Google MUM Impact
Do you also burst into tears when you don't get the right responses to your searches? It can’t just be me, right? Right?
Thankfully, we don’t have that experience so often because Google’s search operations are evolving every day. From the introduction of helpful content updates to E-EAT to now Google MUM, Google has tapped into our hearts. As generative AI expands, Google has been on the cusp of refining its search algorithms to don the crown of “best search engine ever.”
Generative AI has brought a lot of businesses under its belt, but Google is not far behind the race. The newest Google MUM (multitask unified model) update has enhanced search capabilities, SERP relevance, and personalized user journeys in ways unimaginable.
What sort of web content will appeal to which user persona? What is the user's feeling while searching for a resource? The self-evolving architecture of generative AI software in the MUM model can capture all this and more.
What is Google MUM?
Google multitask unified model, or Google MUM is a multimodal technique posed to refine the value of search results. It was announced in May 2021 by Pandu Nayak, VP of Search at Google. MUM has replaced the bidirectional encoder representations from transformers (BERT) based web search responses into a more illustrative and giving search experience.
MUM strives to change Google’s user interface (UI) and bring a cohesive palette of resources to the curious audience. For example, Prabhakar Raghavan, Senior Vice President at Google, asserted that Google MUM can answer anything. It asked Google to compare and contrast climbing Mt. Adams and Mt. Fuji, given that he has already trekked Mt. Addams. Not only did Google return the list of differences or similarities, but it also added additional shop links for trek gear and video links.
As an upgraded AI technology, the MUM update improves the functionality of the BERT model. The primary reason for launching MUM was to give users a 360° search experience.
Google BERT vs. Google MUM
While both neural network architectures have dominated the search algorithm, MUM has a slight edge over BERT.
BERT is a 2019 Google update that uses natural language processing to resolve search queries. Based on a transformer neural network, this model contextualizes and encodes search queries to understand the intent behind it. With this update, Google can personalize answers, summarize text, and define the intent and categories of search queries.
Google MUM is a 2021 update derived from a T5 (text-to-text) framework, specifically catering to long-tail queries or a combination of complex queries. It declutters the SERP data and highlights a slew of resources for brand awareness. MUM uses cookie data, web stream data, user search query data, and crawl data to filter out content from reliable sites.
History of Google MUM
We’ve come a long way from the 1980s when the Advanced Research Projects Agency Network (ARPANET) was launched. The exchange of information was restricted to two or more workstations, as data was transmitted over wired servers. Fast forwarding to the internet era, Google used edge computing and serverless containerization to store, retrieve, and send data from servers. Over time, the strategy by which Google treated its users changed.
In the following years, Google released several updates.
- The Penguin update was released in 2012. Back then, Google was trying to fight back against gamers and web spam. The Penguin update prioritized authentic and whitehat URLs over spammy websites and syndicates.
- Hummingbird was programmed to interpret natural language queries and analyze the sentiment behind particular keywords. Hummingbird contextualizes search queries, adjusts the SERP layout, and makes the overall process more precise.
- Rankbrain (2015) was another natural language understanding enhancement aimed at understanding long-tail keywords. Long-tail keywords are raw search queries that may or may not have search volume – they might confuse the Google crawler. By including techniques of tokenization, word stemming, and emotion detection, Rankbrain made SERP more inclusive and bias-free.
- Neural matching was released in 2018. It interpreted search queries through advanced natural language processing. The neural network sees the word order of a search query and assigns an “attention” parameter to it. While loading search results, web pages that exactly match are displayed.
- BERT’s reactive mechanism increased Google’s knowledge retrieval, content filtering, and language interpretation. While it enabled the search engine to understand keyword meaning, it wasn’t able to decipher who the subject was within the keyword.
- Helpful content update, released in 2022, was designed to prioritize the presence of useful and authoritative content on the web. Search queries were divided into buckets of navigational, commercial, informational, and transactional. Each query returned a set of cohesive search results along with additional images and videos.
- E-EAT, which translates into experience, expertise, authoritativeness, and trustworthiness, came out in 2023. With this new launch, the SERP leaned toward published roundups, subject matter expertise, and authors who have reigned in their areas of knowledge. Google gave credibility to web pages by hosting content from trusted market experts.
- MUM combines the features of previous search updates in Google. The sole purpose of this natural language processing mechanism is to fuel the buyer's journey through the web. With MUM, you can explore options, review products, and purchase them directly without ad clicks or organic page visits.
