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Visualizing Google Core Update Winners & Losers With Python

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Visualizing Google Core Update Winners & Losers With Python


For SEO experts, Google’s core updates are a way of life. They will happen at least once – if not multiple times – a year.

Naturally, there will be winners and losers.

So while Google doesn’t disclose most of the ranking factors behind the algorithm updates, there are things we can do to get a greater understanding of what’s going on, in terms of:

  • Which site content is affected.
  • Sites operating in your search space.
  • Result types.

The limit is your imagination, your questions (based on your SEO knowledge), and of course, your data.

This code will cover aggregations at the search engine results page (SERP) level (inter-site category comparison), and the same principles can be applied to other views of the core update such as result types (think snippets and other views mentioned above).

Using Python To Compare SERPs

The overall principle is to compare the SERPs before and after the core update, which will give us some clues as to what’s going on.

We’ll start by importing our Python libraries:

import re
import time
import random
import pandas as pd
import numpy as np
import datetime
from datetime import timedelta
from plotnine import *
import matplotlib.pyplot as plt
from pandas.api.types import is_string_dtype
from pandas.api.types import is_numeric_dtype
import uritools  
pd.set_option('display.max_colwidth', None)
%matplotlib inline

Defining some variables, we’re going to be focusing on ON24.com as they lost out to the core update.

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root_domain = 'on24.com'
hostdomain = 'www.on24.com'
hostname="on24"
full_domain = 'https://www.on24.com'
site_name="ON24"

Reading in the data, we’re using an export from GetSTAT which has a useful report that allows you to compare SERPs for your keywords before and after.

This SERPs report is available from other rank tracking providers like SEO Monitor and Advanced Web Ranking – no preferences or endorsements on my side!

getstat_ba_urls = pd.read_csv('data/webinars_top_20.csv', encoding = 'UTF-16', sep = 't')
getstat_raw.head()
Screenshot by author, January 2022
getstat_ba_urls = getstat_raw

Construct the URLs by joining the protocol and URL string to get the full ranking URL for both before the update and after.

getstat_ba_urls['before_url'] = getstat_ba_urls['Protocol for Nov 19, 2020'] + '://' + getstat_ba_urls['Ranking URL on Nov 19, 2020']
getstat_ba_urls['after_url'] = getstat_ba_urls['Protocol for Dec 17, 2020'] + '://' + getstat_ba_urls['Ranking URL on Dec 17, 2020']
getstat_ba_urls['before_url'] = np.where(getstat_ba_urls['before_url'].isnull(), '', getstat_ba_urls['before_url'])
getstat_ba_urls['after_url'] = np.where(getstat_ba_urls['after_url'].isnull(), '', getstat_ba_urls['after_url'])


To get the domains of the ranking URLs, we create a copy of the URL in a new column, remove the subdomains using an if statement embedded in a list comprehension:

getstat_ba_urls['before_site'] = [uritools.urisplit(x).authority if uritools.isuri(x) else x for x in getstat_ba_urls['before_url']]
stop_sites = ['hub.', 'blog.', 'www.', 'impact.', 'harvard.', 'its.', 'is.', 'support.']
getstat_ba_urls['before_site'] = getstat_ba_urls['before_site'].str.replace('|'.join(stop_sites), '')

The list comprehension is repeated to extract the domains post update.

getstat_ba_urls['after_site'] = [uritools.urisplit(x).authority if uritools.isuri(x) else x for x in getstat_ba_urls['after_url']]
getstat_ba_urls['after_site'] = getstat_ba_urls['after_site'].str.replace('|'.join(stop_sites), '')
getstat_ba_urls.columns = [x.lower() for x in getstat_ba_urls.columns]
getstat_ba_urls = getstat_ba_urls.rename(columns = {'global monthly search volume': 'search_volume'
                                                   })
getstat_ba_urls
Before and after URLsScreenshot by author, January 2022

Dedupe Multiple Ranking URLs

The next step is to remove the multiple ranking URLs by the same domain per keyword SERP. We’ll split the data into two sets, before and after.

