The ultimate guide to log file analysis for SEO

What can log file analysis do for my site’s SEO?

And what is Log File Analysis, anyway?

Every request made to your hosting web server for content is being recorded in a log file. This is important because you can see exactly what resources search engines like Google are crawling on your site. All you have to do is export the data and filter for requests made by Googlebot (by user-agent and IP range). So if you have your log file data to hand, follow this guide to learn how to perform a log file audit for your site.

Using this guide to Log file analysis can help you improve your site’s SEO by:

  1. Showing you how much “crawl budget” is being wasted and where
  2. Helping you improve accessibility errors such as 404 and 500 errors
  3. Locating pages that aren’t being crawled often
  4. Identifying static resources that are being crawled too much!

With your log file data at hand, you’ll have an incredibly powerful way of investigating and understanding search engine crawl behaviour for your website. The insight you glean is better than any other external crawl tool available, and the outcome is higher performance for your SEO channel.


1.1 What is a web server log file?

A file output from the web server containing a record of all requests (or ‘hits’) that the server receives.

The data received is stored anonymously, and includes details such as the time and date in which the request was made, the request IP address, the URL/content requested, and the user-agent from the browser.

These files exist typically for technical site auditing & troubleshooting, but can be extremely valuable for your SEO audits as well.

1.2 How does it work?

When a user types a URL into a browser, the browser first translates the URL into 3 components:
  1. Protocol
  2. Server name
  3. File name

The server name ( is converted into an IP address via the domain name server, which allows a connection (typically via port 80) between the browser and corresponding web server, where the requested file is located, to be made.

An HTTP Get request is then sent to the web server via the associated protocol for the desired page (or file), with the HTML being returned to the browser, which is then interpreted to format the visible page you see on your screen.

Each of these requests is then recorded as a ‘hit’ by the web server.

1.3 Breakdown of a server log file

The structure of the log file will ultimately depend on the type of server and configurations applied, e.g. Apache, IIS etc, but there are several common attributes that are almost always included:

  1. Server IP
  2. Timestamp (date & time)
  3. Method (GET / POST)
  4. Request URI (aka: URI stem + URI query)
  5. HTTP status code
  6. User-agent

Example record including the above data: - - [30/Sep/2013:08:07:05 -0400] "GET /resources/whitepapers/ retail-whitepaper/ HTTP/1.1" 200  "-" "Mozilla/5.0 (compatible; Googlebot/2.1; +"

Additional attributes that can sometimes be seen include:

What is crawl budget and how is it calculated?

Not to be mistaken with ‘crawl rate’, which refers to the speed at which search engines request pages, ‘crawl budget’ refers to the number of pages a search engine will crawl each time it visits your site.

Crawl budget/allocation is very much determined based on the authority of the domain (historically PageRank) in question, and proportional to the flow of link equity through the site. The higher the authority, the more URLs crawled.

Why is crawl utilisation a fundamental consideration for SEO?

Having a larger crawl budget doesn’t necessarily translate into a positive trait, however, especially if search engines are crawling thousands of irrelevant, content thin URLs the majority of the time.

In the above scenario, in areas of lower authority the longer tail traffic drivers (or product pages) will be competing across a much wider net of URLs than necessary, leading to slower indexation rates, irregular crawl frequencies and ultimately restricting their ability to rank well organically. As such, conservation of your site’s crawl budget is key to ensuring its continued organic performance.

If you’ve been hit by Google’s Panda update, and are relying on the sole use of ‘noindex’ to set you free, you may as well give up now. If search engines are still crawling irrelevant, thin content types and they make up the majority of URLs on your site then you haven’t resolved the problem.

Conservation is the key to survival & growth

Working with Log Files

2.1 Tracking Search Engines

Combing your logs for search engine crawlers can be done broadly by locating the appropriate user-agent and filtering accordingly:

"Mozilla/5.0 (compatible; Googlebot/2.1;"

You’ll then need to validate the IP range by carrying out a reverse DNS lookup to avoid any potential ‘fake’ user agents coming through.

There are a number of online tools that allow you to pop in the IP and return the corresponding host name, thus validating the IP against the user-agent.

