LinkedIn Ads to JSON: How to Export and Simplify Your Campaign Data

Have you ever wished you could pull your LinkedIn Ads data into something clean and structured like JSON Format, you’re not the only one. Every marketer and data geek hits that same wall: LinkedIn’s dashboards look great, but they don’t always play nicely with your own tools or reports.

What does “LinkedIn Ads to JSON” mean?

It means exporting or converting your LinkedIn Ads performance data into JSON, a structured data format that’s easy to use in APIs, dashboards, and automation systems.

This guide is your shortcut to doing it right whether you want to use the LinkedIn Ads API, a quick CSV export, or a simple Python script. I’ll walk you through every method, what works best for who, and how to keep things easy enough to maintain without breaking your brain.

Why You’d Even Want LinkedIn Ads in JSON Format

Think about how messy raw marketing data can get. You might have impressions here, clicks there, and spend data hiding in some export folder. JSON fixes that chaos.

Unlike spreadsheets, JSON speaks the same language as your analytics platforms, dashboards, or any app you build on top of it. It’s compact, structured, and easy for both humans and machines to understand.

So if you’re tracking campaign performance, automating reports, or feeding data into a visualization tool like Power BI or Google Data Studio — JSON is gold.

Using the LinkedIn Ads API for Direct JSON Access

Now for the fun part: automation.

LinkedIn’s Marketing Developer Platform gives you an API that can deliver your ad performance data straight in JSON. Once you set it up, you can schedule reports, pull data automatically, and skip the “download-convert-upload” routine forever.

Here’s how it works in plain steps:

1. Create a LinkedIn Developer App — this gives you a Client ID and Secret.
2. Authenticate using OAuth 2.0 (basically proving your app’s identity).
3. Use the Reporting API to ask for metrics like impressions, clicks, and spend.
4. Save the output directly as JSON.

That’s it. Once you have access, the data comes back neatly formatted — ready for analysis, automation, or storage.

Grabbing LinkedIn Ads Data with Python

If you’re the “show me” type, here’s a super-simple version that just gets the job done:

import requests, json

token = "YOUR_ACCESS_TOKEN"
headers = {"Authorization": f"Bearer {token}", "X-Restli-Protocol-Version": "2.0.0"}

params = {
  "q": "analytics",
  "dateRange.start.year": 2025,
  "dateRange.start.month": 10,
  "dateRange.end.year": 2025,
  "dateRange.end.month": 10,
  "accounts": "urn:li:sponsoredAccount:123456"
}

response = requests.get("https://api.linkedin.com/v2/adAnalyticsV2", headers=headers, params=params)
data = response.json()

with open("linkedin_ads.json", "w") as file:
    json.dump(data, file, indent=2)

Convert LinkedIn Ads CSV to JSON — The No-Code Shortcut

Let’s say APIs aren’t your thing. Totally fine. You can still get to JSON without writing a single line of code.

Here’s what you do:

1. Head to LinkedIn Campaign Manager.
2. Click your ad account, pick a time range, and hit Export.
3. Save the file to CSV.
4. Upload it to a free online CSV-to-JSON converter or use Excel alternatives like Coupler.io or Apipheny if you want to automate.

import pandas as pd
pd.read_csv("linkedin_ads.csv").to_json("linkedin_ads.json", orient="records", indent=2)

The Easy Way: LinkedIn Ads Data Pipeline via ETL Tools

If you run multiple campaigns or manage clients, doing this manually becomes a headache fast. That’s where ETL tools (Extract-Transform-Load) come in.

Apps like Coupler.io, Apipheny, and Singer taps can automatically fetch your LinkedIn Ads data, clean it up, and save it as JSON — daily, hourly, or however often you like.

You can connect your LinkedIn account once, pick which metrics you want, and the tool does the heavy lifting. It’s a great setup for agencies or teams that need reliable, repeated exports.

Designing a Simple LinkedIn Ads JSON Schema

Once your data is flowing, structure becomes everything. A tidy schema helps you keep track of what matters most:

{
  "account_urn": "urn:li:sponsoredAccount:98765",
  "campaign_urn": "urn:li:sponsoredCampaign:54321",
  "date": "2025-10-18",
  "impressions": 15300,
  "clicks": 284,
  "spend": 372.50,
  "currency": "USD",
  "ctr": 0.0185,
  "campaign_name": "Q4 Product Launch"
}

LinkedIn Ads Automation: Set It and Forget It

Once you’ve got JSON flowing automatically, the real magic starts.

Imagine pulling live ad stats into a dashboard every morning without opening LinkedIn. Or syncing daily metrics into a Google Sheet your team checks over coffee.

Automation turns messy marketing data into a system that just runs.

The main things to remember:

• Keep your API tokens refreshed.
• Handle pagination for large datasets.
• Watch your rate limits (LinkedIn can throttle requests).
• Validate your JSON before saving.

Follow those, and your automation won’t skip a beat.

When JSON Beats CSV (and Why It Matters)

CSV is fine for one-off exports. But once you’re integrating or scaling, JSON wins — every time.

It’s flexible enough for nested data (like targeting demographics), compatible with nearly every programming language, and far less error-prone.

In short: CSV is for quick checks; JSON is for serious work.

Bringing LinkedIn Ads data to JSON isn’t about chasing trends — it’s about owning your data story.

Whether you use an API, a script, or a simple export, the goal is freedom. Freedom from clunky interfaces, repetitive downloads, and inconsistent metrics. Start small: export one report, test your JSON, and see how much cleaner it feels to work with. Once you’ve seen that flow in action, you’ll never go back to spreadsheets again.