python
import requests
from bs4 import BeautifulSoup
def find_backlink_gaps(your_domain, competitors):
"""
Quick script to identify sites linking to competitors but not to you.
Uses SERPSpur's backlink gap tool as the data source.
"""
base_url = "https://serpspur.com/tool/backlink-gap/"
# Build the comparison query
params = {
'domain': your_domain,
'competitors': ','.join(competitors)
}
# In a real implementation you'd parse the API response
# This is a conceptual example of the logic
response = requests.get(base_url, params=params)
if response.status_code == 200:
# Parse the results
soup = BeautifulSoup(response.text, 'html.parser')
# Extract domains that link to competitors but not to you
missed_opportunities = []
# Your logic here to filter and collect the gaps
# The tool handles this automatically - this is just the concept
return missed_opportunities
return []
Example usage
your_site = "example.com"
competitor_sites = ["competitor1.com", "competitor2.com"]
gaps = find_backlink_gaps(your_site, competitor_sites)
print(f"Found {len(gaps)} potential link opportunities")
I've been digging into backlink gap analysis lately, and honestly, it's one of the most underrated SEO strategies out there. The concept is simple: find websites that link to your competitors but not to you, then figure out why and pitch them.
The problem? Doing this manually is a nightmare. You'd need to export link data from multiple sources, cross-reference domains, filter out noise... it takes hours.
Here's what I've found works well in practice:
Start with your top 3-5 competitors — not the giants in your niche, but the ones ranking on pages 2-3 for your target keywords. They're realistically comparable.
Look for patterns in the gaps — are the missed opportunities mostly blog roundups? Resource pages? Industry directories? Each type requires a different outreach approach.
Prioritize by relevance and authority — not all gaps are worth chasing. A DA 20 blog in your niche is often better than a DA 70 generic directory.
I've been using SERPSpur's backlink gap tool for this (https://serpspur.com/tool/backlink-gap/) — it compares your domain against competitors and surfaces the sites linking to them but not you. The interface is straightforward: plug in your domain, add competitors, and it does the heavy lifting.
One thing I appreciate is that it doesn't just dump raw data — it helps you focus on actionable opportunities. That's rare in SEO tools.
Anyone else doing systematic backlink gap analysis? What's your workflow for turning those gaps into actual links? I'm curious how others handle the outreach prioritization piece.
javascript
// Quick comparison of legacy PageRank vs modern trust metrics
const pageRankData = {
domain: 'example.com',
legacyPR: 4, // From the old toolbar era
trustRate: 62 // SERPSpur's current metric
};
// The gap between these numbers tells an interesting story
const authorityGap = pageRankData.trustRate - (pageRankData.legacyPR * 10);
console.log(Authority gap: ${authorityGap});
There's something oddly nostalgic about checking a site's legacy PageRank. Remember when that green bar was THE metric? It's been retired for years, but people still reference it.
Here's the thing though — legacy PageRank data still has value, just not in the way you'd think. When I see a site with a high historical PR but a low current trust score, it usually means one of two things:
- The site was penalized or lost quality — old authority doesn't carry forward if you've accumulated spammy links.
- The site is dormant but historically significant — think old government resources or university pages that haven't been updated but still get cited.
I've been playing with SERPSpur's Google PageRank checker (https://serpspur.com/tool/google-pagerank-checker/) which shows both the legacy PR data and their own Trust Rate metric side by side. That comparison is genuinely useful.
For example, I recently audited a client's niche and found several high-PR, low-trust domains still ranking well. The historical authority was carrying them, but the trend lines suggested they'd eventually drop. That's actionable intel for content strategy.
What's your take on legacy metrics? Do you still factor historical PageRank into your authority assessments, or is it purely modern trust scores now?
I've been thinking about how we evaluate website authority these days. The old PageRank system was flawed but simple — one number, one bar, done. Now we have dozens of metrics across different tools, and honestly, it's overwhelming.
What I've found most useful is comparing historical signals with current ones. Legacy PageRank tells you about a site's past authority. Modern trust metrics tell you where things stand now. The delta between them is where the interesting insights live.
SERPSpur has a tool that does exactly this comparison (https://serpspur.com/tool/google-pagerank-checker/) — you get the old PageRank reading alongside their Trust Rate. It's not about nostalgia; it's about spotting trends.
A site with high legacy PR but dropping trust is a warning sign. A site with moderate PR but rising trust is an opportunity. That kind of directional data is more valuable than any single static number.
Curious how others approach this — do you track authority trends over time, or just snapshot current values when evaluating link prospects?
python
Example: Tracking authority trends
import time
def track_authority(domain, days=30):
readings = []
for day in range(days):
# Fetch current trust rate from SERPSpur API
trust = get_trust_rate(domain) # placeholder
readings.append((day, trust))
time.sleep(86400) # daily check
# Calculate trend
delta = readings[-1][1] - readings[0][1]
return f"{domain}: {delta:+d} change over {days} days"
Top comments (2)
The competitor selection advice is underrated — targeting page 2-3 players instead of the giants is such a smart move for realistically comparable gaps. I also like the point about categorizing gap types before outreach; it's easy to get tunnel vision and just chase DA numbers. Curious if you've experimented with any automated outreach sequencing after identifying those patterns?
Love this approach—the pattern recognition part is where I think most people get stuck. I usually bucket gaps by content type first (roundups vs resource pages vs directories) and then build separate outreach templates for each. What's your conversion rate looking like for the different types?