When requiring a bulk lookup, however, these tools can become a bit of a chore. As we use Excel as the preferred tool for our log file analysis at Builtvisible, it also makes sense for us to look for a solution directly from our workbook, and thankfully there is one…

First detailed on Protocol Syntax, with the help of AlonHircsh and Arkham79 of, we can create an Excel VBA module to run this lookup for us:

  1. Open an Excel workbook and press Alt-F11 to open the VBA editor
  2. From the main toolbar, click ‘Insert’ > ‘Module’
  3. Copy and paste this code
  4. Head back to the Excel workbook, and use the following formula to activate the lookup on an IP address: =gethostname(A2)

A big thank you to Stephan for this incredibly useful and efficient module!

Pro Tip: Open a new Excel workbook and follow steps 2 & 3, then head back to the workbook and click ‘Save As’. Within the ‘Save as type’ field, select ‘Excel Add-In’:

The formula will then be available across all Excel workbooks, providing the add-in has been activated (File > Options > Add-Ins > select add-in).

2.2 Log File Analysers

Using a log file analyser tool can help to sift through large data sets with relative ease, and even export to smaller or more manageable files for auditing in Excel.

Log file analysers include:

AnalyserPrice GuideResources
Screaming Frog Log Analyser£99 per year, but one of the best!Announcing the SF Log File Analyser
GamutLogViewer$20 – single license
Licenses for 15, 40, and unlimited users available
Training videos
SplunkEnterprise – free for 60 days
Yearly & data consumption based subscriptions available
Basic Splunk for log file analysis
Command LineFree
There are also a number of log file utilities you can run directly from the command line.
Top command line tips: apache logs

It’s worth noting that, despite their ability to crunch vast amounts of data, we have often found that using these tools can only take your analysis so far. Exporting to Excel opens up almost endless possibilities for investigation, with the ability to extend, combine, pivot and compare data points from additional sources.

2.3 Formatting Your Log Files for Excel

1. Convert .log to .csv

When you’ve extracted a web server log you’ll end up with a number of files with the .log extension. Converting these into a format that can be understood by Excel couldn’t be easier, just select the file and re-type the file extension as .csv. Excel will open the file without corrupting the contents (surprising I know, but it works!).

Working with multiple .log or .csv files?

We can quickly and easily convert and merge these files using the command line:

  1. Pop the .log (or .csv) files into a single folder on your desktop
  2. Head to ‘Run’ from the Windows start bar
  3. Type cmd and hit return
  4. Type cd Desktop/your folder and hit return
  5. Type
    1. If converting & merging multiple .log files to .csv type: copy *.log mergedlogs.csv
    2. If merging multiple .csv files type: copy *.logs

2. Sample Size

Upon opening the file in Excel, check to see how many rows of data it contains (this also reflects the number of hits). A good sample size/range to work with is between 60-120K rows of data, any more than that and Excel will likely become unresponsive once you start filtering, pivoting and combining other data sets.

3. Text-to-columns

Opening in Excel will usually result in the ‘hit’ data being recorded in a single column like the below:

To organise your data set into a manageable format we need to spread the data across multiple columns, this is where the ‘Text to Columns’ function comes in to play:

4. Create a table

Now to start organising the data:

Useful Tools for Technical SEO Auditing

Desktop Software:

Online Tools:

Identifying Crawl Accessibility Errors (Excel)

4.1 Exploring the Initial Data

Following the steps in the previous points, each data point will have been separated into individual columns in an Excel spreadsheet. The following data points are of interest at this stage of the analysis:

4.1.1 Crawl Frequency vs User Agent

Creating a pivot table & chart based on the timestamp (date) property and filtering via specific User-Agents, such as Googlebot, G-bot Images, G-bot Video, G-bot Mobile etc, allows us to monitor crawl behaviour over time at a high level.

Your pivot table set up should reflect the following format:

This can be extremely useful for quickly identifying anomalies with specific search engine user-agents.

4.1.2 Most vs Least Crawled URLs

Pivoting against request count of URL string allows us to see where search engines are spending the majority of their time during crawl, whilst also segmenting areas that have become crawl deficient.

Your pivot table set up should reflect the following format:

Whilst running through the data you’ll soon start to identify URL types that search crawlers just don’t need to be accessing (crawl waste).

Pro Tip: Create a column next to the request URL (excluding the domain) in your main data set, and add the following formula:

=IFERROR(LEFT(A1,SEARCH("/",A1,2)), "Root URL")

This will trim the URL down to the top level directory, allowing you to then filter crawl behaviour by site sections to spot anomalies at an aggregate level.

