Claude Code for PPC Professionals: Automating Campaign Management With AI

March 11, 2026

Most PPC professionals are drowning in repetitive tasks. You're manually pulling reports at 7 AM, copy-pasting bid adjustments across dozens of ad groups, writing the same headline variations you've written a hundred times, and building the same Excel pivot tables your clients have requested for three years running. Meanwhile, the campaigns you actually need to think about — the ones with real strategic problems — sit waiting while you fight fires in a spreadsheet.

Here's what changes when you put Claude Code in your workflow: those repetitive tasks become scripts. Those scripts run while you sleep. And you get your mornings back.

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This guide is specifically for PPC marketers — not developers, not data scientists — who want to start automating real campaign work using Claude Code. We'll walk through five high-impact automation projects, the exact steps to build them, and how to avoid the mistakes that waste your time before you write a single line of code. By the end, you'll have a working foundation for a PPC automation stack that would have taken a developer weeks to build from scratch.

What Is Claude Code and Why Should PPC Managers Care?

Claude Code is Anthropic's agentic coding tool that runs directly in your terminal and can read, write, and execute code on your local machine. Unlike a chatbot you type questions into, Claude Code can actually interact with your file system, run scripts, call APIs, and complete multi-step technical tasks autonomously. For PPC professionals, this distinction matters enormously.

Traditional AI chatbots can help you brainstorm ad copy or explain a concept. Claude Code can actually build the tool that generates and tests that ad copy against your account data, automatically. It bridges the gap between "I have an idea for an automation" and "the automation is running and saving me four hours a week."

The reason PPC specifically benefits from this kind of tool comes down to the nature of the work. Pay-per-click campaign management is unusually data-dense and rule-driven. Most of what a mid-level PPC manager does in a given week follows patterns: if this metric crosses this threshold, do this thing. If this search term shows up in the query report, add it as a negative. If conversion rate drops below this level, reduce bids here. These are logical, repeatable rules — exactly the kind of thing that should be automated.

What's kept PPC professionals from automating this work historically is the technical barrier. Google Ads scripts require JavaScript knowledge. The Google Ads API requires OAuth setup, library installation, and backend architecture knowledge. Even Microsoft Advertising's automation options assume you're comfortable in a development environment. Claude Code collapses that barrier dramatically. You describe what you want in plain language, and it writes, tests, and refines the code for you in real time.

What You'll Need Before You Start

Before diving into the step-by-step projects below, make sure you have the following in place:

Claude Code installed: You'll need a Claude Pro or API subscription from Anthropic and Claude Code running in your terminal. Anthropic's official Claude Code documentation walks through the installation process clearly.
Python 3.10 or higher: Most of what we'll build uses Python. If you don't have it, download it from python.org and install it — Claude Code will help you verify it's working.
A Google Ads or Microsoft Advertising account with API access: You'll need developer tokens and credentials. Both platforms have free sandbox environments for testing.
Basic comfort with your terminal: You don't need to know how to code, but you should be comfortable opening Terminal (Mac) or Command Prompt/PowerShell (Windows) and running simple commands.
Estimated setup time: 45–90 minutes to get your environment ready before building anything.

Step 1 — Set Up Your PPC Automation Environment (Without Breaking Anything)

Your first task is building a safe, isolated workspace where Claude Code can experiment without touching live campaign data. This is the step most people skip, and it's the step that prevents disasters. Estimated time: 30–45 minutes.

Open your terminal and navigate to a folder where you want to keep your PPC automation projects. If you don't have one, create it now:

Type: mkdir ppc-automation && cd ppc-automation

Now start Claude Code by typing claude in that directory. You'll see the Claude Code interface load. From here, everything you build will be contained in this folder unless you explicitly tell it otherwise.

Create a Virtual Environment

Tell Claude Code: "Set up a Python virtual environment in this directory, activate it, and install the google-ads library, pandas, and python-dotenv. Then create a .env file template with placeholder values for GOOGLE_ADS_DEVELOPER_TOKEN, CLIENT_ID, CLIENT_SECRET, REFRESH_TOKEN, and CUSTOMER_ID."

Claude Code will write and execute all of this. Watch what it does — you'll learn the pattern quickly. The virtual environment isolates your project dependencies so nothing conflicts with other software on your machine.

Connect to the Google Ads API in Test Mode

Once your environment is set up, you need credentials. In your Google Ads account, go to Tools & Settings → API Center and apply for a developer token. While waiting for approval (it can take a few days), you can use the test account environment, which gives you a sandbox to run API calls against without affecting real spend.

Tell Claude Code: "Write a Python script that loads credentials from my .env file and makes a simple test call to the Google Ads API to verify the connection — just pull the account name and currency code for the customer ID in my .env file."

When Claude Code writes this script, it will typically create a test_connection.py file. Run it, and if you see your account name returned without errors, you're connected. If you see an error, paste it back to Claude Code and it will diagnose and fix it.

Common Mistakes to Avoid in Step 1

Don't put real credentials in code files. Always use the .env file. Claude Code will sometimes inline credentials in examples — immediately ask it to move them to environment variables instead.
Don't test against live production accounts until you've verified scripts in the test environment. One miswritten script can modify hundreds of ad groups simultaneously.
Don't skip the virtual environment. Package conflicts will cause cryptic errors that Claude Code will waste time debugging when they could have been avoided entirely.

Pro Tip: Ask Claude Code to create a README.md in your project folder documenting what each script does and how to run it. Future-you will be grateful.

Step 2 — Build an Automated Bid Adjustment Script That Actually Makes Sense

Automated bid adjustments based on real performance data are one of the highest-ROI automations a PPC manager can build. The goal here is to create a script that pulls keyword performance data, applies your own bidding logic, and either executes changes directly or outputs a review file for your approval. Estimated time: 60–90 minutes.

This is where Claude Code starts to feel genuinely powerful. You're not writing code from scratch — you're describing your bidding strategy in plain English and letting Claude Code translate it into executable logic.

Define Your Bidding Rules Before Touching Code

Before you open Claude Code, write out your bidding logic on paper. A simple example:

If a keyword has more than 50 conversions in the last 30 days and CPA is more than 20% below target: increase bid by 15%
If a keyword has more than 30 clicks and zero conversions in the last 30 days: decrease bid by 25%
If a keyword's impression share is below 40% and CPA is on target: increase bid by 10%
Never let a bid go above $45 or below $0.50

Having these rules written down before you start means Claude Code is translating your strategy into code — not making strategic decisions for you. That's an important distinction. The AI handles the implementation; you own the logic.

