Nov 21 2024
YouTube Takes On TikTok With New ‘Jewels’ Tipping System via @sejournal, @MattGSouthern

YouTube is introducing “Gifts, Powered by Jewels,” which lets people support creators during vertical livestreams.

This signals YouTube’s push into the virtual gifting space that TikTok has capitalized on for years.

Key Features

“Gifts, Powered by Jewels,” is available for members of the YouTube Partner Program in the United States and offers new ways to earn money from live content.

The new gifting system introduces two virtual currencies: Jewels for viewers and Rubies for creators.

Viewers can purchase Jewels in bundles to send animated gifts that appear as overlays during vertical livestreams.

This feature is similar to TikTok’s popular gift animations that occur during live sessions.

Earnings

Creators earn Rubies from these gifts, with each Ruby worth one cent in real currency.

For example, if a creator receives 100 Rubies, they earn $1.

To incentivize adoption, YouTube offers a 50% bonus on gift earnings (up to $1,000 per month) to qualified creators for the first three months.

This bonus structure appears designed to attract creators who might currently be prioritizing TikTok livestreams for their virtual gifting revenue.

Technical Requirements

The feature is currently restricted to:

  • Vertical format livestreams only
  • U.S.-based creators in the YouTube Partner Program
  • Creators who have accepted the Virtual Items Module
  • U.S.-based viewers for purchasing Jewels

Only mobile app users can buy and send gifts, but creators can receive them while streaming directly on YouTube or through third-party software.

Retiring Super Stickers

YouTube is phasing out Super Stickers for vertical livestreams with the introduction of gifts.

Once creators enable gifts on their channels, Super Stickers will no longer be available for their vertical live content.

Unlike traditional monetization features such as Super Chats and channel memberships, gifts don’t have a fixed revenue share since their prices can vary based on bundle pricing and promotional offers.

Looking Ahead

YouTube is beta testing “Gifts, Powered by Jewels” in the United States and plans to roll it out to more countries soon.

You can enable gifting through the Earn hub in YouTube Studio.

This launch represents YouTube’s direct challenge to TikTok’s live streaming monetization.

While TikTok has the edge in virtual gifting, YouTube’s large user base and monetization infrastructure could make it a strong competitor.

Featured Image: Screenshot from support.google.com, November 2024. 

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Nov 20 2024
Wix Rolling Out AI-Powered Site Planning & Visualization Tool via @sejournal, @martinibuster

Wix announced it is rolling out an AI-based tool that simplifies the site planning and visualization step, dramatically compressing the time from planning to site rollout.  The  new tool enables agencies and enterprise users to create a visual map and wireframe website representation for planning new websites, simplifying one of the most fundamental tasks of creating a high performing website.

Wix

Wix is a cloud-based website builder for small to medium size businesses to enterprise level companies that handles all of the back-end technology necessary for creating a professional online web presence with a state of the art website performance and search optimization features. It offers complete customization, marketing and integrations with Google business features that small businesses, agencies and advanced users require.

Site Planning And Visualization

One of the first steps for creating a high performance website that is user friendly, easy to navigate and search optimized is planning the site structure. This is important for every website but especially important for large websites with thousands of products or topics. A taxonomical topic structure that makes it easy for users to locate what they’re looking for begins with creating a visual representation of major category sections with hierarchical nodes that represent subcategories and all of the associated web pages.

Wix’s new tool is an AI-powered tool that can create the visualization after users input the project details. The visual representation allows agencies and designers to view what the site structure will look like and make decisions ahead of time. The visual representation can then be exported to share with clients and stakeholders. The AI can even pre-insert content suggestions. The resulting visual representation can be fully customized and edited. A task that ordinarily can take weeks to months is compressed to days.

Wix explains how it works:

“Agencies and web professionals can input project details, including business type, site description, goals, target audience, and tone of voice. After filling in the information, a tailored sitemap structure is created detailing pages and sections.

If preferred, bespoke wireframes can be generated to kick off the creation process. Both the tailor-made visual sitemap and wireframes are created with pages, sections and relevant business applications.”

The tool’s built-in collaboration functions can reflect changes made to the sitemap in real-time, speeding up the process of getting project buy-in and moving forward.

Read Wix’s announcement:

Visual Sitemap And Wireframe Generator: Site planning, accelerated

Featured Image by Shutterstock/Graphic farm

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Nov 20 2024
LinkedIn Report: Most In-Demand Marketing Jobs & Skills via @sejournal, @MattGSouthern

LinkedIn’s Marketing Jobs Outlook report reveals a rebound in industry job postings, with a 76% increase compared to last year.