As you come across error URL types, it’s recommended that you make a log of this within the main data set. This can later be used to calculate things like overall % crawl waste as a snapshot of search engine behaviour.

For each error type create a new column in your data set adding the following formula:


This will carry out a look up directly on the request URL column for a specific text string and return a True/False value. The specific text should be the unique identifier from the URL generating the error e.g. ‘?parameter’, ‘/directory/’, file extension etc.

It’s not bullet proof for keeping a record of all error types particularly when addressing at a content level, and for anything other than a high level analysis of what search engines are encountering, it’s a time consuming process – which is why we need some more data!

4.1.3 HTTP Status – Requests vs Request URLs

Segmenting by server header response allows you to quickly assess direct crawl errors encountered by search engines. Pivoting request URLs vs count of requests will show if search engines are encountering the same error for a particular URL, or a range.

Seeing a % breakdown by HTTP response can help put into perspective crawl performance.

500 Server Error

If search engines are frequently encountering 500 server errors, not only will site crawl be impeded but you run the risk of those URLs dropping out of the index altogether.

Instead, returning a 503 response will inform search engines that the server is temporarily unavailable for maintenance, and they are thus to resume crawl at a later date (of which can also be specified as part of the header response).

404 Page Not Found

Sort these URLs by the number of requests by search engines to prioritise the handling of the 404 errors. A few 404 errors are not going to hurt your organic visibility, and is a perfectly natural occurrence for any website where a page is no longer available, and a related/similar page cannot be found.

However, to efficiently identify the appropriate action for the 404 requested URLs we’re going to need data from a site crawler like Screaming Frog, and traffic data from your site analytics.

We can then investigate the types of 404s occurring based on traffic and links (both internal & external) received to better gauge whether a 301 re-direct is the appropriate course of action.

302 Temporary Redirect

If your site employs 302 re-directs, check to see whether the ‘temporary’ response is valid for the requested URLs, as this type of redirect does not pass on the link equity accrued and maintains the index status of the origin URL.

In the majority of cases 302 re-directs will never be required, and should be replaced by 301 permanent re-directs. But in the slim chance that they are, you should be assessing whether the URLs should be crawled in the first place.

301 Permanent Redirect

Again, a totally natural occurring part of the web that search engines will expect to find.

If you’re seeing a large volume of 301s in your log files, chances are that you’ve either migrated platform/domain/structure recently, or there is a misconfiguration within the site itself that is firing the 301s, e.g. broken links, invalid rel=canonical status, legacy URLs within the XML sitemaps etc.

Any unnecessary 301 is of course going to delay site crawl, but also inhibit the flow link equity accrued by the origin URL. Although 301 redirects carry the vast amount of link equity over to the final destination, it isn’t passing all of that equity, and so, the more redirects encountered, the less link equity you can expect to be passed through the site.

Updating internal site navigation, XML sitemaps etc to point directly to the final destination URL will ensure that as much link equity as possible is being passed through the site.

This brings us on to another limitation of the raw log files, and that is the inability to identify ‘re-direct chains’. By combining the log file data with your crawler of choice, you’ll be able to pin down the number of re-direct chains (and count of ‘hops’ per chain) vs actual requests.

Pro Tip: Depending on the size of the data set and time span, you can carry out a lookup on the actual re-direct paths, to assess whether search crawlers actually followed the full ‘chain’, or halted their crawl short (a % overall complete crawl vs partial crawl would make for an extremely interesting study!).

4.1.4 Time Taken

With the time taken data point (measured in milliseconds) we can start to see which requested URLs on average by search engine crawlers have been downloaded the fastest/slowest.

Bucketing those URLs by directory will allow us to measure performance by site sections, to see which areas have been affected by slower performance.

However with the standard output we’ll be unable to see how much of the page is typically being downloaded, but we can still assess at high level overall site speed.

4.2 Expanding Your Data Set

Right, it’s time to start expanding our data set.

It’s worth bearing in mind that if you’re working with a large website, you may find it more useful to first split your log file data (based on category or product type) into separate documents, before then pulling the new data to allow for a deeper level analysis.

For now, just export the data from the tools below to new sheets within the same Excel document as your log file data:

Analytics (Google Analytics, Omniture, Core Metrics etc)

What data?

Additional notes:

Screaming Frog

What data?

Additional notes:

Google WMT

What data?

XML Sitemaps

What data?

Additional notes:

SEOGadget for Excel

What data?

