Discover How Machine Learning Helps in Competitor Research

To improve their performance, many SEO experts in digital marketing company are studying Python and other programming languages in 2021. Machine learning is another important innovation in the Internet Marketing services. Machine learning, or ML, may aid you with competition research and other tasks. We’ll show you how to do competitive research using machine learning to make your work simpler.

Machine learning is a subfield of artificial intelligence (AI) and computer science that employs data and algorithms to imitate how humans learn. It operates by examining data and identifying patterns.

In Internet Marketing, best SEO techniques often examine the SERPs and their competitors’ sites to determine what they do to rank higher. During the early days of SERPs, we collected data using spreadsheets, providing columns indicative of relevant metrics such as the number of backlinks to home pages, number of pages, etc. However, the execution was hopeless due to Excel’s limitations in completing a comprehensive statistical analysis in the limited time that was available.
And, as if the limitations of spreadsheets weren’t enough, the landscape has evolved significantly since then, as seen by the following:

1. 1. 1. Search engine results for mobile devices.
      2. The use of social media.
      3. A far more refined Google Search experience.
      4. Page Loading Time.
      5. Search that is tailored to you.
      6. Schema.
      7. JavaScript frameworks and other cutting-edge web technologies.
      
      

      The preceding is by no means a complete list of trends, but it does serve as an example of the ever-expanding variety of factors that will explain the benefit of your higher-ranked competitors in Google.

      • Machine Learning in the SEO Context

      We are no longer limited by spreadsheets, thanks to gadgets like Python/R. Python/R can handle tens of thousands to billions of rows of data.For one thing, the limit is the amount of information you can feed into your ML model and the clever queries you can ask of your data. As an SEO professional in digital marketing company, you may also make a significant difference in your SEO advertising and Internet marketing strategy by cutting through the noise and using machine learning on competition data to discover:

      • Which score components can most clearly show the differences in rankings across websites?
      • What is the profitable benchmark?
      • When it comes to rank, how much a unit shift throughout the topic is worth.
      
      

      As with any (information) science project, there are several issues that must be answered before we begin coding.

      • Is competitor analysis a ML problem?

      ML addresses a wide range of issues, including categorizing objects (classification) and predicting an infinite number of outcomes (regression). In our situation, because a competitor’s SEO standard is represented by its position in Google, which might be a continuous quantity, the ML problem is one of regression.

      • What are the features?

      The next step is to identify the independent variables, or model inputs, based on the resulting measure. Feature information will be different, for example:

      • The initial paint would be timed in seconds.
      • Sentiment in the good, neutral, and negative categories would be an aspect.
      
      

      Naturally, you want to conceal as many significant aspects as possible, including technical, content/UX, and offsite features, for the most thorough competition investigation.

      • What is the math?

      Given that rankings are numerical, we’d like to explain the difference in rank using mathematics:w 1*feature 1 + w 2*feature 2 +… + w n*feature n (known as the “tilde”) signifies “explained by”

      n denotes the nth feature

      w denotes the feature’s weighting

      • Uncovering Competitor Secrets Using Machine Learning

      With the answers to those questions in hand, we can look at what secrets machine learning may disclose about your competitors.At this point, we’ll assume that your data (referred to as “serps data” in this example) has been joined, converted, and cleaned, and is now ready for modelling. This data will at the very least include the Google rank and the data you wish to examine. Our columns may include the following:

      • Google_rank
      • Page_Speed
      • Flesch_kincaid_reading-Ease
      • Sentiment
      • Site_depth
      • Amp_version_available
      • Internal_page_rank
      • Referring_domains_count
      • Avg_domain_authority_backlinks
      • Title_keyword_string_distance
      
      
      • Training ML Model

      We’re using XGBoost to train your model since it produces better outcomes than other ML models.LightGBM (particularly for much bigger datasets), RandomForest, and Adaboost are alternatives you should try in parallel. For your SERPs dataset, use the following Python code for XGBoost:

      # import the libraries
      # import the libraries
      import xgboost as xgb
      import pandas as pd
      serps_data = pd.read_csv(‘serps_data.csv’)
      # set the model variables
      # your SERPs data with everything but the google_rank column
      serp_features = serps_data.drop(columns = [‘Google_rank’])
      # your SERPs data with just the google_rank column
      rank_actual = serps_data.Google_rank

      # Instantiate the model
      serps_model = xgb.XGBRegressor(objective=’reg:linear’, random_state=1231)

      # fit the model
      serps_model.fit(serp_features, rank_actual)

      # generate the model predictions
      rank_pred = serps_model.predict(serp_features)

      # evaluate the model accuracy
      mse = mean_squared_error(rank_actual, rank_pred)

      • Here are a few examples of what you may learn from your model:

      In order of significance, the data will show you the most significant SERP characteristics or ranking criteria. Each market or industry will be unique.
      You may also check how much rank you’ll get depending on each aspect.
      The statistics will also inform you which benchmark is the best for each ranking element. For example, even if you follow certain broad SEO principles for each ranking component, the exact benchmark for each factor may differ based on the market or sector.This data may be used to assist you optimize your content to outperform your competitors in the SERPs.

      • A Machine Learning Approach for Automating SEO Competitor Analysis

      It is essential to analyse SEO competitor data in a continuous manner, as it is even more useful. The SERPs are continually evolving and changing, much like Google’s algorithm, so a one-time machine learning study is only a snapshot in time. As a result, you will have a better overall picture of what is happening on the SERPs in your sector (or your clients’). Machine learning is vital for automating your SEO competitor analysis.This is when SEO-specific data warehousing and dashboard solutions come into play. These systems collect data from your preferred SEO tools daily, integrate it, and use machine learning to provide insights via a front-end application of your choosing, such as Google Data Studio.

      To create your own automated system, you will need to put it onto a cloud infrastructure such as Amazon Web Services or Google Cloud Platform using an ETL, or extract, transform, and load. The term “extracting” refers to the everyday use of your SEO tool’s APIs. Transforming is the process of cleansing and analyzing your data. Loading is the process of placing the final product into your data warehouse. This allows you to centralize your data gathering, processing, and visualization.

      • SEO Competitor Research and Machine Learning

      Conducting SEO competitor research might be tough, but machine learning can help make it easier, especially if you automate the process. When you apply machine learning on your rivals, you may discover what the primary drivers are, find successful benchmarks among them, and determine how much rank lift your improvements can provide.