Using Data Science to Improve Internal Link Structures<\/h2>\n\n\n\n
Our data-driven approach will concentrate on just one aspect of optimizing internal link architecture: modeling the distribution of internal links by site depth and then targeting the pages that are lacking links for their specific site depth.<\/p>\n\n\n\n
We begin by importing the libraries and data, then we clean up the column names before previewing them:<\/p>\n\n\n\n
import pandas as pd\r\nimport numpy as np\r\nsite_name = 'ON24'\r\nsite_filename = 'on24'\r\nwebsite = 'www.on24.com'\r\n\r\n# import Crawl Data\r\ncrawl_data = pd.read_csv('data\/'+ site_filename + '_crawl.csv')\r\ncrawl_data.columns = crawl_data.columns.str.replace(' ','_')\r\ncrawl_data.columns = crawl_data.columns.str.replace('.','')\r\ncrawl_data.columns = crawl_data.columns.str.replace('(','')\r\ncrawl_data.columns = crawl_data.columns.str.replace(')','')\r\ncrawl_data.columns = map(str.lower, crawl_data.columns)\r\nprint(crawl_data.shape)\r\nprint(crawl_data.dtypes)\r\nCrawl_data\r\n\r\n(8611, 104)\r\n\r\nurl object\r\nbase_url object\r\ncrawl_depth object\r\ncrawl_status object\r\nhost object\r\n ... \r\nredirect_type object\r\nredirect_url object\r\nredirect_url_status object\r\nredirect_url_status_code object\r\nunnamed:_103 float64\r\nLength: 104, dtype: object<\/pre>\n\n\n\n![\"\"](\"https:\/\/seopolarity.com\/blog\/wp-content\/uploads\/2021\/11\/1_crawl_data-1024x441.png\")
<\/figure><\/div>\n\n\n\nThe data imported from the Sitebulb desktop crawler application is shown above as a preview. There are over 8,000 rows, and not all of them will be unique to the domain, as resource URLs and external outbound link URLs will be included.<\/p>\n\n\n\n
We also have over 100 columns that aren’t required, so some column selection will be necessary.<\/p>\n\n\n\n
But, before we get there, let’s take a look at how many site levels there are:<\/p>\n\n\n\n
crawl_depth\r\n0 1\r\n1 70\r\n10 5\r\n11 1\r\n12 1\r\n13 2\r\n14 1\r\n2 303\r\n3 378\r\n4 347\r\n5 253\r\n6 194\r\n7 96\r\n8 33\r\n9 19\r\nNot Set 2351\r\ndtype: int64<\/pre>\n\n\n\nSo, as we can see from the above, there are 14 site levels, the majority of which are found in the XML sitemap rather than the site architecture.<\/p>\n\n\n\n
You’ll notice that Pandas (the Python data-handling package) sorts the site levels by digit.<\/p>\n\n\n\n
This is because the site levels are currently charactered strings rather than numeric. This will be changed in later code because it affects data visualization (‘viz’).<\/p>\n\n\n\n
We’ll now filter the rows and select the columns.<\/p>\n\n\n\n
# Filter for redirected and live links<\/pre>\n\n\n\nredir_live_urls = crawl_data[['url', 'crawl_depth', 'http_status_code', 'indexable_status', 'no_internal_links_to_url', 'host', 'title']]\nredir_live_urls = redir_live_urls.loc[redir_live_urls.http_status_code.str.startswith(('2'), na=False)]\nredir_live_urls['crawl_depth'] = redir_live_urls['crawl_depth'].astype('category')\nredir_live_urls['crawl_depth'] = redir_live_urls['crawl_depth'].cat.reorder_categories(['0', '1', '2', '3', '4',\n '5', '6', '7', '8', '9',\n '10', '11', '12', '13', '14',\n 'Not Set',\n ])\nredir_live_urls = redir_live_urls.loc[redir_live_urls.host == website]\ndel redir_live_urls['host']\nprint(redir_live_urls.shape)\nRedir_live_urls\n\n(4055, 6)<\/pre>\n\n\n\n![\"\"](\"https:\/\/seopolarity.com\/blog\/wp-content\/uploads\/2021\/11\/2_redir_live_urls-1024x335.png\")
<\/figure><\/div>\n\n\n\nWe now have a more streamlined data frame after filtering rows for indexable URLs and selecting the relevant columns (think Pandas version of a spreadsheet tab).<\/p>\n\n\n\n
