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How To Visualize & Customize Backlink Analysis With Python

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How To Visualize & Customize Backlink Analysis With Python

Chances are, you’ve used one of the more popular tools such as Ahrefs or Semrush to analyze your site’s backlinks.

These tools trawl the web to get a list of sites linking to your website with a domain rating and other data describing the quality of your backlinks.

It’s no secret that backlinks play a big part in Google’s algorithm, so it makes sense as a minimum to understand your own site before comparing it with the competition.

While using tools gives you insight into specific metrics, learning to analyze backlinks on your own gives you more flexibility into what it is you’re measuring and how it’s presented.

And although you could do most of the analysis on a spreadsheet, Python has certain advantages.

Other than the sheer number of rows it can handle, it can also more readily look at the statistical side, such as distributions.

In this column, you’ll find step-by-step instructions on how to visualize basic backlink analysis and customize your reports by considering different link attributes using Python.

Not Taking A Seat

We’re going to pick a small website from the U.K. furniture sector as an example and walk through some basic analysis using Python.

So what is the value of a site’s backlinks for SEO?

At its simplest, I’d say quality and quantity.

Quality is subjective to the expert yet definitive to Google by way of metrics such as authority and content relevance.

We’ll start by evaluating the link quality with the available data before evaluating the quantity.

Time to code.

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

root_domain = 'johnsankey.co.uk'
hostdomain = 'www.johnsankey.co.uk'
hostname="johnsankey"
full_domain = 'https://www.johnsankey.co.uk'
target_name="John Sankey"

We start by importing the data and cleaning up the column names to make it easier to handle and quicker to type for the later stages.

target_ahrefs_raw = pd.read_csv(
    'data/johnsankey.co.uk-refdomains-subdomains__2022-03-18_15-15-47.csv')

List comprehensions are a powerful and less intensive way to clean up the column names.

target_ahrefs_raw.columns = [col.lower() for col in target_ahrefs_raw.columns]

The list comprehension instructs Python to convert the column name to lower case for each column (‘col’) in the dataframe’s columns.

target_ahrefs_raw.columns = [col.replace(' ','_') for col in target_ahrefs_raw.columns]
target_ahrefs_raw.columns = [col.replace('.','_') for col in target_ahrefs_raw.columns]
target_ahrefs_raw.columns = [col.replace('__','_') for col in target_ahrefs_raw.columns]
target_ahrefs_raw.columns = [col.replace('(','') for col in target_ahrefs_raw.columns]
target_ahrefs_raw.columns = [col.replace(')','') for col in target_ahrefs_raw.columns]
target_ahrefs_raw.columns = [col.replace('%','') for col in target_ahrefs_raw.columns]

Though not strictly necessary, I like having a count column as standard for aggregations and a single value column “project” should I need to group the entire table.

target_ahrefs_raw['rd_count'] = 1
target_ahrefs_raw['project'] = target_name
target_ahrefs_raw
Screenshot from Pandas, March 2022

Now we have a dataframe with clean column names.

The next step is to clean the actual table values and make them more useful for analysis.

Make a copy of the previous dataframe and give it a new name.

target_ahrefs_clean_dtypes = target_ahrefs_raw

Clean the dofollow_ref_domains column, which tells us how many ref domains the site linking has.

In this case, we’ll convert the dashes to zeroes and then cast the whole column as a whole number.

# referring_domains
target_ahrefs_clean_dtypes['dofollow_ref_domains'] = np.where(target_ahrefs_clean_dtypes['dofollow_ref_domains'] == '-',
                                                              0, target_ahrefs_clean_dtypes['dofollow_ref_domains'])
target_ahrefs_clean_dtypes['dofollow_ref_domains'] = target_ahrefs_clean_dtypes['dofollow_ref_domains'].astype(int)


# linked_domains
target_ahrefs_clean_dtypes['dofollow_linked_domains'] = np.where(target_ahrefs_clean_dtypes['dofollow_linked_domains'] == '-',
                                                           0, target_ahrefs_clean_dtypes['dofollow_linked_domains'])
target_ahrefs_clean_dtypes['dofollow_linked_domains'] = target_ahrefs_clean_dtypes['dofollow_linked_domains'].astype(int)

First_seen tells us the date the link was first found.

