Analyzing the Impact of SEO Metrics on Website Ranking and Identifying Low Competition Keywords

Introduction

In the ever-evolving landscape of digital marketing, understanding the dynamics of search engine rankings is crucial for online visibility and success. This study is motivated by the need to demystify the factors that influence website ranking, particularly the roles of backlinks, domain authority, and keyword optimization. By analyzing real-world data, this research aims to shed light on these factors, offering a data-driven approach to SEO strategy. Additionally, identifying low-competition keywords is essential for developing effective content strategies, especially for new or smaller websites looking to establish a foothold in competitive markets.

Methodology

This research was conducted using a comprehensive and systematic approach to collect and analyze data. The primary tool for data collection was the ValueSERP API, a robust service providing detailed search result data. The methodology comprised several stages, as detailed below:

Data Collection:

1. Keyword Generation: A diverse list of keywords was generated to ensure a broad representation of various search queries. This list aimed to cover a wide range of topics and intents to provide a comprehensive view of the search landscape.
2. Search Result Retrieval: For each keyword, the top 100 search results were retrieved using the ValueSERP API. This API was chosen for its reliability and depth of information, providing detailed data for each search result, including titles, URLs, and snippets.
3. Backlink Metrics and Authority Scores: For each of the 40,000 search results obtained, detailed backlink metrics were collected. This included the total number of backlinks, the number of do-follow and no-follow backlinks, and the number of backlinks from educational (.edu) and government (.gov) domains. Additionally, the domain and page authority scores were retrieved to assess the overall strength and credibility of each webpage.

Data Analysis:

1. Python as a Tool: Python, a powerful programming language known for its versatility in data analysis, was employed to handle and analyze the large dataset. Various libraries and tools, such as Pandas for data manipulation and Matplotlib and Seaborn for visualization, were utilized to process the data and derive insights.
2. Correlation Analysis: To understand the relationship between different SEO metrics and website ranking, correlation analysis was conducted. This statistical method helped identify the strength and direction of the association between variables such as backlinks, domain authority, page authority, and search result ranking.
3. Identification of Low Competition Keywords: An algorithmic approach was used to identify low competition keywords. This involved analyzing the average backlinks and domain authority of pages ranking for each keyword. Keywords where top-ranking pages had relatively low backlink counts and domain authority were flagged as potential low competition opportunities.

The methodology was designed to be rigorous and replicable, ensuring that the study’s findings are reliable and can be validated by others in the field. The use of Python for both data collection and analysis streamlined the research process and allowed for the handling of a large dataset, ensuring a comprehensive study of the factors influencing website ranking and the identification of low competition keywords.

The dataset contains information about various web pages, including their rank in search results, title, URL, snippet, domain, and several metrics related to SEO such as the total number of backlinks, domain authority, page authority, and whether keywords are used in the title and meta descriptions. Here’s a breakdown of the key columns:

• rank: The rank of the website in the search results.
• title: The title of the web page.
• url: The web page’s URL.
• snippet: A short excerpt from the web page.
• domain: The domain of the website.
• total_backlinks: The total number of backlinks to the page.
• edu_backlinks: The number of backlinks from educational domains.
• gov_backlinks: The number of backlinks from government domains.
• referring_domains: The number of unique domains referring to the page.
• do_follow_backlinks: The number of do-follow backlinks.
• no_follow_backlinks: The number of no-follow backlinks.
• domain_authority: A score representing the domain’s overall authority.
• page_authority: A score representing the page’s authority.
• keyword_in_title: Whether the keyword is present in the page title.
• keyword_in_meta: Whether the keyword is present in the page’s meta description.
• keyword: The search keyword associated with the data.

[SHARE The code for data collection] and screenshots from streamlit app.

Analysis of Backlink Metrics and Ranking

The correlation matrix provides insights into the relationship between the rank of a website and various types of backlinks.

Here’s what the correlations suggest:

• Rank and Total Backlinks: There’s a slight negative correlation of -0.065, indicating that pages with more backlinks tend to rank slightly higher, but the relationship is not very strong.
• Rank and Do-Follow Backlinks: Similarly, there’s a slight negative correlation of -0.064, suggesting a weak relationship where pages with more do-follow backlinks might rank slightly higher.
• Rank and No-Follow Backlinks: The correlation is slightly stronger at -0.072, indicating a similar but weak trend where more no-follow backlinks might correspond to a slightly higher rank.
• Rank and Edu Backlinks: With a correlation of -0.075, pages with more .edu backlinks might rank slightly higher, but again, the relationship is not strong.
• Rank and Gov Backlinks: The correlation of -0.067 suggests a very weak relationship where more .gov backlinks might be associated with a slightly higher rank.

In all cases, the negative correlation indicates that as the number of backlinks increases, the rank (which is lower for higher positions) tends to decrease slightly. However, it’s important to note that all these correlations are quite weak. This suggests that while there might be a trend where more backlinks are associated with better rankings, backlinks alone don’t have a strong direct influence on ranking. Other factors are likely also at play.

