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Is Google's MUM a search ranking factor?

With MUM, machine learning is getting closer and closer to fully and fully experiencing the content as your intended readers.But is it a ranking factor?

At Google I/O earlier last year, Google announced that it was internally exploring a new technology called MUM (Multitask Unified Model) to help its ranking system better understand language.

Billed as a "new AI milestone in understanding information," MUM aims to make it easier for Google to meet complex demands in search.

Google promises that MUM is 1000 times more powerful than its NLP transfer learning predecessor, BERT.

It uses a model called T5, the Text-to-Text Transfer Transformer, to reframe NLP tasks into a unified text-to-text format and develop a more comprehensive understanding of knowledge and information.

According to Google, MUM can be applied to document summarization, question answering, and classification tasks such as sentiment analysis.

Clearly, MUM is a major priority within the Googleplex – what’s important to the search team is also better on the SEO industry’s radar.

But is it a ranking factor in Google's search algorithm?

Disclaimer: MUM as a ranking factor

Many who read the news when MUM was first revealed naturally wondered how it would affect search rankings (especially their own rankings).

Google makes thousands of updates to its ranking algorithm every year, and while the vast majority are ignored, some are influential.

BERT is one such example.

It rolled out globally in 2019 and was hailed by Google itself as the most important update in five years.

Sure enough, BERT affects about 10% of search queries.

Launched in spring 2015, RankBrain is another example of an algorithm update that has a significant impact on the SERPs.

Now that Google is talking about MUM, it's clear that SEO professionals and the clients they serve should take note.

Roger Montti recently wrote about a patent he believes could provide more insight into the inner workings of MUM.

This is an interesting read if you want to peek at what's possible under the hood.

Now, let's consider whether MUM is a ranking factor.

Evidence of MUM as a ranking factor

When RankBrain launched, it wasn't announced until about six months later.Most updates are not announced or confirmed at all.

However, Google has done better before the impactful update.

For example, BERT was first released in November 2018, launched for English queries in October 11, and rolled out globally in December of that year..

We have more time to prepare the page to experience the signals and core network vitals, which were announced more than a year before the final launch in June 2021..

Google has already said that MUM is coming, and it's going to be a big deal.

But will MUM be responsible for the drop in rankings that many sites experienced in the spring and summer of 2021?

Evidence against MUM as a ranking factor

In a May 2021 presentation to MUM, Google researcher and VP of search Pandu Nayak made it clear that technology is not playing a role.Anyway, not yet:

"Today's search engines are not sophisticated enough to answer questions like an expert.But with a new technology called the Multitask Unified Model (MUM), we're getting closer to helping you meet these types of complex needs.therefore,in the future, you will need less searching to get the job done.

The timeline given at the time as to when the MUM-powered features and updates would go live was "in the coming months and years."

When asked if the industry would be alerted when MUM goes live for search, Google search liaison Danny Sullivan said yes.

Recently, Nayak explained how Google is using AI in search, writing:

"While we're still in the early stages of unlocking MUM's potential, we're already using it to improve searches for COVID-19 vaccine information, and we'll be providing a more intuitive way to use text and images in Google Lens in the coming months search in combination.

These are very specialized applications - so MUM is currently not like RankBrain, Neural Matching and BERT systemsUsed to help rank and improve the quality of search results.

He also added that any future applications of MUM will be subject to a rigorous evaluation process, including special attention to the responsible use of artificial intelligence.

MUM as a ranking factor: our verdict

Bottom line: Google doesn't use MUM as a search ranking signal.This is a language AI model based on Google's open source neural network architecture Transformer.

Google will train MUM like BERT on large datasets, and then fine-tune it for specific applications on smaller datasets.That's what it's testing MUM for to improve vaccine search results.

Google has mentioned specific ways it might be used in the (near) future, including:

  • Presents insights based on their in-depth knowledge of the world.
  • Displays useful subtopics for deeper exploration.
  • Break down language barriers by transferring knowledge across languages.
  • Also understand information from different formats, such as web pages, images, etc.

How would you optimize for MUM?

Observable (how to observe the analysis to find米国生活consult).

One thing is for certain: the intelligence of Google searches is growing by leaps and bounds.

As Google's search algorithms become more sophisticated and better able to determine the intent and nuances of language, attempts to deceive and manipulate will become less effective (and possibly easier to spot).

With NLP technology 1000x more powerful than RankBrain, optimizing the human experience is more important than ever.

If you want to go beyond MUM, focus on what the content you are creating means to those who are designed to meet their needs.

Machines are getting closer and closer to fully and fully experiencing this content as your intended readers/viewers do.

Extended reading:

Mobile friendliness as a Google ranking factor: what you need to know

Is language a Google ranking factor?

Are Local Citations (NAP) a Google Ranking Factor?

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