We’ll convert the string to a date format that Python can process and then use this to derive the age of the links later on.

# first_seen
target_ahrefs_clean_dtypes['first_seen'] = pd.to_datetime(target_ahrefs_clean_dtypes['first_seen'], format="%d/%m/%Y %H:%M")

Converting first_seen to a date also means we can perform time aggregations by month and year.

This is useful as it’s not always the case that links for a site will get acquired daily, although it would be nice for my own site if it did!

target_ahrefs_clean_dtypes['month_year'] = target_ahrefs_clean_dtypes['first_seen'].dt.to_period('M')

The link age is calculated by taking today’s date and subtracting the first_seen date.

Then it’s converted to a number format and divided by a huge number to get the number of days.

# link age
target_ahrefs_clean_dtypes['link_age'] = datetime.datetime.now() - target_ahrefs_clean_dtypes['first_seen']
target_ahrefs_clean_dtypes['link_age'] = target_ahrefs_clean_dtypes['link_age']
target_ahrefs_clean_dtypes['link_age'] = target_ahrefs_clean_dtypes['link_age'].astype(int)
target_ahrefs_clean_dtypes['link_age'] = (target_ahrefs_clean_dtypes['link_age']/(3600 * 24 * 1000000000)).round(0)
target_ahrefs_clean_dtypes

 

backlink analysis ahrefs dataScreenshot from Pandas, March 2022

With the data types cleaned, and some new data features created, the fun can begin!

Link Quality

The first part of our analysis evaluates link quality, which summarizes the whole dataframe using the describe function to get descriptive statistics of all the columns.

target_ahrefs_analysis = target_ahrefs_clean_dtypes
target_ahrefs_analysis.describe()

 

python backlink data tableScreenshot from Pandas, March 2022

So from the above table, we can see the average (mean), the number of referring domains (107), and the variation (the 25th percentile and so on).

The average Domain Rating (equivalent to Moz’s Domain Authority) of referring domains is 27.

Is that a good thing?

In the absence of competitor data to compare in this market sector, it’s hard to know. This is where your experience as an SEO practitioner comes in.

However, I’m certain we could all agree that it could be higher.

How much higher to make a shift is another question.

domain rating over yearsScreenshot from Pandas, March 2022

The table above can be a bit dry and hard to visualize, so we’ll plot a histogram to get an intuitive understanding of the referring domain’s authority.

dr_dist_plt = (
    ggplot(target_ahrefs_analysis, aes(x = 'dr')) + 
    geom_histogram(alpha = 0.6, fill="blue", bins = 100) +
    scale_y_continuous() +   
    theme(legend_position = 'right'))
dr_dist_plt
bar graph of link dataScreenshot from author, March 2022

The distribution is heavily skewed, showing that most of the referring domains have an authority rating of zero.

Beyond zero, the distribution looks fairly uniform, with an equal amount of domains across different levels of authority.

Link age is another important factor for SEO.

Let’s check out the distribution below.

linkage_dist_plt = (
    ggplot(target_ahrefs_analysis, 
           aes(x = 'link_age')) + 
    geom_histogram(alpha = 0.6, fill="blue", bins = 100) +
    scale_y_continuous() +   
    theme(legend_position = 'right'))
linkage_dist_plt
bar graph for link ageScreenshot from author, March 2022

The distribution looks more normal even if it is still skewed with the majority of the links being new.

The most common link age appears to be around 200 days, which is less than a year, suggesting most of the links were acquired recently.

Out of interest, let’s see how this correlates with domain authority.

dr_linkage_plt = (
    ggplot(target_ahrefs_analysis, 
           aes(x = 'dr', y = 'link_age')) + 
    geom_point(alpha = 0.4, colour="blue", size = 2) +
    geom_smooth(method = 'lm', se = False, colour="red", size = 3, alpha = 0.4)
)

print(target_ahrefs_analysis['dr'].corr(target_ahrefs_analysis['link_age']))
dr_linkage_plt

0.1941101232345909
data chart of link ageScreenshot from author, March 2022

The plot (along with the 0.19 figure printed above) shows no correlation between the two.