Investigating the Distribution of Internal\u00a0Links<\/h2>\n\n\n\n
We’re now ready to data visualize the data and see how the internal links are distributed overall and by site depth.<\/p>\n\n\n\n
from plotnine import *\r\nimport matplotlib.pyplot as plt\r\npd.set_option('display.max_colwidth', None)\r\n%matplotlib inline\r\n\r\n# Distribution of internal links to URL by site level\r\nove_intlink_dist_plt = (ggplot(redir_live_urls, aes(x = 'no_internal_links_to_url')) + \r\n geom_histogram(fill = 'blue', alpha = 0.6, bins = 7) +\r\n labs(y = '# Internal Links to URL') + \r\n theme_classic() + \r\n theme(legend_position = 'none')\r\n )\r\n\r\nove_intlink_dist_plt<\/pre>\n\n\n\n![\"\"](\"https:\/\/seopolarity.com\/blog\/wp-content\/uploads\/2021\/11\/1_overall_intlink_dist_plt-1024x977.png\")
<\/figure><\/div>\n\n\n\nWe can see from the above that most pages have no links, so improving internal linking would be a significant opportunity to improve SEO here.<\/p>\n\n\n\n
Let’s look at some statistics at the site level.<\/p>\n\n\n\n
crawl_depth\r\n0 1\r\n1 70\r\n10 5\r\n11 1\r\n12 1\r\n13 2\r\n14 1\r\n2 303\r\n3 378\r\n4 347\r\n5 253\r\n6 194\r\n7 96\r\n8 33\r\n9 19\r\nNot Set 2351\r\ndtype: int64<\/pre>\n\n\n\nThe table above depicts the approximate distribution of internal links by site level, including the average (mean) and median values (50 percent quantile).<\/p>\n\n\n\n
This is in addition to the variation within the site level (std for standard deviation), which tells us how close the pages within the site level are to the average; i.e., how consistent the internal link distribution is with the average.<\/p>\n\n\n\n
Except for the home page (crawl depth 0) and the first level pages (crawl depth 1), we can deduce that the average by site-level ranges from 0 to 4 per URL.<\/p>\n\n\n\n
For a more visual approach, consider:<\/p>\n\n\n\n
# Distribution of internal links to URL by site level\r\nintlink_dist_plt = (ggplot(redir_live_urls, aes(x = 'crawl_depth', y = 'no_internal_links_to_url')) + \r\n geom_boxplot(fill = 'blue', alpha = 0.8) +\r\n labs(y = '# Internal Links to URL', x = 'Site Level') + \r\n theme_classic() + \r\n theme(legend_position = 'none')\r\n )\r\n\r\nintlink_dist_plt.save(filename = 'images\/1_intlink_dist_plt.png', height=5, width=5, units = 'in', dpi=1000)\r\nintlink_dist_plt<\/pre>\n\n\n\nThe plot above confirms our previous observations that the home page and the pages directly linked from it receive the lion’s share of the links.<\/p>\n\n\n\n
With the scales as they are, we don’t have a good idea of how the lower levels are distributed. We’ll fix this by taking the y axis logarithm:<\/p>\n\n\n\n
# Distribution of internal links to URL by site level\r\nfrom mizani.formatters import comma_format\r\n\r\nintlink_dist_plt = (ggplot(redir_live_urls, aes(x = 'crawl_depth', y = 'no_internal_links_to_url')) + \r\n geom_boxplot(fill = 'blue', alpha = 0.8) +\r\n labs(y = '# Internal Links to URL', x = 'Site Level') + \r\n scale_y_log10(labels = comma_format()) + \r\n theme_classic() + \r\n theme(legend_position = 'none')\r\n )\r\n\r\nintlink_dist_plt.save(filename = 'images\/1_log_intlink_dist_plt.png', height=5, width=5, units = 'in', dpi=1000)\r\nintlink_dist_plt<\/pre>\n\n\n\n![\"\"](\"https:\/\/seopolarity.com\/blog\/wp-content\/uploads\/2021\/11\/1_intlink_dist_plt-1024x944.png\")
<\/figure><\/div>\n\n\n\nThe graph above depicts the same distribution of the links in a logarithmic view, which helps us confirm the distribution averages for the lower levels. This is much easier to picture.<\/p>\n\n\n\n