See also  Bumper Ads: A Smart Way To Boost Your YouTube Ad Campaign [Infographic]

Then we’ll group by keyword and perform the deduplication:

getstat_bef_unique = getstat_ba_urls[['keyword', 'market', 'location', 'device', 'search_volume', 'rank',
       'result types for nov 19, 2020', 'protocol for nov 19, 2020',
       'ranking url on nov 19, 2020', 'before_url', 'before_site']]
getstat_bef_unique = getstat_bef_unique.sort_values('rank').groupby(['before_site', 'device', 'keyword']).first()
getstat_bef_unique = getstat_bef_unique.reset_index()
getstat_bef_unique = getstat_bef_unique[getstat_bef_unique['before_site'] != '']
getstat_bef_unique = getstat_bef_unique.sort_values(['keyword', 'device', 'rank'])
getstat_bef_unique = getstat_bef_unique.rename(columns = {'rank': 'before_rank', 
                                                          'result types for nov 19, 2020': 'before_snippets'})
getstat_bef_unique = getstat_bef_unique[['keyword', 'market', 'device', 'before_snippets', 'search_volume', 
                                         'before_url', 'before_site', 'before_rank'
                                        ]]
getstat_bef_unique
keyword trackingScreenshot by author, January 2022

The procedure is repeated for the after data set.

getstat_aft_unique = getstat_ba_urls[['keyword', 'market', 'location', 'device', 'search_volume', 'rank',
       'result types for dec 17, 2020', 'protocol for dec 17, 2020',
       'ranking url on dec 17, 2020', 'after_url', 'after_site']]
getstat_aft_unique = getstat_aft_unique.sort_values('rank').groupby(['after_site', 'device', 'keyword']).first()
getstat_aft_unique = getstat_aft_unique.reset_index()
getstat_aft_unique = getstat_aft_unique[getstat_aft_unique['after_site'] != '']
getstat_aft_unique = getstat_aft_unique.sort_values(['keyword', 'device', 'rank'])
getstat_aft_unique = getstat_aft_unique.rename(columns = {'rank': 'after_rank', 
                                                          'result types for dec 17, 2020': 'after_snippets'})
getstat_aft_unique = getstat_aft_unique[['keyword', 'market', 'device', 'after_snippets', 'search_volume', 
                                         'after_url', 'after_site', 'after_rank'
                                        ]]

Segment The SERP Sites

When it comes to core updates, most of the answers tend to be in the SERPs. This is where we can see what sites are being rewarded and others that lose out.

With the datasets deduped and separated, we’ll work out the common competitors so we can start segmenting them manually which will help us visualize the impact of the update.

serps_before = getstat_bef_unique
serps_after = getstat_aft_unique
serps_before_after = serps_before_after.merge(serps_after, left_on = ['keyword', 'before_site', 'device', 'market', 'search_volume'], 
                                                right_on = ['keyword', 'after_site', 'device', 'market', 'search_volume'], how = 'left')

Cleaning the rank columns of null (NAN Not a Number) values using the np.where() function which is the Panda’s equivalent of Excel’s if formula.

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serps_before_after['before_rank'] = np.where(serps_before_after['before_rank'].isnull(), 100, serps_before_after['before_rank'])
serps_before_after['after_rank'] = np.where(serps_before_after['after_rank'].isnull(), 100, serps_before_after['after_rank'])


Some calculated metrics to show the rank difference before vs after, and whether the URL changed.

serps_before_after['rank_diff'] = serps_before_after['before_rank'] - serps_before_after['after_rank']
serps_before_after['url_change'] = np.where(serps_before_after['before_url'] == serps_before_after['after_url'], 0, 1)
serps_before_after['project'] = site_name
serps_before_after['reach'] = 1
serps_before_after
rank difference before vs afterScreenshot by author, January 2022

Aggregate The Winning Sites

With the data cleaned, we can now aggregate to see which sites are the most dominant.

See also  Google Says Site Duplicated In Search With IP Address Won't Lead To Your Site Being Removed

To do this, we define the function which calculates weighted average rank by search volume.

Not all keywords are as important which helps make the analysis more meaningful if you care about the keywords that get the most searches.

def wavg_rank(x):
    names = {'wavg_rank': (x['before_rank'] * (x['search_volume'] + 0.1)).sum()/(x['search_volume'] + 0.1).sum()}
    return pd.Series(names, index=['wavg_rank']).round(1)

rank_df = serps_before_after.groupby('before_site').apply(wavg_rank).reset_index()
reach_df = serps_before_after.groupby('before_site').agg({'reach': 'sum'}).sort_values('reach', ascending = False).reset_index()

commonstats_full_df = rank_df.merge(reach_df, on = 'before_site', how = 'left').sort_values('reach', ascending = False)
commonstats_df = commonstats_full_df.sort_values('reach', ascending = False).reset_index()
commonstats_df.head()
calculate weighted average rank by search volumeScreenshot by author, January 2022

While the weighted average rank is important, so is the reach as that tells us the breadth of the site’s presence in Google i.e. the number of keywords.

The reach also helps us prioritize the sites we want to include in our segmentation.

