Reddit might not be the first place you think of for product research. However, it’s one of the most honest and high-signal communities online. The problem is, it’s noisy and hard to monitor consistently unless you automate it. That’s exactly what we did.
In this post, we break down how we built an automated Reddit scraping system to monitor mentions of HubSpot. From scheduling and filtering to AI-powered relevance checks, here’s how we cut through the clutter and find valuable insights multiple times a week.
Why Are We Tracking HubSpot Mentions?
HubSpot is a major player in the CRM and marketing automation space. Whether you're using it, evaluating it, or competing with it, understanding how people talk about HubSpot in the wild provides:
Insight into real user pain points and feature gaps
Signals about adoption, switching behaviour, or churn
Competitive intel on what other tools users are comparing it to
We track HubSpot mentions because they serve as a reliable proxy for broader CRM and marketing tech conversations. HubSpot is frequently discussed across subreddits, and those threads often surface real user pain points, switching signals, and competitive comparisons. Automating the tracking and filtering process helps us stay on top of sentiment and spot relevant trends quickly without the overhead of manual monitoring.
Step 1: Scheduling Scrapes Three Times a Week
Our workflow begins with a simple but important rule. We scrape Reddit on a consistent schedule.
We set up the scraper to run every Sunday, Tuesday, and Thursday. This gives us:
Fresh data without overlap
Coverage of key discussion days (Reddit is often quiet on Fridays and Saturdays)
Regular insight cycles for our team to review
You could use a scheduler within n8n or your preferred automation tool. We use n8n because it gives us full control, flexibility, and open-source transparency.
Step 2: Scraping Posts That Mention HubSpot
Once the workflow kicks off, we use a Reddit search node to pull posts that mention HubSpot or appear in subreddits like:
r/HubSpot
r/CRM
r/SaaS
We focus only on new posts since the last scrape. This avoids redundancy and keeps our dataset focused.
Step 3: Filtering for Relevance
Raw Reddit data is messy. So before anything hits our database, we apply three key filters:
1. Recency
We take only posts from the last two days. This ensures:
Conversations are fresh
Responses haven’t been fully resolved yet (great for sentiment tracking)
We avoid analysing outdated issues
2. Deduplication
Sometimes Reddit threads get re-shared or cross-posted. We remove any post we’ve already processed.
3. Upvotes
Higher-voted posts often reflect broader interest. We sort posts by upvotes and prioritise them for analysis.
This simple logic, recency, uniqueness, and popularity, gives us a clean batch of content worth looking into.
Step 4: AI Compliance Agent: Deciding What Moves Forward
Even with filters, not every post is worth analysing. Some are memes, off-topic rants, or one-liners that don’t help us understand user sentiment.
That’s where the AI compliance agent comes in.
We built an AI node that reviews each Reddit post and assigns a simple tag: compliant: yes or no.
It checks whether a post:
Mentions HubSpot in a relevant context
Discusses a product issue, use case, or vendor comparison
Offers enough context to be useful
This adds another layer of intelligence that keyword filters can’t handle. For example, it knows the difference between “HubSpot sucks lol” and “HubSpot’s CRM workflow has a confusing UI.” The latter is actionable.
Only compliant posts continue through the workflow. The rest are discarded or logged for later review.
What Happens Next (And Why It Matters)
While this post covers just the scraping and filtering portion of our Reddit workflow, it’s the most critical part.
This matters because everything downstream, including enrichment, sentiment analysis, competitor detection, and email reports, depends on getting high-quality Reddit data in the first place.
Without smart scheduling, filtering, and compliance tagging, we’d be buried in noise.
Why This Is Useful for Product and Marketing Teams
If you’re in product ops, customer research, or brand monitoring, Reddit is a hidden gem.
When paired with automation and AI, it becomes:
A free, ongoing focus group
A source of unsolicited customer feedback
A competitive intelligence feed
With just a few steps, including scheduling, scraping, filtering, and AI review, you can build a listening engine that runs on its own.
