{"id":6677,"date":"2022-05-20T15:01:49","date_gmt":"2022-05-20T15:01:49","guid":{"rendered":"https:\/\/puntorojo.com\/blog\/en\/?p=6677"},"modified":"2022-08-03T23:29:31","modified_gmt":"2022-08-03T23:29:31","slug":"data-science-how-to-extract-and-process-data-for-decision-making-in-your-seo-strategy","status":"publish","type":"post","link":"https:\/\/puntorojo.com\/blog\/en\/data-science-how-to-extract-and-process-data-for-decision-making-in-your-seo-strategy\/","title":{"rendered":"Data Science: How to extract and process data for decision making in your SEO strategy"},"content":{"rendered":"<p><b>Data Analytics<\/b><span style=\"font-weight: 400;\"> it\u2019s the science of analyzing raw data and extracting from it patterns and insights that add value to decision making. By raw data, we mean unparsed, unprocessed, and unformatted information.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Many algorithms sift through the information for you and return a complete report with insights into what was analyzed. To put it bluntly, Data Science is the use of technology to analyze data. In this post, we are going to see how you can start extracting data from your keywords using Python.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Before we start, let\u2019s define some terms:<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Data:<\/b><span style=\"font-weight: 400;\"> The information collected<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Analytics:<\/b><span style=\"font-weight: 400;\"> The data analysis<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Insights:<\/b><span style=\"font-weight: 400;\"> What you learn after analyzing the data<\/span><\/li>\n<\/ul>\n<p><span style=\"font-weight: 400;\">In order to bring some added value to the organic SEO industry, I&#8217;m going to stay away from Google Analytics and similar tools in this post as there are thousands of posts written about the topic by people more qualified than me.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">We are going to use the following tools:<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Google Analytics (I know I said I won\u2019t use it, but we are going to extract the data from here in this example)<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Google Chrome Console<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Notepad (or equivalent)<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Python<\/span><\/li>\n<\/ul>\n<h2><b>How to extract search term data from Google Analytics<\/b><\/h2>\n<ol>\n<li><span style=\"font-weight: 400;\">Open Google Analytics in Google Chrome<\/span><\/li>\n<li><span style=\"font-weight: 400;\">In Google Analytics, go to<\/span><span style=\"font-weight: 400;\"> Acquisition &gt; Search Console &gt; Queries<\/span><\/li>\n<li><span style=\"font-weight: 400;\">Filter view to show 5000 rows<\/span><\/li>\n<li><span style=\"font-weight: 400;\">Open <\/span><span style=\"font-weight: 400;\">Google Chrome inspector &gt; &#8220;Elements&#8221; tab<\/span><\/li>\n<li><span style=\"font-weight: 400;\">Navigate to one of the rows that display Google Search Console information to make sure it is in the inspector cache. If this step is not done correctly, keywords may not appear when running the script.<\/span><\/li>\n<li><span style=\"font-weight: 400;\">Go to the &#8220;console&#8221; tab<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Ctrl + L to clean the console<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Paste the following script and press ENTER*.