The report also identifies the most in-demand marketing roles across regions and experience levels, providing a roadmap for those looking to make career moves.

Whether you’re a seasoned executive or just starting out in the field, understanding these trends can help you position yourself for success.

Report Highlights

Most In-Demand Marketing Roles

According to LinkedIn’s data, these are the marketing positions employers are most actively hiring for now:

North America (NAMER)

  • Early Career: Social Media Manager
  • Mid-Career: Marketing Manager
  • Seasoned: Marketing Director

Europe, Middle East & Africa (EMEA)

  • Early Career: Marketing Specialist
  • Mid-Career: Social Media Manager
  • Seasoned: Head of Marketing

Asia Pacific (APAC)

  • Early Career: Digital Marketing Specialist
  • Mid-Career: Marketing Manager
  • Seasoned: Marketing Director

Latin America (LATAM)

  • Early Career: Community Manager
  • Mid-Career: Marketing Analyst
  • Seasoned: Promoter

B2B Marketing Jobs See 21% Growth

While the overall marketing job market saw a 76% year-over-year increase, B2B marketing roles grew by 21%.

This suggests that despite the more modest growth compared to the broader industry, opportunities are expanding again in the B2B space after last year’s slump.

Marketers Satisfied (But Open to New Opportunities)

The report found a 91% job satisfaction rate among B2B marketers, especially those in executive positions.

However, 55% said they are either actively job searching or would consider leaving for the right opportunity.

Rapid Change Creates Overwhelm

The marketing field is changing quickly, and 72% of professionals feel overwhelmed by these changes.

More than half worry about falling behind if they don’t keep up.

This shift is due to the rapid growth of artificial intelligence (AI), and most professionals expect it to significantly impact their work soon.

The report notes that “to stay ahead of the curve, marketers are embracing continual learning,” with 51%seeking guidance on skills to develop.

Collaborative Problem-Solving: The Skill of the Year

With AI taking on more routine tasks, human-centric skills are more crucial than ever.

LinkedIn named “Collaborative Problem-Solving” the top marketing skill of the year, and it has grown by 138% since 2021.

Key technical skills marketers are growing include Creative Execution (443% increase), Artificial Intelligence (392% increase), and Marketing Technology (351% increase).

What Does This Mean For Marketers?

LinkedIn’s new Marketing Jobs Outlook seems promising, but what should marketers do with these insights?

Here’s the breakdown.

Polish Your Profile (Even if You’re Not Looking)

Most B2B marketers are happy where they are, but over half would jump ship for the right gig.

Keep your resume and LinkedIn fresh in case that dream job pops up.

Embrace the Chaos

Marketing moves fast, and most feel overwhelmed by the constant change.

The solution? Never stop learning. Dive into training on the latest skills and tech to stay caught up.

Balance Tech Savvy With People Skills

You can’t escape AI in marketing now. Get comfy with these tools, but don’t sleep on skills like collaboration and creative problem-solving.

As AI handles the routine stuff, these “human” skills will set you apart.

Target High-Growth Roles

Are you eyeing a career move? Social Media Manager, Marketing Manager, and Director roles are hot in North America.

In EMEA, aim for Marketing Specialist or Head of Marketing jobs.

Stay Flexible

Is your niche growing slower than others? Don’t stress. Remote work means more options across locations.

Plus, your marketing chops likely transfer to other industries—pivot as needed.

Full Report

You can explore LinkedIn’s Fall Marketing Jobs Outlook report for insights, in-demand skills, and career tips.

Featured Image: Primakov/Shutterstock

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Nov 20 2024
GraphRAG Update Improves AI Search Results via @sejournal, @martinibuster

Microsoft announced an update to GraphRAG that improves AI search engines’ ability to provide specific and comprehensive answers while using less resources. This update speeds up LLM processing and increases accuracy.

The Difference Between RAG And GraphRAG

RAG (Retrieval Augmented Generation) combines a large language model (LLM) with a search index (or database) to generate responses to search queries. The search index grounds the language model with fresh and relevant data. This reduces the possibility of AI search engine providing outdated or hallucinated answers.

GraphRAG improves on RAG by using a knowledge graph created from a search index to then generate summaries referred to as community reports.

GraphRAG Uses A Two-Step Process:

Step 1: Indexing Engine
The indexing engine segments the search index into thematic communities formed around related topics. These communities are connected by entities (e.g., people, places, or concepts) and the relationships between them, forming a hierarchical knowledge graph. The LLM then creates a summary for each community, referred to as a Community Report. This is the hierarchical knowledge graph that GraphRAG creates, with each level of the hierarchical structure representing a summarization.

