How can marketers prepare for Google MUM?


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Marketers and SEO professionals monitor Google search engine updates with a mixture of anticipation and awe. Google announced its last search engine update in May, when it introduced the Uniform Multitasking Model (MUM) update. MUM is a multimodal algorithm designed to provide answers to complex queries by simultaneously evaluating information through multilingual text, images, video and audio. In September, Google followed up on its previous announcement of MUM during Search On, with further glimpses of how MUM might innovate the way people search for information.

Google MUM and a brief explanation of multimodal search

Since its launch in 1997, Google has consistently dominated the search engine market. Over the years, Google has made thousands of changes to its search, culminating in the current algorithm, BERT. BERT has improved voice search and added features that have revamped the presentation of information on the SERP. With MUM, Google introduced a unique machine learning model to take into account more complex queries and how information is deployed online.

A brief explanation on the importance of the multimodal model: Multimodal is a composite machine learning technique that compares and combines information from multiple sources to form a single answer. The ‘modal’ in multimodal refers to the aggregation of data within media, such as visual data from images and videos, linguistic data from text documents, and audio data from recordings. musical and sound. The modalities are integrated into the training dataset for machine learning models. Multimodal sentiment analysis, for example, can inspect various combinations of text, audio, and visual data to assess sentiment toward an event or occurrence. With MUM, Google treats media as modalities to improve the user experience with its search.

The choice of multimodal models is suitable for Google due to the growing number of non-textual sources, such as video in the form of live streams or the like, and audio files, as in the case of podcasts. To develop MUM, Google trained the algorithm “in 75 different languages ​​and many different tasks at once” to refine its understanding of digital information and details. MUM also takes into account knowledge in all languages, comparing a query to sources that are not written in the user’s native language to provide better information accuracy. As a result, Google claims that MUM is 1,000 times more powerful than BERT.

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What MUM means for SEO, Google … and you

MUM complements a larger trend in the use of multiple forms of media as a method of online communication. Marketers increasingly deploy a variety of media to communicate with customers. Through MUM, Google will revitalize the way it connects people to information about a given brand, potentially repositioning search as a competitor to social media platforms, which people often use to engage brands.

For marketers, adding MUM to search will require further refinement of content marketing strategies, ensuring proper labeling of audio and video files, and creative thinking about how to coordinate content across platforms that appear in search results.

For Google, MUM means to upgrade media match from different platforms that appear in search results. Over the past few years, I’ve been explaining how posts from Pinterest and YouTube can be part of SEO query considerations. MUM is an evolution of that tactic, so marketers should be more savvy about how their white papers, podcasts, memes, and posts are deployed.

MUM is also giving Google the opportunity to address some public concerns about machine learning bias. With its significant technological investment, Google is optimistic that the improved modeling of MUM across the media can minimize bias in search results.

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What’s next for MUM

Google will continue to invest in MUM by launching a variety of updates for search-based products, such as Search Console and Google Analytics 360. MUM’s first notable application will be with Google Lens, a recognition application for MUM. images available on Android. Phone (s. Marketers will soon see more “features and improvements powered by MUM.” In the meantime, Google will continue to test and refine MUM to address a number of concerns, including applying its latest research to how to reduce the carbon footprint of machine learning training systems Most industry experts see MUM as the successor to BERT.

Marketers need to recognize that their search and content strategies require strong and consistent identification when launched online. The ability to link images, videos and supporting documents will therefore be more critical in capturing the attention of MUM and potential customers.

Pierre DeBois is the founder of Zimana, a digital analysis consulting firm for small businesses. It examines data from web analytics and social media dashboard solutions, then provides web development recommendations and actions that improve business marketing strategy and profitability.

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