Why Digital Marketers Should Be Using Coding Copilots

I don't think enough marketers are aware of the power of using coding copilots. I'm not saying this is non-negotiable for everyone in marketing. But there's a class of marketers who are naturally more technical, more systems-oriented, more process-driven, or just plain experimental. For them, this is table stakes.

There's urgency here, but the window isn't closing overnight. It's worth your time to test out these tools and see what you can do, even if it's just generating a simple landing page or a basic backend script that does some web scraping and analysis.

I've been in digital marketing for over 15 years. When I got my start, I worked at a SaaS startup on a high growth trajectory like many early stage companies. There wasn't a whole lot of marketing team to go around.

I was the sole marketer, and I needed to get a lot of stuff done. It wasn't until several years into my tenure that we started building out the team and had some technical resources available to us. I spent time in the website's Drupal backend, fixing things, wishing I had more tooling in place. As we grew, we added roles that could help support us and build automation and integrations into our various systems.

Partnering with technical teams to implement ideas has been a huge part of my success. In other words, I wish I'd had this skill set 15 years ago. I could have accomplished so much more.

What Vibe Coding Actually Means

Plain language instructions automatically become functional code

Vibe coding is really just using coding copilots. You work with an AI copilot to systematically create applications—small, medium, and large—to accomplish your objectives.

Think of it this way: you describe what you want in plain language, and the AI writes the code for you. You're directing the code rather than typing every line yourself. The copilot handles the syntax, the boilerplate, the structure. You handle the strategy and the outcomes.

There's an element of vibe coding that this article focuses on: ad hoc, personalized, hyper-personalized even, applications that do a specific job. These aren't tools you're building to sell. They're systems that solve your specific problems.

A Real Example: The FAQ Writer

Days of manual work reduced to hours

Let me show you what this looks like in practice.

Something I've seen people do (and have done myself to some extent) is adding FAQs to blog posts. You can do this manually by going to Claude, sharing your content, sharing some "People Also Ask" queries from Google, and getting Claude to generate FAQs based on that.

That works fine for one post.

But if you do this programmatically behind the scenes, you could do it for every blog on your website. Query the People Also Ask API, query Claude, and have Claude write FAQs for every blog automatically.

The difference? Manually copying and pasting each blog one at a time will take hours and hours. Creating the script and running a Node.js script might take a day or two to set up. Once it's done, you'll be finished in an hour or so, and then you have your FAQs.

This is the shift. From one-off tasks to systematic solutions.

Three Levels of Building

Progression from prototypes to production-level applications

Level 1: Prototypes and Ad Hoc Processes

How much coding knowledge do you really need to be an effective vibe coder?

For building prototypes or examples, the barrier to entry is extraordinarily low. You need two things:

First, understand how to interact with VS Code. Open the terminal. Understand different file types when you're programming—JavaScript files, Markdown files, JSON files. You don't need to understand a whole lot more than that.

Second, the ability to clearly articulate what you want. Clear outcomes. Instructions. Data points you want to collect. Be comprehensive.

Between familiarity with a code editor and the ability to articulate feature specs, you can build something useful immediately.

Use cases for marketers at this level are about creating systems rather than getting immediate one-off outcomes. For instance, I've used this for setting up things in Google Tag Manager. I'll go into a page, download the source code, share it with Claude, and get Claude to help me identify the proper CSS selector so I can test it in Google Tag Manager and configure web analytics.

That's a basic thing. To do this at scale, you can set up a script that crawls your website, looks at CTA elements, and returns back the proper CSS selectors. This is the idea of doing something once in Claude or ChatGPT versus doing it over and over again.

Other examples: crawling through your entire website and updating meta descriptions against your new positioning strategy. Or doing a general SEO audit. We can do these things quickly and programmatically. Yes, there are tools that can do this, but these are just examples.

Level 2: Sustainable, Scalable Systems

The next layer up is building something sustainable and scalable that works for you consistently over time. This is where we start entering more full-stack development territory.

You need the ability to understand and work with data structures to some extent. Working with third-party APIs for deeper integrations. For example, integrating a web scraping tool into your platform, connecting to LLM APIs, connecting to third parties. In my case, I connect to tools like Moz and People Also Ask to create more robust applications.