And why should there be?

A correlation would only imply that the higher authority links were acquired in the early phase of the site’s history.

The reason for the non-correlation will become more apparent later on.

We’ll now look at the link quality throughout time.

If we were to literally plot the number of links by date, the time series would look rather messy and less useful as shown below (no code supplied to render the chart).

To achieve this, we will calculate a running average of the Domain Rating by month of the year.

Note the expanding( ) function, which instructs Pandas to include all previous rows with each new row.

target_rd_cummean_df = target_ahrefs_analysis
target_rd_mean_df = target_rd_cummean_df.groupby(['month_year'])['dr'].sum().reset_index()
target_rd_mean_df['dr_runavg'] = target_rd_mean_df['dr'].expanding().mean()
target_rd_mean_df
calculate a running average of the Domain RatingScreenshot from Pandas, March 2022

We now have a table that we can use to feed the graph and visualize it.

dr_cummean_smooth_plt = (
    ggplot(target_rd_mean_df, aes(x = 'month_year', y = 'dr_runavg', group = 1)) + 
    geom_line(alpha = 0.6, colour="blue", size = 2) +
    scale_y_continuous() +
    scale_x_date() +
    theme(legend_position = 'right', 
          axis_text_x=element_text(rotation=90, hjust=1)
         ))
dr_cummean_smooth_plt
visualizing the culmulative average domain ratingScreenshot by author, March 2022

This is quite interesting as it seems the site started off attracting high authority links at the beginning of its time (probably a PR campaign launching the business).

It then faded for four years before reprising with a new link acquisition of high authority links again.

Volume Of Links

It sounds good just writing that heading!

Who wouldn’t want a large volume of (good) links to their site?

Quality is one thing; volume is another, which is what we’ll analyze next.

Much like the previous operation, we’ll use the expanding function to calculate a cumulative sum of the links acquired to date.

target_count_cumsum_df = target_ahrefs_analysis
target_count_cumsum_df = target_count_cumsum_df.groupby(['month_year'])['rd_count'].sum().reset_index()
target_count_cumsum_df['count_runsum'] = target_count_cumsum_df['rd_count'].expanding().sum()
target_count_cumsum_df
calculating cumulative sum of linksScreenshot from Pandas, March 2022

That’s the data, now the graph.

target_count_cumsum_plt = (
    ggplot(target_count_cumsum_df, aes(x = 'month_year', y = 'count_runsum', group = 1)) + 
    geom_line(alpha = 0.6, colour="blue", size = 2) +
    scale_y_continuous() + 
    scale_x_date() +
    theme(legend_position = 'right', 
          axis_text_x=element_text(rotation=90, hjust=1)
         ))
target_count_cumsum_plt
line graph of culmulative sum of linksScreenshot from author, March 2022

We see that links acquired at the beginning of 2017 slowed down but steadily added over the next four years before accelerating again around March 2021.

Again, it would be good to correlate that with performance.

Taking It Further

Of course, the above is just the tip of the iceberg, as it’s a simple exploration of one site. It’s difficult to infer anything useful for improving rankings in competitive search spaces.

Below are some areas for further data exploration and analysis.

  • Adding social media share data to both the destination URLs.
  • Correlating overall site visibility with the running average DR over time.
  • Plotting the distribution of DR over time.
  • Adding search volume data on the host names to see how many brand searches the referring domains receive as a measure of true authority.
  • Joining with crawl data to the destination URLs to test for content relevance.
  • Link velocity – the rate at which new links from new sites are acquired.
  • Integrating all of the above ideas into your analysis to compare to your competitors.

I’m certain there are plenty of ideas not listed above, feel free to share below.

More resources:


Featured Image: metamorworks/Shutterstock




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Google’s AI Overviews Go Viral, Draw Mainstream Media Scrutiny

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Google's AI Overviews Go Viral, Draw Mainstream Media Scrutiny

Google’s rollout of AI-generated overviews in US search results is taking a disastrous turn, with mainstream media outlets like The New York Times, BBC, and CNBC reporting on numerous inaccuracies and bizarre responses.

