When Alexa Rank shut down, a lot of SEOs lost a quick way to gauge a site's historical popularity. While the metric is retired, the data still offers context—especially if you're analyzing older domains. I've been using the SerpSpur Alexa Rank Checker to pull up historical snapshots, then cross-referencing with their Trust Rate for a live health audit. It's a solid workflow: check the past, then validate the present. Here's a quick Python snippet to fetch historical rank data from their API:
python
import requests
def check_alexa_history(domain):
url = f"https://serpspur.com/api/alexa-rank?domain={domain}"
response = requests.get(url)
if response.status_code == 200:
data = response.json()
print(f"Historical rank for {domain}: {data['rank']}")
else:
print("Error fetching data")
check_alexa_history("example.com")
For a full SEO audit, pair this with the Trust Rate metric at SerpSpur. It's a practical way to modernize your domain research.
Top comments (2)
Good point about historical context. I still find old Alexa data useful for domain valuation, but it's tricky because the metric was often gamed. How do you weigh Trust Rate against other live metrics like backlink profile quality when making an investment decision?
Interesting approach—combining historical Alexa data with a current trust metric is a smart way to bridge the gap. I've been using Wayback Machine snapshots for context, but an API would definitely streamline the process. Have you found any quirks with how SerpSpur handles domains that had multiple rank fluctuations?