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Claude Code for Marketers

Claude Code for marketers means using Claude Code, Anthropic's AI coding agent, for marketing work on your own computer: cleaning messy CSV exports, building small tools such as a UTM builder page, and analysing ad data locally. You describe the task in plain English, and it writes and runs the necessary code, asking your permission first.

  • Works in a folder: It reads and writes files inside the project folder where you start it.
  • Asks before acting: It asks permission before editing files or running commands, unless you change that setting.
  • Remembers rules: A CLAUDE.md file stores your naming conventions and instructions for every session.
  • Good for repetitive chores: It handles data cleaning, combining exports, small web pages and quick analysis.
  • Not for personal data: Customer names, phone numbers and email addresses always stay out of the folder.
Claude Code for marketers: local files in, clean files outAn Indore home decor brand keeps a project folder with a CLAUDE.md instructions file, Meta Ads and Google Ads exports from its Diwali sale, and its UTM naming rules. Claude Code reads the files, proposes a plan, asks permission before running commands, and writes new files: a combined ads CSV, a summary by channel and a UTM builder page. Personal data is removed before any file goes into the folder.Project folderCLAUDE.mdmeta_ads_diwali.csvgoogle_ads_diwali.csvutm_rules.txtClaude Code1. Reads the files2. Proposes a plan3. Asks before it runs4. Writes new filesNew filesads_combined.csvsummary_by_channel.mdutm-builder.htmlRemove names, phone numbers and emails before any file enters the folderSimplified illustration of an Indore home decor brand's Diwali sale files
Claude Code for marketers: local files in, clean files out

This tutorial follows one example throughout, a home decor brand from Indore running a Diwali sale on Meta Ads and Google Ads. Its marketer downloads exports from both platforms every few days, and the files never match, because they use different column names, date formats and campaign names. She also needs a simple, reliable way for the team to build tagged links. Claude Code handles all three jobs on her laptop, without any customer data leaving the business's control.

Prerequisites for Claude Code for Marketers

  • A Claude plan or API account: Claude Code needs a paid Claude subscription or an Anthropic Console account.
  • A computer with a terminal: A machine running macOS, Linux or Windows, although Claude Code also runs inside some code editors and desktop applications.
  • Clean inputs: Use campaign-level exports only, and if an export contains personal data, delete those columns before copying the file.
  • Basic idea of the tool: The Claude Code tutorial explains how the agent reads files, plans and runs commands.
  • Your UTM rules: Know how the brand names sources, mediums and campaigns, as covered in UTM parameters.

Setup: Install and Create a Project Folder

Install Claude Code using the method on Anthropic's documentation page, then create a folder for the Diwali work and start Claude Code inside it.

Bash
mkdir diwali-sale-data
cd diwali-sale-data
claude

The first time you start it, Claude Code asks you to sign in. Next, create a CLAUDE.md file in the folder containing the brand's rules. You can ask Claude Code to draft this file and then edit it yourself, so that every future session follows the same conventions:

Example
# Diwali sale data: rules
- Never add or keep columns with customer names, phone numbers, emails or addresses.
- Dates are DD-MM-YYYY in outputs. Currency is INR.
- UTM rules: all lowercase, words joined with underscores.
  utm_source: meta, google, whatsapp, email
  utm_medium: paid_social, cpc, broadcast, newsletter
  utm_campaign: festival_offer names, such as diwali_sale
- Keep the original export files unchanged. Write new files to /output.

Step-by-Step: Three Marketing Jobs

Step 1: Put the Exports in the Folder

Copy the Meta Ads and Google Ads exports into the folder, using descriptive names such as meta_ads_diwali.csv and google_ads_diwali.csv. Open each file first and confirm that it contains no personal data columns. Campaign exports usually contain the campaign, the ad set or ad group, the date, spend, impressions, clicks and conversions, which is everything this analysis requires.

Step 2: Clean and Combine the Exports

Type a clear request:

Example
Read meta_ads_diwali.csv and google_ads_diwali.csv.
Make one file, output/ads_combined.csv, with these columns:
date, platform, campaign, spend_inr, impressions, clicks, purchases, purchase_value_inr.
Map each platform's column names to these. Fix date formats. Remove the totals row if there is one.
Before writing, show me the column mapping you plan to use and any rows you will drop.

Claude Code reads both files, displays its proposed mapping, and asks permission before running its script. Check the mapping carefully, because Meta's "Amount spent (INR)" column and Google's "Cost" column should both map to spend_inr.

Step 3: Check the Output

Ask Claude Code to print the total spend per platform from the combined file, then compare those totals with Ads Manager and Google Ads for the same dates. If a total does not match, ask it to explain which rows caused the difference. Never skip this verification, because every later analysis depends on these totals being correct.

Step 4: Analyse the Sale Locally

Ask a focused question:

Example
Using output/ads_combined.csv, make output/summary_by_channel.md with:
spend, purchases, purchase value, cost per purchase and ROAS by platform and by campaign, sorted by spend.
Show the formula for each metric. Note that the two platforms may count the same order.
Do not add benchmarks or guesses about why numbers changed.

