How Long Should An SEO Migration Take? [Study]

Website migrations, specifically domain migrations, are often seen as one of the more complex parts of SEO.

They are becoming increasingly more common as businesses are consolidating websites and assets to reduce costs and consolidate efforts as more channels and platforms come into play.

As SEO professionals, we are tasked with mitigating as many risks and variables as possible so that the business doesn’t see organic performance issues – either at all or for a longer than necessary period of time.

When we ran this study in 2023, we looked at 171 migrations and found that it took 229 days (on average) for third-party tools to reflect organic traffic for the new domain to return to the same pre-migration levels of the original domain; 42% didn’t return at all.

The reason we’ve repeated this study is that we think it’s important that businesses (and SEO marketers) have data to work off to make informed decisions when planning domain migrations.

Over the years, I’ve been in a number of pitch meetings where the other agencies pitching have promised no traffic loss at all during migration, and more often than not, adequate preparation work, monitoring, or expectation setting has been done.

Study Methodology

This study aims to research and provide a data-led answer to “How long should an SEO migration take,” to help both in-house SEOs and consultants provide better estimations and communications with non-SEO stakeholders of their migration projects.

This is building off of last year’s study in which we looked at 171 domain migrations. This year, we’ve expanded the dataset to 892, thanks to fellow SEO professionals responding to information requests on various Slack channels and X (Twitter).

Using third-party tools, we then measured the number of days it took Domain B (the new domain) to achieve the same estimated organic traffic volume as Domain A (the old domain).

Data was collated on October 22nd, 2024.

Bias Factors

Bias in a quantitative study refers to systematic errors that can significantly skew the results, leading to incorrect conclusions.

These biases can arise at any stage of the research process, such as in the design, data collection, analysis, and interpretation stages.

Bias undermines the validity and reliability of the study’s results and can lead to misleading conclusions.

Where possible, we have taken steps to mitigate any bias from impacting the study.

Selection Bias: We have worked to eliminate this as much as possible, with a high percentage of the data coming from Ahrefs (unfiltered) and an open request to the SEO community. This should negate our own inputs from sectors we specialize in. This has led to a variety of domains in a variety of sectors.
Measurement Bias: As we’re using a third-party tool, the bias here is limited to the scope of the tool, database updates, and the keyword set. As we’re comparing two domains and making the assumption that they match in terms of the target keyword set, this bias should be mitigated.
Confounding Bias: As we’re comparing date periods within the same tool, no correlations are being made in terms of data analysis.
Publication Bias: This study was going to be published and submitted regardless of percentage/data findings. So, this bias is mitigated.

The data set contained domains of varying usage, from lead generation in SaaS, legal, and finance to blogs, local retail, and ecommerce.

Study Findings

The key takeaways from the domain study are: