Envy that handbag of the random lady sitting next to you? Found yourself in a new town and don’t know where they serve the best coffee? Got a song stuck in your head? You know where to find answers – Google! ‘Google Search’ is the ultimate solution that has been serving humanity for two decades now. With its classic search features, Google has transformed our everyday lives. Taking it up a notch, Google is launching its one-of-a-kind AI feature – Google MUM. Multitask Unified Model aka Google MUM, is the newest addition to the Google Search engine.
MUM model in a Nutshell
Google MUM or Multitask Unified Model is an algorithm created to search the internet across different languages, text, images and media to help users find answers to detailed questions.
MUM is a transformer-based AI model that has been trained in images + text. This will revolutionize the way Google Search & Google Lens are used.
MUM offers solutions beyond text, based on multimedia such as images, videos, and podcasts. The focus on multimedia means that users can get more specific with their queries instead of relying solely on SEO-friendly language and keywords.
MUM algorithm in Detail
The Google MUMalgorithm is superior because it helps Google give unified results for complex searches. If a user has a complex query, Google will literally run the globe to find an answer that best suits the query.
Pros of Google MUM
1000x more powerful than BERT Focused on multitasking with different elements – text, image and multimedia.
Not only provides exact information but also suggests related information (like FAQs)
Integrates with Google Lens Provides an additional layout in SERP with the Things To Know column
Removes language barriers
Has the added feature of “Refine Search” and “Broaden Search” between the search results.
Making Multimodal Search Possible with MUM
From text to speech, audio, visuals, the Google MUM update offers humanity a way to find things easily.
It is described as one of the first AI models that is truly multimodal as it can simultaneously perceive information across multiple formats like texts, videos, and images.
Not only does Google MUM understand the core intent of a complex query, it also allows you to modify your search and take you closer to what you actually want. Google MUM can understand and translate 75 different languages.
Search in-store inventory from home
A fan of casual window shopping? Google MUM will allow the user to know the availability of the product that you see on the screen. Just hover over the product and the user will get the details like availability of the product in nearest stores, the price, the store timings, related products, etc. And all of this from the comfort of your abode!
Shoppable Photos With Google Lens
Users will be able to snap a photo and ask a question using Google Lens (like finding a bag using an image of your friend’s bag!) Photos become shoppable items.
For example: You like a friend’s necklace design in one of the old photos you are randomly skimming through. You fire up that photo in Google lens and the Google MUM update shows you similar necklace patterns from that image. But what if you want a bracelet with that necklace design imprinted on it? Bam!
This is where Google MUM comes into the picture. Google’s MUM allows you to modify your search, add a text query related to a bracelet, mix and match the combination of your search and help you find what you are looking for.
You will see results from nearby shops, large chains, and online retailers that sell bracelets with the similar necklace design you were looking for. Thus, Google MUM not only allows you to broaden your options but takes you closer to your exact search items.
Window shop right from Search
Aside from adding knowledge and simplifying the general search for a common user, Google MUM aims at making shopping an easier task for the user. Google will be introducing a new button that will enable the user to instantly shop the image they are looking at on the screen.
Google understands the three phases of an online shopper, which are –
Inspiration
Exploration
Purchase
Google Lens has been modified with Google MUM and thus, provides the buyer with an array of options to explore.
Neural nets for Subtopics
MUM breaks the language barrier and helps pull results from varied sites. Users also have the option to “refine this search” and “broaden this,” leading them to more useful answers.
More than Before in SERP
The latest Google MUM update offers better results with refined and broadened search results. Google MUM has expanded its search results to 75 different languages, text, images, and media to help users find answers to detailed questions.
More Insights About the Result
Google is no longer limited to giving you a high-end shopping experience but brings the world closer by giving you access to endless knowledge. Before the Google MUM-embedded search engine, if you typed ‘pottery’ on Google, the search engine would have given you relevant pottery videos, the history of pottery, so on and so forth.
Here’s how Google MUM further adds knowledge to your search. If you are a beginner at pottery, you will see step-by-step instructions, the list of the apparatus required, etc. But if you are an expert in the field, Google MUM also anticipates that you would need more in-depth information about pottery, and so you would also find different techniques and new advancements in pottery technology.
Along with this, you will find the usual relevant information like the famous blogs on pottery, famous pottery articles and artists, etc.
The idea is to revolutionize the search page and compel the user to explore, think about new possibilities, generate opinions and get a better understanding of the search.
Advanced Video Information
A large number of people heavily rely on Google to check facts and dig deeper into those facts. In order to make it easier for the user to check the credibility of the information, Google MUM will hence offer relevant video information about your query. This feature will enable users to dig deep.
When will Google MUM be implemented?
It’s Google and nobody really knows the answer to this question! It’s hard to predict whether the new solution will be introduced in a few months or a few years. At the moment, it’s being tested and refined. We can only speculate until we know that the work is finished.
Even if Google announces the date of launching MUM, we can’t take it for granted. Google has numerously postponed the implementation of previously announced changes.