The segmentation works by using the np.select function which is like a mega nested Excel if formula.

First, we create a list of our conditions.

domain_conds = [
    commonstats_df['before_site'].isin(['google.com', 'medium.com', 'forbes.com', 'en.m.wikipedia.org',
                                        'hbr.org', 'en.wikipedia.org', 'smartinsights.com', 'mckinsey.com',
                                        'techradar.com','searchenginejournal.com', 
                                        'cmswire.com']),
    commonstats_df['before_site'].isin(['on24.com', 'gotomeeting.com', 'marketo.com', 'zoom.us', 'livestorm.co',
                                        'hubspot.com', 'drift.com', 'salesforce.com', 'clickmeeting.com',
                                        'qualtrics.com', 'workcast.com', 'livewebinar.com', 'getresponse.com', 
                                        'superoffice.com', 'myownconference.com', 'info.workcast.com']),
    commonstats_df['before_site'].isin([ 'neilpatel.com', 'ventureharbour.com', 'wordstream.com', 
                                        'business.tutsplus.com', 'convinceandconvert.com']),
    commonstats_df['before_site'].isin(['trustradius.com', 'g2.com', 'capterra.com', 'softwareadvice.com', 
                                        'learn.g2.com']),
    commonstats_df['before_site'].isin(['youtube.com', 'm.youtube.com', 'facebook.com', 'linkedin.com', 
                                        'business.linkedin.com', 
                                       ])
]

 

Then we create a list of the values we want to assign for each condition.

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segment_values = ['publisher', 'martech', 'consulting', 'reviews', 'social_media']

 

Then create a new column and use np.select to assign values to it using our lists as arguments.

commonstats_df['segment'] = np.select(domain_conds, segment_values, default="other")
commonstats_df = commonstats_df[['before_site', 'segment', 'reach', 'wavg_rank']]
commonstats_df
new column to use np.selectScreenshot by author, January 2022

The domains are now segmented which means we can start the fun of aggregating to see which site types benefitted and deteriorated from the update.

# SERPs Before and After Rank
serps_stats = commonstats_df[['before_site', 'segment']]
serps_segments = commonstats_df.segment.to_list()


We’re joining the unique before SERPs data with the SERP segments table created immediately above to segment the ranking URLs using the merge function.

The merge function that uses the ‘eft’ parameter is equivalent to the Excel vlookup or index match function.

serps_before_segmented = getstat_bef_unique.merge(serps_stats, on = 'before_site', how = 'left')
serps_before_segmented = serps_before_segmented[~serps_before_segmented.segment.isnull()]
serps_before_segmented = serps_before_segmented[['keyword', 'segment', 'device', 'search_volume', 'before_snippets', 
                             'before_rank', 'before_url', 'before_site']]
serps_before_segmented['count'] = 1
serps_queries = serps_before_segmented['keyword'].to_list()
serps_queries = list(set(serps_queries))
serps_before_segmented
joining the unique before SERPs data with the SERP segments tableScreenshot by author, January 2022

Aggregating the before SERPs:

def wavg_rank_before(x):
    names = {'wavg_rank_before': (x['before_rank'] * x['search_volume']).sum()/(x['search_volume']).sum()}
    return pd.Series(names, index=['wavg_rank_before']).round(1)

serps_before_agg = serps_before_segmented
serps_before_wavg = serps_before_agg.groupby(['segment', 'device']).apply(wavg_rank_before).reset_index()
serps_before_sum = serps_before_agg.groupby(['segment', 'device']).agg({'count': 'sum'}).reset_index()
serps_before_stats = serps_before_wavg.merge(serps_before_sum, on = ['segment', 'device'], how = 'left')
serps_before_stats = serps_before_stats.rename(columns = {'count': 'before_n'})
serps_before_stats
Aggregating the before SERPs.Screenshot by author, January 2022

Repeat procedure for the after SERPs.

