SEO
A Practical Guide To Multi-touch Attribution
The customer journey involves multiple interactions between the customer and the merchant or service provider.
We call each interaction in the customer journey a touch point.
According to Salesforce.com, it takes, on average, six to eight touches to generate a lead in the B2B space.
The number of touchpoints is even higher for a customer purchase.
Multi-touch attribution is the mechanism to evaluate each touch point’s contribution toward conversion and gives the appropriate credits to every touch point involved in the customer journey.
Conducting a multi-touch attribution analysis can help marketers understand the customer journey and identify opportunities to further optimize the conversion paths.
In this article, you will learn the basics of multi-touch attribution, and the steps of conducting multi-touch attribution analysis with easily accessible tools.
What To Consider Before Conducting Multi-Touch Attribution Analysis
Define The Business Objective
What do you want to achieve from the multi-touch attribution analysis?
Do you want to evaluate the return on investment (ROI) of a particular marketing channel, understand your customer’s journey, or identify critical pages on your website for A/B testing?
Different business objectives may require different attribution analysis approaches.
Defining what you want to achieve from the beginning helps you get the results faster.
Define Conversion
Conversion is the desired action you want your customers to take.
For ecommerce sites, it’s usually making a purchase, defined by the order completion event.
For other industries, it may be an account sign-up or a subscription.
Different types of conversion likely have different conversion paths.
If you want to perform multi-touch attribution on multiple desired actions, I would recommend separating them into different analyses to avoid confusion.
Define Touch Point
Touch point could be any interaction between your brand and your customers.
If this is your first time running a multi-touch attribution analysis, I would recommend defining it as a visit to your website from a particular marketing channel. Channel-based attribution is easy to conduct, and it could give you an overview of the customer journey.
If you want to understand how your customers interact with your website, I would recommend defining touchpoints based on pageviews on your website.
If you want to include interactions outside of the website, such as mobile app installation, email open, or social engagement, you can incorporate those events in your touch point definition, as long as you have the data.
Regardless of your touch point definition, the attribution mechanism is the same. The more granular the touch points are defined, the more detailed the attribution analysis is.
In this guide, we’ll focus on channel-based and pageview-based attribution.
You’ll learn about how to use Google Analytics and another open-source tool to conduct those attribution analyses.
An Introduction To Multi-Touch Attribution Models
The ways of crediting touch points for their contributions to conversion are called attribution models.
The simplest attribution model is to give all the credit to either the first touch point, for bringing in the customer initially, or the last touch point, for driving the conversion.
These two models are called the first-touch attribution model and the last-touch attribution model, respectively.
Obviously, neither the first-touch nor the last-touch attribution model is “fair” to the rest of the touch points.
Then, how about allocating credit evenly across all touch points involved in converting a customer? That sounds reasonable – and this is exactly how the linear attribution model works.
However, allocating credit evenly across all touch points assumes the touch points are equally important, which doesn’t seem “fair”, either.
Some argue the touch points near the end of the conversion paths are more important, while others are in favor of the opposite. As a result, we have the position-based attribution model that allows marketers to give different weights to touchpoints based on their locations in the conversion paths.
All the models mentioned above are under the category of heuristic, or rule-based, attribution models.
In addition to heuristic models, we have another model category called data-driven attribution, which is now the default model used in Google Analytics.
What Is Data-Driven Attribution?
How is data-driven attribution different from the heuristic attribution models?
Here are some highlights of the differences:
- In a heuristic model, the rule of attribution is predetermined. Regardless of first-touch, last-touch, linear, or position-based model, the attribution rules are set in advance and then applied to the data. In a data-driven attribution model, the attribution rule is created based on historical data, and therefore, it is unique for each scenario.
- A heuristic model looks at only the paths that lead to a conversion and ignores the non-converting paths. A data-driven model uses data from both converting and non-converting paths.
- A heuristic model attributes conversions to a channel based on how many touches a touch point has with respect to the attribution rules. In a data-driven model, the attribution is made based on the effect of the touches of each touch point.
How To Evaluate The Effect Of A Touch Point
A common algorithm used by data-driven attribution is called Markov Chain. At the heart of the Markov Chain algorithm is a concept called the Removal Effect.
The Removal Effect, as the name suggests, is the impact on conversion rate when a touch point is removed from the pathing data.