Tell Claude Code Exactly What to Build

In your Claude Code session, say: "I need a Python script that does the following: connects to the Google Ads API using credentials from my .env file, pulls keyword performance data for the last 30 days including clicks, impressions, conversions, cost, average CPC, and impression share, then applies the following bidding rules [paste your rules], and outputs a CSV file called 'bid_recommendations.csv' with columns for keyword ID, current bid, recommended bid, percentage change, and the rule that triggered the change. Don't execute any bid changes yet — just generate the recommendation file."

Notice what you're doing: you're asking for a recommendation output first, not live execution. This is a critical safety layer. Review the CSV, verify the logic is working correctly across different keyword scenarios, and only then build the execution layer.

Add the Execution Layer With Approval Gates

Once you've verified the recommendations look correct across at least two or three real reporting periods, tell Claude Code: "Modify the script to add an execution mode. When run with the flag --execute, it should apply the bid changes in the CSV to the live account via the API, log every change made to a file called 'bid_change_log.txt' with timestamps, and send me a summary email using Python's smtplib."

The logging component is non-negotiable. You need an audit trail of every automated change. If something goes wrong — and at some point, something will — you need to know exactly what the script changed and when so you can reverse it quickly.

Schedule It to Run Automatically

On Mac/Linux, ask Claude Code: "Show me how to add this script to cron so it runs every morning at 6 AM." On Windows, ask it to set up a Windows Task Scheduler entry. Claude Code will write the exact command or configuration file you need.

Warning: Set a maximum spend change cap in your script. Something like: "If the total bid changes across all keywords would increase monthly spend by more than $X, abort and send an alert instead." This prevents a data anomaly from triggering runaway bid increases.

Step 3 — Automate Ad Copy Generation That Matches Your Brand Voice

The most time-consuming part of PPC creative work isn't writing one ad — it's writing fifty variations that stay on-brand, hit character limits, and actually say something different from each other. Claude Code can build you a tool that does this in seconds, not hours. Estimated time: 45–75 minutes.

The key insight here is that you're not just asking an AI to write ads. You're building a repeatable system that takes structured inputs (keyword, landing page URL, value proposition, tone) and outputs complete, review-ready ad copy formatted for Google Ads' Responsive Search Ad format — with headlines, descriptions, and character counts validated automatically.

Create Your Brand Voice Document

Before building the tool, create a plain text file called brand_voice.txt in your project folder. Write 2–3 paragraphs describing your client's brand voice, list words and phrases they always use, list words and phrases they never use, and include three to five examples of approved ad copy they love. This file becomes the context Claude Code injects into every generation request.

Build the Ad Copy Generator

Tell Claude Code: "Build a Python script called ad_copy_generator.py that does the following: reads brand voice context from brand_voice.txt, accepts these inputs from a CSV file called 'ad_requests.csv' with columns for keyword, landing_page_url, primary_value_prop, and campaign_type, uses the Anthropic API to generate 15 headline variations (max 30 characters each) and 4 description variations (max 90 characters each) for each row, validates character counts before outputting, and saves results to 'generated_ads.csv' with all variations in separate columns."

You'll need your Anthropic API key in the .env file for this one. Claude Code will handle the API call structure, but make sure it's prompting the model with your brand voice document as part of the system prompt — not just the keyword. The difference in output quality is dramatic.

Add Quality Filters

Raw AI-generated ad copy often needs filtering. Tell Claude Code to add these validation layers to the script:

Flag any headline that uses superlatives like "best" or "greatest" without qualification (Google policy sensitivity)
Check that the primary keyword appears in at least three of the fifteen headlines
Flag descriptions that don't include a call to action
Remove any duplicate headlines within the same ad unit

Ask it to add a "quality_flags" column to the output CSV that lists any issues found for each ad set. This gives you a fast triage view when reviewing the output.

Build a Batch Processing Mode for Campaign Launches

The real leverage comes when you're launching a new campaign with fifty ad groups. Tell Claude Code: "Add a batch mode that processes every row in the CSV simultaneously using async API calls, and add a progress bar using the tqdm library so I can see how far along it is."

A fifty-ad-group campaign launch that used to take you three hours of creative work now takes four minutes of script runtime and thirty minutes of review. That's the shift you're after.

Pro Tip: Build a "regenerate" mode that accepts a specific row number from the CSV and regenerates just that ad set with a different random seed. This is faster than re-running the whole batch when one ad group's copy doesn't quite land.

Step 4 — Build a Custom Reporting Dashboard Your Clients Will Actually Read

Most client dashboards fail not because they lack data, but because they present data in the way that's easy for the platform to export rather than the way that's meaningful to the client. Building a custom dashboard with Claude Code lets you present exactly the metrics your clients care about, formatted the way they want to see them. Estimated time: 90–120 minutes.

This is one of the most impactful automations in terms of client relationship value. When a client receives a report that speaks their language — revenue impact instead of ROAS, leads instead of conversions, market share instead of impression share — the conversation changes from "explain these numbers" to "what should we do next."

Define the Report Structure Before Building

Ask your client (or yourself, if you're managing in-house) three questions: What's the one number that determines whether this month was good or bad? What do you need to explain to your boss about PPC performance? What decisions do you make based on this data?

The answers become your report structure. Everything else is secondary. Write it down: executive summary section, primary KPI section, campaign performance breakdown, and recommendations section.

Tell Claude Code to Build the Data Pipeline

Tell Claude Code: "Build a Python script that pulls the following data from the Google Ads API for the date range of the previous calendar month: total spend, total conversions, cost per conversion, conversion rate, impression share, top losing IS reason, and performance by campaign. Format the data into a pandas dataframe and export it to 'monthly_data.csv'."

Once the data pipeline works, you'll add the visualization layer. Tell Claude Code: "Using the plotly library, build a second script that reads monthly_data.csv and generates an HTML dashboard file with the following sections: a headline summary box showing total spend, conversions, and CPA with month-over-month arrows indicating direction, a line chart showing daily conversions over the month, a bar chart showing CPA by campaign sorted from lowest to highest, and a data table showing all campaigns with conditional formatting — green if CPA is below target, red if above. Save the output as 'monthly_report.html'."