Here’s the Python code used to calculate the correlation matrix between the rank and various types of backlinks:

import pandas as pd

# Load the data
data = pd.read_csv('data-40k.csv')

# Selecting the relevant columns for correlation analysis.
backlink_columns = ['rank', 'total_backlinks', 'do_follow_backlinks', 'no_follow_backlinks', 'edu_backlinks', 'gov_backlinks']
backlink_data = data[backlink_columns]

# Calculating the correlation matrix.
correlation_matrix = backlink_data.corr()

Detailed Backlink Analysis

We’ll investigate how backlink profiles differ across websites with different domain authorities. We’ll categorize websites based on their domain authority and analyze the average number of total backlinks, do-follow backlinks, no-follow backlinks, .edu backlinks, and .gov backlinks for each category.

I’ll start by categorizing the websites into groups based on their domain authority and then calculate the average number of each type of backlink for these groups. After this, I’ll provide a visualization to help you understand the trends. Let’s begin.

The analysis categorizes websites into groups based on their domain authority and then calculates the average number of different types of backlinks for each group. Here are the key findings:

• Domain Authority 0-20: Websites in this category have the lowest average number of backlinks across all types. This suggests that newer or less established websites might not have a robust backlink profile.
• Domain Authority 21-40: There’s a notable increase in the average number of backlinks compared to the 0-20 category, indicating that as domain authority increases, so does the backlink profile.
• Domain Authority 41-60: These sites have an even higher average number of backlinks, with significant increases particularly in do-follow and educational backlinks.
• Domain Authority 61-80: There’s a substantial jump in the average number of backlinks, especially in do-follow and no-follow types. This indicates that highly authoritative sites tend to have extensive backlink profiles.
• Domain Authority 81-100: This category shows an exponential increase in backlinks, reflecting that the most authoritative domains have massive numbers of backlinks.

The visualization supports these findings, showing a clear trend where the average number of backlinks increases with higher domain authority categories. It’s particularly noteworthy how dramatically the number of backlinks rises for the highest authority domains.

This analysis suggests that a strong backlink profile is associated with higher domain authority. However, it’s important to note that correlation does not imply causation, and other factors might also contribute to both a strong backlink profile and high domain authority.

Domain and Page Authority Analysis

The correlation matrix and heatmap provide insights into the relationship between website rank and its domain and page authority:

• Rank and Domain Authority: There’s a negative correlation of -0.108 between rank and domain authority. This suggests that websites with higher domain authority tend to rank better (lower rank number) in search results, although the correlation is moderate, indicating other factors are also at play.
• Rank and Page Authority: The correlation between rank and page authority is -0.064, which is weaker than the domain authority correlation. This indicates a slight tendency for pages with higher page authority to rank better but again suggests that page authority is one of many factors influencing rank.
• Domain Authority and Page Authority: There’s a strong positive correlation of 0.728 between domain authority and page authority. This indicates that pages on domains with high authority tend to have higher page authority themselves, which is consistent with the understanding that domain authority reflects the cumulative strength of a domain’s pages.

The analysis suggests that while domain and page authority are associated with better rankings, they are not the sole factors. The moderate to weak correlations indicate a complex interplay between various SEO metrics and website ranking. This complexity underscores the importance of a holistic SEO strategy that considers multiple aspects, including but not limited to authority metrics.

Keyword Analysis

The correlation matrix provides insights into the relationship between the rank of a website and the presence of keywords in titles and meta descriptions:

• Rank and Keyword in Title: There’s a slight negative correlation of -0.039, suggesting that pages with the keyword in the title might rank marginally higher, but the relationship is not very strong.
• Rank and Keyword in Meta Description: The negative correlation is -0.031, indicating a very weak relationship where pages with the keyword in the meta description might rank slightly higher.

The heatmap visualizes these correlations. While both correlations are negative (implying that having keywords in the title or meta description might be associated with better rankings), they are quite weak, indicating that these factors alone are not strong predictors of rank.

Additionally, there’s a notable positive correlation (0.653) between having the keyword in the title and in the meta description, indicating that pages often include the keyword in both places.

This analysis suggests that while including keywords in titles and meta descriptions is a common SEO practice, their direct impact on rankings may be relatively minor compared to other factors.

Identifying Low Competition Keywords

Identifying low competition keyword opportunities involves looking for keywords where the top-ranking pages don’t have overwhelmingly strong SEO metrics. This typically means they have fewer backlinks and lower domain authority, making it easier for new content to rank well. Here’s how we’ll approach this:

1. Filter for Lower-Ranked Pages: We’ll look for pages ranking lower for certain keywords, assuming that if a page with weaker metrics is ranking well, the competition might be lower.
2. Analyze SEO Metrics: We’ll analyze the backlinks and domain authority for these pages.
3. Identify Low Competition Keywords: We’ll identify keywords where the top pages have lower average backlinks and domain authority.

I’ll now execute this analysis and identify potential low competition keywords.