This article will not go into the mathematical details of the Markov Chain algorithm.
Below is an example illustrating how the algorithm attributes conversion to each touch point.
The Removal Effect
Assuming we have a scenario where there are 100 conversions from 1,000 visitors coming to a website via 3 channels, Channel A, B, & C. In this case, the conversion rate is 10%.
Intuitively, if a certain channel is removed from the conversion paths, those paths involving that particular channel will be “cut off” and end with fewer conversions overall.
If the conversion rate is lowered to 5%, 2%, and 1% when Channels A, B, & C are removed from the data, respectively, we can calculate the Removal Effect as the percentage decrease of the conversion rate when a particular channel is removed using the formula:
Then, the last step is attributing conversions to each channel based on the share of the Removal Effect of each channel. Here is the attribution result:
| Channel | Removal Effect | Share of Removal Effect | Attributed Conversions |
| A | 1 – (5% / 10%) = 0.5 | 0.5 / (0.5 + 0.8 + 0.9) = 0.23 | 100 * 0.23 = 23 |
| B | 1 – (2% / 10%) = 0.8 | 0.8 / (0.5 + 0.8 + 0.9) = 0.36 | 100 * 0.36 = 36 |
| C | 1 – (1% / 10%) = 0.9 | 0.9 / (0.5 + 0.8 + 0.9) = 0.41 | 100 * 0.41 = 41 |
In a nutshell, data-driven attribution does not rely on the number or position of the touch points but on the impact of those touch points on conversion as the basis of attribution.
Multi-Touch Attribution With Google Analytics
Enough of theories, let’s look at how we can use the ubiquitous Google Analytics to conduct multi-touch attribution analysis.
As Google will stop supporting Universal Analytics (UA) from July 2023, this tutorial will be based on Google Analytics 4 (GA4) and we’ll use Google’s Merchandise Store demo account as an example.
In GA4, the attribution reports are under Advertising Snapshot as shown below on the left navigation menu.
After landing on the Advertising Snapshot page, the first step is selecting an appropriate conversion event.
GA4, by default, includes all conversion events for its attribution reports.
To avoid confusion, I highly recommend you pick only one conversion event (“purchase” in the below example) for the analysis.
Screenshot from GA4, November 2022
Understand The Conversion Paths In GA4
Under the Attribution section on the left navigation bar, you can open the Conversion Paths report.
Scroll down to the conversion path table, which shows all the paths leading to conversion.
At the top of this table, you can find the average number of days and number of touch points that lead to conversions.
Screenshot from GA4, November 2022
In this example, you can see that Google customers take, on average, almost 9 days and 6 visits before making a purchase on its Merchandise Store.
Find Each Channel’s Contribution In GA4
Next, click the All Channels report under the Performance section on the left navigation bar.
In this report, you can find the attributed conversions for each channel of your selected conversion event – “purchase”, in this case.
Screenshot from GA4, November 2022
Now, you know Organic Search, together with Direct and Email, drove most of the purchases on Google’s Merchandise Store.
Examine Results From Different Attribution Models In GA4
By default, GA4 uses the data-driven attribution model to determine how many credits each channel receives. However, you can examine how different attribution models assign credits for each channel.
Click Model Comparison under the Attribution section on the left navigation bar.
For example, comparing the data-driven attribution model with the first touch attribution model (aka “first click model” in the below figure), you can see more conversions are attributed to Organic Search under the first click model (735) than the data-driven model (646.80).
On the other hand, Email has more attributed conversions under the data-driven attribution model (727.82) than the first click model (552).
Screenshot from GA4, November 2022
The data tells us that Organic Search plays an important role in bringing potential customers to the store, but it needs help from other channels to convert visitors (i.e., for customers to make actual purchases).
On the other hand, Email, by nature, interacts with visitors who have visited the site before and helps to convert returning visitors who initially came to the site from other channels.
Which Attribution Model Is The Best?
A common question, when it comes to attribution model comparison, is which attribution model is the best. I’d argue this is the wrong question for marketers to ask.
The truth is that no one model is absolutely better than the others as each model illustrates one aspect of the customer journey. Marketers should embrace multiple models as they see fit.
From Channel-Based To Pageview-Based Attribution
Google Analytics is easy to use, but it works well for channel-based attribution.