Make It Brandable and Client-Ready

Tell Claude Code: "Add a configuration file called report_config.json where I can set the client's name, logo URL, brand color hex code, CPA target, and monthly budget. Use these values to customize the dashboard header, color scheme, and conditional formatting thresholds."

Now you have a report template that takes two minutes to configure per client and generates a professional, branded HTML file that you can email directly or upload to a client portal. The alternative — building this in Data Studio or Looker — requires ongoing maintenance and platform dependencies. Your HTML file works everywhere, forever, with no login required.

Automate the Monthly Send

Tell Claude Code: "Write a wrapper script called send_monthly_report.py that runs the data pipeline script, then the dashboard script, then emails the resulting HTML file as an attachment to the email addresses listed in report_config.json, using a professional email template. Schedule this to run on the first business day of each month at 8 AM."

Your monthly reporting is now fully automated. The time you save — typically three to six hours per client per month — compounds into significant capacity that you can redirect toward actual strategy work.

Step 5 — Build a Search Term Mining and Negative Keyword Tool

Search term analysis is one of the most valuable and most neglected recurring PPC tasks. Most managers do it inconsistently because it's tedious. Automating it means it happens every week without fail, and your accounts stay cleaner as a result. Estimated time: 60–90 minutes.

The workflow you're building here: pull search terms from the last seven days, classify them by intent, flag obvious irrelevant terms for negative keyword addition, surface new positive keyword opportunities, and output a prioritized action list.

Pull the Search Term Report

Tell Claude Code: "Write a Python script that queries the Google Ads API for the search terms report for the last 7 days, including search term, match type, impressions, clicks, conversions, cost, and which ad group it triggered. Output this to search_terms_raw.csv."

Build the Intent Classification Layer

This is where the AI capability of Claude Code becomes genuinely powerful beyond just code generation. Tell Claude Code: "Add a second stage to this script that reads search_terms_raw.csv and for each search term, uses the Anthropic API to classify it into one of these categories: Branded (contains our brand name), Competitor (contains a competitor brand name), Commercial Intent (clearly buying intent), Informational (research/learning intent), Irrelevant (clearly off-topic for our product category), and Ambiguous (unclear). Also ask it to suggest whether each term should be added as a positive keyword, a negative keyword, or monitored for more data. Add these classifications as new columns and save to search_terms_classified.csv."

To make the classification accurate, include your brand name, competitor names, and a one-paragraph description of your product/service in the prompt. Without this context, the AI's classifications will be generic and less useful.

Build the Negative Keyword Upload Function

Tell Claude Code: "Add a third stage that filters search_terms_classified.csv for rows where the recommendation is 'negative keyword' AND the classification confidence is high AND impressions are greater than 20, then formats these as a Google Ads Editor compatible CSV for negative keyword upload, and also prepares them as API calls to add them directly as campaign-level negatives. Include a --dry-run flag that shows what would be added without making changes."

Run this weekly. Over three months, you'll see quality scores improve, irrelevant spend decrease, and your accounts will require significantly less reactive fire-fighting because you're proactively pruning irrelevant traffic before it accumulates significant waste.

Surface Positive Keyword Opportunities

Tell Claude Code: "Add a final output that lists all search terms classified as 'Commercial Intent' with more than 10 clicks and zero conversions, sorted by spend descending, with a column suggesting which existing ad group they're most closely related to. Save this as 'keyword_opportunities.csv'."

This output is gold. These are searches that already triggered your ads, got enough clicks to be statistically meaningful, and converted at zero — which means either they're worth adding and testing properly with dedicated ad copy, or they're irrelevant and should be negated. That's a strategic decision you make; the script just surfaces the data systematically instead of leaving it buried in a report you pull quarterly.

How to Troubleshoot When Claude Code Gets Stuck

Even with an AI coding assistant, you'll hit walls. Knowing how to get unstuck quickly is what separates people who successfully automate their PPC work from people who give up after the first error message. Here's a practical troubleshooting framework.

When the Script Errors Out

Don't try to read the error yourself first. Paste the complete error message back to Claude Code immediately. Include: the full error text, the last thing you asked it to do, and any changes you made manually. Nine times out of ten, Claude Code will identify the problem and fix it in one response. The mistake most people make is trying to interpret the error themselves, making a change, and creating a new problem on top of the original one.

When the API Returns Unexpected Data

Tell Claude Code to add print statements that output the raw API response before any processing happens. This lets you see exactly what the API is returning versus what the script expects. Google Ads API responses in particular have nested structures that can be tricky — seeing the raw JSON output immediately clarifies the mismatch.

When the Script Runs But Produces Wrong Results

Ask Claude Code to add a validation mode: "Add a --validate flag that processes just the first five rows, prints every intermediate calculation step to the console, and shows me what the output would be before writing any files." Seeing the step-by-step logic on real data is the fastest way to find where the logic diverges from your intention.

When You're Not Sure What to Ask For

Start with the output, not the process. Instead of trying to describe the technical implementation, describe what you want to see when the script is done. "I want a CSV with these columns containing this data" gives Claude Code the information it needs to figure out the implementation. If you can show it an example of the desired output — even a manually created fake version — the results improve dramatically.

If you want to go from zero to building your own PPC automation tools with confidence, Adventure Media is running a hands-on Claude Code workshop called "Master Claude Code in One Day" designed specifically for marketers and non-developers. It covers real project builds — not theory — in a structured single-day format. It's worth checking out if you want accountability and guidance alongside independent practice.

Putting It All Together: Your PPC Automation Stack

The five scripts you've built in this guide form a cohesive automation stack that covers the core repetitive work of campaign management. But the real power comes from connecting them into a coherent workflow rather than running them as isolated tools.

Here's how the full stack operates on a weekly rhythm:

Daily (6 AM): Bid adjustment script runs, logs changes, emails summary
Weekly (Monday 7 AM): Search term mining script runs, generates classified CSV and negative keyword upload file for your review
Weekly (Monday 8 AM): You spend 30 minutes reviewing bid change logs and search term recommendations, approving what's correct and flagging anything anomalous
On-demand: Ad copy generator runs when launching new ad groups or refreshing creative
Monthly (first business day): Reporting dashboard generates and sends automatically

The time this stack replaces: roughly eight to twelve hours per week of manual repetitive work per account, depending on account size and complexity. That's real capacity that can be redirected toward landing page strategy, audience testing, competitive analysis, or winning new clients.

