Google's Shift in Personalization with the November 2024 Core Update
by Daniel James Stoker
Last Updated on November 27, 2024
A prominent theme from the November 2024 Core Update is the advancement of personalization in search. Google has been steadily progressing towards offering more personalized search experiences. This includes customizing search results for individual users by considering various factors such as search history, location, device, and even inferred interests. The objective is to deliver the most relevant and useful information tailored to each user's specific context.
With this update, Google appears to be enhancing the way it personalizes search results. As a result, two users searching for the same query may encounter different results depending on their personal data and search context. This increased level of personalization can cause variations in search rankings and visibility that are not easily detected by traditional SEO tracking tools.
In an earlier SEO white paper, we demonstrated that Google's average position metric statistically corresponds to performance categories only for changes in position at the query-page average position (QPAP) level. Consequently, we rely exclusively on QPAPs in our analyses of rank performance changes. We also revealed that Google Search Console data sampling occurs through both the Google Search Console (GSC) UI and third-party API connections, whereas Google BigQuery GSC provides complete data capture of search metrics, including the inclusion of zero-click impression queries. Therefore, all search console rank analysis herein relies on our internal Google BigQuery GSC QPAPs.
We compared our internal BigQuery GSC QPAP changes with unique query-page rankings tracked by Ahrefs. We examined pre- and post-periods that did not align with a Google algorithm update, as well as those related to the Google November 2024 core update. Although our internal QPAP changes do not perfectly align with AHREFs data, we found that during comparison periods not associated with a Google algorithm update, there was an average absolute position change variance of 1.08 between our internal QPAPs and Ahrefs query-page rank tracker across more than a thousand unique query-page combinations. In contrast, when comparing the same set of query-page combinations for the pre- and post-periods of the Google November 2024 core update, the average absolute position change variance increased to 2.82.
These data findings indicate that the variance between how our internal QPAPs track unique query-page rank changes and how Ahrefs tracks them has increased. The two distinct data sources are showing greater deviation in their data collection for specific query-page position changes following the Google November 2024 core update. The chart below visually illustrates the variance in individual query-page position changes between our internal QPAPs changes and Ahrefs query-page rank changes.
A shift towards personalization might be contributing to the observed discrepancies between internal Google BigQuery Google Search Console data and Ahrefs tracking data. Here are a few reasons why this might be happening:
Granular User Context: Google's internal data, accessed via Google Search Console, is inherently tied to the personalized experiences of users. This means that the data reflects how actual users interact with search results based on their unique profiles. On the other hand, Ahrefs and similar tools provide a more generalized view, often using proxies or simulated users that may not account for the same level of personalization.
Dynamic Ranking Changes: As Google becomes more adept at adjusting rankings based on real-time user signals and preferences, the positional changes for specific queries can be more fluid. This dynamic nature can lead to less alignment between internal and third-party data, as the latter may not update as frequently or accurately reflect these changes.
Localized Results: Personalization often includes localization, meaning search results can vary significantly based on the user's geographical location. While Google Search Console can reflect these localized variations, Ahrefs might present an averaged or less localized perspective, leading to apparent discrepancies.
These data findings provide initial insights into the Google November 2024 core update, which is still in the process of rolling out at the time of this publication. The statistically significant shift in positional change variance between our internal QPAPs and the AHREFs query-page rank tracker suggests a need for further investigation into the potential impact of increased personalization in Google SERPs. Additional research will be conducted and updates will be added to this paper shortly.
For SEO professionals, adapting to an increased personalization in Google may require a new, or renewed, focus in strategy:
Focus on User Experience: Prioritize creating high-quality, relevant content that caters to your target audience's needs and preferences. Understanding user intent and context is more important than ever.
Leverage First-Party Data: Use insights from Google BigQuery Search Console metrics data to understand how real users are interacting with your content. Exclusively use query-page analysis where value changes in the metric statistically map to performance categories as we have shown in previous white papers like The Statistics Behind Google's Search Average Position and The Statistics Behind Google's Search Click Metric. This data can help you identify patterns and opportunities that might not be visible through third-party tools.
Monitor Local and Mobile Performance: Pay close attention to how your site performs across different locations and devices. Personalization often means that mobile and local optimizations can have a significant impact on visibility.
Stay Informed: Keep abreast of Google's updates and industry discussions to understand how personalization trends are evolving and how they might affect your SEO strategy.
About the Author: Daniel James Stoker is a member of the FindLaw Performance Team and holds degrees of B.S. in Physics, B.S.H.S. in Physiological Sciences, a M.S. in Computer Sciences Database Technologies, and has completed a PGP in Artificial Intelligence and Machine Learning: Business Applications. LinkedIn