If you want to further understand how customers navigate through your website before converting, and what pages influence their decisions, you need to conduct attribution analysis on pageviews.
While Google Analytics doesn’t support pageview-based attribution, there are other tools you can use.
We recently performed such a pageview-based attribution analysis on AdRoll’s website and I’d be happy to share with you the steps we went through and what we learned.
Gather Pageview Sequence Data
The first and most challenging step is gathering data on the sequence of pageviews for each visitor on your website.
Most web analytics systems record this data in some form. If your analytics system doesn’t provide a way to extract the data from the user interface, you may need to pull the data from the system’s database.
Similar to the steps we went through on GA4, the first step is defining the conversion. With pageview-based attribution analysis, you also need to identify the pages that are part of the conversion process.
As an example, for an ecommerce site with online purchase as the conversion event, the shopping cart page, the billing page, and the order confirmation page are part of the conversion process, as every conversion goes through those pages.
You should exclude those pages from the pageview data since you don’t need an attribution analysis to tell you those pages are important for converting your customers.
The purpose of this analysis is to understand what pages your potential customers visited prior to the conversion event and how they influenced the customers’ decisions.
Prepare Your Data For Attribution Analysis
Once the data is ready, the next step is to summarize and manipulate your data into the following four-column format. Here is an example.
Screenshot from author, November 2022
The Path column shows all the pageview sequences. You can use any unique page identifier, but I’d recommend using the url or page path because it allows you to analyze the result by page types using the url structure. “>” is a separator used in between pages.
The Total_Conversions column shows the total number of conversions a particular pageview path led to.
The Total_Conversion_Value column shows the total monetary value of the conversions from a particular pageview path. This column is optional and is mostly applicable to ecommerce sites.
The Total_Null column shows the total number of times a particular pageview path failed to convert.
Build Your Page-Level Attribution Models
To build the attribution models, we leverage the open-source library called ChannelAttribution.
While this library was originally created for use in R and Python programming languages, the authors now provide a free Web app for it, so we can use this library without writing any code.
Upon signing into the Web app, you can upload your data and start building the models.
For first-time users, I’d recommend clicking the Load Demo Data button for a trial run. Be sure to examine the parameter configuration with the demo data.
Screenshot from author, November 2022When you’re ready, click the Run button to create the models.
Once the models are created, you’ll be directed to the Output tab, which displays the attribution results from four different attribution models – first-touch, last-touch, linear, and data-drive (Markov Chain).
Remember to download the result data for further analysis.
For your reference, while this tool is called ChannelAttribution, it’s not limited to channel-specific data.
Since the attribution modeling mechanism is agnostic to the type of data given to it, it’d attribute conversions to channels if channel-specific data is provided, and to web pages if pageview data is provided.
Analyze Your Attribution Data
Organize Pages Into Page Groups
Depending on the number of pages on your website, it may make more sense to first analyze your attribution data by page groups rather than individual pages.
A page group can contain as few as just one page to as many pages as you want, as long as it makes sense to you.
Taking AdRoll’s website as an example, we have a Homepage group that contains just the homepage and a Blog group that contains all of our blog posts.
For ecommerce sites, you may consider grouping your pages by product categories as well.
Starting with page groups instead of individual pages allows marketers to have an overview of the attribution results across different parts of the website. You can always drill down from the page group to individual pages when needed.
Identify The Entries And Exits Of The Conversion Paths
After all the data preparation and model building, let’s get to the fun part – the analysis.
I’d suggest first identifying the pages that your potential customers enter your website and the pages that direct them to convert by examining the patterns of the first-touch and last-touch attribution models.
Pages with particularly high first-touch and last-touch attribution values are the starting points and endpoints, respectively, of the conversion paths. These are what I call gateway pages.
Make sure these pages are optimized for conversion.
Keep in mind that this type of gateway page may not have very high traffic volume.
For example, as a SaaS platform, AdRoll’s pricing page doesn’t have high traffic volume compared to some other pages on the website but it’s the page many visitors visited before converting.
Find Other Pages With Strong Influence On Customers’ Decisions
After the gateway pages, the next step is to find out what other pages have a high influence on your customers’ decisions.
For this analysis, we look for non-gateway pages with high attribution value under the Markov Chain models.