Building Your Own Custom Automations Beyond This Guide

Once you're comfortable with the pattern — define the logic, describe the output, let Claude Code build and test, add safety layers, schedule it — you can apply it to virtually any repeatable PPC task. Some high-value candidates to build next:

Quality score monitoring with automated alerts when scores drop below 5
Budget pacing tracker that alerts you when campaigns are trending toward over- or under-delivery mid-month
Competitor ad monitoring using the Google Ads Auction Insights report
Automated A/B test significance calculator that emails you when a test reaches statistical significance
Landing page uptime monitor that pauses campaigns if a destination URL returns a 404

Every one of these follows the same pattern you've already learned. The learning curve is front-loaded; each new automation you build takes less time than the last.

Frequently Asked Questions

Do I need to know how to code to use Claude Code for PPC automation?

No — but you need to be comfortable with your terminal and willing to learn basic concepts as you go. Claude Code writes the code for you, but you'll need to understand enough to spot when something looks wrong and describe your logic clearly. Most PPC professionals pick up the necessary basics within their first two or three projects.

Is it safe to connect Claude Code to my live Google Ads account?

Yes, with appropriate safeguards in place. Always use Google's test account environment first, always implement dry-run modes before live execution, always log every automated change, and always build maximum change caps into any script that modifies bids or budgets. The risk isn't the AI — it's running untested logic against live accounts. Test thoroughly and the risk is manageable.

How much does this cost to run?

The primary costs are your Claude Pro or Anthropic API subscription and any Google Ads API usage. The Google Ads API is free for standard usage within the API quota limits. Anthropic's API pricing is usage-based and typically costs a few dollars per month for the volume of API calls a PPC automation stack like this would generate. Claude Pro as a flat subscription is an alternative if you're primarily working interactively. Total monthly cost for most PPC professionals building and running this stack: well under $50.

Will Google penalize my account for using automated bid management scripts?

No — Google explicitly supports API-based automation and provides the API for exactly this purpose. Google's terms of service prohibit automated clicking or fraudulent behavior, not automated bid management. The Google Ads API is designed for building automation tools. What you're building is functionally similar to what third-party bid management platforms do, except you own and control the logic.

How long does it take to build all five automations?

Expect to invest eight to fifteen hours across your first week, depending on your comfort level. The environment setup takes the most time initially. Each subsequent script builds faster because you're reusing patterns and existing code. By your second week, you'll be building new automations in an hour or two rather than an afternoon.

Can I use this with Microsoft Advertising (Bing Ads) as well?

Yes — Microsoft Advertising has its own API with very similar structure to the Google Ads API. Tell Claude Code which platform you're targeting and it will use the appropriate API library. Many of the scripts in this guide can be adapted to Microsoft Advertising with relatively minor modifications. Running parallel scripts for both platforms doubles the automation coverage without doubling the build time.

What happens when Google updates its API and my scripts break?

API updates do occasionally break scripts — this is a real maintenance consideration. Google Ads API versions are typically supported for twelve to eighteen months after release before being deprecated, and they publish breaking changes well in advance. When an update breaks your script, paste the error into Claude Code with the changelog notes from Google and it will update the code to the new API version. This is typically a thirty to sixty minute fix, not a rebuild.

Can Claude Code access my Google Ads account directly without me building API scripts?

Not natively — you need to build the API connection as described in this guide. Claude Code can interact with files, run scripts, and make API calls, but it doesn't have a pre-built Google Ads integration. The API script you build in Step 1 is the bridge between Claude Code's capabilities and your account data. Third-party tools are beginning to offer pre-built integrations, but building your own gives you far more control and customization.

How do I handle accounts with multiple clients in the same automation setup?

Build your scripts to accept a customer ID as a parameter from the start, rather than hardcoding it. This is a simple change to make early and a painful refactor to make later. With parameterized customer IDs, you can run the same bid adjustment script across all your client accounts sequentially with a simple loop, and the report generator can produce branded output for each client from the same base script with different config files.

What's the best way to present AI-generated ad copy to clients for approval?

Export the generated ads to a Google Sheet with a simple approval column. Clients can mark each ad set as Approved, Needs Revision, or Rejected with comments. Claude Code can build a script that reads the approved rows from the sheet and creates a Google Ads Editor upload file automatically, so you move from client approval to campaign upload in minutes. This workflow also gives clients the visibility and control they want without adding significant time to your process.

Can I use these same techniques for Meta Ads or LinkedIn Ads?

Yes — the same pattern applies to any platform with a publicly available API. Meta's Marketing API and LinkedIn's Campaign Manager API both support programmatic campaign management. The specific API libraries and authentication methods differ, but the Claude Code workflow — describe the logic, build the connection, add safety layers, schedule it — is identical. Tell Claude Code which platform you're targeting and it will use the right libraries and API structure.

What's the single most valuable automation to build first if I only have time for one?

Build the search term mining and negative keyword tool first. It requires no live account changes to start (the dry-run mode is safe), it produces immediate visible value by surfacing wasted spend, and it runs weekly so the benefit compounds quickly. The bid adjustment script has higher long-term impact, but requires more careful testing before it runs live. Start with the search term tool, build your confidence with the pattern, then move to bid management.

Conclusion: The Compounding Advantage of Building Your Own PPC Automation Stack

The PPC professionals who will dominate the next three to five years aren't necessarily the ones who know the most about bidding strategy or ad copy theory. They're the ones who build systems that execute good strategy consistently, at scale, without requiring their personal attention for every repetitive task.

Claude Code makes that accessible to marketers who aren't developers. You don't need to learn Python from scratch. You don't need to hire a developer to build automation tools. You need to learn how to describe your logic clearly, test carefully before going live, and invest a few hours upfront to save dozens of hours every month going forward.

The five automations in this guide — environment setup, bid management, ad copy generation, custom reporting, and search term mining — represent a real, buildable PPC automation stack. Each one solves a specific, recurring pain point. Together, they free up ten or more hours per week that you can redirect toward the strategic, creative, and client-facing work that actually grows accounts.

Start with one. Get it working. Build the next one. By the time you've built all five, you'll have a clear sense of which additional automations would create the most value in your specific situation — and you'll have the skills and confidence to build them.

The campaigns that matter most deserve your strategic attention. Let the scripts handle the rest.

