Marketing Engineering Crash Course with Claude Code

0:00In the next 10 minutes, I'm going to teach you how to use your Claude code or your cursor or your Hermes agent or your Grog bot. It doesn't matter what harness that you're using, but I'm going to teach you how to do marketing engineering using these tools. In marketing engineering, what that is is having your coding agent do all of the middle work that you used to have to do by hand as a marketer. That could be writing copy, that could be making creative, doing graphic design, uploading the actual blog posts to your website, making the landing page, researching the keywords for Google ads, researching your target customer,

0:37finding their emails and adding them to a cold emailing software, building an email newsletter so that you can send it out to your audience for a webinar that you're hosting, monitoring your competitors' ad libraries on Facebook, Google, and LinkedIn so you can understand how they're positioning themselves. Everything that I just described, that used to be manual work, a human having to do that work, can now be completely augmented with these AI coding agents. And I'm going to teach you what you need to do to get started with this. I'm going to explain marketing engineering to you. I'm going to explain why it's so important for you

1:08to learn as a marketer so that you have a job 2 years from now. And I'm going to give you real-world examples of the outcomes that companies are making by using this tooling. With that said, I'm Cody Schneider. I've been doing digital marketing for the last 15 years of my life, and for the last 4 years, all I've been doing is figuring out how to get AI to do marketing operations for us. And quickly before we jump in, this YouTube video is brought to you by graph.com, the marketing engineering cloud. Click the link in the description to get started below for free. Okay, so first off, why should you we even be caring

1:38about this? Why should we even be having this conversation? I just want to show you this chart. So, this is marketing engineering searches over time. This is a very quickly growing category, and companies are trying to hire for this role. If you look at the total number of job listings that are happening for this role, it is skyrocketing, and the salaries for this are insane. You can easily make $150,000 plus a year if you understand how to do this. If you take your marketing knowledge and you learn how to do this engineering component by using these coding agents. And this marketing engineering trajectory has a really

2:11similar trajectory to what you're seeing with GTM engineering. Also, an extremely fast-growing category of job. Companies want this more than anything. It's basically somebody who's technical enough to build out these go-to-market motion operations, whether that's in marketing and in the go-to-market. Now, on this channel we actually talk about both of these. We talk about GTM engineering and marketing engineering, so subscribe if you want content like this. But for today, we're just going to be doing this marketing engineering

2:38crash course. And so, let's get into it. What do you need to even do marketing engineering? This is everything This is everything that we have found that is necessary to be able to do this function. To actually get Claude code to do the work for you. To actually get your coding agent like a Grok bot to actually be able to do this marketing to actually be able to do this marketing operations. So, first you need a data

3:00pipeline and you need a data warehouse. What are those? So, a data pipeline is how we collect all of the data from all of our different sources into a single location. So, a data pipeline enables us to basically pull data from Facebook ads, from Google ads, from Google Analytics, from Search Console, from our CRM like a Teo or HubSpot, from our payment processor like Stripe, and it puts it all into a location that the agent has access to. Why do you need to do this? Why can't I just use the APIs to call to these data providers? There's a couple things that will go wrong. One,

3:33you'll run into the rate limits with those API providers. If you spam those APIs too aggressively, for example, with Facebook ads, it can be against their TOS or their terms of service, so they'll ban that Facebook ads account. And also, doing joins across all of this. Joining data across these sources in the agent is extremely hard and you're going to find that it hallucinates. You're also going to run into these challenges of eating up all of the context. If you pull in 50 million rows from Facebook Ads and 75 million rows from Google Analytics and are trying to join that data to understand the historical context and

4:07provide that to your agent, you're going to very quickly run into the token input limits that that data provider has or that coding agent has. All right, so we have to get the data from all these different locations and then we have to put it into a place into a single location that's unified so that our coding agent can then go and access that data whenever it needs it. And why do we even care about this data? Because this is the source of truth that the agent is making decisions on. It has to have this to be able to actually do the work. And if I only have a single source, if I

4:38only pull from Facebook Ads, that can misrepresent what's actually happening. I have to be able to go all the way down into what is actually driving revenue, not just that top initial data source. And I see this mistake happening all the time with people when they're doing this work. They don't realize that like I need to be able to tie the Ads all the way into what is actually producing outcomes. So once I've unified that data, where am I unifying it? I'm unifying it in a data warehouse. And a data warehouse is a very specific type of database. It's built so that it can hold a lot of data and query that data