# SERPs  After Rank
aft_serps_segments = commonstats_df[['before_site', 'segment']]
aft_serps_segments = aft_serps_segments.rename(columns = {'before_site': 'after_site'})
serps_after_segmented = getstat_aft_unique.merge(aft_serps_segments, on = 'after_site', how = 'left')
serps_after_segmented = serps_after_segmented[~serps_after_segmented.segment.isnull()]
serps_after_segmented = serps_after_segmented[['keyword', 'segment', 'device', 'search_volume', 'after_snippets', 
                             'after_rank', 'after_url', 'after_site']]
serps_after_segmented['count'] = 1
serps_queries = serps_after_segmented['keyword'].to_list()
serps_queries = list(set(serps_queries))
def wavg_rank_after(x):
    names = {'wavg_rank_after': (x['after_rank'] * x['search_volume']).sum()/(x['search_volume']).sum()}
    return pd.Series(names, index=['wavg_rank_after']).round(1)
serps_after_agg = serps_after_segmented
serps_after_wavg = serps_after_agg.groupby(['segment', 'device']).apply(wavg_rank_after).reset_index()
serps_after_sum = serps_after_agg.groupby(['segment', 'device']).agg({'count': 'sum'}).reset_index()
serps_after_stats = serps_after_wavg.merge(serps_after_sum, on = ['segment', 'device'], how = 'left')
serps_after_stats = serps_after_stats.rename(columns = {'count': 'after_n'})
serps_after_stats
Repeat procedure for the after SERPsScreenshot by author, January 2022

With both SERPs summarised, we can join them and start making comparisons.

serps_compare_stats = serps_before_stats.merge(serps_after_stats, on = ['device', 'segment'], how = 'left')
serps_compare_stats['wavg_rank_delta'] = serps_compare_stats['wavg_rank_after'] - serps_compare_stats['wavg_rank_before']
serps_compare_stats['sites_delta'] = serps_compare_stats['after_n'] - serps_compare_stats['before_n']
serps_compare_stats
Comparing before and after rank by keywordScreenshot by author, January 2022

Although we can see that publisher sites seemed to gain the most by virtue of more keywords ranked for, a picture would most certainly tell a 1000 more words in a PowerPoint deck.

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We’ll endeavor to do this by reshaping the data into a long format that the Python graphics package ‘plotnine’ favors.

serps_compare_viz = serps_compare_stats
serps_rank_viz = serps_compare_viz[['device', 'segment', 'wavg_rank_before', 'wavg_rank_after']].reset_index()
serps_rank_viz = serps_rank_viz.rename(columns = {'wavg_rank_before': 'before', 'wavg_rank_after': 'after', })
serps_rank_viz = pd.melt(serps_rank_viz, id_vars=['device', 'segment'], value_vars=['before', 'after'],
                     var_name="phase", value_name="rank")
serps_rank_viz
serps_ba_plt = (
    ggplot(serps_rank_viz, aes(x = 'segment', y = 'rank', colour="phase",
                             fill="phase")) + 
    geom_bar(stat="identity", alpha = 0.8, position = 'dodge') +
    labs(y = 'Google Rank', x = 'phase') + 
    scale_y_reverse() + 
    theme(legend_position = 'right', axis_text_x=element_text(rotation=90, hjust=1)) + 
    facet_wrap('device')
)
serps_ba_plt
Google RankScreenshot by author, January 2022

And we have our first visualization, which shows us how most site types gained in ranking which is only half of the story.

Let’s also look at the number of entries into the top 20.

Desktop vs. smartphoneScreenshot by author, January 2022

Ignoring the ‘Other’ segment, we can see Martech and Publishers were the main winners expanding their keyword reach.

Summary

It took a bit of code just to create a single chart with all the cleaning and aggregating.

However, the principles can be applied to achieve extended winner-loser views such as:

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  • Domain level.
  • Internal site content.
  • Result Types.
  • Cannibalised results.
  • Ranking URL Content types (blogs, offer pages etc).

Most SERP reports will have the data to perform the above extended views.

While it might not explicitly reveal the key ranking factor, the views can tell you a lot about what is going on, help you explain the core update to your colleagues, and generate hypotheses to test if you’re one of the less lucky ones looking to recover.

More resources:  


Featured Image: Pixels Hunter/Shutterstock

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How Data Is Reshaping The SEO & Digital Marketer’s Landscape

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How Data Is Reshaping The SEO & Digital Marketer's Landscape

There is a new data revolution happening, and it’s sweeping across the industry so quickly that many SEO and digital marketers are struggling to make sense of the insights and intelligence at their disposal.

To utilize this opportunity, marketers need to evolve their mindsets and use technology to analyze multiple data formats and understand the new opportunities it can bring.

SEO marketers of today and digital marketers of tomorrow will need to extract, structure quickly, and manipulate data to drive the most critical business outcomes.

Data has always been mission-critical to digital decision-making.

The Economist, back in 2017, declared it the world’s most valuable resource.

Fast forward to today and the future, and we can see that the exponential growth of data fuelling this revolution is staggering.

According to the IDC, the amount of digital data created over the next five years will be greater than twice the amount of data made since the advent of digital storage.

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Think about that for a second!

Flash drives, for example, were introduced in 2000.