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Most PPC professionals are drowning in repetitive tasks. You're manually pulling reports at 7 AM, copy-pasting bid adjustments across dozens of ad groups, writing the same headline variations you've written a hundred times, and building the same Excel pivot tables your clients have requested for three years running. Meanwhile, the campaigns you actually need to think about — the ones with real strategic problems — sit waiting while you fight fires in a spreadsheet.

Here's what changes when you put Claude Code in your workflow: those repetitive tasks become scripts. Those scripts run while you sleep. And you get your mornings back.

Limited Event

Master Claude Code in One Day — Live Workshop by Adventure Media

Go from zero coding experience to building real AI-powered tools. Hands-on projects, expert guidance, no fluff.

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This guide is specifically for PPC marketers — not developers, not data scientists — who want to start automating real campaign work using Claude Code. We'll walk through five high-impact automation projects, the exact steps to build them, and how to avoid the mistakes that waste your time before you write a single line of code. By the end, you'll have a working foundation for a PPC automation stack that would have taken a developer weeks to build from scratch.

What Is Claude Code and Why Should PPC Managers Care?

Claude Code is Anthropic's agentic coding tool that runs directly in your terminal and can read, write, and execute code on your local machine. Unlike a chatbot you type questions into, Claude Code can actually interact with your file system, run scripts, call APIs, and complete multi-step technical tasks autonomously. For PPC professionals, this distinction matters enormously.

Traditional AI chatbots can help you brainstorm ad copy or explain a concept. Claude Code can actually build the tool that generates and tests that ad copy against your account data, automatically. It bridges the gap between "I have an idea for an automation" and "the automation is running and saving me four hours a week."

The reason PPC specifically benefits from this kind of tool comes down to the nature of the work. Pay-per-click campaign management is unusually data-dense and rule-driven. Most of what a mid-level PPC manager does in a given week follows patterns: if this metric crosses this threshold, do this thing. If this search term shows up in the query report, add it as a negative. If conversion rate drops below this level, reduce bids here. These are logical, repeatable rules — exactly the kind of thing that should be automated.

What's kept PPC professionals from automating this work historically is the technical barrier. Google Ads scripts require JavaScript knowledge. The Google Ads API requires OAuth setup, library installation, and backend architecture knowledge. Even Microsoft Advertising's automation options assume you're comfortable in a development environment. Claude Code collapses that barrier dramatically. You describe what you want in plain language, and it writes, tests, and refines the code for you in real time.

What You'll Need Before You Start

Before diving into the step-by-step projects below, make sure you have the following in place:

Claude Code installed: You'll need a Claude Pro or API subscription from Anthropic and Claude Code running in your terminal. Anthropic's official Claude Code documentation walks through the installation process clearly.
Python 3.10 or higher: Most of what we'll build uses Python. If you don't have it, download it from python.org and install it — Claude Code will help you verify it's working.
A Google Ads or Microsoft Advertising account with API access: You'll need developer tokens and credentials. Both platforms have free sandbox environments for testing.
Basic comfort with your terminal: You don't need to know how to code, but you should be comfortable opening Terminal (Mac) or Command Prompt/PowerShell (Windows) and running simple commands.
Estimated setup time: 45–90 minutes to get your environment ready before building anything.

Step 1 — Set Up Your PPC Automation Environment (Without Breaking Anything)

Your first task is building a safe, isolated workspace where Claude Code can experiment without touching live campaign data. This is the step most people skip, and it's the step that prevents disasters. Estimated time: 30–45 minutes.

Open your terminal and navigate to a folder where you want to keep your PPC automation projects. If you don't have one, create it now:

Type: mkdir ppc-automation && cd ppc-automation

Now start Claude Code by typing claude in that directory. You'll see the Claude Code interface load. From here, everything you build will be contained in this folder unless you explicitly tell it otherwise.

Create a Virtual Environment

Tell Claude Code: "Set up a Python virtual environment in this directory, activate it, and install the google-ads library, pandas, and python-dotenv. Then create a .env file template with placeholder values for GOOGLE_ADS_DEVELOPER_TOKEN, CLIENT_ID, CLIENT_SECRET, REFRESH_TOKEN, and CUSTOMER_ID."

Claude Code will write and execute all of this. Watch what it does — you'll learn the pattern quickly. The virtual environment isolates your project dependencies so nothing conflicts with other software on your machine.

Connect to the Google Ads API in Test Mode

Once your environment is set up, you need credentials. In your Google Ads account, go to Tools & Settings → API Center and apply for a developer token. While waiting for approval (it can take a few days), you can use the test account environment, which gives you a sandbox to run API calls against without affecting real spend.

Tell Claude Code: "Write a Python script that loads credentials from my .env file and makes a simple test call to the Google Ads API to verify the connection — just pull the account name and currency code for the customer ID in my .env file."

When Claude Code writes this script, it will typically create a test_connection.py file. Run it, and if you see your account name returned without errors, you're connected. If you see an error, paste it back to Claude Code and it will diagnose and fix it.

Common Mistakes to Avoid in Step 1

Don't put real credentials in code files. Always use the .env file. Claude Code will sometimes inline credentials in examples — immediately ask it to move them to environment variables instead.
Don't test against live production accounts until you've verified scripts in the test environment. One miswritten script can modify hundreds of ad groups simultaneously.
Don't skip the virtual environment. Package conflicts will cause cryptic errors that Claude Code will waste time debugging when they could have been avoided entirely.

Pro Tip: Ask Claude Code to create a README.md in your project folder documenting what each script does and how to run it. Future-you will be grateful.

Step 2 — Build an Automated Bid Adjustment Script That Actually Makes Sense

Automated bid adjustments based on real performance data are one of the highest-ROI automations a PPC manager can build. The goal here is to create a script that pulls keyword performance data, applies your own bidding logic, and either executes changes directly or outputs a review file for your approval. Estimated time: 60–90 minutes.

This is where Claude Code starts to feel genuinely powerful. You're not writing code from scratch — you're describing your bidding strategy in plain English and letting Claude Code translate it into executable logic.

Define Your Bidding Rules Before Touching Code

Before you open Claude Code, write out your bidding logic on paper. A simple example:

If a keyword has more than 50 conversions in the last 30 days and CPA is more than 20% below target: increase bid by 15%
If a keyword has more than 30 clicks and zero conversions in the last 30 days: decrease bid by 25%
If a keyword's impression share is below 40% and CPA is on target: increase bid by 10%
Never let a bid go above $45 or below $0.50

Having these rules written down before you start means Claude Code is translating your strategy into code — not making strategic decisions for you. That's an important distinction. The AI handles the implementation; you own the logic.

