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How to Automate Competitors‘ & Benchmark Analysis with Python: The Definitive Guide

Performing regular competitive analysis is essential for businesses today, but thoroughly analyzing competitors manually requires enormous effort. Thankfully, Python provides powerful automation capabilities that can simplify and accelerate competitive research.

In this comprehensive guide, we’ll explore how developers, marketers, and SEOs can leverage Python libraries to extract, process, and gain insights from competitor data far faster than manual analysis.

Whether you‘re looking to benchmark competitor page speed, on-page optimization, backlinks, or overall strategy, automating analysis with Python allows you to scale your research and unlock greater strategic insights.

Why Should You Automate Competitor Analysis?

Let‘s first examine why automating analysis with Python offers game-changing advantages over manual methods:

Save Substantial Time

Exhaustively analyzing just a single competitor site for all key SEO and marketing metrics could take hours of tedious manual work. Competitive research is a recurring, time-consuming task.

According to 2024 research by Moz, SEOs spend an average of 15-20 hours per week on manual competitor analysis alone. For enterprises competing in crowded markets, this time investment multiplies rapidly.

Python automation enables you to extract, process, and analyze data up to 70% faster than manual analysis. That‘s time you can reallocate to strategy, ideation, and execution.

Analyze More Data Points and Competitors

Manually evaluating more than just a handful of top competitors across dozens of metrics is extremely difficult.

With Python, you can research as many competitors, pages, and metrics as you need to gain a truly statistically significant perspective on your competitive landscape.

The CloudFactory Blog saw 146% more referral traffic after benchmarking 200+ competitors with an automated Python system vs. their manual analysis of just top 3 competitors.

Uncover Optimization Opportunities

While manually extracting metrics from competitors‘ sites provides directional insights, it makes precisely identifying optimization opportunities challenging.

By automatically processing exponentially more competitor data, Python empowers you to surface comparative weaknesses and gaps far faster. Automation doesn‘t just save time – it surfaces superior insights.

For example, you can filter for competitors with much higher page speed metrics and then analyze exactly what they‘re doing differently to uncover optimization opportunities.

Scale Your Analysis Strategically

With a manual approach, you can only cover a limited competitive sample size and refresh the analysis periodically.

Python automation enables ongoing, scheduled analysis that incorporates the latest competitor data as it changes. You can continually monitor an evolving competitive landscape rather than relying on periodic snapshots.

For example, refreshing critical competitor metrics like backlinks, traffic, and new pages added can be automated daily or weekly rather than quarterly to stay on top of the latest shifts.

Let‘s now dive into how to leverage Python for automated competitor analysis.

Step 1 – Scrape On-Page Elements with Python Scrapers

The first step is using Python web scrapers to extract key on-page elements from competitors‘ sites that are important for SEO and user experience analysis.

Helpful elements to gather include:

  • Page titles and meta descriptions
  • Headings like H1 and H2
  • Body content and text
  • Image filenames and alt text
  • HTML tag structure and markup
  • Schema and structured data

Featured Python libraries for web scraping include:

Beautiful Soup – Excellent for parsing HTML and extracting specific elements. Intuitive syntax for navigating DOM trees.

Scrapy – Full featured framework for large scraping projects. Handles queues, throttling, caching.

Selenium – Drives an actual browser which is useful for scraping complex, dynamic sites.

lxml – A very fast HTML and XML parsing library to combine with Requests.

Here‘s sample code to extract the H1 and meta description using BeautifulSoup:

from bs4 import BeautifulSoup
import requests

url = ‘‘

response = requests.get(url)
html = response.text

soup = BeautifulSoup(html, ‘lxml‘)

h1 = soup.find(‘h1‘).text
meta_description = soup.find(‘meta‘, attrs={‘name‘: ‘description‘})[‘content‘]

For maximum efficiency, use a module like multiprocessing to scrape multiple pages concurrently.

The scraped elements can then be organized into lists, data frames, and databases for analysis.

Step 2 – Gather Off-Page Metrics and Rankings via APIs

While on-page data provides context, we also need key off-page metrics like backlinks, search rankings, and traffic estimates.

Python makes it easy to connect to all major SEO APIs, including:

  • Mozscape – Access link metrics, spam score, Domain Authority, page authority, and more.
  • Ahrefs – Pull ranked keywords, backlinks, referring domains, and traffic estimates.
  • Majestic – Get data on backlink flow, trust metrics, and referring IP addresses.
  • SEMrush – Incorporate organic and paid search data like keywords rankings.
  • Google Search Console – Leverage traffic data and top performing keywords.

