Quickstart: refining consideration lists

Most Oxzeon reports contain two lists of domains that should be considered as candidates to approach for backlinks:

These lists are shown in the report interactive, and also appear as separate worksheets in the data tables (MS Excel file). Our goal is to save you time by only listing domains that meet specific criteria, but these lists can still contain dozens of domains.

Fortunately, the domain attributes and metrics in the data tables (MS Excel file) provide many ways to refine these lists using a data-driven approach. Here are a few:

Focus on a particular competitor

Your strategy may be to look for domains that are referrers of a particular competitor.

Domains of the domain type shared referrer, competitor referrer, or competitor shared referrer will have a relationship with one or more of the competitor domains. To filter domains in a list by their associated competitor domain(s), use the Target competitors column.

Use metadata similarity

If your strategy is to look for domains that focus on similar topics as the principal domain, metadata is a good place to start. Wherever possible, we obtain metadata from listed domains and provide several comparative metrics:

Meta title similarity and Meta description similarity are powered by OpenAI, and provide scores between 0 and 1 for how 'similar' a domain's meta title or description are to those of the principal domain's. Scores above 0.9 are 'significant', so filter for this value (or above).

Alternatively, Meta title common # and Meta description common # will show how many words (excluding high frequency words like 'and') are common to a domain and the principal domain. Try sorting these columns by Largest to Smallest. To see the words themselves, look in:

NOTE It is not always possible to obtain metadata for a domain, so this score is not always available for every domain. To see only domains for which we could obtain full metadata, filter the column Meta data status for the value 'OK'.

Use quality metrics

You may want to investigate domains that are likely to be higher quality websites. Our data partners provide many measures, all available as columns in the worksheets, that are a good starting point:

The Ahrefs domain rating column provides scores between 1 and 100 that are one estimate of a domain's quality. Try sorting this by Largest to Smallest to put domains that are more likely to be of higher quality at the top of the list.

Alternatively, the Spam score column provides scores between 0 and 100 that are an estimate of how likely the domain is to be 'spam' or junk, with 100 being the worst score. Try sorting this by Smallest to Largest to put domains that are less likely to be junk to the top of the list.

Use domain connectivity

If you strategy is to focus on domains that are highly connected, several relevant graph metrics are available:

The Degree column shows the number of other domains with which a domain has a links relationship, where links can be in either (or both) directions between the domains. Try sorting this column from Largest to Smallest to focus on domains that are in some way connected with many domains.

To focus on domains which are connected with many other domains by outbound links, try sorting the Out degree column from Largest to Smallest. Conversely, to see domains that are connected with many domains by inbound links (backlinks), try sorting the In degree column from Largest to Smallest.

You may also want to consider the number of individual inbound links (backlinks) and outbound links detected for a domain:

The Eco backlinks column shows the number of backlinks it has from other domains in the ecosystem. Sorting this column from Largest to Smallest will put domains with the highest number of backlinks to the top of the list. Alternatively, sorting the Eco backlinks column from Largest to Smallest will put domains with the highest number of outbound links to the top of the list.

Use graph centrality metrics

Your strategy may be to focus on domains that occupy key positions in the ecosystem, for which a number of graph centrality metrics are available:

The Betweenness column shows a score between 0 and 1 that rates each domain on how it appears on the shortest path between any given pair of domains (see Distance for more about paths). Sort this column from Largest to Smallest to focus on domains with higher Betweenness centrality scores.

The Directed betweenness column is almost identical to Betweenness, but in this case the scores are calculated using a more stringent methodology: shortest paths between any given pair of domains must follow the direction of links.

Further graph centrality metrics are also available:

The Closeness column shows a score between 0 and 1 that rates each domain by considering the lengths of shortest paths between it and all other domains in the ecosystem (see Distance for more about paths). Sort this column from Largest to Smallest to focus on domains with higher Closeness centrality scores.

The Outward closeness column is almost identical to Closeness, but in this case the scores are calculated using a more stringent methodology: shortest paths between any given pair of domains must follow the direction of links.

The Inward closeness column is identical to Outward closeness, but in this case the shortest paths between any given pair of domains must follow the opposite direction of links.

TIP The Open Multiple URLs Google Chrome extension lets you to open a list of URLs as separate tabs. This is very useful when evaluating domains, as you can paste in selections from the data tables (MS Excel file).