Tell Claude Code Exactly What to Build

In your Claude Code session, say: "I need a Python script that does the following: connects to the Google Ads API using credentials from my .env file, pulls keyword performance data for the last 30 days including clicks, impressions, conversions, cost, average CPC, and impression share, then applies the following bidding rules [paste your rules], and outputs a CSV file called 'bid_recommendations.csv' with columns for keyword ID, current bid, recommended bid, percentage change, and the rule that triggered the change. Don't execute any bid changes yet — just generate the recommendation file."

Notice what you're doing: you're asking for a recommendation output first, not live execution. This is a critical safety layer. Review the CSV, verify the logic is working correctly across different keyword scenarios, and only then build the execution layer.

Add the Execution Layer With Approval Gates

Once you've verified the recommendations look correct across at least two or three real reporting periods, tell Claude Code: "Modify the script to add an execution mode. When run with the flag --execute, it should apply the bid changes in the CSV to the live account via the API, log every change made to a file called 'bid_change_log.txt' with timestamps, and send me a summary email using Python's smtplib."

The logging component is non-negotiable. You need an audit trail of every automated change. If something goes wrong — and at some point, something will — you need to know exactly what the script changed and when so you can reverse it quickly.

Schedule It to Run Automatically

On Mac/Linux, ask Claude Code: "Show me how to add this script to cron so it runs every morning at 6 AM." On Windows, ask it to set up a Windows Task Scheduler entry. Claude Code will write the exact command or configuration file you need.

Warning: Set a maximum spend change cap in your script. Something like: "If the total bid changes across all keywords would increase monthly spend by more than $X, abort and send an alert instead." This prevents a data anomaly from triggering runaway bid increases.

Step 3 — Automate Ad Copy Generation That Matches Your Brand Voice

The most time-consuming part of PPC creative work isn't writing one ad — it's writing fifty variations that stay on-brand, hit character limits, and actually say something different from each other. Claude Code can build you a tool that does this in seconds, not hours. Estimated time: 45–75 minutes.

The key insight here is that you're not just asking an AI to write ads. You're building a repeatable system that takes structured inputs (keyword, landing page URL, value proposition, tone) and outputs complete, review-ready ad copy formatted for Google Ads' Responsive Search Ad format — with headlines, descriptions, and character counts validated automatically.

Create Your Brand Voice Document

Before building the tool, create a plain text file called brand_voice.txt in your project folder. Write 2–3 paragraphs describing your client's brand voice, list words and phrases they always use, list words and phrases they never use, and include three to five examples of approved ad copy they love. This file becomes the context Claude Code injects into every generation request.

Build the Ad Copy Generator

Tell Claude Code: "Build a Python script called ad_copy_generator.py that does the following: reads brand voice context from brand_voice.txt, accepts these inputs from a CSV file called 'ad_requests.csv' with columns for keyword, landing_page_url, primary_value_prop, and campaign_type, uses the Anthropic API to generate 15 headline variations (max 30 characters each) and 4 description variations (max 90 characters each) for each row, validates character counts before outputting, and saves results to 'generated_ads.csv' with all variations in separate columns."

You'll need your Anthropic API key in the .env file for this one. Claude Code will handle the API call structure, but make sure it's prompting the model with your brand voice document as part of the system prompt — not just the keyword. The difference in output quality is dramatic.

Add Quality Filters

Raw AI-generated ad copy often needs filtering. Tell Claude Code to add these validation layers to the script:

Flag any headline that uses superlatives like "best" or "greatest" without qualification (Google policy sensitivity)
Check that the primary keyword appears in at least three of the fifteen headlines
Flag descriptions that don't include a call to action
Remove any duplicate headlines within the same ad unit

Ask it to add a "quality_flags" column to the output CSV that lists any issues found for each ad set. This gives you a fast triage view when reviewing the output.

Build a Batch Processing Mode for Campaign Launches

The real leverage comes when you're launching a new campaign with fifty ad groups. Tell Claude Code: "Add a batch mode that processes every row in the CSV simultaneously using async API calls, and add a progress bar using the tqdm library so I can see how far along it is."

A fifty-ad-group campaign launch that used to take you three hours of creative work now takes four minutes of script runtime and thirty minutes of review. That's the shift you're after.

Pro Tip: Build a "regenerate" mode that accepts a specific row number from the CSV and regenerates just that ad set with a different random seed. This is faster than re-running the whole batch when one ad group's copy doesn't quite land.

Step 4 — Build a Custom Reporting Dashboard Your Clients Will Actually Read

Most client dashboards fail not because they lack data, but because they present data in the way that's easy for the platform to export rather than the way that's meaningful to the client. Building a custom dashboard with Claude Code lets you present exactly the metrics your clients care about, formatted the way they want to see them. Estimated time: 90–120 minutes.

This is one of the most impactful automations in terms of client relationship value. When a client receives a report that speaks their language — revenue impact instead of ROAS, leads instead of conversions, market share instead of impression share — the conversation changes from "explain these numbers" to "what should we do next."

Define the Report Structure Before Building

Ask your client (or yourself, if you're managing in-house) three questions: What's the one number that determines whether this month was good or bad? What do you need to explain to your boss about PPC performance? What decisions do you make based on this data?

The answers become your report structure. Everything else is secondary. Write it down: executive summary section, primary KPI section, campaign performance breakdown, and recommendations section.

Tell Claude Code to Build the Data Pipeline

Tell Claude Code: "Build a Python script that pulls the following data from the Google Ads API for the date range of the previous calendar month: total spend, total conversions, cost per conversion, conversion rate, impression share, top losing IS reason, and performance by campaign. Format the data into a pandas dataframe and export it to 'monthly_data.csv'."

Once the data pipeline works, you'll add the visualization layer. Tell Claude Code: "Using the plotly library, build a second script that reads monthly_data.csv and generates an HTML dashboard file with the following sections: a headline summary box showing total spend, conversions, and CPA with month-over-month arrows indicating direction, a line chart showing daily conversions over the month, a bar chart showing CPA by campaign sorted from lowest to highest, and a data table showing all campaigns with conditional formatting — green if CPA is below target, red if above. Save the output as 'monthly_report.html'."