On social media, users are sharing endless examples of the feature’s nonsensical and sometimes dangerous output.

From recommending non-toxic glue on pizza to suggesting that eating rocks provides nutritional benefits, the blunders would be amusing if they weren’t so alarming.

Mainstream Media Coverage

As reported by The New York Times, Google’s AI overviews struggle with basic facts, claiming that Barack Obama was the first Muslim president of the United States and stating that Andrew Jackson graduated from college in 2005.

These errors undermine trust in Google’s search engine, which more than two billion people rely on for authoritative information worldwide.

Manual Removal & System Refinements

As reported by The Verge, Google is now scrambling to remove the bizarre AI-generated responses and improve its systems manually.

A Google spokesperson confirmed that the company is taking “swift action” to remove problematic responses and using the examples to refine its AI overview feature.

Google’s Rush To AI Integration

The flawed rollout of AI overviews isn’t an isolated incident for Google.

As CNBC notes in its report, Google made several missteps in a rush to integrate AI into its products.

In February, Google was forced to pause its Gemini chatbot after it generated inaccurate images of historical figures and refused to depict white people in most instances.

Before that, the company’s Bard chatbot faced ridicule for sharing incorrect information about outer space, leading to a $100 billion drop in Google’s market value.

Despite these setbacks, industry experts cited by The New York Times suggest that Google has little choice but to continue advancing AI integration to remain competitive.

However, the challenges of taming large language models, which ingest false information and satirical posts, are now more apparent.

The Debate Over AI In Search

The controversy surrounding AI overviews adds fuel to the debate over the risks and limitations of AI.

While the technology holds potential, these missteps remind everyone that more testing is needed before unleashing it on the public.

The BBC notes that Google’s rivals face similar backlash over their attempts to cram more AI tools into their consumer-facing products.

The UK’s data watchdog is investigating Microsoft after it announced a feature that would take continuous screenshots of users’ online activity.

At the same time, actress Scarlett Johansson criticized OpenAI for using a voice likened to her own without permission.

What This Means For Websites & SEO Professionals

Mainstream media coverage of Google’s erroneous AI overviews brings the issue of declining search quality to public attention.

As the company works to address inaccuracies, the incident serves as a cautionary tale for the entire industry.

Important takeaway: Prioritize responsible use of AI technology to ensure the benefits outweigh its risks.



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New Google Search Ads Resemble AI Assistant App

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New Google Search Ads Resemble AI Assistant App

A keynote at Google’s Marketing Live event showed a new AI-powered visual search results that feature advertisements that engage users within the context of an AI-Assisted search, blurring the line between AI-generated search results and advertisements.

Google Lens is a truly helpful app but it becomes unconventional where it blurs the line between an assistant helping users and being led to a shopping cart. This new way of engaging potential customers with AI is so far out there that the presenter doesn’t even call it advertising, he doesn’t even use the word.

Visual Search Traffic Opportunity?

Google’s Group Product Manager Sylvanus Bent, begins the presentation with an overview of the next version of Google Lens visual search that will be useful for surfacing information and for help finding where to buy them.

Sylvanus explained how it will be an opportunity for websites to receive traffic from this new way to search.

“…whether you’re snapping a photo with lens or circling to search something on your social feed, visual search unlocks new ways to explore whatever catches your eye, and we recently announced a newly redesigned results page for Visual search.

Soon, instead of just visual matches, you’ll see a wide range of results, from images to video, web links, and facts about the knowledge graph. It gets people the helpful information they need and creates new opportunities for sites to be discovered.”

It’s hard to say whether or not this will bring search traffic to websites and what the quality of that traffic will be. Will they stick around to read an article? Will they engage with a product review?

Visual Search Results

Sylvanus shares a hypothetical example of someone at an airport baggage claim who falls in like with someone else’s bag. He explains that all the person needs to do is snap a photo of the luggage bag and Google Lens will take them directly to shopping options.

He explains:

“No words, no problem. Just open Lens, take a quick picture and immediately you’ll see options to purchase.