The disparity between the first two site levels and the remaining site suggests a skewed distribution.<\/p>\n\n\n\n
As a result, I’ll logarithmize the internal links to help normalize the distribution.<\/p>\n\n\n\n
We now have the normalized number of links, which we will depict:<\/p>\n\n\n\n
# Distribution of internal links to URL by site level\r\nintlink_dist_plt = (ggplot(redir_live_urls, aes(x = 'crawl_depth', y = 'log_intlinks')) + \r\n geom_boxplot(fill = 'blue', alpha = 0.8) +\r\n labs(y = '# Log Internal Links to URL', x = 'Site Level') + \r\n #scale_y_log10(labels = comma_format()) + \r\n theme_classic() + \r\n theme(legend_position = 'none')\r\n )\r\n\r\nintlink_dist_plt<\/pre>\n\n\n\n![\"\"](\"https:\/\/seopolarity.com\/blog\/wp-content\/uploads\/2021\/11\/1c_loglinks_dist_plt-1024x991.png\")
<\/figure><\/div>\n\n\n\nThe distribution appears to be less skewed from the above, as the boxes (interquartile ranges) have a more gradual step change from site to site.<\/p>\n\n\n\n
This sets us up nicely for analyzing the data and determining which URLs are under-optimized in terms of internal links.<\/p>\n\n\n\n
Quantifying the Problems<\/h2>\n\n\n\n
For each site depth, the code below will compute the lower 35th quantile (data science term for percentile).<\/p>\n\n\n\n
# internal links in under\/over indexing at site level\r\n# count of URLs under indexed for internal link counts\r\n\r\nquantiled_intlinks = redir_live_urls.groupby('crawl_depth').agg({'log_intlinks': \r\n [quantile_lower]}).reset_index()\r\nquantiled_intlinks = quantiled_intlinks.rename(columns = {'crawl_depth_': 'crawl_depth', \r\n 'log_intlinks_quantile_lower': 'sd_intlink_lowqua'})\r\nquantiled_intlinks<\/pre>\n\n\n\n![\"\"](\"https:\/\/seopolarity.com\/blog\/wp-content\/uploads\/2021\/11\/2_lowqua.png\")
<\/figure><\/div>\n\n\n\nThe calculations are shown above. At this point, the numbers are meaningless to an SEO practitioner because they are arbitrary and serve only to provide a cut-off for under-linked URLs at each site level.<\/p>\n\n\n\n
Now that we have the table, we’ll combine it with the main data set to determine whether the URL is under-linked row by row.<\/p>\n\n\n\n
# join quantiles to main df and then count\r\nredir_live_urls_underidx = redir_live_urls.merge(quantiled_intlinks, on = 'crawl_depth', how = 'left')\r\n\r\nredir_live_urls_underidx['sd_int_uidx'] = redir_live_urls_underidx.apply(sd_intlinkscount_underover, axis=1)\r\nredir_live_urls_underidx['sd_int_uidx'] = np.where(redir_live_urls_underidx['crawl_depth'] == 'Not Set', 1,\r\n redir_live_urls_underidx['sd_int_uidx'])\r\n\r\nredir_live_urls_underidx<\/pre>\n\n\n\nWe now have a data frame with each URL marked as under-linked as a 1 in the “sd int uidx’ column.<\/p>\n\n\n\n
This allows us to calculate the number of under-linked site pages by site depth:<\/p>\n\n\n\n
# Summarise int_udx by site level\r\nintlinks_agged = redir_live_urls_underidx.groupby('crawl_depth').agg({'sd_int_uidx': ['sum', 'count']}).reset_index()\r\nintlinks_agged = intlinks_agged.rename(columns = {'crawl_depth_': 'crawl_depth'})\r\nintlinks_agged['sd_uidx_prop'] = intlinks_agged.sd_int_uidx_sum \/ intlinks_agged.sd_int_uidx_count * 100\r\nprint(intlinks_agged)<\/pre>\n\n\n\n crawl_depth sd_int_uidx_sum sd_int_uidx_count sd_uidx_prop\n0 0 0 1 0.000000\n1 1 41 70 58.571429\n2 2 66 303 21.782178\n3 3 110 378 29.100529\n4 4 109 347 31.412104\n5 5 68 253 26.877470\n6 6 63 194 32.474227\n7 7 9 96 9.375000\n8 8 6 33 18.181818\n9 9 6 19 31.578947\n10 10 0 5 0.000000\n11 11 0 1 0.000000\n12 12 0 1 0.000000\n13 13 0 2 0.000000\n14 14 0 1 0.000000\n15 Not Set 2351 2351 100.000000<\/pre>\n\n\n\nWe can now see that, even though the site depth 1 page has a higher than average number of links per URL, 41 pages are under-linked.<\/p>\n\n\n\n
To be more specific:<\/p>\n\n\n\n