Make It Brandable and Client-Ready

Tell Claude Code: "Add a configuration file called report_config.json where I can set the client's name, logo URL, brand color hex code, CPA target, and monthly budget. Use these values to customize the dashboard header, color scheme, and conditional formatting thresholds."

Now you have a report template that takes two minutes to configure per client and generates a professional, branded HTML file that you can email directly or upload to a client portal. The alternative — building this in Data Studio or Looker — requires ongoing maintenance and platform dependencies. Your HTML file works everywhere, forever, with no login required.

Automate the Monthly Send

Tell Claude Code: "Write a wrapper script called send_monthly_report.py that runs the data pipeline script, then the dashboard script, then emails the resulting HTML file as an attachment to the email addresses listed in report_config.json, using a professional email template. Schedule this to run on the first business day of each month at 8 AM."

Your monthly reporting is now fully automated. The time you save — typically three to six hours per client per month — compounds into significant capacity that you can redirect toward actual strategy work.

Step 5 — Build a Search Term Mining and Negative Keyword Tool

Search term analysis is one of the most valuable and most neglected recurring PPC tasks. Most managers do it inconsistently because it's tedious. Automating it means it happens every week without fail, and your accounts stay cleaner as a result. Estimated time: 60–90 minutes.

The workflow you're building here: pull search terms from the last seven days, classify them by intent, flag obvious irrelevant terms for negative keyword addition, surface new positive keyword opportunities, and output a prioritized action list.

Pull the Search Term Report

Tell Claude Code: "Write a Python script that queries the Google Ads API for the search terms report for the last 7 days, including search term, match type, impressions, clicks, conversions, cost, and which ad group it triggered. Output this to search_terms_raw.csv."

Build the Intent Classification Layer

This is where the AI capability of Claude Code becomes genuinely powerful beyond just code generation. Tell Claude Code: "Add a second stage to this script that reads search_terms_raw.csv and for each search term, uses the Anthropic API to classify it into one of these categories: Branded (contains our brand name), Competitor (contains a competitor brand name), Commercial Intent (clearly buying intent), Informational (research/learning intent), Irrelevant (clearly off-topic for our product category), and Ambiguous (unclear). Also ask it to suggest whether each term should be added as a positive keyword, a negative keyword, or monitored for more data. Add these classifications as new columns and save to search_terms_classified.csv."

To make the classification accurate, include your brand name, competitor names, and a one-paragraph description of your product/service in the prompt. Without this context, the AI's classifications will be generic and less useful.

Build the Negative Keyword Upload Function

Tell Claude Code: "Add a third stage that filters search_terms_classified.csv for rows where the recommendation is 'negative keyword' AND the classification confidence is high AND impressions are greater than 20, then formats these as a Google Ads Editor compatible CSV for negative keyword upload, and also prepares them as API calls to add them directly as campaign-level negatives. Include a --dry-run flag that shows what would be added without making changes."

Run this weekly. Over three months, you'll see quality scores improve, irrelevant spend decrease, and your accounts will require significantly less reactive fire-fighting because you're proactively pruning irrelevant traffic before it accumulates significant waste.

Surface Positive Keyword Opportunities

Tell Claude Code: "Add a final output that lists all search terms classified as 'Commercial Intent' with more than 10 clicks and zero conversions, sorted by spend descending, with a column suggesting which existing ad group they're most closely related to. Save this as 'keyword_opportunities.csv'."

This output is gold. These are searches that already triggered your ads, got enough clicks to be statistically meaningful, and converted at zero — which means either they're worth adding and testing properly with dedicated ad copy, or they're irrelevant and should be negated. That's a strategic decision you make; the script just surfaces the data systematically instead of leaving it buried in a report you pull quarterly.

How to Troubleshoot When Claude Code Gets Stuck

Even with an AI coding assistant, you'll hit walls. Knowing how to get unstuck quickly is what separates people who successfully automate their PPC work from people who give up after the first error message. Here's a practical troubleshooting framework.

When the Script Errors Out

Don't try to read the error yourself first. Paste the complete error message back to Claude Code immediately. Include: the full error text, the last thing you asked it to do, and any changes you made manually. Nine times out of ten, Claude Code will identify the problem and fix it in one response. The mistake most people make is trying to interpret the error themselves, making a change, and creating a new problem on top of the original one.

When the API Returns Unexpected Data

Tell Claude Code to add print statements that output the raw API response before any processing happens. This lets you see exactly what the API is returning versus what the script expects. Google Ads API responses in particular have nested structures that can be tricky — seeing the raw JSON output immediately clarifies the mismatch.

When the Script Runs But Produces Wrong Results

Ask Claude Code to add a validation mode: "Add a --validate flag that processes just the first five rows, prints every intermediate calculation step to the console, and shows me what the output would be before writing any files." Seeing the step-by-step logic on real data is the fastest way to find where the logic diverges from your intention.

When You're Not Sure What to Ask For

Start with the output, not the process. Instead of trying to describe the technical implementation, describe what you want to see when the script is done. "I want a CSV with these columns containing this data" gives Claude Code the information it needs to figure out the implementation. If you can show it an example of the desired output — even a manually created fake version — the results improve dramatically.

If you want to go from zero to building your own PPC automation tools with confidence, Adventure Media is running a hands-on Claude Code workshop called "Master Claude Code in One Day" designed specifically for marketers and non-developers. It covers real project builds — not theory — in a structured single-day format. It's worth checking out if you want accountability and guidance alongside independent practice.

Putting It All Together: Your PPC Automation Stack

The five scripts you've built in this guide form a cohesive automation stack that covers the core repetitive work of campaign management. But the real power comes from connecting them into a coherent workflow rather than running them as isolated tools.

Here's how the full stack operates on a weekly rhythm:

Daily (6 AM): Bid adjustment script runs, logs changes, emails summary
Weekly (Monday 7 AM): Search term mining script runs, generates classified CSV and negative keyword upload file for your review
Weekly (Monday 8 AM): You spend 30 minutes reviewing bid change logs and search term recommendations, approving what's correct and flagging anything anomalous
On-demand: Ad copy generator runs when launching new ad groups or refreshing creative
Monthly (first business day): Reporting dashboard generates and sends automatically

The time this stack replaces: roughly eight to twelve hours per week of manual repetitive work per account, depending on account size and complexity. That's real capacity that can be redirected toward landing page strategy, audience testing, competitive analysis, or winning new clients.