Taking the group of product feature pages on AdRoll.com as an example, the pattern of their attribution value across the four models (shown below) shows they have the highest attribution value under the Markov Chain model, followed by the linear model.
This is an indication that they are visited in the middle of the conversion paths and played an important role in influencing customers’ decisions.
Image from author, November 2022
These types of pages are also prime candidates for conversion rate optimization (CRO).
Making them easier to be discovered by your website visitors and their content more convincing would help lift your conversion rate.
To Recap
Multi-touch attribution allows a company to understand the contribution of various marketing channels and identify opportunities to further optimize the conversion paths.
Start simply with Google Analytics for channel-based attribution. Then, dig deeper into a customer’s pathway to conversion with pageview-based attribution.
Don’t worry about picking the best attribution model.
Leverage multiple attribution models, as each attribution model shows different aspects of the customer journey.
More resources:
Featured Image: Black Salmon/Shutterstock
SEO
How To Become an SEO Expert in 4 Steps
With 74.1% of SEOs charging clients upwards of $500 per month for their services, there’s a clear financial incentive to get good at SEO. But with no colleges offering degrees in the topic, it’s down to you to carve your own path in the industry.
There are many ways to do this; some take longer than others.
In this post, I’ll share how I’d go from zero to SEO pro if I had to do it all over again.
Understanding what search engine optimization really is and how it works is the first state of affairs. While you can do this by reading endless blog posts or watching YouTube videos, I wouldn’t recommend that approach for a few reasons:
- It’s hard to know where to start
- It’s hard to join the dots
- It’s hard to know who to trust
You can solve all of these problems by taking a structured course like our SEO course for beginners. It’s completely free (no signup required), consists of 14 short video lessons (2 hours total length), and covers:
- What SEO is and why it’s important
- How to do keyword research
- How to optimize pages for keywords
- How to build links (and why you need them)
- Technical SEO best practices
Here’s the first lesson to get you started:
It doesn’t matter how many books you read about golf, you’re never going to win a tournament without picking up a set of clubs and practicing. It’s the same with SEO. The theory is important, but there’s no substitute for getting your hands dirty and trying to rank a site.
If you don’t have a site already, you can get up and running fairly quickly with any major website platform. Some will set you back a few bucks, but they handle SEO basics out of the box. This saves you time sweating the small stuff.
As for what kind of site you should create, I recommend a simple hobby blog.
Here’s a simple food blog I set up in <10 minutes:
Once you’re set-up, you’re ready to start practicing and honing your SEO skills. Specifically, doing keyword research to find topics, writing and optimizing content about them, and (possibly) building a few backlinks.
For example, according to Ahrefs’ Keywords Explorer, the keyword “neopolitan pizza dough recipe” has a monthly traffic potential of 4.4K as well as a relatively low Keyword Difficulty (KD) score:
Even better, there’s a weak website (DR 16) in the top three positions—so this should definitely be quite an easy topic to rank for.
Given that most of the top-ranking posts have at least a few backlinks, a page about this topic would also likely need at least a few backlinks to compete. Check out the resources below to learn how to build these.
It’s unlikely that your hobby blog is going to pay the bills, so it’s time to use the work you’ve done so far to get a job in SEO. Here are a few benefits of doing this:
- Get paid to learn. This isn’t the case when you’re home alone reading blog posts and watching videos or working on your own site.
- Get deeper hands-on experience. Agencies work with all kinds of businesses, which means you’ll get to build experience with all kinds of sites, from blogs to ecommerce.
- Build your reputation. Future clients or employers are more likely to take you seriously if you’ve worked for a reputable SEO agency.
To find job opportunities, start by signing up for SEO newsletters like SEO Jobs and SEOFOMO. Both of these send weekly emails and feature remote job opportunities:
You can also go the traditional route and search job sites for entry-level positions. The kinds of jobs you’re looking for will usually have “Junior” in their titles or at least mention that it’s a junior position in their description.
Beyond that, you can search for SEO agencies in your local area and check their careers pages.
Even if there are no entry-level positions listed here, it’s still worth emailing and asking if there are any upcoming openings. Make sure to mention any SEO success you’ve had with your website and where you’re at in your journey so far.
This might seem pushy, but many agencies actually encourage this—such as Rise at Seven:
Here’s a quick email template to get you started:
Subject: Junior SEO position?