And for the first time, shopping ads will appear at the very top of the results on linked searches, where a business can offer what a consumer is looking for.

This will help them easily purchase something that catches their eye.”

These are image-heavy shopping ads at the top of the search results and as annoying as that may be it’s nowhere near the “next level” advertising that is coming to Google’s search ads where Google presents a paid promotion within the context of an AI Assistant.

Interactive Search Shopping

Sylvanus next describes an AI-powered form advertising that happens directly within search. But he doesn’t call it advertising. He doesn’t even use the word advertising. He suggests this new form of AI search experience is more than offer, saying that, “it’s an experience.”

He’s right to not use the word advertisement because what he describes goes far beyond advertising and blurs the boundaries between search and advertising within the context of AI-powered suggestions, paid suggestions.

Sylvanus explains how this new form of shopping experience works:

“And next, imagine a world where every search ad is more than an offer. It’s an experience. It’s a new way for you to engage more directly with your customers. And we’re exploring search ads with AI powered recommendations across different verticals. So I want to show you an example that’s going live soon and you’ll see even more when we get to shopping.”

He uses the example of someone who needs to store their furniture for a few months and who turns to Google to find short term storage. What he describes is a query for local short term storage that turns into a “dynamic ad experience” that leads the searcher into throwing packing supplies into their shopping cart.

He narrated how it works:

“You search for short term storage and you see an ad for extra space storage. Now you can click into a new dynamic ad experience.

You can select and upload photos of the different rooms in your house, showing how much furniture you have, and then extra space storage with help from Google, AI generates a description of all your belongings for you to verify. You get a recommendation for the right size and type of storage unit and even how much packing supplies you need to get the job done. Then you just go to the website to complete the transaction.

And this is taking the definition of a helpful ad to the next level. It does everything but physically pick up your stuff and move it, and that is cool.”

Step 1: Search For Short Term Storage

1716722762 15 New Google Search Ads Resemble AI Assistant App

The above screenshot shows an advertisement that when clicked takes the user to what looks like an AI-assisted search but is really an interactive advertisement.

Step 2: Upload Photos For “AI Assistance”

1716722762 242 New Google Search Ads Resemble AI Assistant App

The above image is a screenshot of an advertisement that is presented in the context of AI-assisted search.  Masking an advertisement within a different context is the same principal behind an advertorial where an advertisement is hidden in the form of an article. The phrases “Let AI do the heavy lifting” and “AI-powered recommendations” create the context of AI-search that masks the true context of an advertisement.

Step 3: Images Chosen For Uploading

1716722762 187 New Google Search Ads Resemble AI Assistant App

The above screenshot shows how a user uploads an image to the AI-powered advertisement within the context of an AI-powered search app.

The Word “App” Masks That This Is An Ad

Screenshot of interactive advertisement for that identifies itself as an app with the words

Above is a screenshot of how a user uploads a photo to the AI-powered interactive advertisement within the context of a visual search engine, using the word “app” to further the illusion that the user is interacting with an app and not an advertisement.

Upload Process Masks The Advertising Context

Screenshot of interactive advertisement that uses the context of an AI Assistant to mask that this is an advertisement

The phrase “Generative AI is experimental” contributes to the illusion that this is an AI-assisted search.

Step 4: Upload Confirmation

1716722762 395 New Google Search Ads Resemble AI Assistant App

In step 4 the “app” advertisement is for confirming that the AI correctly identified the furniture that needs to be put into storage.

Step 5: AI “Recommendations”

1716722762 588 New Google Search Ads Resemble AI Assistant App

The above screenshot shows “AI recommendations” that look like search results.

The Recommendations Are Ad Units

1716722762 751 New Google Search Ads Resemble AI Assistant App

Those recommendations are actually ad units that when clicked takes the user to the “Extra Space Storage” shopping website.

Step 6: Searcher Visits Advertiser Website

1716722762 929 New Google Search Ads Resemble AI Assistant App

Blurring The Boundaries

What the Google keynote speaker describes is the integration of paid product suggestions into an AI assisted search. This kind of advertising is so far out there that the Googler doesn’t even call it advertising and rightfully so because what this does is blur the line between AI assisted search and advertising. At what point does a helpful AI search become just a platform for using AI to offer paid suggestions?