Here are ten potential low competition keywords identified based on the average total backlinks and domain authority for pages ranking beyond the top 10:

1. Digital Marketing Chapter 6 Quizlet: Very low domain authority and no backlinks, suggesting it’s a niche area with low competition.
2. Digital Marketing Business for Sale: No backlinks and relatively low domain authority among ranking pages.
3. Digital Marketing Conference: No backlinks and low domain authority, which might indicate an opportunity in a specific event or niche market.
4. Digital Marketing Bachelor Degree: No backlinks and low domain authority, potentially a niche educational topic.
5. Digital Marketing Coordinator Jobs: No backlinks and low domain authority, suggesting low competition in this job market niche.
6. Digital Marketing Agency Detroit: No backlinks and relatively low domain authority, indicating a local or regional opportunity.
7. 3 Email Marketing Services Lookinglion: Despite a higher number of backlinks, the low domain authority suggests it might be a specific or new topic with fewer established competitors.
8. Digital Marketing Bootcamp Free: No backlinks and low domain authority, suggesting an opportunity in the educational space.
9. Digital Marketing Assistant: No backlinks and low domain authority, indicating a potential opportunity in job-related content.
10. Digital Marketing Detroit: No backlinks and low domain authority, another local or regional opportunity.

These keywords represent areas where the top-ranking pages don’t have strong backlinks or high domain authority, suggesting that these might be easier topics to rank for. However, it’s essential to further validate this by analyzing search volumes, user intent, and the quality of the current top-ranking content to ensure these are valuable opportunities.

Here’s the Python code that I used to identify potential low competition keywords based on average total backlinks and domain authority for pages ranking lower for certain keywords:

import pandas as pd

# Load the data
file_path = 'your_data_file.csv'  # Replace with your actual file path
data = pd.read_csv(file_path)

# Identifying low competition keyword opportunities

# Step 1: Filter for lower-ranked pages.
# Assuming pages ranking beyond a certain threshold (e.g., rank > 10) might indicate lower competition.
lower_ranked_pages = data[data['rank'] > 10]

# Step 2: Analyze SEO Metrics.
# Calculate the average total backlinks and domain authority for each keyword.
average_metrics = lower_ranked_pages.groupby('keyword').agg({
    'total_backlinks': 'mean',
    'domain_authority': 'mean',
    'rank': 'mean'
}).sort_values(by=['domain_authority', 'total_backlinks'])

# Step 3: Identify Low Competition Keywords.
# We'll consider keywords with lower average domain authority and backlinks as low competition.
low_competition_keywords = average_metrics.head(10)

# Display the result in a more readable format
print(low_competition_keywords.reset_index()[['keyword', 'total_backlinks', 'domain_authority', 'rank']])

Conclusion

This study embarked on a detailed examination of the factors influencing website rankings in search engine results, with a special focus on the impact of backlinks, domain authority, and the presence of keywords in titles and meta descriptions. Furthermore, it aimed to uncover low competition keyword opportunities that could be beneficial for strategizing content and SEO efforts. The research utilized a dataset of 40,000 search results, with data collected via the ValueSERP API and analyzed using Python.

Key findings from the research include:

1. Correlation between Backlinks and Ranking: The analysis revealed a weak but noticeable negative correlation between the number of backlinks (including specific types like do-follow, no-follow, edu, and gov) and the ranking of websites. This indicates that while backlinks are a factor in ranking, their impact is not as strong or direct as traditionally perceived.
2. Influence of Domain and Page Authority: The study also observed that higher domain and page authority scores generally corresponded with better rankings. However, similar to backlinks, the correlation wasn’t strongly pronounced, suggesting other factors also play significant roles in determining rankings.
3. Keyword Presence in Titles and Meta Descriptions: The presence of keywords in titles and meta descriptions showed a very slight correlation with better rankings. This reinforces the notion that while keyword optimization is a common SEO practice, it’s just one of many factors that search engines consider.
4. Identification of Low Competition Keywords: The research successfully identified potential low competition keywords by analyzing the SEO metrics of pages ranking for various keywords. This provides a valuable starting point for content creators and marketers looking to target less saturated niches.

The findings of this study contribute to the broader understanding of SEO and website rankings, offering insights into the complex nature of search engine algorithms and the myriad factors they consider. It also highlights the importance of a well-rounded SEO strategy that goes beyond just focusing on traditional metrics like backlinks and keywords.

However, it’s important to acknowledge the limitations of the study. The ever-changing nature of search engine algorithms means that the factors influencing rankings can shift over time. Additionally, the study’s focus on quantitative data means it doesn’t capture the qualitative aspects of content, such as relevance, readability, and user engagement, which are increasingly important in SEO.

In conclusion, this research provides valuable insights into the factors influencing website rankings and identifies potential low competition keywords. It serves as a reminder of the complexity of SEO and the need for continuous learning and adaptation in digital marketing strategies. Future research could build on these findings by incorporating additional variables, exploring the qualitative aspects of content, and examining the impact of recent updates to search engine algorithms.