Hey folks,
Do you have any upcoming openings for junior SEOs?
I’ve been learning SEO for [number] months, but I’m looking to take my knowledge to the next level. So far, I’ve taken Ahrefs’ Beginner SEO course and started my own blog about [topic]—which I’ve had some success with. It’s only [number] months old but already ranks for [number] keywords and gets an estimated [number] monthly search visits according to Ahrefs.
[Ahrefs screenshot]
I checked your careers page and didn’t see any junior positions there, but I was hoping you might consider me for any upcoming positions? I’m super enthusiastic, hard-working, and eager to learn.
Let me know.
[Name]
You can pull all the numbers and screenshots you need by creating a free Ahrefs Webmaster Tools account and verifying your website.
SEO is a broad industry. It’s impossible to be an expert at every aspect of it, so you should niche down and hone your skills in the area that interests you the most. You should have a reasonable idea of what this is from working on your own site and in an agency.
For example, link building was the area that interested me the most, so that’s where I focused on deepening my knowledge. As a result, I became what’s known as a “t-shaped SEO”—someone with broad skills across all things SEO but deep knowledge in one area.
Marie Haynes is another great example of a t-shaped SEO. She specializes in Google penalty recovery. She doesn’t build links or do on-page SEO. She audits websites with traffic drops and helps their owners recover.
In terms of how to build your knowledge in your chosen area, here are a few ideas:
Here are a few SEOs I’d recommend following and their (rough) specialties:
Final thoughts
K Anders Ericsson famously theorized that it takes 10,000 hours of practice to master a new skill. Can it take less? Possibly. But the point is this: becoming an SEO expert is not an overnight process.
I’d even argue that it’s a somewhat unattainable goal because no matter how much you know, there’s always more to learn. That’s part of the fun, though. SEO is a fast-moving industry that keeps you on your toes, but it’s a very rewarding one, too.
Here are a few stats to prove it:
- 74.1% of SEOs charge clients upwards of $500 per month for their services (source)
- $49,211 median annual salary (source)
- ~$74k average salary for self-employed SEOs (source)
Got questions? Ping me on Twitter X.
SEO
A Year Of AI Developments From OpenAI
Today, ChatGPT celebrates one year since its launch in research preview.
Try talking with ChatGPT, our new AI system which is optimized for dialogue. Your feedback will help us improve it. https://t.co/sHDm57g3Kr
— OpenAI (@OpenAI) November 30, 2022
From its humble beginnings, ChatGPT has continually pushed the boundaries of what we perceive as possible with generative AI for almost any task.
a year ago tonight we were probably just sitting around the office putting the finishing touches on chatgpt before the next morning’s launch.
what a year it’s been…
— Sam Altman (@sama) November 30, 2023
In this article, we take a journey through the past year, highlighting the significant milestones and updates that have shaped ChatGPT into the versatile and powerful tool it is today.
a year ago tonight we were placing bets on how many total users we’d get by sunday
20k, 80k, 250k… i jokingly said “8B”.
little did we know… https://t.co/8YtO8GbLPy— rapha gontijo lopes (@rapha_gl) November 30, 2023
ChatGPT: From Research Preview To Customizable GPTs
This story unfolds over the course of nearly a year, beginning on November 30, when OpenAI announced the launch of its research preview of ChatGPT.
As users began to offer feedback, improvements began to arrive.
Before the holiday, on December 15, 2022, ChatGPT received general performance enhancements and new features for managing conversation history.
As the calendar turned to January 9, 2023, ChatGPT saw improvements in factuality, and a notable feature was added to halt response generation mid-conversation, addressing user feedback and enhancing control.
Just a few weeks later, on January 30, the model was further upgraded for enhanced factuality and mathematical capabilities, broadening its scope of expertise.
February 2023 was a landmark month. On February 9, ChatGPT Plus was introduced, bringing new features and a faster ‘Turbo’ version to Plus users.
This was followed closely on February 13 with updates to the free plan’s performance and the international availability of ChatGPT Plus, featuring a faster version for Plus users.
March 14, 2023, marked a pivotal moment with the introduction of GPT-4 to ChatGPT Plus subscribers.
Screenshot from ChatGPT, March 2023This new model featured advanced reasoning, complex instruction handling, and increased creativity.