Additional notes:

=majesticAPI_toFit("GetIndexItemInfo", "fresh", A1:A1000)

Combining Key Data

Before we can continue the analysis, we need to combine the key elements of these data sets with our log file data, so let’s break out some VLOOKUP’s to pull the following entities & columns from each sheet into our main log file Excel sheet:

Unfortunately Screaming Frog doesn’t yet provide an easy way for us to see what the final destination URL or status is for these URLs in a single column, so we’re going to use a slightly different set of lookups here:

Final Destination URL:

=IF(C2=1,VLOOKUP(A2,redirects,7,0),IF(C2=2,VLOOKUP(A2,redirects,11,0),IF(C2=3,VLOOKUP(A2,redirects,15,0), IF(C2=4,VLOOKUP(A2,redirects,19,0), IF(C2=5,VLOOKUP(A2,redirects,23,0), "N/A")))))

‘C2’ = the cell containing the value for the ‘number of redirects’ in our log file data set

‘A2’ = the cell containing the URL we want to look up.

‘redirects’ = the name of our table containing the Screaming Frog redirect chains export.

This formula will check URLs that cycle through 5 redirects and return the final destination URL from the corresponding column. Just bolt on a couple more IF statements in the rare case you encounter more redirects.

Final Destination HTTP Status:

=IF(C2=1,VLOOKUP(A2,redirects,9,0),IF(C2=2,VLOOKUP(A2,redirects,13,0),IF(C2=3,VLOOKUP(A2,redirects,17,0), IF(C2=4,VLOOKUP(A2,redirects,21,0), IF(C2=5,VLOOKUP(A2,redirects,25,0), "N/A")))))

Works in the exact same way as the previous point, but returns the HTTP status of the final destination.

=ISNUMBER(SEARCH("~?parameter", A2))

Note the use of the ‘~’, which is placed in front of special characters/symbols so that Excel treats it as text. You could just use “~?” as the criteria to view all URLs containing a parameter for example.

=IF(ISNUMBER(MATCH(A2, sf[Address],0)),"Yes","No")

‘A2’ = the cell containing the URL we want to check against the sitemap.

‘sf[Address]’ = tablename[ColumnName] – case sensitive

We should end up with a large table looking something like:

You can now tell why we recommend working with no more than ~100,000 requests!

4.3 Data Mining

I’m often asked our process behind mining log files for errors, and whilst there are common traits you can look out for, each website is unique in some way, and so the errors encountered will always be slightly different (even those using the same platforms!).

There will always be a need for analysing the data set beyond a set template, and so we’ll start by exploring different techniques for extracting the most value from your log files.

String Search & Pivot Tables

Remember this formula we referenced earlier:


Extending this lookup across our new data will help to pin point specific error types, or can just be used as another mechanism to filter the data set.

Combining this with the use of simple pivot tables i.e. URL vs count of requests vs HTTP Status, or URLs that are set to ‘noindex’ is another way to identify accessibility or even content (relevancy) related errors.

Index Search & Negative Filters

An often over looked resource is the search engines themselves. Mining the ‘site:’ operator in both Google & Bing in combination with multiple ‘-inurl:[property]’ commands can quickly highlight areas for review within the log file data set.

You can typically insert ~10 ‘-inurl:’ operators before Google stops displaying results, at which point just switch those operators out, or view the remaining URLs with a normal inurl: command.

It doesn’t matter how large the site is either, you’re just looking for patterns, trends or anomalies at this point. Using this process you’ll soon come across something…

For example: -inurl:/radio/ -inurl:/iplayer/ -inurl:/news/ -inurl:/weather/ -inurl:/food/

That 4th result above took me to the following page (jumping in the middle/end of the pagination on search results is another way to uncover issues):

The URL returned a 200OK at the time despite the ‘internal server error’ message displayed, but clicking the result underneath (5th result in the previous screenshot) returned content correctly.

Leveraging On-Page Components to Power Your Filters

This lead to another search investigating the scale of indexation by using a common component of the page template, in this case: “Sorry, the server encountered a problem”

The search returned nothing out of the ordinary, so we then use the next common entity from the same page, “Please try again later”, and we find ~9K results:

From checking those results we find that the /dna/ directory refers to a forum containing genuine FAQ’s regarding problems with iPlayer.