<br \/>\n<\/span><span style=\"font-weight: 400;\">for(var kwds=document.getElementsByClassName(&#8220;_GApu&#8221;),clicks=document.getElementsByClassName(&#8220;_GAtjb&#8221;),total=kwds.length,content='&lt;table align=&#8221;center&#8221;&gt;&lt;tr&gt;&lt;th&gt;Keywords&lt;\/th&gt;&lt;th&gt;Clicks&lt;\/th&gt;&lt;th&gt;Impresiones&lt;\/th&gt;&lt;th&gt;CTR&lt;\/th&gt;&lt;th&gt;Ranking&lt;\/th&gt;&lt;\/tr&gt;&#8217;,i=0;i&lt;total;i++){var current_kwd=kwds[i].innerHTML,current_clicks=clicks[i].innerText.split(&#8220;(&#8220;)[0].replace(&#8220;%&#8221;,&#8221;&#8221;).replace(&#8220;.&#8221;,&#8221;&#8221;).replace(&#8220;,&#8221;,&#8221;.&#8221;).trim(),current_impresiones=clicks[i+1].innerText.split(&#8220;(&#8220;)[0].replace(&#8220;%&#8221;,&#8221;&#8221;).replace(&#8220;.&#8221;,&#8221;&#8221;).replace(&#8220;,&#8221;,&#8221;.&#8221;).trim(),current_ctr=clicks[i+2].innerText.split(&#8220;(&#8220;)[0].replace(&#8220;%&#8221;,&#8221;&#8221;).replace(&#8220;.&#8221;,&#8221;&#8221;).replace(&#8220;,&#8221;,&#8221;.&#8221;).trim(),current_posicion_media=clicks[i+3].innerText.split(&#8220;(&#8220;)[0].replace(&#8220;%&#8221;,&#8221;&#8221;).replace(&#8220;.&#8221;,&#8221;&#8221;).replace(&#8220;,&#8221;,&#8221;.&#8221;).trim();i+=3;var row=&#8221;&lt;td&gt;&#8221;+current_kwd+'&lt;\/td&gt;&lt;td align=&#8221;center&#8221;&gt;&#8217;+current_clicks+'&lt;\/td&gt;&lt;td align=&#8221;center&#8221;&gt;&#8217;+current_impresiones+'&lt;\/td&gt;&lt;td align=&#8221;center&#8221;&gt;&#8217;+current_ctr+'&lt;\/td&gt;&lt;td align=&#8221;center&#8221;&gt;&#8217;+current_posicion_media+&#8221;&lt;\/td&gt;&#8221;;content+=&#8221;&lt;tr style=\\&#8221;font-family: &#8216;Open Sans&#8217;\\&#8221;&gt;&#8221;+row+&#8221;&lt;\/tr&gt;&#8221;}with(output='&lt;html&gt;&lt;head&gt;&lt;title&gt;Punto Rojo Tools &#8211; Google Analytics Keyword Extractor&lt;\/title&gt;&lt;link rel=&#8221;stylesheet&#8221; type=&#8221;text\/css&#8221; href=&#8221;https:\/\/fonts.googleapis.com\/css?family=Open+Sans&#8221; \/&gt;&lt;link rel=&#8221;stylesheet&#8221; type=&#8221;text\/css&#8221; href=&#8221;\/\/fonts.googleapis.com\/css?family=Open+Sans&#8221; \/&gt;&lt;\/head&gt;&lt;body&gt;&#8217;,output='&lt;table align=&#8221;center&#8221; width=&#8221;50%&#8221;&gt;&lt;tr&gt;&lt;td align=&#8221;left&#8221;&gt;&lt;img src=&#8221;https:\/\/puntorojo.com\/blog\/wp-content\/themes\/punto_rojo\/img\/logo_mini.png&#8221; width=&#8221;200px&#8221;&gt;&lt;\/td&gt;&lt;td align=&#8221;right&#8221;&gt;&lt;h1 style=&#8221;font-family: \\&#8217;Open Sans\\'&#8221;&gt;Google Analytics Keyword Extractor&lt;\/h1&gt;&lt;\/td&gt;&lt;\/tr&gt;&lt;\/table&gt;&lt;br&gt;&lt;br&gt;&#8217;,output+=content,output+=&#8221;&lt;\/body&gt;&lt;\/html&gt;&#8221;,window.open())document.write(output),document.close();<\/span><\/li>\n<li><span style=\"font-weight: 400;\">If you want to see directly the information without formatting to export it to a CSV, paste the following code:<br \/>\n<\/span><span style=\"font-weight: 400;\">for(var kwds=document.getElementsByClassName(&#8220;_GApu&#8221;),clicks=document.getElementsByClassName(&#8220;_GAtjb&#8221;),total=kwds.length,content=&#8221;Keywords;Clicks;Impresiones;CTR;Ranking&lt;br&gt;&#8221;,i=0;i&lt;total;i++){var current_kwd=kwds[i].innerHTML,current_clicks=clicks[i].innerText.split(&#8220;(&#8220;)[0].replace(&#8220;%&#8221;,&#8221;&#8221;).replace(&#8220;.&#8221;,&#8221;&#8221;).replace(&#8220;,&#8221;,&#8221;.&#8221;).trim(),current_impresiones=clicks[i+1].innerText.split(&#8220;(&#8220;)[0].replace(&#8220;%&#8221;,&#8221;&#8221;).replace(&#8220;.&#8221;,&#8221;&#8221;).replace(&#8220;,&#8221;,&#8221;.&#8221;).trim(),current_ctr=clicks[i+2].innerText.split(&#8220;(&#8220;)[0].replace(&#8220;%&#8221;,&#8221;&#8221;).replace(&#8220;.&#8221;,&#8221;&#8221;).replace(&#8220;,&#8221;,&#8221;.&#8221;).trim(),current_posicion_media=clicks[i+3].innerText.split(&#8220;(&#8220;)[0].replace(&#8220;%&#8221;,&#8221;&#8221;).replace(&#8220;.&#8221;,&#8221;&#8221;).replace(&#8220;,&#8221;,&#8221;.