This $5,000 n8n system monitors competitors 24/7 (template available)

What's up guys, in this video I'm going to be showing you a system that I built that can scrape all of your competitors and constantly give you updates on what they're all doing. With this system, all you have to do is fill out a spreadsheet one time with all of your competitors' names and their website domains, and then this system will automatically every single day see if anything with them is updated and how you can act on that. If you stick around for the full video, I'll show you how you can even get the template for this so you can start using it straight away, either in your own business or you can start selling it straight away to other businesses. So if you want a system that can constantly monitor all of your competitors and give you regular insights on what you should be doing differently, then make sure you watch till the end of the video. Okay, so here we are in N8N and this is the system. I'll give you a quick rundown of basically what's going on here and then we'll run it through to give you a demo and then we'll break down exactly what happened. So to begin with here, we have a scheduled trigger and you can set this to whatever you want. This could be 5 times a day, once a day, once a week, once a month, once a quarter, whatever you like. And then what it's going to do is it's going to grab your competitors that you've put in a spreadsheet that will look something like this. And for this example, we're going to be pretending that we're a clothing company. So in here we've just put Gymshark, Zara, and Ralph Lauren. So sort of some competitors, some big competitors, if you was a clothing company. And then what it does is it grabs each of those competitors and it loops through them one by one. So we've got 3 in there, so it's going to run through 3 times. What it does is it will first use Explorium MCP. And if you don't know what that is, don't worry. I mentioned in all of my videos pretty much by this point. So I'll run you through how that works, but essentially it's just a platform where you can get information on loads of different businesses. So it's going to use Explorium MCP through this AI agent here to basically see what they've been up to. If anything's changed with their tech stack, if they've got a new office, hired a new employee, anything like that, this agent will pick up on that. And then we're just going to do a routine check of their LinkedIn followers and employee count. So we'll just get that kind of information and also look at their website visits so we can see a change in things like followers, and website visits to basically see, you know, if they've been posting, if they've been doing something different on their website, if there's an increase, if there's a decrease, and what we can do with that information. Then we move to the insights section where we grab all of that information and we pass it to this AI agent here. That's basically going to take all that information that we scraped on these competitors and it's going to give us actionable insights. So it's going to look at exactly what our company does and then it will give us actionable insights based on all of that information. So you can see here, this is going to be basically the information we're going to get from the company. So things like the company name, their website traffic, content output, ad activity, product launches, partnerships and integrations, this kind of information. If we go to my company here, you can see we have put in the exact same information for us. So essentially, this AI agent here is going to be comparing our data, which looks like this, next to the exact same data points from our competitors. That way it can do actual good insights instead of just kind of guessing on everything. It can look at every single thing, like our website visits compared to theirs, our ad activity compared to theirs, and that kind of thing. So like I showed you, we have 3 competitors in there. So let's run this system through and we can see exactly how it works. So first of all, here you can see it's grabbed this first one. So we've got the information from the Google Sheet, looks like this. You grab the first one, which I think is— yeah, Gymshark. Perfect. We use the Explorium MCP here. As you can see, it's working. It does take a couple seconds for this to run through because it's getting quite a lot of information. Yeah, so you can see that's done now. It's given us the content output, the ad activity, product launches, partnership integrations, press mentions, tech stack changes. Now it's going to run through, it's going to get their LinkedIn followers and employee count, get that information. And now it's getting their website visits, and again it's going to get that information. And then it's going to add that into a Google Sheet— or actually first it's going to get our information from the Google Sheet with the same data points, and it's going to pass that to this Insights Agent here, which will then update the Google Sheet with insights for us. So we get information that looks like this. You can see we have the company name, which is Gymshark. It couldn't find their website traffic just because sometimes it can't— it can't always find their website traffic. It's just one of those things. And then the content output, you can see here, recent content output from Gymshark includes multiple LinkedIn posts about their store openings and community engagement, notably the opening of their first US flagship store in New York City and a second permanent store in Dubai in early December 2025. Then ad activity, uh, no specific advertising campaign data found in the recent enrichment data or events within the last week. Product launches, no new product launches. Of course, that's going to happen sometimes. And again, there's no new partnerships. Again, that's going to happen sometimes. This is their current LinkedIn followers. This is their current employee count. Uh, press mentions. They haven't had much activity in the press recently. They also haven't had many tech stack changes. Of course, sometimes this is going to happen. It's not always going to be rich information, but of course that is also a sign of the fact that I haven't had much noise in the press recently. I haven't had any new product launches or ad activity. These, although it's not like actual real data that we're getting back, these are still signs, you know what I mean? These are still stuff that we can do. It's the fact that they haven't invested in ads or invested in a new product is still a sign for us. So with the insights, we get something like this: Comparing Threadform with Gymshark reveals distinct strategic focuses and scales. Gymshark, with a large workforce of nearly 5,000 and nearly 394,000 