Knowing that this directory contains active content (I could investigate the ?thread URLs but I’m about to show you a clearer problem), the next step is to exclude this from my search so:

The URLs returned contain the parameter ‘?epoch’, which all result in blank pages on the BBC e.g.

There are ~4.8K URLs reportedly indexed in Google, and so by logging ‘?epoch’ as a lookup in our Excel document we can then view how many requests have been made for associated URLs.

The second screen above actually highlights another type of issue, one related to the archiving of content. So again we lift the common entity from the page and incorporate in a site command: “This page has been archived and is no longer updated”

Archived URLs equate to over 1 million indexed results, which at this scale becomes more of a concern for crawl budget and content relevancy.

We’re just demonstrating the different sequences to locate potential problems, but if we were to extend this analysis, crawling the URLs within the log files and applying a custom filter within Screaming Frog to look out for “This page has been archived…” etc, we could see just how much crawl budget within our data set is being assigned to those URL types.

In summary:

The following points define some other steps to locate more specific accessibility issues using the data we’ve collected in the previous section.

4.4 Crawl % by File Type

Required data:


Part of the Screaming Frog crawl returned the property ‘Content’, which reports on the file type of the requested URL i.e. HTML, plain text, CSS, JavaScript, JPG, PNG etc.

Creating a pivot chart with this data shows a high level view of what URL types are being crawled and how frequently.

Add a report filter for the HTTP Status obtained via the SF crawl, and you can clearly see a breakdown of requests vs the primary file types associated with specific errors, or just files consuming unnecessary crawl budget:


For example in the above chart, ‘text/html’ makes up the largest portion of all 200OK requests, but 2nd on the list is ‘text/plain’ files –What are they? Why are they being crawled?

4.5 HTTP Status vs Redirect Errors vs Redirect Chains

Required data:


Within a new column in your Excel table add the following formula:

=IF(D2>1,"Error",IF(C2=404,"Error", IF(C2=302,"Error", IF(C2=0,"Error",IF(C2=400,"Error",IF(C2=403,"Error",IF(F2=404,"Error","ok")))))))

C2 = HTTP status of the ‘request’ URL

D2 = Cell (column) containing the number of redirect hops

F2 = HTTP status of ‘final destination’ URL


This formula will tell us how much crawl budget has then been burned via requests resulting in one of the most common server errors, redirect error or chains.

4.6 Canonical Miss-Match & Status Errors

Required Data:


The following formulas will allow us to understand whether the rel=canonical (if applied) matches that of the request URL, and if not, whether the HTTP status returns anything other than a 200OK response.

We first need to check if the canonical matches the request URL, and if one isn’t present ignore, so add a new column in your data set with:

=IF(M2="", "N/A", IF(M2=B2, "Yes", "No"))

M2 = Cell containing the canonical URL

B2 = Request URL

Then add another column with the following formula:

=IF(AND(O2="No", N2=404),"Error", IF(AND(O2="No", N2=302), "Error", IF(AND(O2="No", N2=301), "Error", "Ok")))

O2 = Cell containing the result of our previous formula

N2 = Canonical HTTP Status


A simple pivot table using this data and we can quickly see:

Both of which can lead to the canonical tag being considered invalid across the domain, the tag after all is only a suggestion to search engines as opposed to a directive.

If the non-canonical URLs are being crawled more frequently, this is another indication that there is a problem with your site architecture and internal link structure.

4.7 Meta Robots vs Crawl Frequency

Data Required:


Creating the following pivot table on the master data, will provide a clear overview as to the crawl frequency of specific Meta robots directives:


Similar to the true canonical vs non-canonical URLs, if search engines are spending more time crawling URLs set to ‘noindex’ than those set to ‘index’, you’ll likely be experiencing issues with Panda.

4.8 Crawl Terminated or Restricted

Data Required:


Create a pivot table using the following properties:

The use of the ‘Content’ and ‘HTTP Status’ fields as report filters will prevent false positives from being returned via file types such as CSS or JS that naturally do not include link elements, or URLs returning a 404 response etc.

Set these fields to only show text/html page types that return a 200OK response.


Applying the pivot table directly to the Screaming Frog crawl data (instead of the master log file data) will return unique requested URLs only as opposed to a total number of requests.

When search engines access a URL with 0 or very few ‘outlinks’ – defined as links pointing to other pages on the same domain – their crawl reaches a dead end, or they have limited options as to which page to crawl next, hampering overall crawl efficiency.