Google MUM’s working methodology
Google MUM combines several technologies to make Google search more holistic and contextual. The large language model (LLM) behind MUM works in over 75 languages. Initially, this Google search algorithm functioned on the concept of retrieval systems. That means the search keyword was compared against a set of keys in the Google database. If there was a match, that value of the key was displayed.
Now, Google MUM uses sequence-to-sequence template matching to enhance user knowledge. Usually, when someone is stuck between a decision to purchase a product or a service, a hearty call to action helps. But MUM’s strategic approach puts forth a ton of images, videos, and media resources for that query and also presents answers for alternate questions.
MUM produces a calculated SERP that contains a far-stretched perspective of user needs in the main interface. This is also known as “simultaneous query processing.” The machine learning (ML) algorithm converts words into vectors, transfers knowledge to the server, and responds with valuable information. With MUM, non-organic content ranks faster, resulting in lower click-through rates (CTRs) but more content engagement.
Essentially, in a sales funnel, customers struggle to make decisions between the “evaluation” and the “awareness” stage. Organic websites and content are used to convert web experiences into sales, whereas MUM focuses on bringing a swath of digital assets in the form of multimedia. Users are treated to the best of the best so that they “evaluate all options” before striking a deal.
Core focus areas of Google MUM:
- Facilitating a deep understanding of human sentiments and world knowledge.
- Providing translation services in up to 75 languages to reduce language barriers.
- Deciphering the grammatical and literary context of search queries.
- Employing knowledge graphs to analyze the “unspoken” concerns of end users.
- Enhancing readers’ retention and extrapolation so that they explore SERP for more time before visiting a specific URL.
Do you remember iGoogle? It was a personalized Google homepage custom set with Ajax in 2005. By analyzing previous web behavior, it offered immersive insights in one window. The concept of iGoogle formed the foundation of Google MUM, where the idea was hardwired with AI.
Currently, no one can predict the gamut of features Google MUM will bring with its release. It’s still being cross-validated for accuracy. When launched, MUM might represent three main levels.
Levels of Google MUM
For different systems, servers, and data transfers, MUM will work with a certain degree of efficiency. For now, three existing levels have already been implemented using Google MUM:
- Short-term development: MUM uses “knowledge transfer” to filter its dataset and display results in 75 languages for different users. It helps people steer clear of confusion when they have to simplify difficult information in their mother tongue.
- Medium-term development: With the medium-level MUM update, the SERP will be a kaleidoscope of content resources. From images to carousels to PR podcasts to audio articles, SERP will become a mix and match of the best knowledge assets.
- Long-term development: MUM, in the long term, will customize SERP according to the user’s present state of mind. Behind every long-tail keyword, a particular orientation is set. MUM aims to use sentiment analysis and feedback mapping to analyze user needs and engage them for a long duration.
Search changes after Google MUM
Currently, SERP is viewed as a “length x breadth” interface experience. Every search engine result page has a featured snippet and a length of blue links with the most suitable content. But with MUM, a newer spectrum of features will come into play that will make search more responsive, user-friendly, and fun.
- Google Lens: Using Google Lens, you’ll be able to classify different components of an image with visual annotations and text overlays. It’ll help refine the search based on which images fit best with user needs.
- Larger images: You’ll be able to zoom in on banner images or product images of a particular company directly on the main search page. It will also increase the pixel adjustment of URL images.
- Refine and broaden: Similar to “people also searched for,” this feature will widen the horizon of user thoughts, inspirations, and desires by offering them access to more resources.
- Things to know: “Things to know” is like a recommendation section on Google. Answering queries with “people also ask” will change with “things to know.” The feature will be able to lead users to completely different buyer journeys and products.
Benefits of Google MUM
The MUM algorithm will be a turning point for search engine optimization (SEO) enthusiasts. In the future, a lot of Google response techniques will be driven by MUM. Not only will this benefit web teams but also audiences.
- Video analysis: Google MUM’s release will put a special emphasis on video marketing and visual production. The new mechanism will scrutinize video content, extract timestamps, and apply this data to personalize video suggestions. While searching for a particular video, users will get direct video results and closely-related video links.
- Google featured snippet: As a longstanding SEO metric, featured snippets will appear in a different format with Google MUM. There could be multiple featured snippets for different audiences. MUM might also aim to reduce paid or sponsored permits by 40%.
- Non-organic SERP: After the release of MUM, blogs and articles wouldn't be credible enough to rank higher on the SERP. Other sites that provide 360* information, including images, alternate keywords, and videos for a particular keyword, would rank higher in search results. Some forums like Reddit and Quora are already following this technique to rank higher and engage large communities with their content.