Watch The Keynote At The 32 Minute Mark

Featured Image by Shutterstock/Ljupco Smokovski

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How Do I Get A Job With A PPC Agency

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Conversion Tracking In PPC Campaigns

This month’s “Ask A PPC” question is particularly significant because the job market has been quite volatile.

“How do I get a job with a PPC agency when I have only worked in-house. What experience would they want?” – Karl Toronto

It’s understandable that people want to know which skills employers seek when hiring for a PPC team. There can be a disparity between what people think they need and what the market actually demands.

We’ll delve into some data and commentary to explain why various traits are valued.

It’s crucial to understand that the ideal candidates will be versatile and have an aptitude for all aspects of digital marketing.

However, no one can excel at everything, so leveraging your strengths or preferences is beneficial.

Ensure that you’re securing the best role for yourself while the company hiring you finds the best fit for them.

Here Are The Essential Skills

  • Analytics.
  • Creativity.
  • Ad network knowledge.
  • Willingness to test/learn.
  • Culture fit.

Discrepancy Between Market Demands And Perceived Needs

I conducted a poll on my LinkedIn to gauge the skills desired by current employers and practitioners.

Screenshot from author, LinkedIn, April 2024

Analytical skills emerged as the most sought-after trait. Employers seek individuals who can interpret numbers and discern the story behind them.

However, relying solely on analytical prowess may overlook the importance of creativity.

Creative skills are vital in today’s ad networks, especially emphasizing visual content like videos and campaign types that force visual content (Performace Max/Demand Gen). Neglecting creativity can hinder a company’s branding efforts.

Unexpectedly, ad network skills and cultural fit were deemed far less critical than analytical skills. Brands should prioritize team cohesion for long-term success, yet this aspect is often undervalued.

The disparity between job descriptions and actual skill requirements contributes to the difficulty in the job market.

Agencies that hire for how PPC used to work will be left wanting. Practitioners who only focus on popular skills instead of needed ones will be made obsolete by the privacy-first era obscuring data and AI owning creative.

Analytical Skills

Analytical abilities involve knowing where to find relevant data sources and understanding how they contribute to success.

While PPC historically relied on measurable outcomes, the landscape is evolving, necessitating adaptability in data analysis. Technical proficiency and strategic acumen are crucial for navigating different data sources.

These include:

  • Customer relationship management (CRM) systems.
  • Google Analytics 4 (GA4).
  • Ecommerce platforms.
  • Content management platforms (CMS).

Empathy for various ad channels improves your candidacy, and knowing how to work with post-click data will give you an edge over those who can only work with ad platform data.

While being highly technical isn’t required, having empathy for coding and scripts will give you a better chance to stay current with evolving data mechanics (especially as APIs become even more important for accessing data blocked by privacy-first regulations).

Here are some takes from PPC experts on why analytics is the most important:

A screenshot of a LinkedIn comment by Georgi Zayakov, who describes himself as analytical Screenshot from author, LinkedIn, April 2024
A LinkedIn post by Kathryn B., a paid media specialist at a PPC agencyScreenshot from LinkedIn, April 2024
Screenshot of a LinkedIn post by Nikolaos B., discussing how marketers must become data-savvyScreenshot from author, LinkedIn, April 2024

Creativity

Creativity is essential for crafting compelling ad content, yet many PPC agencies struggle in this area.

Clients are often tasked with providing creative materials due to cost or complexity constraints.

You’ll get a competitive edge if you have these skills:

  • Video Editing: With the rise of PMax, as well as many ad networks leaning heavily into connected TV, having video editing chops will be a huge asset for any team. If you’re not comfortable using conventional editing tools, AI tools like Descript are a great way to take on those tasks.
  • Graphic Design: No matter the ad network your potential employer is hiring for, you will need some ability to design static images. Whether you use stock photos or AI-generated images or come up with the creative yourself, the days of purely text ads are over. Tools like Canva can help bridge the gap for less technical designers, but don’t discount ad network AI.
  • Content Creation: While the first two categories leaned toward visual content, written content is still important (i.e., most ad formats include some text). Having the ability to understand how diverse audiences prefer to be addressed while respecting the specific requirements of each format is a great skill to hone.