Less than ten days later, on March 23, experimental AI plugins, including browsing and Code Interpreter capabilities, were made available to selected users.
On May 3, users gained the ability to turn off chat history and export data.
Plus users received early access to experimental web browsing and third-party plugins on May 12.
On May 24, the iOS app expanded to more countries with new features like shared links, Bing web browsing, and the option to turn off chat history on iOS.
June and July 2023 were filled with updates enhancing mobile app experiences and introducing new features.
The mobile app was updated with browsing features on June 22, and the browsing feature itself underwent temporary removal for improvements on July 3.
The Code Interpreter feature rolled out in beta to Plus users on July 6.
Plus customers enjoyed increased message limits for GPT-4 from July 19, and custom instructions became available in beta to Plus users the next day.
July 25 saw the Android version of the ChatGPT app launch in selected countries.
As summer progressed, August 3 brought several small updates enhancing the user experience.
Custom instructions were extended to free users in most regions by August 21.
The month concluded with the launch of ChatGPT Enterprise on August 28, offering advanced features and security for enterprise users.
Entering autumn, September 11 witnessed limited language support in the web interface.
Voice and image input capabilities in beta were introduced on September 25, further expanding ChatGPT’s interactive abilities.
An updated version of web browsing rolled out to Plus users on September 27.
The fourth quarter of 2023 began with integrating DALL·E 3 in beta on October 16, allowing for image generation from text prompts.
The browsing feature moved out of beta for Plus and Enterprise users on October 17.
Customizable versions of ChatGPT, called GPTs, were introduced for specific tasks on November 6 at OpenAI’s DevDay.
Screenshot from ChatGPT, November 2023On November 21, the voice feature in ChatGPT was made available to all users, rounding off a year of significant advancements and broadening the horizons of AI interaction.
And here, we have ChatGPT today, with a sidebar full of GPTs.
Screenshot from ChatGPT, November 2023Looking Ahead: What’s Next For ChatGPT
The past year has been a testament to continuous innovation, but it is merely the prologue to a future rich with potential.
The upcoming year promises incremental improvements and leaps in AI capabilities, user experience, and integrative technologies that could redefine our interaction with digital assistants.
With a community of users and developers growing stronger and more diverse, the evolution of ChatGPT is poised to surpass expectations and challenge the boundaries of today’s AI landscape.
As we step into this next chapter, the possibilities are as limitless as generative AI continues to advance.
Featured image: photosince/Shutterstock
SEO
Is AI Going To E-E-A-T Your Experience For Breakfast? The LinkedIn Example
Are LinkedIn’s collaborative articles part of SEO strategies nowadays?
More to the point, should they be?
The search landscape has changed dramatically in recent years, blurring the lines between search engines and where searches occur.
Following the explosive adoption of AI in content marketing and the most recent Google HCU, core, and spam updates, we’re looking at a very different picture now in search versus 12 months ago.
User-generated and community-led content seems to be met with renewed favourability by the algorithm (theoretically, mirroring what people reward, too).
LinkedIn’s freshly launched “collaborative articles” seem to be a perfect sign of our times: content that combines authority (thanks to LinkedIn’s authority), AI-generated content, and user-generated content.
What could go wrong?
In this article, we’ll cover:
- What are “collaborative articles” on LinkedIn?
- Why am I discussing them in the context of SEO?
- The main issues with collaborative articles.
- How is Google treating them?
- How they can impact your organic performance.
What Are LinkedIn Collaborative Articles?
First launched in March 2023, LinkedIn says about collaborative articles:
“These articles begin as AI-powered conversation starters, developed with our editorial team, but they aren’t complete without insights from our members. A select group of experts have been invited to contribute their own ideas, examples and experiences within the articles.“
Essentially, each of these articles starts as a collection of AI-generated answers to FAQs/prompts around any given topic. Under each of these sections, community members can add their own perspectives, insights, and advice.
What’s in it for contributors? To earn, ultimately, a “Top Voice” badge on their profile.
The articles are indexable and are all placed under the same folder (https://www.linkedin.com/advice/).
They look like this:
On the left-hand side, there are always FAQs relevant to the topic answered by AI.
On the right-hand side is where the contributions by community members get posted. Users can react to each contribution in the same way as to any LinkedIn post on their feed.