Building Your Own Custom Automations Beyond This Guide

Once you're comfortable with the pattern — define the logic, describe the output, let Claude Code build and test, add safety layers, schedule it — you can apply it to virtually any repeatable PPC task. Some high-value candidates to build next:

Quality score monitoring with automated alerts when scores drop below 5
Budget pacing tracker that alerts you when campaigns are trending toward over- or under-delivery mid-month
Competitor ad monitoring using the Google Ads Auction Insights report
Automated A/B test significance calculator that emails you when a test reaches statistical significance
Landing page uptime monitor that pauses campaigns if a destination URL returns a 404

Every one of these follows the same pattern you've already learned. The learning curve is front-loaded; each new automation you build takes less time than the last.

Frequently Asked Questions

Do I need to know how to code to use Claude Code for PPC automation?

No — but you need to be comfortable with your terminal and willing to learn basic concepts as you go. Claude Code writes the code for you, but you'll need to understand enough to spot when something looks wrong and describe your logic clearly. Most PPC professionals pick up the necessary basics within their first two or three projects.

Is it safe to connect Claude Code to my live Google Ads account?

Yes, with appropriate safeguards in place. Always use Google's test account environment first, always implement dry-run modes before live execution, always log every automated change, and always build maximum change caps into any script that modifies bids or budgets. The risk isn't the AI — it's running untested logic against live accounts. Test thoroughly and the risk is manageable.

How much does this cost to run?

The primary costs are your Claude Pro or Anthropic API subscription and any Google Ads API usage. The Google Ads API is free for standard usage within the API quota limits. Anthropic's API pricing is usage-based and typically costs a few dollars per month for the volume of API calls a PPC automation stack like this would generate. Claude Pro as a flat subscription is an alternative if you're primarily working interactively. Total monthly cost for most PPC professionals building and running this stack: well under $50.

Will Google penalize my account for using automated bid management scripts?

No — Google explicitly supports API-based automation and provides the API for exactly this purpose. Google's terms of service prohibit automated clicking or fraudulent behavior, not automated bid management. The Google Ads API is designed for building automation tools. What you're building is functionally similar to what third-party bid management platforms do, except you own and control the logic.

How long does it take to build all five automations?

Expect to invest eight to fifteen hours across your first week, depending on your comfort level. The environment setup takes the most time initially. Each subsequent script builds faster because you're reusing patterns and existing code. By your second week, you'll be building new automations in an hour or two rather than an afternoon.

Can I use this with Microsoft Advertising (Bing Ads) as well?

Yes — Microsoft Advertising has its own API with very similar structure to the Google Ads API. Tell Claude Code which platform you're targeting and it will use the appropriate API library. Many of the scripts in this guide can be adapted to Microsoft Advertising with relatively minor modifications. Running parallel scripts for both platforms doubles the automation coverage without doubling the build time.

What happens when Google updates its API and my scripts break?

API updates do occasionally break scripts — this is a real maintenance consideration. Google Ads API versions are typically supported for twelve to eighteen months after release before being deprecated, and they publish breaking changes well in advance. When an update breaks your script, paste the error into Claude Code with the changelog notes from Google and it will update the code to the new API version. This is typically a thirty to sixty minute fix, not a rebuild.

Can Claude Code access my Google Ads account directly without me building API scripts?

Not natively — you need to build the API connection as described in this guide. Claude Code can interact with files, run scripts, and make API calls, but it doesn't have a pre-built Google Ads integration. The API script you build in Step 1 is the bridge between Claude Code's capabilities and your account data. Third-party tools are beginning to offer pre-built integrations, but building your own gives you far more control and customization.

How do I handle accounts with multiple clients in the same automation setup?

Build your scripts to accept a customer ID as a parameter from the start, rather than hardcoding it. This is a simple change to make early and a painful refactor to make later. With parameterized customer IDs, you can run the same bid adjustment script across all your client accounts sequentially with a simple loop, and the report generator can produce branded output for each client from the same base script with different config files.

What's the best way to present AI-generated ad copy to clients for approval?

Export the generated ads to a Google Sheet with a simple approval column. Clients can mark each ad set as Approved, Needs Revision, or Rejected with comments. Claude Code can build a script that reads the approved rows from the sheet and creates a Google Ads Editor upload file automatically, so you move from client approval to campaign upload in minutes. This workflow also gives clients the visibility and control they want without adding significant time to your process.

Can I use these same techniques for Meta Ads or LinkedIn Ads?

Yes — the same pattern applies to any platform with a publicly available API. Meta's Marketing API and LinkedIn's Campaign Manager API both support programmatic campaign management. The specific API libraries and authentication methods differ, but the Claude Code workflow — describe the logic, build the connection, add safety layers, schedule it — is identical. Tell Claude Code which platform you're targeting and it will use the right libraries and API structure.

What's the single most valuable automation to build first if I only have time for one?

Build the search term mining and negative keyword tool first. It requires no live account changes to start (the dry-run mode is safe), it produces immediate visible value by surfacing wasted spend, and it runs weekly so the benefit compounds quickly. The bid adjustment script has higher long-term impact, but requires more careful testing before it runs live. Start with the search term tool, build your confidence with the pattern, then move to bid management.

Conclusion: The Compounding Advantage of Building Your Own PPC Automation Stack

The PPC professionals who will dominate the next three to five years aren't necessarily the ones who know the most about bidding strategy or ad copy theory. They're the ones who build systems that execute good strategy consistently, at scale, without requiring their personal attention for every repetitive task.

Claude Code makes that accessible to marketers who aren't developers. You don't need to learn Python from scratch. You don't need to hire a developer to build automation tools. You need to learn how to describe your logic clearly, test carefully before going live, and invest a few hours upfront to save dozens of hours every month going forward.

The five automations in this guide — environment setup, bid management, ad copy generation, custom reporting, and search term mining — represent a real, buildable PPC automation stack. Each one solves a specific, recurring pain point. Together, they free up ten or more hours per week that you can redirect toward the strategic, creative, and client-facing work that actually grows accounts.

Start with one. Get it working. Build the next one. By the time you've built all five, you'll have a clear sense of which additional automations would create the most value in your specific situation — and you'll have the skills and confidence to build them.

The campaigns that matter most deserve your strategic attention. Let the scripts handle the rest.

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