This means that in the next five years, marketers will have to analyze and make sense of 2x the data created in the last 22 years!

The Data Revolution Means More Sources & Complexity For SEO

The data revolution has gone on for some time now, and it’s changed our concept of what counts as “data,” rightfully so.

In the past, we thought only numbers mattered.

But, in this new digital world where everything is converted into ones and zeros, data is broader and contains text, audio, and visual information – all bits waiting to be processed!

  • Machine-based and human-generated data are growing at a rate of 10x faster than conventional business data.
  • Machine-created data is increasing exponentially at a 50x the growth rate. This data revolution is primarily marketing-driven and consumer-oriented who are “always on.”
  • In just the last 18 months, the volume of site processing data we have been generating at BrightEdge has increased by 11x!
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As a result of these increasingly demanding trends, SEO and digital marketers need to adapt and become more like data analysts and scientists in approaching the extraction of structured data insights and business intelligence – without adding more manual work.

Fortunately, SEO is well-positioned to take advantage of this new data revolution.

  • Increasing your keyword universe – More keywords mean more data points to look at with reporting and fuelling insights. While focusing on conversion rate metrics is very important, it wouldn’t be possible without opening the scope of your audience and getting more people in the door. SEO has drifted away from writing for a primary dedicated keyword and is now way more advanced with advancements in search engines like Google’s understanding of intent of searches through RankBrain and BERT.
  • Increasing your search footprint – will also help you discover unexplored of informing your future content strategy or ideate new keyword ideas. However, sometimes you might miss the boat, like the transition of Content Management Systems slowly turning into “Experience Platforms” as they offer more functionality to meet the needs of today’s webmaster or marketer.

Read More On SEJ

Data Is The Currency Of An Accelerated SEO & Digital Age

By 2025, Worldwide data will reach 175 zettabytes.

But unfortunately, the human brain can’t process, structure, and analyze all that data.

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So technology engines have to help, and digital marketers should be the driver.

There is a massive opportunity for companies that can utilize data to create more engaging experiences.

A recent study showed that 95% of business leaders recognize this as their biggest growth lever over the next three years, which means there’s plenty at stake here!

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Robust data analysis ensures decisions are evidence-based and have more accountability.

Drawing on existing and new data sources to fully integrate business acumen and analytical skills into decision making, sourcing, managing, and analyzing large amounts of unstructured data will ensure continued use and success.

SEO began with data and has evolved.

From the introduction of real-time SEO in 2019 and Page Experience Updates in 2021, SEO’s future lies again with data and the creation of intelligent systems. Here marketers can leverage combined data sources that structures data for them.

As a result, they can achieve business objectives and stay ahead during all data and digital transformation stages.

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Technology & AI Are Helping SEO Evolve

Advancements in technology and, in particular, AI and Natural Language Processing has meant that SEO and digital marketers can become data analysts without having to become an actual data scientist.

This is key to unlocking structured insights from your company’s big data to make more precise predictions about what is coming next based on existing information.

Digital marketers can evolve, understand key trends, and learn in new areas such as:

  • Predictive modeling of future trends and forecasting based on multiple types of data.
  • Real-time identification of opportunities and intelligence.
  • Digital research at scale with both historical and real-time data.
  • Leveraging automated visualizations for various stakeholders.
  • Improved data security and compliance.
  • Market and business intelligence at a macro level.
  • Consumer behavior at the most granular level.

SEO and digital marketers can learn critical skills such as statistics, data analysis, data visualization, and strategy.

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AI, NLP, and machine learning are helping them do this without needing expertise in computer programming and software applications.

What digital marketers must do is combine their communication skills and analytics skills with stakeholders who cannot think outside of the advertising box.

Read More On SEJ

Data Analysis & Intelligence As Competitive Advantage

The application of technology will be the driving force behind the next generation of data analysis technology.

Therefore, SEO and digital marketers of today should learn how to better utilize insights from data analysis.

It’s becoming more apparent that the marketing platforms of tomorrow will require the capabilities of data analysis and science infrastructure at their core.

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The future of marketing will blend technological know-how, business sense, and an understanding of data analysis.

The next generation of SEO will touch all components of marketing, from video, email, and voice, to digital performance of content.

SEO and data science will converge into one evolved discipline that drives omnichannel acquisition and democratizes data.

Marketers who embrace this new era of SEO will be well-positioned to succeed in the years to come.

Data is reconfirming its role as the new competitive advantage, and as SEO and digital marketers, you must evolve if you want to be part of the future.

More resources:


Featured Image: ra2 studio/Shutterstock

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