For this skill level, having an introduction to coding knowledge helps. There'll be a lot of troubleshooting. Your projects are now escaping beyond just a few core files in a small app into something built for scale—multiple folders, directories, being able to navigate between those and manage all that information.

The key skill I'd add at this stage is the ability to organize information systematically. As you start developing apps that take multiple days, having a system in place to manage all your instructions, your architecture, your overall project becomes really important. Without that system, you find copilots replacing large sections of your code base without any understanding of what they're doing.

A detailed example: My content audit process

Here's what I do. First, I run the site through Screaming Frog and identify the URLs I want to use in my content audit. Then I load those up in a CSV.

I have a script that looks through that CSV and processes it in batches. I use a web scraping tool to pull in relevant content from the body of each article. Then I have Claude analyze that content and return back a data file.

Manually, I can collect maybe 25 data points—both qualitative and quantitative—per page. A single page would take me 20 to 30 minutes because I have to read all the content and then check and select the right data points.

Doing this programmatically? In that same 20 minutes, I can usually go through about 100 blogs. Once I run the script, I can process literally thousands in a day.

It's extremely time-saving. And it's a great use case for agencies who might be looking to do the same thing, because once you write the code to do it for one website, changing it for another website is very simple.

Understanding the relationships between different data sources matters here. From a marketing perspective, understanding what that data will tell you matters even more. We have the ability to collect endless amounts of data, but data is only useful as the knowledge and insights it provides.

I spend time thinking about that and getting it all set up. I create what I call a data structure companion—a markdown file that provides definitions and the reasoning for each data property in my applications. This becomes your reference guide when the copilot inevitably forgets what you're trying to do.

Level 3: Production-Level Applications

The last level is creating production-level applications. I'd argue here you'd want junior to intermediate level understanding of code. This has been my experience.

When I built Citebots, my LLM visibility tool, I had an aspiration to make it production level. I ended up opting not to for several reasons.

One of those reasons was the technical architecture required to scale something like an LLM visibility tool. It was something I realized I probably could handle, but then I had to do the trade off: is this something I want to handle?

When you're getting into production-level apps, security has to be first and foremost. Twitter and Reddit are littered with examples of vibe coders leaking API keys and suddenly finding themselves with thousands of dollars in charges because bad actors found those keys. Not to mention securing user data, which can result in major issues with different regulations.

Then there's the complexity of separation of concerns and application management. All of this is non-negotiable when building a production-level app.

Having been through all three stages, I can say this is the level where my own skill set hits its limitations. Building production-level apps that are fairly complex in their nature requires time and understanding.

There's a misconception that vibe coding automatically creates applications from our imagination. To some extent, this is true. But the truth is much more nuanced.

Building out Citebots, I not only needed to consider the outcomes I wanted (being able to programmatically and systematically query LLM APIs), but I needed to be able to store that data, use it for data visualizations, and access it for insights and AI analysis.

The data structure component took me almost two weeks, and I wasn't vibe coding during that. I was making sure I was collecting all the data points I wanted and that those data points would make sense together.

There's a lot that goes into vibe coding your own apps. Reading this, you might be thinking this skill set is out of reach. Some of what I'm talking about is complex. But there's an opportunity here for digital marketers to embrace Visual Studio Code, jump in the cold water, and use it to accomplish tasks.

When to Build vs. When to Buy

Strategic decision between internal builds and external purchases

The types of people who are going to get the most out of this are those who recognize issues and want to put a system in place to solve those issues.

As we graduate from using Claude or ChatGPT interfaces for one-off chats and goals, we're going to start seeing patterns in what we want to do and be able to automate these things. There are great tools out there today that allow you to do that.

Visual Studio Code combined with a coding copilot puts that power in your hands. You don't have to wait for a demo. You can throw something together, almost passively, build it, and see what you get back.

I'm unique in that I'm increasingly building out my own software instead of even looking for existing tools. Nine times out of ten, building something has so much benefit across all my clients that it's worth investigating. But it definitely doesn't make sense for all use cases.

Marketers should be cognizant of the time they spend on this. The types of applications that are most useful are those that integrate AI with them. The reason I say this: your out-of-the-box tool, while it has some good AI functionality (and most tools are starting to integrate AI), there's a level of personalization you can achieve with your own custom applications that's unmatched.