FAQs About Automating Hubspot Community Listening on Reddit
1. Can I adapt this workflow for tools other than HubSpot?
Yes. Just change the search terms and target subreddits to match your brand or product.
2. Why not just monitor Twitter or LinkedIn?
Reddit discussions are often more detailed, honest, and use-case specific. It’s a different kind of signal: less PR, more reality.
3. What tools are needed to set this up?
You can use n8n or any automation tool with Reddit and AI integrations. We also recommend a place to store and view the data, like Google Sheets or Airtable.
4. How long did this take to build?
A few days of setup and testing. The hardest part is tuning the AI prompts so they tag posts accurately.
We’ve found huge value in building Reddit listening workflow. Not just in insights, but in mindset.
If you’re curious about setting up something similar, contact us. We’d be happy to talk through your use case.
![The Dummy Node That Saved Our Reddit Scraper (And Why Every Workflow Needs One) Sometimes, the smallest fixes make the biggest difference. That was the case when a simple "dummy node" turned our struggling Reddit scraper into a stable, reliable automation engine. If you've ever built a complex n8n workflow involving APIs or AI, you might have hit a wall with execution limits or bloated outputs. We did too. Here's how one insignificant-looking node fixed our problem and why it's worth considering in your own automations. The Problem With Complex AI-Driven Workflows When too much data becomes too much The primary keyword here is "dummy node", but the real issue started with our Reddit scraper pumping out massive payloads. Each run retrieved thousands of posts and comments, often funnelled through AI tools like GPT for classification and summarisation. The result? Bloated data outputs and overloaded downstream nodes. In platforms like n8n, every node passes data along the chain. If the payloads aren't trimmed or controlled, each subsequent node processes more than it needs, causing timeouts, memory issues, or workflow crashes. Real-world symptoms of system overload Here's what we noticed: Workflows began timing out unpredictably Logs were flooded with unnecessary JSON Debugging became a nightmare due to massive execution histories Cloud-hosted workflows hit memory caps and stopped mid-loop This wasn’t just inconvenient. It made our automation brittle and unscalable. Why Reddit scrapers are especially prone to bloated outputs Reddit's API returns rich metadata: post titles, bodies, comments, scores, flairs, and more. While this is great for comprehensive analysis, most of it is useless for immediate processing. Add AI tools into the mix, and the problem compounds. Each AI call might return lengthy completions or nested objects, multiplying the size of each item travelling through the flow. Meet the Dummy Node What is a dummy node in workflow automation? A dummy node is a no-op (no operation) node. It doesn’t transform, filter, or manipulate data. It simply returns a static or minimal response. In n8n, this can be accomplished using the "Set" node with an empty field configuration or a Function node that returns a hard-coded value. Its purpose is to interrupt or isolate data flow intentionally. Why we added it to our Reddit scraper After weeks of patching and optimising, we realised our loops and conditional paths were carrying forward bulky outputs from AI nodes. We didn’t always need those results, especially after decision points. So, we introduced a dummy node right after key branching logic. This node served one purpose: to strip away prior output and return a clean slate. How it works: static return, dynamic impact The dummy node outputs a single object like: [ { "clean": true } ] This minimal payload dramatically lightens the load for the next step. The result? Lighter memory use No carry-over of unnecessary GPT or Reddit API data Clearer logs More predictable workflow performance The Results: Small Fix, Big Stability Gains Reduced memory footprint After implementing dummy nodes, we saw a 40 percent decrease in memory usage per execution. According to Cloudflare, managing execution payload size is critical in serverless environments. Our flows instantly became more efficient with no code changes elsewhere. Faster execution and fewer timeouts Execution time dropped from 2+ minutes to under 45 seconds in some cases. By stripping payloads after key nodes, we avoided pushing unneeded data into loops or webhook responses. Timeout