How Easy Is It To Contribute And Earn A Badge For Your Insights?
Pretty easy.
I first got invited to contribute on September 19, 2023 – though I had already found a way to contribute a few weeks before this.
Screenshot from LinkedIn, November 2023My notifications included updates from connections who had contributed to an article.
By clicking on these, I was transferred to the article and was able to contribute to it, too (as well as additional articles, linked at the bottom).
I wanted to test how hard it was to earn a Top SEO Voice badge. Eight article contributions later (around three to four hours of my time), I had earned three.
Screenshots from LinkedIn, November 2023How? Apparently, simply by earning likes for my contributions.
A Mix Of Brilliance, Fuzzy Editorial Rules, And Weird Uncle Bob
Collaborative articles sound great in principle – a win-win for both sides.
- LinkedIn struck a bullseye: creating and scaling content (theoretically) oozing with E-E-A-T, with minimal investment.
- Users benefit from building their personal brand (and their company’s) for a fragment of the effort and cost this usually takes. The smartest ones complement their on-site content strategy with this off-site golden ticket.
What isn’t clear from LinkedIn’s Help Center is what this editorial mix of AI and human input looks like.
Things like:
- How much involvement do the editors have before the topic is put to the community?
- Are they only determining and refining the prompts?
- Are they editing the AI-generated responses?
- More importantly, what involvement (if any) do they have after they unleash the original AI-generated piece into the world?
- And more.
I think of this content like weird Uncle Bob, always joining the family gatherings with his usual, unoriginal conversation starters. Only, this time, he’s come bearing gifts.
Do you engage? Or do you proceed to consume as many canapés as possible, pretending you haven’t seen him yet?
Why Am I Talking About LinkedIn Articles And SEO?
When I first posted about LinkedIn’s articles, it was the end of September. Semrush showed clear evidence of their impact and potential in Search. (Disclosure: I work for Semrush.)
Only six months after their launch, LinkedIn articles were on a visible, consistent upward trend.
- They were already driving 792.5K organic visits a month. (This was a 75% jump in August.)
- They ranked for 811,700 keywords.
- Their pages were ranking in the top 10 for 78,000 of them.
- For 123,700 of them, they appeared in a SERP feature, such as People Also Ask and Featured Snippets.
- Almost 72% of the keywords had informational intent, followed by commercial keywords (22%).
Here’s a screenshot with some of the top keywords for which these pages ranked at the top:
Screenshot from Semrush US database, desktop, September 2023Now, take the page that held the Featured Snippet for competitive queries like “how to enter bios” (monthly search volume of 5,400 and keyword difficulty of 84, based on Semrush data).
It came in ahead of pages on Tom’s Hardware, Hewlett-Packard, or Reddit.
Screenshots from LinkedIn, November 2023See anything weird? Even at the time of writing this post, this collaborative article had precisely zero (0) contributions.
This means a page with 100% AI-generated content (and unclear interference of human editors) was rewarded with the Featured Snippet against highly authoritative and relevant domains and pages.
A Sea Of Opportunity Or A Storm Ready To Break Out?
Let’s consider these articles in the context of Google’s guidelines for creating helpful, reliable, people-first content and its Search Quality Rater Guidelines.
Of particular importance here, I believe, is the most recently added “E” in “E-E-A-T,” which takes experience into account, alongside expertise, authoritativeness, and trustworthiness.
For so many of these articles to have been ranking so well must mean that they were meeting the guidelines and proving helpful and reliable for content consumers.
After all, they rely on “a select group of experts to contribute their own ideas, examples and experiences within the articles,” so they must be worthy of strong organic performances, right?
Possibly. (I’ve yet to see such an example, but I want to believe somewhere in the thousands of pages these do exist).
But, based on what I’ve seen, there are too many examples of poor-quality content to justify such big rewards in the search engine results pages (SERPs).
The common issues I’ve spotted:
1. Misinformation
I can’t tell how much vetting or editing there is going on behind the scenes, but the amount of misinformation in some collaborative articles is alarming. This goes for AI-generated content and community contributions alike.
I don’t really envy the task of fact-checking what LinkedIn describes as “thousands of collaborative articles on 2,500+ skills.” Still, if it’s quality and helpfulness we’re concerned with here, I’d start brewing my coffee a little stronger if I were LinkedIn.