5:11really quickly using a coding language called SQL. And your Claude code or your cursor, they're incredible at writing SQL. All of these modern models are really good at writing it. And this is actually what we give the coding agent. So it has that source of truth. The data is all going to the data pipeline in the data warehouse. We give the coding agent access to the data warehouse. That's where it's pulling its data to do its analysis. And then with that analysis that it does, it then can go and do actions. So when we think about the structure of this, I'm pulling the data into the data warehouse. The data

5:41warehouse is going to something like Claude code. So let's just type that here, Claude code. And then I only use the API keys, for example, for Facebook Ads, for Google Ads, whatever other channels that I'm running, I'm only using the APIs to write to those platforms. And what do I mean by write? So, a read is what's happening here. I'm reading from this data warehouse, and then I'm writing to, for example, Facebook Ads via the API to upload media, to turn Ads on and off, to move Ads inside of ad sets, and again, to reiterate, why are we doing this? Why are we having this separation? It's

6:17because we will run into rate limits if we're trying to query all the data that's in Facebook Ads and not having this kind of circular loop that's occurring. And in reality, what this actually looks like is something more like this. So, basically, we are writing back into Facebook Ads, the data is going back into the data warehouse, and it's this circular loop that's kind of happening continuously. And this is the

6:38prop- This is the proper way to do this. And I say this is the proper way to do this because you can probably run a small account on the back of these APIs, a single channel, but as soon as you get to scale, so if you're a real business, you're going to be making so much data that you have to structure it in this way to be able to actually understand and give the coding agent what it needs to be successful without constantly having to solve for the API rate limits, the context window that you're burning, all of these things that you'll run into. And I promise you, you will run

7:06into these. We've been doing this for 2 years now. We always see the same thing. We always get companies that come to us and they're like, "Oh, yeah, I did this locally and then I realized that I ran into these problems." And that's great. I'm happy that they're discovering this, right? Because it's so game-changing when you think about the amount of work that a person can do, but structurally, this is the correct way to do this. All marketing, everything that we're doing is now code. We're trying to give the coding agent what it needs to be able to program and do marketing with code successfully. All right, so coming back

7:36to this, next item is a cloud server. So, a server, when you hear that word, just think this is a computer that's in the cloud that I can go and deploy software to. That can run on any timeframe, and that computer is always on. It's always accessible. And I need this if I want to be able to run one of these scripts or one of these pieces of software, one of these agents without having to have my own personal laptop on constantly. So, whenever I hear a server, just think about that. The other thing you need is media storage. Where do I store The other thing you need is media

8:06storage. Where do The other thing you need is media storage. Where do I store images and videos? For example, we make a bunch of media and marketing, whether that's social posts or ads or AI avatars. The best You need to place to store those The best place to store those is some type of S3 bucket. Super cheap to do this. And then the other thing that you also need to provide is databases. So, Postgres databases is a database type that enables you to store information so you can access later. How do we see this used a lot and how we use this a lot is by giving the agents a database for it

8:38to store, for example, information that it extracted for its total addressable market mapping for cold email, a way to organize the images and videos with a specific queue and or a specific skew, and the JSON that was actually sent to, for example, ChatGPT image 2.5 or Seedance to actually make that piece of media. And these databases are flexible. You need to be able to create these on a whim as you need them. The next thing that you need to do is have recurring tasks, or what are called cron jobs. So, these are things that can run on some type of cadence. It could be hourly, it could be minute, it could be

9:11daily. But it's basically a way to run a piece of software, you know, on some type of timeframe. You also need artifact hosting. So, this is This is like if you need to share a dashboard or some type of webpage that maybe has all of the ads that you made so that you can

9:23get approval. Some way to share that. And as soon as you have that artifact hosting that's at an accessible URL, this is like a share link. You have this problem is you have to think about authentication. So, how do I give people the appropriate authentication so that they can go and access it but other people can't. And that kind of relates to the shareable links thing. And then the final piece is you want to make this multiplayer. You want to make this so multiple people can work on these systems simultaneously. So you have to have what's called a Git manager. So a Git manager is basically a way for you