&#8221;).trim();i+=3;var row=current_kwd+&#8221;;&#8221;+current_clicks+&#8221;;&#8221;+current_impresiones+&#8221;;&#8221;+current_ctr+&#8221;;&#8221;+current_posicion_media+&#8221;&lt;br&gt;&#8221;;content+=row}var output=&#8221;&lt;html&gt;&#8221;;with(output+=content,output+=&#8221;&lt;\/html&gt;&#8221;,window.open())document.write(output),document.close();<\/span><\/li>\n<li><span style=\"font-weight: 400;\">Copy keywords, paste them into Notepad<\/span><\/li>\n<li><span style=\"font-weight: 400;\">Add &#8216;Keywords&#8217; to the first row of the txt (without quotation marks and with capital K)<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Save as &#8216;keywords.csv&#8217;<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">If you want to save the script for future reference, follow the steps below:<\/span>\n<ol style=\"list-style-type: lower-alpha;\">\n<li style=\"font-weight: 400;\" aria-level=\"2\"><span style=\"font-weight: 400;\">In the Google Chrome inspector, go to th<\/span><span style=\"font-weight: 400;\">e &#8216;Sources&#8217; tab<\/span><span style=\"font-weight: 400;\">.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"2\"><span style=\"font-weight: 400;\">In the menu on the left, click on the<\/span><span style=\"font-weight: 400;\"> &#8216;Snippets&#8217; tab (if it does not appear, click on the little arrow on the right)<\/span><span style=\"font-weight: 400;\">.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"2\"><span style=\"font-weight: 400;\">Click on &#8216;+ New Snippet&#8217;.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"2\"><span style=\"font-weight: 400;\">Name the script &#8216;Google Analytics &#8211; Keyword Extractor&#8217; or similar.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"2\"><span style=\"font-weight: 400;\">Paste the code in the window next to it<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"2\"><span style=\"font-weight: 400;\">Ctrl + S to save (or its MAC equivalent)<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"2\"><span style=\"font-weight: 400;\">That&#8217;s it! Each time you want to run it, double click on the snippet name in the left window of <\/span><span style=\"font-weight: 400;\">Sources &gt; Snippets<\/span><\/li>\n<\/ol>\n<\/li>\n<\/ol>\n<h2><b>How to manipulate data with Python<\/b><\/h2>\n<ol>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><a href=\"https:\/\/lmgtfy.es\/?q=c%C3%B3mo+instalar+python+3\"><span style=\"font-weight: 400;\">Install Python3<\/span><\/a><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Install modules:<\/span>\n<ol>\n<li style=\"font-weight: 400;\" aria-level=\"2\"><span style=\"font-weight: 400;\">pandas<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"2\"><span style=\"font-weight: 400;\">matplotlib<\/span><\/li>\n<\/ol>\n<\/li>\n<\/ol>\n<p><span style=\"font-weight: 400;\">Now we are going to see how to load data from a CSV in Python and how to manipulate it. For practicality, the code to read the CSV will be shown only once, but it must always be in the script.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Load the data with the following snippet:<\/span><\/p>\n<p><span style=\"font-weight: 400;\">import pandas as pd<\/span><\/p>\n<p><span style=\"font-weight: 400;\">import matplotlib.pyplot as plt<\/span><\/p>\n<p><span style=\"font-weight: 400;\">data = pd.read_csv(\u2018keywords.csv\u2019, delimiter = \u2018;\u2019, encoding = \u2018ISO-8859-1\u2019)<\/span><\/p>\n<p><span style=\"color: #ff0000;\"><b>Show the first 10 rows of the CSV:<\/b><\/span><\/p>\n<p><span style=\"font-weight: 400;\">print(data.head(10))<\/span><\/p>\n<p><span style=\"color: #ff0000;\"><b>Count the number of rows:<\/b><\/span><\/p>\n<p><span style=\"font-weight: 400;\">print(len(data)<\/span><\/p>\n<p><span style=\"color: #ff0000;\"><b>Count the number of rows 2:<\/b><\/span><\/p>\n<p><span style=\"font-weight: 400;\">print(\u00ab<\/span><span style=\"font-weight: 400;\">number of rows<\/span><span style=\"font-weight: 400;\">: %i\u00bb % len(data))<\/span><\/p>\n<p><span style=\"font-weight: 400;\">#The %i indicates that an integer variable goes there.