LinkedIn followers, is emphasizing physical retail expansion through flagship store openings in prime locations like New York City and Dubai, highlighting community engagement but no recent product launches or ad campaigns. In contrast— and then it says, um, what we're doing— additionally, Threadform's partnerships with local eco-conscious designers and a collaboration with Earthloom Denim amplifies sustainable brand positioning, further supported by notable press coverage in Vogue Green Edition and event presence at London Fashion Week. Tech stack-wise, Threadform shows agility— Threadform shows agility by upgrading its Shopify platform, integrating tools like, uh, I can't pronounce either of those tools, uh, enhancing customer engagement and service areas where Gymshark shows no recent tech update overall. And it just gives us kind of an overall insight there. So you can see here, this is the kind of information we get back. And you can do this for as many competitors as you like. By the way, guys, if you want to grab the template, all you have to do is come over to my school community, which will be the first link in the description of this video. Go over to templates, and then you can see along with all of these other templates in here, you'll have access to this specific template. So if you want to grab it, just come over to my community and you can grab it. And along with all of those templates, you'll also have access to a full course that will basically take you from a complete beginner with NAN all the way up to being able to sell this to real businesses for thousands and thousands of dollars. There's also a resource hub that is constantly being updated related with things like prompt libraries, API libraries, all of that kind of good stuff. And by the way, there's a Christmas offer on at the moment that is ending very, very, very soon. So if you want to grab this template for the cheapest price that the community will ever be, then make sure you grab it straight away. Okay, so now let's break down exactly what happened here. So we had a scheduled trigger. Of course, I manually executed this, so it's a little bit different, but this would go, um, every day at midnight. So the days between triggers, we've just set it to 1. Trigger hour, midnight. So right now it's just running every day at midnight. So you can adjust that, like I said at the beginning of the video, to whatever you like. And then what it does is it gets our competitors from this sheet. So we've linked it up to the sheet, which is competitor research, and then competitor details. So we've just got the company name and the company domain, which is literally all we need. And then we use a loop over items node. So we grab that first one, which was Gymshark, and then gymshark.com for the domain. And then we just edit fields to basically make sure we have those variables set in stone, because it's really weird with the loop over items node Um, the data that's passed through the Loop Over Items node, which in this case is the company name and company domain, you can only use that variable in the node straight after the Loop Over Items node. So basically, if I wanted to use those two variables straight from the Loop Over Items node all the way down here, let's say, uh, you can see if I go to Loop Over Items, we don't have access to that. So the little hack to get around that is just by adding an Edit Field step after your Loop Over Items node to basically just set the variables. So we've got company name and company domain in there. And then what we do is we pass this to an AI agent. So we give it the company name and the company domain, and we basically say to it, we give this prompt and we say, you're part of a competitor research system that is looking for useful insights on competitors. You'll be provided with company information, including their domain. Your task is to research and extract the following key business metrics for this company. So content output, ad activity, product launches, partnerships/integrations added, press mentions/PR events, and technology stack changes. You might be wondering, okay, how do we actually get this information? And like I said earlier, we use something called the Explorium MCP. And if you don't know what Explorium is, it's basically a platform that allows you to just input any kind of company by inputting their domain, and it will give you any information you like on them. So similar to Apollo, that they have way more data points, to my knowledge, um, that you can look up for these creators. So to give you an example, this is their API docs here. We go to businesses, you can see if we go to, um, business enrichments We can look up their firmographics, technographics, social media presence, ratings by employee, keyword search on their website. You can see here there's loads, lots of different things like company hierarchy, uh, website content changes, loads of different things. And there's also event types, so it could be IPO announcement, new product launch, new funding round, investment, uh, new office opening, anything like this. You can see the amount of data that we can get from here. This is all for a platform called Explorium. And essentially what the MCP does is allows us to put all of those tools into one kind of, kind of like a folder basically. It's called like an MCP server. Put all of those tools into an MCP server, connect it up to an AI agent as a tool, and then essentially this AI agent is just going to say, okay, I need to look up— if we have a look at the prompt here, it's going to say I need to find that AddActivity. Cool. So it's going to use the MCP client here and query it with all of the different tools it can use, and then find that one that is going to most likely give it the information it wants back. In this case, it is the AddActivity. and it can just use that tool straight away through the MCP. So you don't have to set up any kind of like HTTP requests, you don't have to get the agent to input like an endpoint or JSON body or anything like that. You literally just prompt it with what you want, connect up the Explorium MCP, and then that's it. That's all you do. And the way you do that, by the way, is you put down an MCP client node, set the SSE endpoint to this endpoint here, and then for the authentication, you just go to Bearer Auth, create a new authentication called Explorium, or you can call it whatever you want, but it just makes it easier to call it Explorium. And if you come back over to your dashboard here, if you go to API key, you can just copy this key and then paste it all into here. And that's literally it. That's all you have to do. And it gives us an output that looks like this, and you can see it's structured. So the way you do that, by the way, is you just click on— is you just