9:53to store code and have multiple people work on it without having conflicts of code being overwritten. Imagine two people working on a document or three people working on a document together and they're writing over each other and they don't other changes that people are making. This is a way to basically solve for that. And then the final piece is a API gateway to all of the APIs that you need to actually do this work. APIs to make images, APIs to get emails, APIs to make videos, APIs to scrape the web, scrape social media accounts, etc. And once you have all these tools, your agent is now the smartest marketer in

10:28the world, full stop. It is the best marketer that exists on planet Earth. But you have to basically remove these barriers so that it can actually go and do this work. You have to give it the right resources for it to be able to do these actions. And everything that I just described, you can just go and build some type of amalgamation of all this, a data pipeline that's open source is called Airbyte, a data warehouse

10:49that's open source is called ClickHouse. Cloud server you can run on something like Netlify. Media storage could be an S3 bucket. Databases could be stored on something like a railway. Recurring tasks, just a cron job function that's running on that server. Artifact hosting, potentially shipping that to Vercel. Application authentication you make a login database, shareable links,

11:10again, related to that login database. And a Git manager you could use something like a GitHub. And then for the tailored API gateways you could go and get subscriptions to the 40 different APIs that are necessary. But I'm not going to do that today. Instead, I'm going to use the Marketing Engineering Cloud from graph.com. It's literally free to get started. It's pay as you go. Put $10 in and you can go and extract 1,000 emails or something in that range. It's ridiculous. And the reason I'm using this is because everything that I just described as being necessary to do this work, it's all bundled up into that single cloud

11:43provider. And all of that becomes accessible through the Graph CLI. And the Graph CLI is just an NPM package. This is just a code package that you give to your Cloud Code. You literally give it this URL and you say something like Cloud Code install this. And what I just did there is I'm using a transcription software from a friend's company called Super Whister. Shout out Neil. We love the work they're doing there. And this NPM package, it has all of the knowledge and everything is bundled up into that CLI so that you can do all of this work. The data pipeline, the warehouse, etc. It's all captured

12:15within there. Just to show you what I mean by this, you can go and connect any data source, whether it's GoHighLevel or Google Analytics or Google Search Console or instantly or Facebook Ads or Post Ads or Twilio. There's 800 plus data sources that you connect that you can connect into this. And then all of these other tools, they're now accessible to you within Cloud Code. So now, so now let's actually go do some work together. So, I've installed this CLI already. And the first thing that I want to do is I want to make an ad and I want to upload it to Facebook Ads. And

12:45that's going to be the first thing that we do today. So, I'm going to start a new chat. I just did command new and again, I'm just working within Cloud Code. And I'm going to say, "Use the Graph CLI. I want to make a static ad that's a 1080 by 1080 pixel ratio using the ChatGPT Image 2.5 API endpoint." And that ad, I want it to say something like

13:04"Marketing Engineering Crash Course." And go to graph.com to see what the design style needs to be for this ad. All right, I'm just going to hit enter. And now what's going to happen? With that Graph CLI installed, it knows that it needs to go and pull through the unified API, so this here, whatever's necessary to call the ChatGPT Image 2.5 URL. And it's going to go and make an ad using that endpoint. Now, we're just going to make a single ad, but I can make as many variations of these that I want. And this is how I can make creative at scale, statics at scale. And

13:36I can put other requirements into them. I can have it be specific fonts. I can have it be specific colors. I can have it be specific language that I can and can't use if I'm a regular if I'm in a regulated industry. And while that's working in the background, I want to now talk about how do you get API key for Facebook Ads. So, we're going to use the Facebook Ads Marketing API. And the Marketing API is basically our bridge to access Facebook Ads. It enables us to go and manage an account. And this is the entire process to actually go and set up

14:08the Facebook Ads token that's necessary. So, first you're going to go and create a developer app by going to business settings, click add in the top right. I'll include this Notion document in the description below as well so that you can go and access this. And then you name it whatever you want. For example, Graph Ads. And then you'd select the use cases that you want to create and manage ads with the Marketing API, and create and manage ads with the Meta Ads Manager, connect the business portfolio to its own account. On requirements, click next, confirm the overview and create the app. And the last thing you

14:39need to do is add a privacy policy. I won't bore you with this. It takes about 10 minutes to get this set up. But once you have it set up, you'll have an API key that never expires. All you have to do is add a system user, and that system user enables that API token. And then you just assign the assets that you want it to have access to, and that token gets generated, and then you just provide that to your coding harness once you've given the Graph CLI access and set that up. And you can store this token locally, but I would not suggest doing that largely just because it's a

