SEO, short for Search Engine Optimization, is the cornerstone of digital marketing. Most brands do some level of optimization for different parameters to increase their online visibility, let it be keywords, backlinks, technical optimizations, and quality of content.
However, it is not an easy feat for a marketer to perfect SEO as search engines constantly tweak their algorithm. So, tracking SEO parameter data, especially keywords, is essential to identify how your website performs in SERP ranking.
By analyzing data for the keywords that drive traffic to your website, doing the same for your competitor's websites, and comparing the differences, you can gain insights into the search intent of your target audience and create content that aligns with their interests. This process is known as Keyword Gap Analysis. And with tools like Gigasheet, you do not need to be an expert programmer to analyze data.
So, let's explore the keyword dataset and see how Gigasheet makes big data analysis hassle-free.
For our keyword gap analysis, we are using a dataset with keyword and SEO parameters data for a website. The dataset will help us assess how good a website’s optimization efforts are and how it fares against competitors. We will analyze our dataset to reveal ranking opportunities, competitors’ traffic, and keyword gaps. And we will use Gigasheet to accomplish that.
Gigasheet is a no-code big analytics tool designed to work as an intuitive spreadsheet, but better. There is no need for users to be experts at programming or dataset. All you need is a dataset, and you can clean it, filter and group data, perform calculations, and more using Gigasheet.
We are using a CSV dataset with columns for information like site links, categories, keyword names, keyword traffic, etc. So let us begin with our exploration.
Before we can analyze our keyword dataset, we must prepare and clean it to get the most accurate insights from our analysis. To do so, we use Gigasheet’s Data Cleanup functions. Gigasheet allows us to find and delete duplicate rows, split and combine columns, trim whitespaces, and more.
Let's check to see if we have duplicate entries in the site link column. We simply head to the bottom of the site link column, click to open the drop-down menu, and select Percent Unique. Our dataset has around 86% unique website names rest are duplicate values.
Next, we will head over to the Data Cleanup tab in the menu bar and click Delete Duplicates. Then in the pop-up window, we will select the column site link and click Remove.
Now, we want to do keyword analysis and use Gigasheet to identify ranking opportunities for wired.com, a technology-centered website. First, we will head over to the Gigasheet menu bar, select the Filter option, and create a filter, as shown below.
Here is what our data shows us about wired.com’s keyword opportunities:
1. We can see that wired.com is getting traffic of around 454,000 visitors.
2. The website has 99,400 keyword gaps.
3. There are 15,400 easy-to-rank opportunities.
4. There are 7,500 opportunities to rank for keywords with purchase intent.
5. Facebook, Google Translate, Facebook Login, and Godaddy are the top four keywords bringing traffic to wired.com
6. iphone x, google hangouts, iphone se, and google one are the top four organic search keywords that do not bring any traffic to wired.com.
7. apple store, iphone xr price, the boring company, and boring company are top keywords with purchase intent driving traffic to competitor websites.
We can also visualize our findings in Gigasheet using bar graphs, pie charts, and more. Let us visualize the traffic wired.com’s competitors are getting from these four keywords: iphone x, google hangouts, iphone se, and google one.
We will select the following columns with estimated traffic to competitor websites:
1. all_topics_keyword_gaps_Avg_traffic_parameter_1 (iphone x)
2. all_topics_keyword_gaps_Avg_traffic_parameter_2 (google hangouts)
3. all_topics_keyword_gaps_Avg_traffic_parameter_3 (iphone se)
4. all_topics_keyword_gaps_Avg_traffic_parameter_4 (google one)
Then we will right-click and select Chart Range > Column > Grouped.
Similarly, we can find more ranking opportunities for wired.com around these keywords. For instance, we can create a new filter to look for all the keywords that contain either of the four terms: iphone x, google hangouts, iphone se, and google one. Resultant data will help us identify how other websites perform for these keywords.
The opportunity ratio is metric businesses use to measure their sales efficiency. It compares the total value of closed deals to the total value of opportunities that were available to close. We can replicate that to create a metric to measure the efficiency of a website’s SEO efforts.
We want to identify the top three website categories with a high opportunity ratio (0.75 or above) for our analysis. A high opportunity ratio signifies a higher keyword gap, i.e., the listed website loses significant traffic to the competitor for a keyword.
We will divide the traffic going to competitor websites by traffic coming to the listed websites. Gigasheet offers a quick way to perform simple calculations. In the Functions option, click Calculation, select the columns you want to perform calculations, and select the mathematical operator. And click Apply.
Gigasheet will perform the calculations and display the results in a new column.
Now, we want to filter data for websites that have an opportunity ratio of 0.75 or more using a filter, as shown below.
Next, we will group our data by website category. For that, we will right-click on the header of the category column and select Group in the context menu.
Computer/Consultants, Business/Aerospace_and_Defense, and Computer/Robotics are the top three categories, with most websites having higher keyword gaps.
Analyzing competitor keyword data allows marketers to identify organic search terms that generate traffic for other websites and replicate them in their own content to bring more visitors.
So, let’s analyze competitor keyword data for wired.com and identify websites with overlapping audiences for keywords.
We will again filter data for wired.com using the filter we created earlier. Our dataset already has columns (audience_overlap_similar_to_this_site_parameter1, 2, 3…) for websites that have overlapping audiences, so here are five websites that have overlapping audiences with wired.com:
But which website shares a bigger audience with wired.com for a keyword search? Our dataset also provides us with columns that show the relative level of overlapping, as shown below:
1. audience_overlap_similar_to_this_site_parameter1 shows the audience for theverge.com.
2. audience_overlap_similar_to_this_site_parameter2 shows numbers for techcrunch.com.
3. audience_overlap_similar_to_this_site_parameter3 contains numbers for businessinsider.com.
4. audience_overlap_similar_to_this_site_parameter4 shows the audience for gizmodo.com.
5. audience_overlap_similar_to_this_site_parameter5 shows numbers for digitaltrends.com.
Now let’s visualize our findings using a bar graph.
Let's dive deeper into our competitor analysis and identify keywords that generate traffic for the three competitors that share an audience with wired.com, namely theverge.com, techcrunch.com, and businessinsider.com.
First, we will need to filter data for the three competitors using the filter shown below:
Here is the list of top search terms bringing traffic to theverge.com, techcrunch.com, and businessinsider.com, respectively.
Similarly, we can identify keyword gaps in the content of our competitor’s website and create content to win more audiences before they do.
Wasn’t that quick? Gigasheet is an easy-to-use, cloud-based no-code big data analytics tool that makes it easier for non-tech users like marketers to explore data and draw actionable insights. There are no restrictions on files you can upload or rows you can process. And Gigasheet is free to use for everyone.
To try it yourself, sign up and create a free Gigasheet account today.