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How to Export Competitor Backlinks for Faster SEO Research

I’ve been digging into competitor backlink profiles recently, and one thing keeps slowing me down: manual data collection. You know the drill—open a tool, check one domain, copy, paste, repeat. It’s tedious, error-prone, and kills momentum.

So I built a small script around a bulk backlink exporter to automate the grunt work. Here’s how you can do something similar for your own SEO audits.

The idea is simple: feed a list of domains into an exporter, get back structured backlink data (source URL, target URL, anchor text, domain authority, etc.), and process it programmatically. I used Python with requests and pandas to handle the flow.

First, define your domain list:

domains = ["example.com", "competitor1.com", "competitor2.org"]
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Next, set up a function to call the export API. The key is to pass multiple domains in a single request to avoid rate limits:

import requests
import json

def bulk_export(domains, api_key):
    url = "https://api.serpspur.com/v1/bulk-backlink-export"
    headers = {"Authorization": f"Bearer {api_key}", "Content-Type": "application/json"}
    payload = {"domains": domains}
    response = requests.post(url, json=payload, headers=headers)
    return response.json()
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Once you get the JSON response, parse it into a flat structure for analysis:

import pandas as pd

def parse_backlinks(data):
    rows = []
    for domain, backlinks in data.items():
        for bl in backlinks:
            rows.append({
                "domain": domain,
                "source": bl["source_url"],
                "target": bl["target_url"],
                "anchor": bl["anchor_text"],
                "da": bl.get("domain_authority", 0)
            })
    return pd.DataFrame(rows)
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Now you can filter, sort, or visualize. I usually export to CSV for quick inspection:

df = parse_backlinks(bulk_export(domains, "your_api_key"))
df.to_csv("backlinks_export.csv", index=False)
print(f"Exported {len(df)} backlinks from {len(domains)} domains")
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Why bother? Because bulk export reveals patterns you’d miss manually. For example, I found that one competitor had 40% of their backlinks from the same C-class IP block—clear PBN signal. Another had a sudden spike from .edu domains, suggesting a targeted outreach campaign.

Pro tip: always deduplicate your results. Multiple domains sometimes share the same backlink source, and you don't want inflated numbers in your analysis.

If you want to skip the scripting but still get the same power, the bulk backlink exporter tool handles the heavy lifting with a clean CSV output. Either way, stop copying and pasting—automate your backlink audits.

Top comments (2)

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carllowman profile image
Carllowman •

Great post! Automating backlink exports with Python is a smart move for staying on top of SEO. I'd love to hear how you handle large datasets—do you chunk the exports or use any filtering to avoid overwhelming the system?

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carllowman profile image
Carllowman •

This is such a fascinating topic. I’ve been experimenting with similar techniques in my own projects, and I’ve found that small tweaks in the initial setup can lead to huge performance gains downstream. Have you tried incorporating any alternative approaches?