But there’s no harm in readying yourself for the update: you can start by improving the quality and scale of your media content, integrate schema markup, and work on your knowledge graph – this ensures your audience gets the answers they are looking for!
The idea behind Google MUM is to revolutionize the search page and compel the user to explore, think about new possibilities, generate opinions and get a better understanding of the search. And when your audience does run a search, creating MUM-friendly pages and content will ensure you will be found.
Google MUM represents a fundamental shift from keyword matching to genuine intent understanding across various media formats. This is critical because it allows the search engine to answer nuanced, multi-layered questions that a person might ask, mirroring human conversation rather than robotic data retrieval. It moves beyond text to interpret the user’s core need, whether expressed through images, text, or a combination.
The model's power comes from its *truly multimodal capabilities* and deep learning architecture:
It simultaneously processes and understands information from text, images, and videos in a unified way.
It grasps context and subtext, providing related information and anticipating next steps, as seen in the 'Things To Know' feature.
Trained across 75 different languages, it dismantles information silos, retrieving answers from sources regardless of their original language.
This evolution requires a strategic pivot from pure keyword optimization to creating rich, interconnected content. Discover how this new model interprets user intent by reading the full analysis.
The Google MUM update aims to create a truly global information ecosystem by understanding concepts and context, not just translating words literally. This means users can receive relevant search results from content written in other languages, even if their query is in English, breaking down long-standing barriers to knowledge. It essentially makes the entire web accessible, regardless of linguistic differences.
This is achieved through its advanced AI training, which allows it to:
Identify the core intent of a query and find answers in other languages that match the concept.
Generate answers by synthesizing information from multiple languages into a single, cohesive result.
Present information from global sources that would have previously been invisible due to the language mismatch.
For users, this opens up a world of niche information and diverse perspectives. Learn more about how this global reach impacts content strategy by exploring the complete article.
The 'Things To Know' column demonstrates Google MUM's ability to think ahead and understand the user's entire discovery journey, not just a single query. It functions as an intelligent guide, anticipating related topics and subsequent questions to provide a holistic overview, which prevents users from needing to perform multiple follow-up searches. This feature transforms the SERP from a list of links into an interactive knowledge panel.
The effectiveness of this feature stems from MUM's deep contextual understanding, enabling it to:
Analyze the primary query to map out related concepts and common informational needs.
Identify adjacent topics and sub-topics that a user is likely to explore next.
Present this information in an organized, easy-to-digest format directly within the search results.
This proactive approach to information delivery signals a major shift in how Google evaluates content value. To understand how to structure your content to appear in these features, read the full post.
While BERT was a major leap in understanding language context, Google MUM is exponentially more powerful because it is genuinely multimodal, not just text-focused. The key advancement is its ability to process and connect information from different formats, like images and text, simultaneously within a single query. This allows it to deliver highly specific and relevant results for complex e-commerce needs that combine visual desire with textual modifiers.
The primary differences impacting search quality include:
True Multimodality: MUM understands an image and a text query together, whereas BERT primarily analyzes text.
Power and Scale: The new model is stated to be 1000x more powerful, enabling it to grasp much more complex relationships between concepts.
Task Unification: It can multitask, understanding and generating language, which leads to more synthesized and helpful answers instead of just retrieved snippets.
For e-commerce, this means users can finally search the way they think. See how this technology is changing online retail by checking out the full article.
This example perfectly illustrates Google MUM's core strength, which is its capacity to synthesize multimodal inputs into a single, actionable search. Instead of just finding visually similar necklaces, the user can layer a text query ('for a bracelet') on top of an image search, demonstrating a level of conversational refinement that was previously impossible. This turns a vague inspiration into a precise, shoppable request.
The technology makes this happen by following a clear process:
First, Google Lens analyzes the initial photo to identify key visual elements, like the necklace's specific pattern.
Next, Google MUM processes the added text query ('bracelet') to understand the user wants to apply that pattern to a different product type.
Finally, it searches its vast index of product images and data to find bracelets that match the specified visual design.
This powerful combination of visual and text search fundamentally changes online product discovery. Dive deeper into how this impacts e-commerce strategy in the complete guide.
Google MUM bridges the gap between online discovery and offline purchasing by connecting a user's visual search to structured data from local retailers. When you hover over a product in an image, MUM not only identifies the item but also cross-references it with local inventory feeds that retailers provide to Google. This transforms a simple image into a powerful local shopping tool.
The process works through a sophisticated integration of different data sources:
The user initiates a search with an image or a product they see on screen.
MUM's visual recognition capabilities identify the specific product, including brand and model.
The algorithm then queries Google's Shopping Graph, which contains real-time inventory data from participating local stores.
The results display availability, price, and store details, enabling a seamless transition from online browsing to an in-store purchase.
This feature is a game-changer for local businesses. Explore more about capitalizing on this technology in the full article.