# plot the table\ndepth_uidx_plt = (ggplot(intlinks_agged, aes(x = 'crawl_depth', y = 'sd_int_uidx_sum')) + \n geom_bar(stat = 'identity', fill = 'blue', alpha = 0.8) +\n labs(y = '# Under Linked URLs', x = 'Site Level') + \n scale_y_log10() + \n theme_classic() + \n theme(legend_position = 'none')\n )\n\ndepth_uidx_plt.save(filename = 'images\/1_depth_uidx_plt.png', height=5, width=5, units = 'in', dpi=1000)\ndepth_uidx_plt<\/pre>\n\n\n\n![\"\"](\"https:\/\/seopolarity.com\/blog\/wp-content\/uploads\/2021\/11\/1_depth_uidx_plt-1024x959.png\")
<\/figure><\/div>\n\n\n\nThe distribution of under-linked URLs appears normal, except the XML sitemap URLs, as indicated by the near bell shape. The majority of the unlinked URLs are in site levels 3 and 4.<\/p>\n\n\n\n
Exporting a List of Underlying URLs<\/h2>\n\n\n\n
Now that we’ve identified the under-linked URLs by site level, we can export the data and devise creative solutions to bridge the site depth gaps, as shown below.<\/p>\n\n\n\n
# data dump of under performing backlinks\nunderlinked_urls = redir_live_urls_underidx.loc[redir_live_urls_underidx.sd_int_uidx == 1]\nunderlinked_urls = underlinked_urls.sort_values(['crawl_depth', 'no_internal_links_to_url'])\nunderlinked_urls.to_csv('exports\/underlinked_urls.csv')\nunderlinked_urls<\/pre>\n\n\n\n![\"\"](\"https:\/\/seopolarity.com\/blog\/wp-content\/uploads\/2021\/11\/3_export_underlinked_urls-1024x442.png\")
<\/figure><\/div>\n\n\n\nOther Internal Linking Data Science Techniques<\/h2>\n\n\n\n
We briefly discussed why improving a site’s internal links is important before delving into how internal links are distributed across the site by site level.<\/p>\n\n\n\n
Then, before exporting the results for recommendations, we quantified the extent of the under-linking problem both numerically and visually.<\/p>\n\n\n\n
Naturally, site-level internal links are only one aspect of internal links that can be statistically explored and analyzed.<\/p>\n\n\n\n
Other factors that could be used to apply data science techniques to internal links include, but are not limited to:<\/p>\n\n\n\n
- Page-level authority offsite.<\/li>
- The importance of anchor text.<\/li>
- Intention to search.<\/li>
- Look for the user journey.<\/li><\/ul>\n\n\n\n
Need help with our free SEO tools? Try our free Plagiarism Checker<\/a>, Article Rewriter<\/a>, Word Counter<\/a>.<\/a><\/ins><\/p>\n\n\n\nLearn more from SEO <\/a>and read 11 Methods for Creating a Google Algorithm Update Resistant SEO Strategy<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"Learn the data-driven approach to improving a website’s internal linking for more effective technical site SEO.<\/p>\n","protected":false},"author":1,"featured_media":1889,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_jetpack_memberships_contains_paid_content":false,"footnotes":"","jetpack_publicize_message":"","jetpack_publicize_feature_enabled":true,"jetpack_social_post_already_shared":false,"jetpack_social_options":{"image_generator_settings":{"template":"highway","enabled":false},"version":2}},"categories":[24],"tags":[],"class_list":["post-1876","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-seo"],"jetpack_publicize_connections":[],"yoast_head":"\n
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