15:10security vulnerability. The best way to do this is you do it in like a one pass, and then it only can use it with that like one pass set up. It's just complicated. The way that we do it is we basically do a secrets manager, and that's what's actually happening under the hood is we're basically doing secrets management for you automatically. So, once you have that token, you paste it into Claude Code. I would paste it right here, and I would say add this Facebook Ads API token to my secrets, and then I can say something like list the secrets in the Graph CLI, and you can see all the secrets that are

15:41available here. I've got my Facebook Ads, my Google Ads, my Instantly API, my Strapi, which is my blog post, and all the other API keys that I've got stored in that internal account. But I can add anything that I need to this, but it's largely just these ones that are necessary for the the ads management or for my CMS. Everything else is already built into this. And so, let's take a look. It made this image, just basically decided this is the right way to build it. And how did it actually do that image? So, we can read it here. It basically used the Chat GPT image 2.5

16:13through the Graph tools to send that image to a model called Kai AI, and then that's where it generated that image and brought it back. So, what are Graph tools? List all of the Graph tools. So, Graph tools are the unified API that I was talking about previously. It's like 250 endpoints that we can call to have it do work for us. You can see all these different tools, and and new tools are constantly being added based off of different workflows that we identify and we aggregate. But here's these tools that are available. We can script the

16:43ads from TikTok, from Google, from Meta. We can find emails, we can validate emails with Million Verifiers and Get Leads. We can look up people, we can look up their phones, we can find funding signals, news search, we can research keywords using the data for SEO API. We can create images, we can create videos with Seedance, with HeyGen. We

17:03can do LinkedIn profile extractions. There's tons of these, and all of these are on a pay-per-use basis. So, it's just usage-based. All the data that's syncing is just usage-based, and all of the API tokens or credits that you need are just usage-based as well. So, we've made this app and now let's actually get it into Facebook Ads. So, let me show you how easy this is to do this now. So,

17:24I've got this ad set, this Claude remix. I'm going to go and take that and I'm going to just give this URL to Claude code. I'm going to literally just going to paste it in and I'm going to say upload the marketing engineering crash course ad to this ad set. And I'm just going to add a piece at the front, use the graph CLI and upload this. So, it now knows that it has that API token that we generated previously stored within its secrets. And when I say upload this, I want it to upload it into the platform. And this is how I can get it to do all of this middle work that

17:52used to take me hours. But I can do this at scale. I can write 100 different I can make 100 different ads. I can write 100 different titles, 100 different descriptions based off of the pain points and the desired outcomes that I go and extract from Reddit by using the XAI API. And we can go do that. While that's working in the background, let's go do this. Extract the pain points and desired outcomes by somebody would buy a data pipeline data warehouse solution tool and then give it access, you know, their Claude code access instead of having to use Looker Studio or Power BI or any of these other tools. Extract

18:25from Reddit those desired outcomes, pain points that they're trying to solve. We're going to use this for ad creative. And again, just adding use the graph CLI for this. All right, so that's running in the background. Again, this is how I actually work. This is how you should actually work. It's having multiple things going on in the background. Once I build the system, once I build this process, I then go and deploy this into the cloud so that it can run on its own cadence without me having to jockey or manage it. All right, so it looks like it just completed that upload. Let's go check to see if it's there. I'm just

18:54going to refresh this page and that marketing engineering crash course ad is now in that ad set. That's how easy this is to do this process. I can do this for videos. I can do this for statics. Anything that you can imagine you can upload into this. And I know I personally never want to touch a Facebook Ads Library again. This is a way that you don't have to do this. Now say I wanted to turn off this G Ads questions. I want to turn that ad off within that ad set. Again, I can just go back to Claude Code and say, "In that ad set, turn the ad off that has this

19:24title." All right, it just turned that ad off. Let's go check it. All right, and you're going to see that G Ads questions is now off. So this is how you can manage the entire ad account. You can upload ads, you can turn off ads, you can move ads around. Everything is just you telling the agent what to do from your Claude Code or Codex. And this is what our marketing engineering looks like. So this is still great scraping