errors, especially in cloud environments, reduced by 90 percent. Easier debugging and maintenance Smaller logs meant we could actually read them. Finding failed paths became faster. We also reduced retry attempts because fewer errors occurred from bloated data. It also made our testing cleaner. Dummy nodes helped isolate logic flows for validation without artificial dependencies. Why Every Workflow Should Consider a Dummy Node Situations where a dummy node helps After AI tools like GPT that return large outputs Post-conditional branches, where one path doesn’t require full upstream data Before webhooks, to avoid unnecessary response size Inside loops, where only the loop state matters If your workflow is slow, flaky, or hard to debug, inserting a dummy node might be the fix. Controlling logic vs. adding complexity Some might argue that introducing "do nothing" nodes adds clutter. But in reality, they offer clarity. By intentionally cutting off output, you define clear boundaries in your logic. This makes workflows easier to reason about. It’s like using comments in code — not required, but incredibly helpful. The minimalist mindset for better automation Great automations are lean. Dummy nodes help enforce that by stripping away what doesn’t need to continue. Instead of building around bloated outputs, they let you reset the context mid-flow. In the same way that a licence tracking system limits unnecessary compliance inputs, dummy nodes reduce workflow noise and focus your data flow. Applying This Fix in Your Own Workflows Using dummy nodes in n8n and other platforms In n8n, try this: Insert a Set node Uncheck all input fields Add a single output field like { "pass": true } Or use a Function node: return [ { json: { reset: true } } ]; In Zapier, you can mimic this by using a Code by Zapier step that returns a basic object. In Make (Integromat), a Data Store or simple Tools module can be used similarly. Example scenarios: webhooks, loops, and external APIs Webhook sanitisation: before sending a final response, clear previous outputs Loop resets: use dummy nodes to refresh data inside iterations Post-AI filtering: break chains after language models to avoid output floods Common pitfalls to avoid Don’t overuse dummy nodes; place them only where output trimming is beneficial Ensure essential data isn’t accidentally dropped Test downstream nodes to verify they still function with the minimal payload Final Thoughts: Sometimes Doing Nothing Does Everything Our Reddit scraper was failing for a reason we never expected: too much data, too far downstream. The fix wasn’t a shiny new module or an external integration. It was a dummy node. Sometimes, the best optimisation is subtraction. In the world of automation, especially when pairing scraping and AI, less really is more. If you’re scaling your automations and want to avoid common pitfalls, we’d love to help. Reach out here to explore your options. FAQs Dummy Node in Reddit Scraper What is a dummy node in workflow automation? A dummy node is a placeholder step that returns minimal or static data. It helps control output in workflows by breaking chains of unnecessary data flow. How does a dummy node prevent data overload? By returning a clean payload, dummy nodes stop large outputs (like from GPT or APIs) from travelling through the rest of your workflow, reducing memory use and improving performance. Can I use dummy nodes in platforms other than n8n? Yes. Dummy nodes are a pattern, not a feature. You can implement them in Zapier, Make, or even hand-coded scripts. The goal is to intentionally limit what data moves forward. When should I not use a dummy node? Avoid dummy nodes where essential data is needed later in the flow. Overuse can lead to confusion or broken logic if not properly managed. What are other lightweight fixes for bloated workflows? Use conditional filters to block unnecessary paths Limit API response fields with query parameters Split large flows into smaller sub-workflows Image Prompt Prompt: "Create a flowchart diagram showing two n8n workflows side-by-side. One has bloated data passing through all nodes, the other introduces a dummy node that reduces payload size before continuing. Use clear labels and a minimalist design with light backgrounds." YouTube Embed Video Title: "Why Dummy Nodes Make n8n Workflows Smarter" Link: https://www.youtube.com/watch?v=AC2zuwbHtdc Embed Code:](https://framerusercontent.com/images/Xh4xfmV0hGTfiinnCjOEjSU0uM.jpg?width=930&height=483)
