Unraveling the Butterfly Effect: Understanding Core Updates and User Intent Shifts

Enhance your skills with Growth Memo's weekly expert insights by subscribing for free!

It can be challenging to understand why sites experience a drop in organic traffic after Core Updates, as there may not be clear patterns in rank changes. One possible reason for this is subtle shifts in user intent.

Two outdated models are currently hindering our ability to accurately understand the situation.

Many of us traditionally categorize user intent into transactional, navigational, informational, and commercial. However, the reality is that user intent has evolved to be more sophisticated and precise nowadays.

Not all spots in the search results are equal. Google categorizes the top results into dominant, common, and minor intent.

Keywords can have multiple meanings, unless they are specific long-tail keywords with a clear intent. For instance, when someone searches for "ai ecommerce," some may be looking to understand the use of AI in ecommerce, while others may be interested in how ecommerce is impacted by AI. These distinctions may seem minor, but they are important.

Google aims to provide a variety of search results to address different user intents effectively. During Core Updates, Google adjusts its ranking of search results to match shifts in user intent. This can be particularly significant when the main intent and top 3 results are impacted.

In the grand scheme of things, small rank changes often seem minuscule and chaotic. But what we’re seeing is the Butterfly Effect: a small change making a massive impact.

what we're seeing is the Butterfly Effect: a small change making a massive impact.

Image Credit: Lyna ™

Butterfly Effects From Intent Shifts

Detecting subtle changes in rank due to shifts in user intent (Butterfly Effects) can be challenging if you're not sure what to look for. Our perception of keyword meanings is often too fixed, as topics are constantly evolving.

Some keywords in the news change meaning quickly due to query deserves freshness (QDF) filters. This shows how fast the meaning of a keyword can change in a day. On the other hand, evergreen keywords have meanings that rarely change.

During the period from early February to mid-April, there was a shift in the main user intent for "ecommerce ai." Initially, users were seeking information about AI in ecommerce, but this changed to a focus on how AI is transforming the ecommerce industry.

Bar chart showing user intent changes for "ai ecommerce" between February and April.

Image Credit: Kevin Indig

In the screenshot below, I color-coded the different types of intent to highlight how subtle the differences can be.

Screenshot of a web page displaying a list of the top changes in SEO visibility for e-commerce sites following Core Updates.

Image Credit: Kevin Indig

It's understandable that AI evolves quickly, along with its influence on our lives. As people continue to educate themselves about AI, they often have new questions and insights. This leads to shifts in user intent, which in turn affects search engine rankings.

The significance of user intent in search engine results pages (SERPs) for specific keywords can also shift over time. What was once a dominant user intent may transition to a more common one, while a previously minor intent may gain more prominence.

Google explains that sometimes site owners are not necessarily at fault when an update occurs, and that there may be "nothing to fix" or "recover" from regarding algorithm changes.

A more complex issue occurs when intent shifts result in two URLs competing against each other, causing cannibalization.

Sometimes, when one article gives a basic overview of a topic ("what is...") and another provides a detailed guide, they may end up ranking simultaneously or even competing with each other.

Line graph titled "Two URLs Suddenly Cannibalizing Each Other After Core Updates" showing the traffic trends for Blog Article 1 and Blog Article 2

When organic traffic drops after a Core Update, pay attention to keywords that dropped out of the top 3 positions. This is where it hurts the most. Even a single position drop has an outsized impact. How do you find Butterfly Effects?

To measure changes in user intent, you can examine the titles of search results before and after the update. While titles are not the only factor to consider, they offer valuable insights into what users are looking for.

When dealing with a large amount of data, you can employ your preferred Language Model (LLM) to group titles together based on specific prompts such as:

“Cluster the following titles into one of the following groups: {intent 1}, {intent 2}, {intent 3}, etc.”

“What intention could users have when clicking on the following search result?”

For highly popular searches, we can also examine the trend of searches in Google Suggest to determine if the search volume is increasing or decreasing.

A significant increase in search interest for a related keyword could potentially change the primary, popular, or less common search intent for the main keyword.

We may not be aware of all keywords experiencing an increase in search volume or where Google draws the line to decide when user intent for a keyword changes. Keep in mind that some keywords can quickly evolve in meaning, like "Independence Day" and "Wuhan."

When you notice a shift in intent for a keyword, you have three choices on how to respond:

Fix potential cannibalization (delete or consolidate).

Rewrite affected articles to match new user intent.

Create new content based on changed user intent.

Core Updates Are Multi-Faceted

Refining user intent is not all that Core Updates do. They’ve become kitchen sinks for all sorts of systems:

The Helpful Content classier has been integrated with Core Updates (link).

Other quality systems such as Panda and Penguin have been incorporated into Core Updates for several years now.

Google receives 15% of searches that are completely new, requiring them to evaluate the initial search results and make adjustments based on user feedback.

Core Updates are influenced by various factors, making them multi-faceted, complex, and unpredictable. Despite this, understanding the different types of user intent and subtle adjustments can help us navigate from confusion to finding solutions.

Featured Image: Paulo Bobita/Search Engine Journal

Editor's P/S:

The article highlights the importance of understanding subtle shifts in user intent as a potential reason for drops in organic traffic after Core Updates. It emphasizes that user intent has evolved beyond traditional categories and that Google categorizes search results based on dominant, common, and minor intent. The article also presents the concept of "Butterfly Effects," where small changes in user intent can have a significant impact on rankings.

To detect these changes, the article suggests examining search result titles before and after an update, using Language Models to group titles based on intent, and analyzing search volume trends. It also discusses the multi-faceted nature of Core Updates, which incorporate various quality systems and handle new searches, making them complex and unpredictable. Overall, the article stresses the need to consider user intent and its evolution when analyzing Core Update impacts on organic traffic.