While some roles may prioritize analytics or ad network knowledge, emphasizing creative abilities can distinguish you during the hiring process.

Here are some experts who value creativity:

A screenshot of a LinkedIn post by Erik PetersonScreenshot from author, LinkedIn, April 2024
A screenshot of a Linkedin post by Amy HebdonScreenshot from author, LinkedIn, April 2024

Ad Network Knowledge

Ad network expertise is valuable, but adaptability is paramount as platforms evolve rapidly.

Some agencies will have specialists, while others hire folks they expect to be passable at every network they service. It’s important to understand what workflow will enable you to succeed.

If you’re happy working with all platforms, then don’t shy away from it. However, if you do better in focusing on one aspect of PPC, that’s totally valid. Just know it might limit your ability to get hired into smaller “familyesque” agencies.

Understanding auction dynamics and bidding strategies is crucial.

Many of us who entered the industry when manual bidding was more popular have an unfair advantage over those who came in during the Smart Bidding era (i.e., anything from 2020).

This is because manual bidding requires you to think about the mechanics of each ad platform’s auction and how you could use those mechanics to your advantage in building account structure.

Knowing what to track and allocating appropriate budgets are key considerations.

Understanding that some networks require more conversions than others to run (e.g., Meta Ads’ 50 in a 7-day period vs. Google Ads’ 15 in a 30-day period) should influence what you choose to track, as well as how you report the data.

Additionally, if you are under or over budget, you’ll set yourself up to fail. Knowing which channels require a big investment upfront and what the breaking point for each network is (either on underspending or spending too much) is critical.

Awareness of potential pitfalls, such as false positives or negatives, enhances campaign effectiveness. For example, it’s important to know how to check if automatically applying recommendations is on and what tasks it’s on for.

It’s worth noting that none of the experts who chimed in on the poll made a clear case for ad network knowledge specifically.

Willingness To Test

Success in PPC requires openness to experimentation and a willingness to adapt. While this wasn’t one of the criteria in the poll, it was one of the most popular traits experts look for in hiring.

Perfectionism can hinder progress in a fast-changing environment. Testing new ideas and embracing failure as an opportunity for growth are essential.

While analytical skills aid in test design, empathy and creativity are equally vital for devising effective experiments.

Here is an expert who favors a willingness to test:

Screenshot of a social media post by Mike RhodesScreenshot from author, LinkedIn, April 2024

Cultural Fit

Cultural alignment with an agency fosters productivity and job satisfaction. However, you can only achieve that by being honest with yourself about what you want and the mechanics of how you work.

Agencies demand intense effort and collaboration, making compatibility with colleagues crucial.

Anyone looking to make the shift from in-house to agency needs to be prepared for a much faster pace of work and a lot more agency.

Open communication with leadership regarding preferred management and learning styles will ensure a positive working relationship.

Respect for peers and a supportive atmosphere contribute to a fulfilling work environment.

Here are a few thoughts on cultural fit from polled experts:

The image shows a LinkedIn post by David Zebrout containing text discussing the importance of integrating PPC network knowledge with intertimed optimizations in generating profitable growth.Screenshot from author, LinkedIn, April 2024
LinkedIn post by Lisa Erschbamer discussing the importance of cultural fit and individual personality in team dynamics for effective performance at a PPC Agency.Screenshot from author, LinkedIn, April 2024
A screenshot of a LinkedIn post by Aaron Davies discussing the importance of cultural fit, individual skills, and team communication in marketing for a PPC agency. The post has reactions and a question comment by NavahScreenshot from author, LinkedIn, April 2024

Final Thoughts

Navigating the current job market can be challenging, but understanding industry needs and honing relevant skills increases your chances of success.

Balancing technical proficiency with creativity and cultural fit is essential for thriving in a PPC role. By aligning with market demands and showcasing your strengths, you can secure rewarding opportunities in the field.

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Featured Image: Paulo Bobita/Search Engine Journal

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