If you also imagine these links acting like a funnel for external link equity to be passed around your site, with 0 or few outlinks that link equity is being horded at these URLs.

4.9 Accessible Parameters

Data Required:


Using the search string Excel formula, add a column for each parameter reported by WMT and lookup the parameter name against the request URL.


Assessing the relevance of these parameters in the operation of your site, can determine whether there is a need for keeping these alive, if not that’s an easy fix to help conserving crawl budget.

Yes, if a canonical is installed on the page then it’s not a duplicate content issue, but if you’re not helping search engines reach the true canonical via your main site navigation it’s still leading to crawl delays (or depending on scale, invalidation of the canonical tag).

If search engines do not need to access the parameters then simply disallow in robots.txt.

4.10 Duplicate Content & Request Frequency

Data Required


Create 3 pivot tables using the above components for both the main log file data set, and the SF crawl data, using the ‘HTTP Status’ as a report filter to only display URLs returning a 200OK response:

Replicate for the other properties, and then sort the ‘Count’ column within the pivot table from largest to smallest.


Great for pin pointing potential accessibility issues at a content level, based on the amount of crawl budget allocated.

By applying pivot tables directly to the SF crawl data we can review ‘unique URLs’ as opposed to a count of requests, allowing us to easily extract potential duplicate content types, or at the very least cannibalised Meta data.

The same pivot tables applied to the SF crawl data will also show pages with missing page elements.

Identifying Crawl Deficiencies (Excel)

5.1 Repurposing Your Log Files

When we analyse a server log file for SEO we’re typically concerned by what search engines are crawling and what accessibility errors they’ve encountered during this process. But let’s take a step back for a moment; if we know what search engines are crawling, we can quite easily find what isn’t being crawled just by obtaining a little more data.

The standard log files won’t tell us that we’ve accidentally blocked a directory containing URLs we’d actually want indexed, or that we forgot to put in redirects for a series of legacy URLs that have now become orphaned pages.

The logs will show areas of the site that have been crawled less frequently than others, but we still won’t have an understanding as to why that is the case.

This next section gives a new meaning to the phrase ‘crawl deficiencies’ through a series of steps that will not only identify areas of concern, but provide insight as to why.

5.2 Introducing More Data

New Required Data

Existing Required Data

We’ll also be using the following properties as originally documented in point 4.2:

5.3 Visited Canonical URLs Blocked by Robots.txt

Data Required


Carry out a ‘list’ mode crawl in Screaming Frog on the ‘full site URL list’ and export data from the ‘internal’ tab, placing this data on a new sheet in your Excel document.

Convert the data into a table, and then add a column for ‘Pageviews’ featuring a VLOOKUP formula to pull through the corresponding data for each URL.

Ensure that the ‘list of robots.txt directives’ can be found on a new sheet within the same document, then add a column for ‘Blocked by Robots.txt’ with the following formula contained:

{=IF(SUM(NOT(ISERROR(FIND('Sheet Name'!$A$2:$A$7,$A2)))*1)>0,P$1,"Non-"&LOWER(P$1))}

PS. you may recognise this formula from Richard’s Keyword Research post on Moz.

‘Sheet Name’!$A$2:$A$7 – refers to the name of the sheet where the list of robots.txt directives can be found, with the absolute cell range $A$2:$A$7 reflecting the list size.

P$1 – change this to reflect the cell containing the column heading, where the resulting output for each cell should then be either “Blocked by Robots.txt” or “Non-Blocked by Robots.txt”.

{ } – the curly brackets turn our formula into an array, but can only be activated by pressing Ctrl+Shift+Enter.

Note: if the robots.txt directives contain a ‘?’, place a tilde ‘~’ operator beforehand so that Excel interprets the symbol as text when executing the formula.

Add a further column labelled ‘Canonical Match’ to assess the canonical URL field. If this matches the origin URL or if left blank, the URL is classified as a true canonical. The formula is the same as that used in 4.6:

=IF(M2="", "N/A", IF(M2=B2, "Yes", "No"))

We then need to create a pivot table with the following fields:

In Row Labels – add ‘URL’

In Values – add ‘Pageviews’

In Report Filter – add ‘HTTP Status’, ‘Canonical Match’, ‘URLs Blocked by Robots.txt’ and ‘Content Type’

Set ‘Canonical Match’ to ‘Yes’ & ‘N/A’, and ‘URLs Blocked by Robots.txt’ to true as well. Then filter the content type to only show HTML page types.


