the reliable, professional solution to their problem. Keep it under 100 words.

7. Save as Gmail draft and update records

   Save Email as Draft (Gmail node) saves the LLM output as a draft: subject from the first line (stripping 'Subject:' prefix), message body from the remaining text, recipient set to the email returned by Hunter. Then Update Contact Info matches by Review ID and updates the sheet with Website, Contact Name, Contact Title, Contact Email, Status (Draft Created), and Draft-Email. Finally, Draft Ready Alert sends a Slack block message with contact details and the generated draft.

   Execution prompt · Source text。text field of the Draft Ready Alert node

   Copy prompt

   =✅ Draft ready for {{ $json["Contact Name"] || "The Team" }} at {{ $json["Website"] || "Unknown Website" }}

   Execution prompt · Source text。text field of the Alert New Review Detected node

   Copy prompt

   =🚨 New review detected: {{ $json["Reviewer Domain"] || "Unknown Brand" }} - {{ $json["Rating"] || "N/A" }}⭐

You are setting up an n8n workflow to automatically monitor competitor Shopify app reviews daily, filter new low-star reviews, find merchant contacts, and generate outreach email drafts. Please confirm before proceeding:
1. You have access to an n8n instance;
2. You have or can create a Google Sheets spreadsheet with columns: Review ID, Date, Rating, Review Text, Competitor URL, Reviewer Domain, Website, Contact Name, Contact Title, Contact Email, Status, Draft-Email;
3. You have or can configure credentials for: OpenAI API, Google Sheets OAuth2, Hunter API, Apify API, Serper HTTP Header Auth, Gmail OAuth2, Slack OAuth2;
4. You have identified the competitor Shopify app review page URLs to monitor;
5. You understand the LLM prompt in this template contains the brand name "SATHI", which must be replaced if used for a different product.

Once confirmed, import the workflow.json or install from the n8n template library, configure credentials and parameters step by step, and test with a small URL set and maxItems=1–2.