- Multilingual: The MUM model has been customized to translate input and output into 75 languages. By using the best NLP practices, sentence and semantic correction, and grammar comprehension for these languages, MUM aims to expand its reach. The multilingual move of MUM has encouraged many companies to build multilingual websites to become a part of the daily journeys of different people all over the world.
- Enlarged visuals: With Google MUM, you can zoom into images and infographics. Wearing Google lenses will help enlarge web visuals, study the features, and check out a product from all angles. Not only that, you can access customer reviews, learn about best practices, and raise brand awareness.
Limitations of Google MUM
MUM has stepped up the volatility of web searches and internet browsing. But with every new feature-packed update comes unavoidable bugs and limitations.
- Deplorability of organic content: A MUM update will demand businesses invest more in advertising and media than organic content marketing. This might have an adverse effect on project owners and content marketers.
- Incomprehensible nature: With MUM, a lot more content assets are visible to the user, perhaps displaying some uncanny resources. Users need to be mindful of what they want and should structure their search queries accordingly. If they make errors or type too fast, the AI algorithm might not be able to decode the intention behind the user query and display unrealistic results.
- SEO complications: After the launch of BERT, SEO became a little too difficult to crack. The MUM update would put more stress on SEO marketers to increase their technical knowledge. The consensus on traditional SEO would remain, but more new SEO rules would make Google a “messy middle.”
- Unethical results: Users need to be mindful of what they want and should structure their search query accordingly. In case they typed it in haste, the AI algorithm might not be able to decode the intention behind the user query and display unrealistic results.
MUM isn’t Google’s first AI sprint. For years, Google’s CEO, Sundar Pichai, has pushed the envelope of generative AI and its volumes of possibilities. Google aims to inject diversity, equity, and inclusion guidelines within MUM through artificial intelligence.
Will MUM be different from other Google AI updates?
MUM can be classified as the next big AI milestone. The traditional way of tackling information and finding the best choice for your needs is being revolutionized. Soon, users will be able to virtualize related topics for the primary query. Finding quality content in one place will reduce their frustration and web consumption time. That’s what the network behind MUM is striving for.
Previous machine learning updates leaned towards stabilizing the search experience, avoiding bugs, and detecting blackhat links and plagiarized content on the web. In a couple of later updates, Google reinforced the “intent” mechanism. Using advanced ML, it mapped the search query language with underlying NLP processors to satisfy user intent and make Google more reliable as an engine.
Earlier AI updates like neural matching, Hummingbird, RankBrain, and BERT were focused on technical SEO and structured data alignment. They gave headroom for organic content and expert-written content. But with generative AI, the focus shifts to what is best for the user to see, regardless of it being organic or sponsored. Google aims to achieve the unimaginable by turning SERP into a distributed social and community network. With this in-depth SEO technique, users will be exposed to recent trends and news in the particular industry they’re looking for.
Google will not only minimize research efforts but also provide a wealth of information with AI.
"AI will impact every product across every company. For example, if you think about 5 to 10 years from now, you are going to have an AI collaborator with you. Let's say you have a hundred things to go through, it may say, "these are the most serious cases you need to look at first."
CEO, Google Inc.
Google MUM’s impact on SEO
The good news for SEO marketers is that they can continue with their current analysis of how to make their websites rank better on Google. People are still debating on whether MUM will be a search engine ranking factor or simply a data-dispersing bridge.
To compete with the MUM update, brands need to bolster both organic and earned media strategies. While paid media doesn’t always give in CPCs, organic search, and SEO will help brands stay ahead. Even if a fair share of SERP does get affected by MUM, the highest-ranking pages and featured snippets will still be preferred.
Brands should start taking their on-page SEO strategies more seriously. Not just to rank higher but to identify their target audience and transfer learnings. Ideating and designing image packs, making introductory videos, and building awareness will help brands weather the MUM thunderstorm.
With MUM, newly sprung SEO strategies will come into play. Things-to-know sections, video search, visual search, zoom-ins, and voice search will lessen user tedium by giving them all answers in one place. At the same time, it is not a question-answer mechanism. Google aims to create a network of like-minded people to “go smart.”
“MUM” knows it all.
MUM is an ocean of knowledge, information, and understanding of sentiments. It’s the beginning of a new web search era. Nothing will be too complex on the web or in real life with MUM. This newfound theoretical machine-learning technique has led us to a new digital path.
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