These URLs will naturally not be reported in WMT, but are still a concern for users especially when these URLs are generating traffic.

If they are generating a substantial amount of traffic and are useful to users, you should be considering removal of corresponding disallow rules.

5.4 Externally Linked to Canonical URLs Blocked by Robots.txt

Data Required


Building on point 5.4, by collecting link metrics for the true canonical URLs we can see where external link equity is also being supplied but not carried due to the robots.txt disallow rules.

We’re going to be using the formula described in point 4.2 for the SEOgadget for Excel add-on, to retrieve a count of external linking root domains for each URL:

=majesticAPI_toFit("GetIndexItemInfo", "fresh", A1:A1000)

The below formula can then be used to mine the backlinks for specific URLs that have generated a larger number of linking root domains:


A quick pivot table with the following fields:

In Row Labels – add ‘URL’

In Values – add ‘External Linking Root Domains’

In Report Filter – add ‘HTTP Status’, ‘Canonical Match’, ‘URLs Blocked by Robots.txt’, ‘Content Type’ and ‘External Linking Root Domains’.

Set ‘Canonical Match’ to ‘Yes’ and ‘N/A’, ‘URLs Blocked by Robots.txt’ to true, ‘Content Type’ to show only HTML page types, and filter the linking root domains to only show URLs generating more than 1.

Sort the values column from high to low.


A list of top externally linked to true canonical URLs currently blocked by robots.txt

Assess whether the URLs are still relevant to users and contain unique content, then consider removing the disallow rules or making exceptions.

If the URLs are no longer required, consider implementing 301 redirects and again lifting the robots.txt rules so the link equity can be passed.

It’s not always that simple however, especially if there are other URLs within the same directory that offer little value for example, but at least by understanding how much link equity is being lost you can make an informed decision as to whether exploring an alternative is a viable option.

5.5 True Canonical URLs Missing from XML Sitemap

Data Required


Within the same data set we should already have a column for ‘Canonical Match’, returning a value of N/A, Yes, or No.

We then need to add a column with the heading ‘URL in XML Sitemap’ and carry out a VLOOKUP on the table containing a download of the XML sitemap(s).

And lastly, add a column with the heading ‘No. of requests’ and apply the following formula to the column:

=COUNTIF(range, A2)

This will then count the number of requests made by GoogleBot for the specific URL.

Once this data is in place, create a pivot table with the following properties:

In Row Labels – add ‘URL’

In Values – add ‘No. of requests’

In Report Filter – apply and set the following filters:


List of 200OK HTML pages not in the XML sitemap, by total requests made by Googlebot

5.6 % URLs Crawled by Click Depth

Data Required

Master log files:

Separate SF full site crawl data:


Create a pivot table for both the main log file data set, and the separate crawl carried out by Screaming Frog.

The ‘Depth’ (or Level) field should be used as both a row label and as a value with the field settings of ‘Count’. The HTTP status, content type and blocked by robots.txt fields should then be used as report filters, set to only display HTML pages returning a 200OK status accessible to search engines.


Overlaying the two data series on a single chart and you can quickly see whether there is an architecture problem based on crawl frequency:

In any scenario where the number of URLs outweighs or is near equal to the number of requests, there’s a problem with your site architecture. The deeper the architecture, the less link equity / authority passed to deeper level URLs, and the harder search engines have to work to crawl & index your content.

From the separate SF crawl data we immediately have a list of URLs that haven’t been crawled.

Now we may want to look at the URLs that are at a depth of 4 onwards to check whether those content types should be crawled & indexed in the first place. Beyond that, you should be assessing your internal link structure to aid in flattening the architecture e.g. main site navigation (drop down menus), popular searches, hot products, new products etc (see point 5.7 for more details).

For large ecommerce sites, leverage your most common content type, product URLs. A site may have a thousand categories (top, sub, sub sub etc), but well over 10,000+ products. A single link on each product is an additional 10,000+ links in your architecture, so why not use this as a way to leverage further exposure of your deeper level categories.

Pro Tip: Run another Screaming Frog crawl, but change the start point of your crawl to a deeper internal page. Overlay this data with the chart above and compare the differences. A strong architecture doesn’t just revolve around the number of clicks from your home page, but the number of clicks from any page on your website.