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">#When closing double quotes, the % indicates what should replace %i<\/span><\/p>\n<p><span style=\"color: #ff0000;\"><b>How to do data splicing:<\/b><\/span><\/p>\n<p><span style=\"font-weight: 400;\">print(data[:10]) #displays the first 10 rows<\/span><\/p>\n<p><span style=\"font-weight: 400;\">print(data[5:]) #displays all but the first 5 rows<\/span><\/p>\n<p><span style=\"font-weight: 400;\">print(data[-3:]) #displays the last 3 rows<\/span><\/p>\n<p><span style=\"font-weight: 400;\">print(data[:-2]) #displays all but the last 2 rows<\/span><\/p>\n<p><span style=\"font-weight: 400;\">print(data[-5:-2]) #sample from the 5th from the end to 2nd from the end<\/span><\/p>\n<p><span style=\"color: #ff0000;\"><b>Change all keywords to lowercase:<\/b><\/span><\/p>\n<p><span style=\"font-weight: 400;\">data = data[\u2018Keyword\u2019].str.lower()<\/span><\/p>\n<p><span style=\"font-weight: 400;\">#Translated, it would be: the keywords are now the keywords but in lower case.<\/span><\/p>\n<p><span style=\"color: #ff0000;\"><b>How to get the average CTR per number of words<\/b><\/span><\/p>\n<p><span style=\"font-weight: 400;\">As a demonstration, let&#8217;s take a look at some of what can be done to get SEO insights with Python.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Using the code above we are going to:<\/span><\/p>\n<ol>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Load pandas and matplotlib.pyplot modules<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Load the dataset with the search terms and dump them into a variable called &#8220;data&#8221;.<\/span><\/li>\n<\/ol>\n<p><span style=\"font-weight: 400;\">The data is now in what is called a Pandas DataFrame. Pandas is a Python module that allows you to manipulate data as if it were a spreadsheet, except that it does not render the content until you ask it to. This saves system resources: imagine loading millions of rows, clicking on a cell, rendering millions of rows again, and so on&#8230; it is not practical and that is why we use Pandas.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Pandas works with columns and rows, just like Excel or Google Sheets. In these examples, our DataFrame is going to be called &#8220;data&#8221;.<\/span><\/p>\n<p><span style=\"color: #ff0000;\"><b>How to see the columns of a DataFrame in Pandas:<\/b><\/span><\/p>\n<p><span style=\"font-weight: 400;\">print(data.columns)<\/span><\/p>\n<p><span style=\"color: #ff0000;\"><b>How to see the content of a column in Pandas:<\/b><\/span><\/p>\n<p><span style=\"font-weight: 400;\">print(data[\u2018CTR\u2019]) #the column is called CTR<\/span><\/p>\n<p><span style=\"color: #ff0000;\"><b>How to average the values of a column in Pandas:<\/b><\/span><\/p>\n<p><span style=\"font-weight: 400;\">print(data[\u2018CTR\u2019].mean())<\/span><\/p>\n<p><span style=\"color: #ff0000;\"><b>How to obtain the average of a column in Pandas grouped according to another column:<\/b><\/span><\/p>\n<p><span style=\"font-weight: 400;\">average = data.groupby([\u2018Ranking\u2019])[\u2018CTR\u2019].mean()<\/span><\/p>\n<p><span style=\"font-weight: 400;\">print(average)<\/span><\/p>\n<p><b>The output should be similar to this (varies depending on the data)<\/b><\/p>\n<p><img loading=\"lazy\" class=\"\" src=\"https:\/\/res.cloudinary.com\/scuba-dive-argentina\/image\/upload\/c_scale,w_760,h_1094\/f_webp,q_auto\/v1639602909\/imagen-1_4631e2528.jpg?