19:43the pain points and desired outcomes. While that's working in the background, let's do a couple other things. Now say I want to analyze this data. I want to analyze my Facebook Ads data and my Google Analytics data. And maybe my Search Console data. Say I wanted to see if branded searches are increasing. So I'm going to just say that. Look at my Facebook Ads data, how many sign-ups have happened from Facebook Ads, what Facebook Ads pixel is recording. Also tell me how much traffic is coming from Google Analytics 4 with referral as Facebook. And tell me how many branded searches are happening. Like are we

20:13seeing an increase in branded searches? Uh use the data source Google Search Console for this. So again, all I'm doing is getting it to go and aggregate this data for me, answer these questions. And because I have all this data unified into a data warehouse, it can pull all that data from all of those locations extremely quickly and respond back. And this enables me to do conversational analytics with all of my live data stream that's coming from my data pipeline to data warehouse. It syncs on an hourly cadence. So this is in real time, you're getting up to speed. And because this is happening in real time, you can build agents on top

20:46of this that are making it decisions autonomously. You can build dashboards on top of this that you need to share with your team. And while it's pulling that data, let's do some traditional go-to-market engineering. I'm going to find all the people that engaged with the post on LinkedIn, and then I'm going to find their emails so that we can cold

21:02outbound to them. So let's do that now. So I'm going to LinkedIn, I'm I'm to find a post that I think anybody who engaged with would be my target customer. Cool. Claude just booked these meetings. Awesome. Let's take this one. So, I'm literally just going to take this post URL. I'm going to go back to Claude, and I'm going to paste it in, and then I'm going to say, "Use the Graph CLI, extract all of the engagers from this post, and then use the get

21:23leads endpoint to find their emails." And I'm going to hit go, and that's going to start running in the background. What we're going to get back is all of the emails that we can find for all the people that engaged with that post. While that's happening, let's do some keyword research. So, I'm going to say, "Use the data for SEO API. I want you to find all of the versus content for customer data platforms. I'm thinking like HighTouch versus X, ClickHouse versus X, anything that would be in this stack. We're going to write some blog posts about this, but just find me all of those keywords, and

21:51again, use the Graph CLI for this." All right. So, it's going to do that keyword research for me. And again, because it has the data for SEO API built into that unified API, it's just going to go and use the credits that it needs to basically do these pulls. And I can interject or stop at any point. Let's go

22:07back to those static ads. Here we go. It's now pulled that data from the data warehouse. We can see the sign-up data from Facebook. We can see the Facebook pixel records for landing page views. We can see the branded searches in Search Console. We can see the Google Analytics 4 traffic. And then I can say something like, "All right. Build a dashboard for me, an artifact that has all this data that we just pulled using the Graph CLI." And now we're back at that LinkedIn post engagers. It found those 69 reactions, and there's 316 comments that exist. So, it looks like it's probably duping those is what I'm

22:38guessing is happening. And that research that we had it using XAI for, it is pulling all of the relevant threads, the pain points, the desired outcomes, the objections, and it's building clusters around that for it to basically, you know, categorize for our ads. Now, it's making this Facebook ads funnel artifact for us. It's going to give us a URL in a

22:57second for us to go check that out. Let's do some analysis on what are all the keywords that are ranking on page two of Google that we're ranking for organically? So, let's go do that. Find all of the keywords that we're ranking for organically on page two of the search results for Google. Use the Google Search Console data source and the Graph SEO I data warehouse to source

23:15this data and this information for us. I'm basically looking for like high impact keywords or keywords that, you know, we could potentially rank for really easily if we just write some content or designate some content to it. Now, that started doing that research for me. That CDP versus content came back. We've got results of 765. Isolate the ones that are the only the same or unique search intent. Like if they're like X versus Y or Y versus X, I don't want them. I just want the unique search intent ones. Again, getting Claude code to do that work for me. And then once I have these, I can actually

23:45go and write the articles and publish them. You can see now that it found 398 keywords that sit on page two of positions 11 to 20 that have a thousand or a hundred plus impressions in the last 90 days. And we have that analysis now. We can go and say, "Okay. Okay, should we make some new content for any of these? Rank stack the top 10 that we should based off of where they're positioned currently." I've got that X versus Y keywords. Let's just post one of these just for the fun of it. So, what I'm going about to do is I'm going to use a plugin that is already created

24:13on Graph. It's called the SEO plugin. So, if you wanted to install this plugin, all you'd have to say is, "Install the Graph SEO plugin. Help me set it up to my CMS." But, I've already done this. So, I can instead say, "Use the Graph CLI. Use the SEO plugin that we have installed. And I want you to write a blog post and publish it uh to