At the moment, it feels a little too much like a free-for-all.
Here are some examples of topics like SEO or content marketing.
Screenshots from LinkedIn, November 20232. Thin Content
To a degree, some contributions seem to do nothing more than mirror the points made in the original AI-generated piece.
For example, are these contributions enough to warrant a high level of “experience” in these articles?
Screenshots from LinkedIn, November 2023The irony to think that some of these contributions may have also been generated by AI…
3. Missing Information
While many examples don’t provide new or unique perspectives, some articles simply don’t provide…any perspectives at all.
This piece about analytical reasoning ranked in the top 10 for 128 keywords when I first looked into it last September (down to 80 in October).
Screenshot from LinkedIn, November 2023It even held the Featured Snippet for competitive keywords like “inductive reasoning examples” for a while (5.4K monthly searches in the US), although it had no contributions on this subsection.
Most of its sections remain empty, so we’re talking about mainly AI-generated content.
Does this mean that Google really doesn’t care whether your content comes from humans or AI?
I’m not convinced.
How Have The Recent Google Updates Impacted This Content?
After August and October 2023 Google core updates (at the time of writing, the November 2023 Google core update is rolling out), the September 2023 helpful content update, and the October 2023 spam update, the performance of this section seems to be declining.
According to Semrush data:
Screenshot from Semrush, November 2023- Organic traffic to these pages was down to 453,000 (a 43% drop from September, bringing their performance close to August levels).
- They ranked for 465,100 keywords (down by 43% MoM).
- Keywords in the Top 10 dropped by 33% (51,900 vs 78,000 in September).
- Keywords in the top 10 accounted for 161,800 visits (vs 287,200 in September, down by 44% MoM).
The LinkedIn domain doesn’t seem to have been impacted negatively overall.
Screenshot from Semrush, November 2023Is this a sign that Google has already picked up the weaknesses in this content and has started balancing actual usefulness versus the overall domain authority that might have propelled it originally?
Will we see it declining further in the coming months? Or are there better things to come for this feature?
Should You Already Be On The Bandwagon If You’re In SEO?
I was on the side of caution before the Google algorithm updates of the past couple of months.
Now, I’d be even more hesitant to invest a substantial part of my resources towards baking this content into my strategy.
As with any other new, third-party feature (or platform – does anyone remember Threads?), it’s always a case of balancing being an early adopter with avoiding over-investment. At least while being unclear on the benefits.
Collaborative articles are a relatively fresh, experimental, external feature you have minimal control over as part of your SEO strategy.
Now, we also have signs from Google that this content may not be as “cool” as we initially thought.
This Is What I’d Do
That’s not to say it’s not worth trying some small-scale experiments.
Or, maybe, use it as part of promoting your own personal brand (but I’ve yet to see any data around the impact of the “Top Voice” badges on perceived value).
Treat this content as you would any other owned content.
- Follow Google’s guidelines.
- Add genuine value for your audience.
- Add your own unique perspective.
- Highlight gaps and misinformation.
Experience shows us that when tactics get abused, and the user experience suffers, Google eventually steps in (from guest blogging to parasite SEO, most recently).
It might make algorithmic tweaks when launching updates, launch a new system, or hand out manual actions – the point is that you don’t know how things will progress. Only LinkedIn and Google have control over that.
As things stand, I can easily see any of the below potential outcomes:
- This content becomes the AI equivalent of the content farms of the pre-Panda age, leading to Google clamping down on its search performance.
- LinkedIn’s editors stepping in more for quality control (provided LinkedIn deems the investment worthwhile).
- LinkedIn starts pushing its initiative much more to encourage participation and engagement. (This could be what makes the difference between a dead content farm and Reddit-like value.)
Anything could happen. I believe the next few months will give us a clearer picture.
What’s Next For AI And Its Role In SEO And Social Media?
When it comes to content creation, I think it’s safe to say that AI isn’t quite ready to E-E-A-T your experience for breakfast. Yet.
We can probably expect more of these kinds of movements from social media platforms and forums in the coming months, moving more toward mixing AI with human experience.
What do you think is next for LinkedIn’s collaborative articles? Let me know on LinkedIn!
More resources:
Featured Image: BestForBest/Shutterstock
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