For instance, for some applications I've built, I'm able to load up extremely detailed knowledge files—not just your standard "this is what the brand is and what the products do," but insights and strategy and positioning. That provides much higher quality output.

That's a good framework to keep in mind: if you're going to use AI to augment or enhance scripting or programmatic processes, having the ability to build custom knowledge files in and use those is extremely helpful.

I'm not advocating that we replace all software with stuff we build in-house. There are lots of reasons why we wouldn't want to do that. You'd want to start small and validate your approaches and make sure you're able to get solid outcomes.

Another criteria: having an outcome-driven process. Something that produces an artifact or has an output you can evaluate is key here.

Getting Started Tomorrow

Try building a simple web scraper using Claude Code or another copilot. Make sure it goes to your website, crawls a page, gets some insights back, and see what you can do with that.

The challenge facing digital marketers today is being able to upskill ourselves rapidly. I'd argue this is one of those skills that very few marketers are going to take the time to learn.

But if you've learned HubSpot, you've learned Marketo, you've learned Google Tag Manager, whatever the system is—this is already very complex stuff. Adding a tool like Visual Studio Code with a coding copilot isn't any harder, in my opinion, than learning Marketo and becoming a Marketo expert in your organization.

It takes time. It takes trial and error. But it's well worth it at the end of the day.

Your literal first steps:

Download Visual Studio Code. Install a command-line copilot. I'd use ChatGPT or Claude to help guide you through that process—those tools are very well-adept at doing this.

Then build a simple landing page with a working form. Something that just saves entries to your local environment. Something very simple to test.

The key here: having experience and trial and error is probably the most tried and true method for being successful. If you're already adept at prompt engineering, you're going to have a lot of success very quickly with this. And then you're going to have to learn how these applications work. But this is no different than any other tool.

It's like having a blank canvas and being handed paints and brushes. You need to start somewhere, and putting paint on the canvas is the first step.

Mistakes I've Made (So You Don't Have To)

I've made plenty of mistakes in my journey. One stands out.

While building Citebots, I implemented something called Row Level Security (RLS) policies in my database. These are rules that control who can see what data. I wanted to make sure clients could only see their own information.

I got ambitious. I created complex policies that checked multiple tables at once—linking users to clients to analysis runs to queries, all in a cascade of database checks. It seemed logical at the time.

Then I crashed my entire Supabase database.

What happened was this: every time someone loaded a dashboard page, the database had to run these complex checks across multiple tables. Each check triggered more checks. Within hours, I hit 100% CPU usage, 100% memory, 100% disk I/O. The whole thing ground to a halt.

I had to emergency-disable features, strip out the complex queries, and spend a weekend rebuilding the access control system with much simpler logic. What I learned: start simple, test under load, and don't get fancy with database policies until you understand the performance implications.

The broader lesson? Strong organizational principles matter. Having knowledge files and docs for your code and copilot, making sure it understands how you want to develop, spending time up front thinking and architecting the application—this stuff saves you from disasters.

When I built Citebots, I used Supabase for my backend. Making sure my visualizations correctly accessed the backend, having these patterns stored and reusable, is really important. Sometimes Claude Code doesn't really understand the database architecture or how things are interlinked, or the importance and significance of how data fields work together. Mapping this out is crucial.

I also think there's an aha moment that exists when you first successfully vibe code something. You realize how powerful this really is. Then you think, "I'm going to build a commercial app." I think that can be a very big distraction from what you're trying to accomplish.

I would focus on incremental, small, actionable wins versus trying to launch new software.

Tool, Not Tool Maker

Using Claude Code as a tool, not as a tool maker, is a really interesting paradigm. It gives us the ability to understand that coding copilots are about helping us accomplish our objectives. They're not about us trying to make a tool to change the world.

There's a class of people (myself included) who probably have the skill set to make a production-ready application. But for marketers, having this anchored in your head matters: we don't need to build the next million dollar app. We can just build something that makes our life easier and gives us more time to have coffee and chat with our colleagues.

That's the real win here. Not becoming a software company. Not pivoting to SaaS. Just solving your own problems faster and better than waiting for someone else to build the perfect tool for you.