5.7 Requests vs. Internal & Inbound Links

Data Required

All data is located within the separate SF crawl used in the previous points:


Building on point 5.6, create a pivot table using the following settings:

In Row Labels – add ‘URL’

In Values – add ‘No. of requests’, ‘Inlinks’, and ‘External Linking Root Domains

In Report Filter – apply and set the following filters:


Sort the ‘No. of requests’ column low to high, and begin investigating the URLs returned by the number of internal and inbound links to see if either is a potential contributing factor to the limited number of requests generated.

5.8 Incomplete Crawl / Download

Data Required


This time using the main log file data set, we’re interested in the fields ‘Page Size’ and ‘Bytes Downloaded’.

Create a pivot table with the following properties:

In Row Labels – add ‘URL’

In Values – add ‘Bytes Downloaded’ and ‘Page Size’, altering the value field settings both to show ‘Average’. The ‘URL String’ (or URL path) field should also be added and set to ‘Count’, so we can compare the data against the number of requests for each URL.

In Report Filter – apply and set the following filters:

Sort the average of bytes downloaded column from lowest to highest.


By comparing the bytes downloaded data from the log files with the actual size of each URL, and averaging across the duration of the log file data we can see just how much of each page is typically being downloaded by search engines.

The ‘least downloaded pages’ view is a fantastic way of quickly identifying pages that typically possess characteristics associated with Panda, and is generally in line with the number of requests for those URLs.

5.9 Aged & Thin Content Types

Data Required


From the standalone SF crawl, create a pivot table with the following properties:

In Row Labels – add ‘URL’

In Values – add ‘Last Mod Date’, ‘No. of Requests’, and ‘Word Count’

In Report Filter – apply and set the following filters:

Switch between sorting the ‘Word Count’ & ‘Last Mod Date’ from low to high/old to new.


This is another tool for helping to assess why a particular URL is being crawled less frequently. If a page hasn’t been updated for quite some time, contains very little useful & unique content, and is experiencing low crawl frequency, you can probably bucket those URLs with the others most at risk of Panda.

Impact Reporting

As part of the auditing process we’ve already created several charts & reports which are extremely useful when getting down to the nitty gritty details, but don’t provide us with a high level view of overall crawl performance.

As mentioned at the beginning, we’re trying to answer the following questions:

The below charts (although not limited to) will help to sum up overall crawl performance for your website.

Wasted Crawl Budget

The above chart was generated from an analysis of a well-known high street retailer (and yes that figure is correct, 95% of their overall crawl allocation was being wasted!).

As we’re logging every error we identify somewhere within Excel, a few lookups across our data and we can combine a simple “is this URL an X error? Yes/No” style series of columns within the main log file data set, where filtering to ‘Yes’ returns the total number of requests made for URLs resulting in that error type.

Aggregating that information again and we have the data to generate the chart you see above.

Here is the formula you’ll need to carry out the lookup:


Crawl Waste by Product

If you understand which product types generate the core of your revenue, you could even break this data down by product just by aggregating at a directory level e.g.

Crawl Deficiencies

The exact same applies to logging each type of crawl deficiency within Excel, with the output being a high level snapshot of URLs that have been affected in some way:

Crawl Waste by Error Type

Provide a clearer view of the errors identified based on the number of requests for URLs of that nature:

This report can also then be pulled for ‘crawl deficiencies’.

Crawl Waste by User Agent

Comparing overall requests by user-agent can yield some interesting behaviour:

In the above instance, Bing tends to work with a much smaller bucket of URLs but crawl those more frequently, whereas Googlebot has a much larger bucket of URLs which are crawled less frequently.

There is also very little cross over in what both user-agents are crawling, with over 50% of requests by each user-agent unique to that crawler.

Extending Your Log File Analysis

There are so many data sets of which this log file auditing process can be applied, that we’ve really just scratched the surface by focusing on a typical search engine crawler in this guide (maybe I’ll release a follow up post on that :)).

If you really want to assess the overall performance of your site, experiment and compare other user agents e.g.

Think of the typical SEO (and even user) generated issues associated with that type of content e.g. is Googlebot Mobile incorrectly crawling the desktop site, despite redirects being in place to a standalone live mobile site? Log files can show you that information!

Again, you may even need to combine your analysis with other data sets like we’ve done in this guide to explore at a much deeper level.

Log files – the possibilities are endless!


Hungry for more? Then be sure to take a look at some of these fantastic articles: