In August, Google rolled out one of its most significant updates of the year. Many SEOs believed it was a partial rollback of the controversial Helpful Content Update – a potential mea culpa from Google.
While some sites have benefited from the changes, others have experienced sharp declines in traffic.
This case study dives into how the update impacted our client’s site, the key metrics affected and the strategies we’re implementing to recover lost visibility and boost SEO performance.
Diagnosing the drop
I recently attended the SEO Office Hours podcast, where a participant asked:
“We haven’t been affected by the August update, but the indexing bug that happened around the same time has decreased our traffic. How do we solve this?”
My first thought was, what confidence! How sure are you that it was the indexing error if you are looking just at traffic?
The error was resolved in a few days so how come there was no recovery of the traffic and rankings?
Yes, we should ignore drops that happened in those two days during the bug, but if they continue after this, might it impact the core update?
Diagnosing what actually happened is not always easy. We see it all the time.
Sometimes, clients don’t know where to start. Other times, they simply don’t have time to investigate closely enough. The latter was true for our new client.
Their excellent SEO team needed help auditing the website and a sounding board for solving any issues.
After a Google core update rollout is completed, you could examine multiple metrics.
Like everything in SEO, these depend on several factors, such as your key performance indicators (KPIs), the type of website you manage and the countries you target.
To diagnose the impact of the Google core update for our client, we used a simple, three-step approach:
Understand what has happened sitewide.
Use the sitewide indicators to segment the data for deeper analysis.
Deep dive into the most affected pages and sections of the website.
Understand what happened sitewide
Our client has a large site. Digging into each page would take too much time. We needed to understand what happened overall to segment and prioritize.
For this, we looked at:
Traffic and conversion trends.
Overall content health.
Link profile.
Technical SEO.
Wild card: persona-based sitewide signals.
Traffic and conversion trends
Two of the main KPIs for our client were traffic and conversions, which made it clear that our analysis needed to begin in these areas.
We examined their Google Analytics 4 (GA4) and Google Search Console (GSC) data to assess organic trends and overall traffic patterns.
This holistic approach is crucial because it establishes a baseline for our analysis. For instance, if we observe a significant drop in overall traffic, we can infer that the decline is likely not solely attributed to organic search issues.
We noticed a slight decrease in sessions overall and no impact on conversions. This was particularly noticeable in August (the timing of the Google core update), suggesting the update may have played a role and the culprit was likely organic.
The hypothesis was soon confirmed by digging into organic data in both GA4 and GSC.
Traffic was down significantly, both looking at GSC and GA4.
Interestingly, just like overall traffic, the organic conversions were much more stable.
It was clear that the issue wasn’t the website’s relevance to its audience; those who visited it were converting. However, the overall number of users hitting the site had significantly decreased.
Before reviewing the rankings, we segmented the data by country to identify where the impact was most pronounced.
This prompted us to ask an essential question: do we care?
In our case, one of the countries most affected was India. While it would have been easy to dive into auditing this market for rankings, we recognized that the devil is in the details.
iOS is not the dominant operating system in India. In 2023, Android had a 95.17% market share, while iOS only had 3.98%.
Since our client’s solution only works with iOS, India is not the right market to focus on. Instead, we should concentrate on the U.S., which is their most relevant and larger market.
Overall content health
The second sitewide signal we explored initially is overall content health.
This gave us an initial understanding of possible issues with content at scale. We used Screaming Frog content analysis for this.
To use any tool effectively, it’s crucial to identify what truly matters in the vast amount of data you’re presented with.
In this case, it would be easy to focus on the wrong metric, like readability.
While some pages may have readability challenges, this aligns with our technical audience’s expectations.
The more pressing issue wasn’t readability but the significant number of near duplicates. Most of the affected pages were in the template directory. This was a key insight for segmenting the data based on website structure.
Note: This analysis differs from evaluating what Google defines as helpful content. There are ways to evaluate content at scale for helpfulness, mainly using Google’s NLP API to pick out entities and analyze sentiment. Something we currently have in the pipeline.
Link profile
The client had already said they wanted to enhance their internal linking at Stage 2 of the project.
For now, our focus was on understanding the situation, which we accomplished by using Screaming Frog.
Overall, the links looked healthy. We found valuable insights for improving our SEO strategies.
For example, when we examined the issue of internal outlinks with no anchor text, we discovered it originated from a specific template. While it’s not a top priority, it’s an easy fix at the template level.
Technical SEO
Not every technical fix will affect rankings, no matter how interesting they may be.
By this point in the analysis, I suspected the real issue was in the content and changes in the SERPs.
I’ve seen many websites struggle after the latest Google updates, and we suspected that Reddit and AI Overviews were taking over the search results.
From what we’ve observed, AIO functions similarly to a featured snippet. Therefore, one area we should focus on is structured data markup.
This Screaming Frog analysis helped us identify missing structured data at the template level and reveal any obvious gaps.
Wild card: Persona-based sitewide signals
Creating well-defined personas can be beneficial for SEO and overall marketing.
The client expressed concern that their recent expansion of personas, as they shifted focus to a different market segment, may have diluted their website’s authority.
While I don’t believe proprietary metrics such as DA or AS are a ranking factor, the recent Google API leak did indicate that Google has its own understanding of domain authority.
I can see why a company might think that expanding into a new market could make Google question its target audience.
From a technical perspective, this concern aligns with how semantic SEO works – websites must focus on delivering closely related content.
To determine if our client faced challenges with this new vertical, we used a combination of tools:
Ahrefs to export the keywords and pages ranking in the U.S.
A Python script that employed NLP to analyze the pages and assign a persona.
The findings were intriguing: most pages were assigned to both personas, indicating the content was closely aligned.
This suggested that the website didn’t have the problem of straying too far from its core topic.
Use the sitewide indicators to segment the data for deeper analysis
So far, everything suggests that the website remains valuable and that the traffic drop is not due to sitewide issues.
We already identified some challenges linked to specific page sections, such as the template directory.
We know that most traffic comes from the blog section.
However, with thousands of pages involved, it raises the question: where do you even start?
Using exports from various sources (GA4, GSC and the Ahrefs Top Pages report), we mapped the data to analyze the impact at the keyword level.