The analysis runs entirely on your computer, and the output is a compact table that you can paste into a report or share with the owner. For the weekly version of this, see automate marketing reports. For deeper questions, see analyze marketing data with AI.

Step 5: Build a UTM Builder Page

The team keeps tagging links by hand and making spelling mistakes, so Diwali traffic in GA4 is split across "Meta", "meta" and "facebook" as separate sources. Ask Claude Code for a small tool that prevents these inconsistencies:

Example
Build output/utm-builder.html: a single page with no external libraries.
Fields: website URL, source (dropdown from CLAUDE.md), medium (dropdown), campaign name (text).
Force lowercase and underscores in the campaign name.
Show the final tagged URL with a copy button, and a table of the last 10 links built in this browser.

Open the file in a browser and test it with a real product page URL. Check that the tagged link opens the correct page and appears correctly in GA4's Realtime report. If the team finds it useful, a developer can review the code and host it on an internal company page.

Step 6: Save the Steps for Next Time

Ask Claude Code to save the cleaning steps as a reusable script, such as clean_ads.py, and add a line to CLAUDE.md explaining how to run it. Next week, the marketer copies in the new exports and runs the same script, getting identical columns every time without repeating the instructions.

Common Mistakes

  • Personal data in the folder: Order exports with customer names and phone numbers do not belong there, so pull campaign-level data only.
  • Approving every prompt without reading: Permission prompts are your safety check, so ask "what does this command do?" whenever you are unsure.
  • Editing original files: Keep raw exports unchanged and write results to a separate folder.
  • Trusting totals without checking: Always compare at least one total with the original advertising platform.
  • Vague requests: An instruction like "analyse my data" produces vague output, so name the file, the columns and the exact result you want.
  • Hosting tools publicly without review: Anything the team uses on the website should be reviewed by a developer first.

Next Steps

How AI Changes Marketing Data Work

What AI Automates Now

Coding agents write the scripts that marketers used to ask a developer or analyst for: cleaning exports, joining files, building simple pages and producing summary tables. They also explain the code in plain words, so a marketer can learn as they go.

What Still Needs a Human

Deciding the question, checking the numbers against the platforms, judging whether a tool is safe to share, and choosing what to do with the results all stay with the marketer. Claude Code does not know that one campaign was paused because a popular product ran out of stock.

Risk to Watch

A coding agent with broad permissions can change or delete important files. Work on copies of your data, keep permission prompts switched on, and never put personal data or passwords in the project folder. Follow the DPDP Act guidance on handling customer data.

Do It with AI

Use this prompt in Claude Code, or adapt it for another coding agent, to plan a data task before running it.

Prompt for ChatGPT, Claude or Gemini

You are helping a marketer at a D2C brand in India with a data task on their own computer. Files in this folder: [list the export files and what each contains] Goal: [e.g., one clean file of spend, clicks and purchases by platform and campaign] Rules: [date format, currency, naming rules, or "see CLAUDE.md"] 1. Read the files and list their columns. Flag any column that may contain personal data and do not use it. 2. Propose the column mapping and cleaning steps, and wait for my approval. 3. After approval, write the output to /output and keep the original files unchanged. 4. Print totals I can compare with the ad platforms, and explain any rows you dropped. Do not invent benchmarks or reasons for changes in the numbers.

  1. Put clean, campaign-level exports in a new folder.
  2. Paste the prompt with your file list and goal.
  3. Approve the mapping only after reading it.
  4. Compare the printed totals with each ad platform before using the output.

Check Before You Use It

  • Facts: Match output totals against the ad platforms for the same date range.
  • Brand fit: Check that campaign names and UTM values follow the brand's naming rules.
  • Compliance: Keep personal data and passwords out of the folder, and follow the company's policy on AI tools.

Quick Quiz

Pick an answer to check yourself. Nothing is saved.

Question 1 / 3

  1. 1. What should the Indore brand remove from its exports before putting them in the project folder?

Frequently Asked Questions

Do marketers need to know coding to use Claude Code?

No, but it helps to be comfortable opening a terminal and reading what the tool proposes. You describe the task in plain English, Claude Code writes and runs the code, and you check the output files. Start with small, low-risk tasks on copies of your data.

What is the difference between Claude Code and the Claude chat app?

The chat app answers in a conversation and works on what you paste or upload. Claude Code works inside a folder on your computer: it can read files, write new ones and run commands, asking your permission first. That makes it better for repeated data cleaning and small tools.

Is it safe to give Claude Code my customer data?

Do not. Remove names, phone numbers, email addresses and addresses before any file goes into the working folder. Campaign-level exports, such as spend and clicks by ad, rarely need personal data at all.

What does Claude Code cost?

It needs a paid Claude plan or an Anthropic API account, and the included usage differs by plan. Check Anthropic's current pricing page before you start, because plans and limits change.

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