click on require specific output format in here. It's going to open up another prong, uh, if I show you. So this prong here, and you just connect that up to a structured output parse that and basically put in a JSON body how you want it to output. So we have content output, ad activity, product launches, partnership integrations added, press mentions or PR events, and then tech stack changes. And then outputs like this as, um, separate variables that we can actually use. If you go to schema, you can see these are variables that we can actually use later on, which just makes our lives so much easier. And then we just have a simple Tavly step after that where we take the company's name. So in this case, it's Gymshark. And we look up their LinkedIn profile. So it looks like Gymshark's LinkedIn profile URL, and we get a LinkedIn URL back that looks something like this, which is of course their company LinkedIn URL. Then what we do is we have an Apify step here. And if you don't know what Apify is, it's basically a big database of scraping APIs. At this point, you probably do know what Apify is because it's been used by absolutely everyone for ages, and I use it in pretty much all my videos. But it's essentially just a community-made platform where there's lots of different scraping APIs that you can use through things like like NAN. So we have it linked up to this, uh, Apify actor here, and I'm going to show you inside of Apify. This is how it looks. It's the LinkedIn profile scraper for cheap lead enrichment. So basically what we do is we just connect that up in NAN. We use that Apify node here, and then we go to actors from list and just type in the name, simple as that, and just click on the one you want. And then to get the JSON body that we input into here, if you come back over to Apify, switch this from form to JSON it just gives you the body here and you simply just copy that, paste it into here, and then adjust the variables. So for this case, instead of the URL being like hard set like this, we want it as that variable, which is their LinkedIn company URL that we just got from the step before. So we basically just put that in. The way these Apify nodes work is you put one down to run the actor and it will essentially just say to you, it will run the actor for however long it takes. Let's say it takes a minute, it will sit there running for a minute and then you'll get a status back that says succeeded. And then what you do after that is you do a get dataset items, put in the dataset ID from this step before, and then it gives you the actual data back. So it looks like this. We get their name, their website, their tagline, description, follow account, founded year, uh, company size, and lots of other information here. So absolutely perfect. The only thing we wanted was their company size and their, uh, follow account. And then we do that exact same thing but with website visits. And we have this actor on Apify linked up, which exact same thing. It's just linked up in the same way through Apify, and this one uses SimilarWeb. Basically, all we do is just input the domain, which you can see here, gymshark.com. As you can see, the status was failed just because sometimes it can't find the website visitor. So I found the majority of the time it can, but it's just sometimes it cannot. In this case, it couldn't, which is just unfortunate. Then what happens after that is we go to the insights section here, and we're basically grabbing the our company data. If we come back over to the sheet and look at my company, so the my company sheet here, we have all of this information, which is the same as the one that we got on that company, and we just return all of that. So we go to competitor research and my company, and it's just get rows. We just get all of this information back. And then if we aggregate that to put it into one list, that's basically what aggregating is. We put it into one list that looks like this. That way we can just drag that entire variable in to our Insights agent here. If you look, we— I'll open this up actually. I have the competitor data, which is basically all that stuff from the AI agent, um, here. We've just dragged it in. There's a few things from the Appify steps. So we have content, ad activity, product launches, partnership integrations, press mentions, tech stack, LinkedIn followers, employees, and then website visits. Of course, we couldn't find the website visits for this one. That's the reason why it's red. And then we have my data here, which is that data we just aggregated. So this is how it actually looks to the agent. All of this info here, and then my data, which basically matches it. It's like the same data points. Then this is the prompt that we've given it. So very, very simple. You are part of a competitor research system that is looking for useful signals on competitors You'll be given data on my competitors as well as the same data points for my company. Your job is to go through the data points, then compare my competitor to my company and generate key insights. Your insights should be a short paragraph around 200 words long. So very, very simple. And output something that looks like this. Of course, we've already seen it in the spreadsheet, but this is how it looks. So pretty short paragraph, basically breaking down the differences between what we're both doing and then some actionable insights. And then we just add all of that information into the Google Sheets. So specifically, uh, this data here in the signals table with our competitor research, and then also the insights. So we're just dragging the variables for company, uh, web traffic growth, content output, ad activity, product launch frequency, partnership integrations, LinkedIn followers, employee count, press mentions, technology stack changes, and then insights. And then that is the whole system, and that will run completely autonomously. Now that we have the scheduled trigger set up, you can put 100 competitors in there. You can put 5, you can do 3 like I did. It really doesn't make a difference. Of course, you just have to account for the cost of the Apify Actors and also the Explorium MCP server. But apart from that, in terms of what the system can handle, it can handle pretty much any number of competitors that you put in there. So this is a massively valuable system like I showed you in this video. If you want to grab the template, get started with this straight away and start scraping your own competitors, or even start selling this to businesses so you can scrape their competitors for them. Community is the cheapest it will ever be due to the Christmas offer that's on at the moment. So if you want to grab it, be quick because this Christmas offer will not last long at all. That's it for this video. I'll see you in the next one.