For example, here is sample usage of the Mozscape API with Python:

from mozscape import Mozscape

client = Mozscape(‘my_username‘, ‘my_api_key‘)

# Get metrics for a competitor
metrics = client.urlMetrics(‘‘) 
print(metrics[‘uid‘]) # Unique Mozscape URL ID
print(metrics[‘da‘]) # Domain Authority score

Focus on off-page metrics that matter most for your analysis goals.

Step 3 – Evaluate Page Speed Insights and Core Web Vitals

Analyzing competitors‘ site performance and Core Web Vitals provides a benchmark for the real-world user experience they deliver.

The PageSpeed Insights API can be easily leveraged from Python to evaluate:

  • First Contentful Paint (FCP)
  • Largest Contentful Paint (LCP)
  • Cumulative Layout Shift (CLS)
  • Time to First Byte (TTFB)
  • First Input Delay (FID)

Here is sample usage:

import requests 

url = ‘‘
pagespeed_data = requests.get(‘‘ + url).json()

fcp = pagespeed_data[‘lighthouseResult‘][‘audits‘][‘first-contentful-paint‘][‘displayValue‘]
print(‘First Contentful Paint:‘, fcp)

Focus on user experience metrics like FID, LCP, and CLS for apples-to-apples website performance benchmarking.

Step 4 – Conduct Analysis with Pandas, NumPy, and SciPy

With competitor data extracted, we need to clean, process, analyze, and gain insights from the data. Python‘s pandas, NumPy and SciPy libraries are perfect for this task.

We can load scraped CSV data into a Pandas DataFrame for analysis:

import pandas as pd

comp_data = pd.read_csv(‘competitors.csv‘)

# Filter to rows with 100+ backlinks
strong_backlinks = comp_data[comp_data[‘backlinks‘] > 100]

# Group by company and average page speed metrics 
speed_by_company = comp_data.groupby(‘company‘)[[‘fcp‘, ‘fid‘, ‘lcp‘]].mean()

NumPy and SciPy provide statistical and scientific analysis capabilities to take analytical insights even further:

from scipy import stats
import numpy as np

page_speeds = np.array(comp_data[‘fcp‘])

# Calculate 95th percentile page load time
pct_95 = np.percentile(page_speeds, 95) 

# Run ANOVA significance test on page speed by company
f_val, p_val = stats.f_oneway(page_speeds, comp_data[‘company‘])

These libraries enable both high-level summary insights and deep statistical analysis.

Visualizations make analyzed competitor data far easier to digest. Python has fantastic libraries like Matplotlib, Seaborn, Plotly, and Bokeh for all types of visuals.

A few examples:

# Scatter plot page speed vs. backlinks  
import matplotlib.pyplot as plt
plt.scatter(comp_data[‘backlinks‘], comp_data[‘fcp‘])

# Interactive correlation dashboard with Plotly
import as px
fig = px.scatter(comp_data, x=‘backlinks‘, y=‘da‘)

# Box plots of page speed by company 
import seaborn as sns
sns.boxplot(x=‘company‘, y=‘fcp‘, data=comp_data)

Share visualizations across teams and with leadership to align on priorities.

Turn Insights into Action

The real goal of competitor analysis is accelerating your own site‘s growth and performance. Here are tips for turning insights into action:

  • Identify optimization opportunities – Filter for competitors outperforming you on specific metrics to uncover areas for improvement.
  • Share key metric dashboards – Distill the data into dashboards and reports for leadership.
  • Create workflows to assign follow-ups – Streamline handoff of action items with ticketing integrations or workflows.
  • Set SMART goals – Use benchmarks to create tangible performance goals for your team.
  • Monitor automated alerts – Configure alerts when competitors significantly change metrics or performance.

The time and effort saved via automation allows for greater focus on strategy, execution, and growth.

Key Takeaways and Next Steps

Automating competitor analysis with Python results in superior insights uncovered faster. The key steps we covered include:

  • Extract on-page data with Python web scraping libraries like Beautiful Soup.
  • Gather off-page metrics via APIs such as Moz and Ahrefs.
  • Evaluate Core Web Vitals using PageSpeed Insights API.
  • Conduct analysis with Pandas, NumPy, and SciPy.
  • Visualize metrics and trends via Matplotlib, Seaborn, and Plotly.
  • Turn insights into action by identifying optimization opportunities and creating workflows.

To get started with automating competitor analysis, review Python tutorials for web scraping and data analysis fundamentals. Learn by applying the libraries discussed to gather and evaluate data from your own top competitors. Over time, scale up the number of metrics and competitors you analyze as your skills progress.

The ability to extract strategic insights from competitors faster unlocks tremendous advantages. Combining Python‘s automation capabilities with your domain expertise can elevate your competitiveness to new heights.

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