24:31the Graph blog for this target keyword." So, it's going to go now. It's going to research the article that it should write for this target keyword phrase by going to the front page of Google and finding what's ranking currently. It's then going to go and write the article using Sonnet or Claude Sonnet and then upload it to the CMS and it will give me that URL in a second. So, we're going to let that run in the background. Again, all I did was wire this together by using the Graph SEO plugin. you can

24:57modify this plugin however you want. This is just what we have found to work and that's available to you. All right, so we're coming back to those LinkedIn engagers extractions. We have now found all of the emails that we can. Found 44 people that don't have any emails, five emails found. This is kind of a terrible one to be totally honest with you. We'd typically see about half. But what I can do is a waterfall enrichment. So I can say, "Hey, those 44, let's do a waterfall email enrichment now with whatever the cheapest provider is. Work down that for these other 44. And then also let's just guess the emails of the

25:28other 89 and use Million Verifier to see if they're actually real." So what's about to happen is this going to do a waterfall enrichment through the other service providers that are on the platform, something like Lead Magic, Apollo. You're only going to pay for the credit that you use. And then it's just going to guess the emails of the other ones. It's only 32 different 32 variations of what an email can be and then checking the emails um is so cheap that sometimes it's just cheaper to guess the person's email at this point

25:56basically than you know anything else. All right, so pull back those data pipeline pain points and I'm going to say something like write summit scripts [laughter] for AI avatar UGC. For UGC, um have the hooks be something like, "Okay, this is kind of crazy." Or "I wish I found this sooner." You know, basically something like that. Um and then talk to one of the pain points and the outcomes. Um and again this is an ad script that a UGC creator is going to read. Write 10 of these for me based off of uh you know, the most uh impactful ideas. All right, so it's going to write those scripts and

26:27I'm going to be able to go and make an AI avatar out or do the script read myself if that's something that I want. All right, coming back to that dashboard, we now have made that dashboard with that data. So this artifact is now created. I have these visualizations. I can modify this in any way that I want. Like for example, say I wanted to change this to a bar chart, change this to a line chart, the spend by month instead of a bar chart. And then we'll just go and do that vibe code modification of this. This is a shareable URL your team has access to

26:56and it's not public. So nobody else can go to it, but your team can go to it. You can also build interactive software here. So add a date range picker to the dashboard. It's going to go and create that for me or add that date range selection back at the select LinkedIn profiles and finding those emails. Wrote those scripts for me as well. Okay, this is kind of crazy. Every Monday I used to spend like 3 hours pulling CSVs that my Shopify made in Google a bad spend and updating the pivot table all just to answer one question. Did we actually make money last week? And there you have

27:26this ad. You can do all these ad reads. You can send these to the AI Avatar to do the ad reads. Let's check back on that SEO and these page to organics has come back. Okay, Google Ads versus Facebook Ads for X industry. Fascinating. Hearing ends, that's a great actually angle for us to go after. We'll probably

27:43do that. I can add those to the queue. So I'm going to save that and find all the other keywords that are similar to this one. Use the data for SEO API in the Gracia line. Cool. It's still adding and it's still adding that date picker to the artifact that we created. Found all the keyword variations that matched what we were going for. It's pulling those leads from Apollo. And that your URL post got published based off of what was ranking currently in the research that it did. And I'm going to end it there guys. If you want to get started with graph.com, go to

28:10graph.com/platform. Just click the link in the description wherever you're watching this. You can get started for free for as small as 10 bucks. You can do a ton of work. Almost everything that I just showed you today you can do. And if you want more content like this, subscribe to my YouTube kit. If you want more content like this, subscribe to my YouTube channel Cody Schneider. We'll be dropping marketing engineering content like this every week. If I don't drop a video, my team gets paid $200 for the all hands offsite. I've been slacking, so hold me accountable. And I talk about everything that we are learning in real time on

28:44both Twitter and on LinkedIn. Go follow me there to learn all of this as we learn it. And if you want to be notified when every video like this drops, go to gtmengineeringcourse.com. Every video that I make, I'm building into a free course that is sponsored by Grapht. It is entirely free. I teach you everything that I know about marketing, about GTM engineering, about marketing engineering. Go sign up for free. All right, and that's it. We'll see you next

29:07time.