_i=AA\" width=\"308\" height=\"443\" \/><\/p>\n<p><span style=\"color: #ff0000;\"><b>How to plot the average of a column in Pandas with Matplotlib:<\/b><\/span><\/p>\n<p>#continued from previous script<\/p>\n<p>average.plot(kind=\u2019bar\u2019)<\/p>\n<p>plt.title(\u2018Average CTR per ranking\u2019)<\/p>\n<p>plt.xlabel(\u2018Ranking\u2019)<\/p>\n<p>plt.ylabel(\u2018CTR\u2019)<\/p>\n<p>plt.show()<\/p>\n<p><b>The output should look like this (varies according to data)<\/b><\/p>\n<p><img loading=\"lazy\" class=\"\" src=\"https:\/\/puntorojo.com\/blog\/wp-content\/uploads\/2019\/05\/imagen-2-1024x575.jpg\" width=\"658\" height=\"369\" \/><\/p>\n<p><span style=\"color: #ff0000;\"><b>To plot only the positions from 1 to 10, it is necessary to reduce the DataFrame before drawing the averages.<\/b><\/span><\/p>\n<p>df = df[df[\u2018Ranking\u2019] &lt;= 10]<\/p>\n<p>average = df.groupby([\u2018Ranking\u2019])[\u2018CTR\u2019].mean()#.plot(kind=\u2019bar\u2019)#, cmap=\u2019coolwarm\u2019)<\/p>\n<p><span style=\"color: #ff0000;\"><b>If we want to round the rankings and change the type of data displayed in the column so that they do not show decimals (rounding would treat, for example, a rank 1.9 as a rank 2):<\/b><\/span><\/p>\n<p>df = df[df[\u2018Ranking\u2019] &lt;= 10]<\/p>\n<p>df[\u2018Ranking\u2019] = df[\u2018Ranking\u2019].round().astype(int)<\/p>\n<p>average = df.groupby([\u2018Ranking\u2019])[\u2018CTR\u2019].mean()<\/p>\n<p><img loading=\"lazy\" class=\"\" src=\"https:\/\/res.cloudinary.com\/scuba-dive-argentina\/image\/upload\/c_fill,g_auto,w_778,h_666\/f_webp,q_auto\/v1639602903\/imagen-3_4633e258e.jpg?_i=AA\" width=\"221\" height=\"189\" \/><\/p>\n<p><span style=\"color: #ff0000;\"><b>And if we plot that by adding the plotting lines shown above and adding these two lines to show the X-axis numbers horizontally and without decimals:<\/b><\/span><\/p>\n<p><span style=\"color: #000000;\">plt.xticks(rotation=\u2019horizontal\u2019)<\/span><\/p>\n<p><img loading=\"lazy\" class=\"\" src=\"https:\/\/puntorojo.com\/blog\/wp-content\/uploads\/2019\/05\/imagen-4-1024x860.jpg\" width=\"360\" height=\"303\" \/><\/p>\n<p>And that&#8217;s it! We got to start getting into all that is <strong>Data Science for SEO<\/strong>. There are a lot of graphs that can be born from this data and even more after starting to generate data from the data with feature engineering. An example could be to look at positioning according to the number of words in a search term, some short, medium, and long-tail keywords, etc.<\/p>\n<p>What programming language do you use to extract graphs and insights from your data? Let us know below so we can shape future posts. Until next time and good rankings!<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Data Analytics it\u2019s the science of analyzing raw data and extracting from it patterns and insights that add value to decision making. By raw data, we mean unparsed, unprocessed, and unformatted information. Many algorithms sift through the information for you and return a complete report with insights into what was analyzed. To put it bluntly,&hellip;<\/p>\n<a class=\"read-more-link\" href=\" https:\/\/puntorojo.com\/blog\/en\/data-science-how-to-extract-and-process-data-for-decision-making-in-your-seo-strategy\/ \">Read More<\/a>","protected":false},"author":31,"featured_media":6941,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":[],"categories":[436,15],"tags":[430,429,431],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v17.8 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Data Science: How to extract and process data for decision making in your SEO strategy | Agencia SEO Punto Rojo<\/title>\n<meta name=\"description\" content=\"Data Analytics it\u2019s the science of analyzing raw data and extracting from it patterns and insights that add value to decision making.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link 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