To capitalize on Google MUM, an online retailer must provide rich, structured data that gives the AI clear context for its products and images. Your goal is to move beyond simple alt-text and create a comprehensive data ecosystem around each visual asset, making it easy for the algorithm to connect your products to complex, multimodal user queries.
A practical approach involves these key steps:
Implement Detailed Product Schema: Use structured data to clearly label every product attribute, such as color, material, style, and dimensions, connecting it to high-quality images.
Optimize Image Quality and Context: Provide multiple high-resolution images showing the product from various angles and in real-world use cases.
Maintain Accurate Inventory Feeds: Ensure your Google Merchant Center feed is constantly updated with real-time stock levels and pricing for local and online availability.
Develop Descriptive, Natural Language Content: Create product descriptions that answer common user questions and reflect conversational language.
Preparing your digital storefront for this AI-driven future is crucial for visibility. The full post offers more detailed guidance on optimizing for these changes.
The rise of AI like Google MUM will shift user behavior toward more conversational, multi-layered, and visually-led queries. Users will expect search engines to understand complex needs without requiring them to break down questions into simple keywords, leading to longer and more natural search inputs. For marketers, this means the era of targeting short-tail keywords is fading.
To stay visible, your SEO strategy must evolve to prioritize *topical authority and contextual relevance*:
Focus on creating comprehensive content hubs that cover a subject from multiple angles, incorporating text, images, and videos.
Optimize images and videos with descriptive metadata and structured data to make them machine-readable.
Build content that directly answers complex, long-tail questions that reflect natural human curiosity.
Prioritize user experience and ensure your content provides direct, valuable solutions to anticipated problems.
Adapting to this new paradigm is no longer optional. Discover more future-proofing strategies in the complete article.
The long-term implication is that *content quality and format diversity will replace keyword density* as the primary drivers of search performance. Google MUM's ability to understand context across text, images, and video means it will reward creators who provide the most comprehensive and genuinely helpful answer, regardless of how well it is optimized for a specific term. This pushes the industry toward creating true user-centric experiences.
Content creators should prepare for a future where success depends on:
Developing deep expertise and demonstrating authority on a topic through various media formats.
Creating interconnected content that guides users through a complete learning or shopping journey.
Producing high-quality visual and video assets that add unique value, not just decorate a page.
Writing for human understanding and natural language, focusing on answering complex questions thoroughly.
This shift demands a more strategic and integrated approach to content. The full article explores how to build these multimedia experiences effectively.
Google MUM directly solves this limitation by allowing users to search in a more natural, human way, combining the things they can see with the things they can describe. This hybrid approach overcomes the constraints of a text-only vocabulary, where it can be difficult or impossible to articulate a visual concept accurately. It bridges the gap between seeing something you like and being able to find it.
This problem-solving capability is built on its unique architecture:
It accepts an image as the core of the query, establishing the primary visual context.
It allows users to add text modifiers to refine, alter, or specify their intent (e.g., 'in a different color' or 'as a scarf').
This *conversational refinement* process narrows down possibilities far more effectively than sequential keyword searches.
This innovation makes search more intuitive for everyone. To see more examples of how it addresses user frustrations, explore the full post.
The shoppable photo feature, powered by Google MUM and Google Lens, directly closes the gap between inspiration and transaction. It solves the frustrating dead-end of seeing a product in a social media post or a random photo with no information on how to purchase it. By making images interactive and queryable, it transforms passive visual content into an active starting point for e-commerce.
This solution effectively removes friction from the customer journey by:
Allowing users to initiate a search directly from any image they find or capture.
Using powerful AI to identify the specific product or visually similar alternatives.
Instantly providing links to online retailers and information on local store availability.
Enabling further refinement with text to find precisely what the user wants.
This capability creates a seamless path from discovery to purchase. The full article provides more insight into how retailers can leverage this powerful tool.
The primary challenge is that Google MUM makes individual keywords less important than the overall topical context and user intent. A strategy built solely on ranking for specific high-volume keywords will fail because MUM is designed to answer complex questions by synthesizing information from multiple sources, not just by matching strings of text. This fundamentally disrupts traditional keyword-centric SEO.
To adapt and succeed, marketers must shift their focus from *keywords to topics*:
Conduct research to understand the full spectrum of questions and problems your audience has around a core subject.
Develop comprehensive content clusters that cover a topic in-depth, using various media formats.
Structure content logically with clear headings and use structured data to give search engines contextual clues.
Prioritize creating genuinely helpful resources that satisfy user intent rather than just targeting popular search terms.
This new landscape requires a more holistic and user-focused strategy. Read our complete analysis to learn how to build a future-proof SEO plan.
Chandala Takalkar is a young content marketer and creative with experience in content, copy, corporate communications, and design. A digital native, she has the ability to craft content and copy that suits the medium and connects. Prior to Team upGrowth, she worked as an English trainer. Her experience includes all forms of copy and content writing, from Social Media communication to email marketing.
Growth Strategy and Planning