There’s a misconception that GraphRAG uses knowledge graphs. While that’s partially true, it leaves out the most important part: GraphRAG creates knowledge graphs from unstructured data like web pages in the Indexing Engine step. This process of transforming raw data into structured knowledge is what sets GraphRAG apart from RAG, which relies on retrieving and summarizing information without building a hierarchical graph.

Step 2: Query Step
In the second step the GraphRAG uses the knowledge graph it created to provide context to the LLM so that it can more accurately answer a question.

Microsoft explains that Retrieval Augmented Generation (RAG) struggles to retrieve information that’s based on a topic because it’s only looking at semantic relationships.

GraphRAG outperforms RAG by first transforming all documents in its search index into a knowledge graph that hierarchically organizes topics and subtopics (themes) into increasingly specific layers. While RAG relies on semantic relationships to find answers, GraphRAG uses thematic similarity, enabling it to locate answers even when semantically related keywords are absent in the document.

This is how the original GraphRAG announcement explains it:

“Baseline RAG struggles with queries that require aggregation of information across the dataset to compose an answer. Queries such as “What are the top 5 themes in the data?” perform terribly because baseline RAG relies on a vector search of semantically similar text content within the dataset. There is nothing in the query to direct it to the correct information.

However, with GraphRAG we can answer such questions, because the structure of the LLM-generated knowledge graph tells us about the structure (and thus themes) of the dataset as a whole. This allows the private dataset to be organized into meaningful semantic clusters that are pre-summarized. The LLM uses these clusters to summarize these themes when responding to a user query.”

Update To GraphRAG

To recap, GraphRAG creates a knowledge graph from the search index. A “community” refers to a group of related segments or documents clustered based on topical similarity, and a “community report” is the summary generated by the LLM for each community.

The original version of GraphRAG was inefficient because it processed all community reports, including irrelevant lower-level summaries, regardless of their relevance to the search query. Microsoft describes this as a “static” approach since it lacks dynamic filtering.

The updated GraphRAG introduces “dynamic community selection,” which evaluates the relevance of each community report. Irrelevant reports and their sub-communities are removed, improving efficiency and precision by focusing only on relevant information.

Microsoft explains:

“Here, we introduce dynamic community selection to the global search algorithm, which leverages the knowledge graph structure of the indexed dataset. Starting from the root of the knowledge graph, we use an LLM to rate how relevant a community report is in answering the user question. If the report is deemed irrelevant, we simply remove it and its nodes (or sub-communities) from the search process. On the other hand, if the report is deemed relevant, we then traverse down its child nodes and repeat the operation. Finally, only relevant reports are passed to the map-reduce operation to generate the response to the user. “

Takeaways: Results Of Updated GraphRAG

Microsoft tested the new version of GraphRAG and concluded that it resulted in a 77% reduction in computational costs, specifically the token cost when processed by the LLM. Tokens are the basic units of text that are processed by LLMs. The improved GraphRAG is able to use a smaller LLM, further reducing costs without compromising the quality of the results.

The positive impacts on search results quality are:

  • Dynamic search provides responses that are more specific information.
  • Responses makes more references to source material, which improves the credibility of the responses.
  • Results are more comprehensive and specific to the user’s query, which helps to avoid offering too much information.

Dynamic community selection in GraphRAG improves search results quality by generating responses that are more specific, relevant, and supported by source material.

Read Microsoft’s announcement:

GraphRAG: Improving global search via dynamic community selection

Featured Image by Shutterstock/N Universe

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Nov 19 2024
Google: Page-Level & Site-Wide Signals Both Matter For Rankings via @sejournal, @MattGSouthern

Google updates search documentation to clarify how both page-level and site-wide signals influence ranking in search results.

  • Google’s ranking systems evaluate content primarily at the page level, but site-wide signals also matter.
  • Good site-wide signals won’t guarantee high rankings for all pages, and poor site-wide signals won’t doom all pages.
  • This documentation update clarifies existing practices rather than introducing new ranking factors.
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Nov 19 2024
Google Clarifies Site Reputation Abuse Policy via @sejournal, @MattGSouthern

Google has issued new clarification for its site reputation abuse policy, first launched earlier this year, which targets “parasite SEO” practices where websites leverage established domains to manipulate search rankings through third-party content.

Chris Nelson from the Google Search Quality team states:

“We’ve heard very clearly from users that site reputation abuse – commonly referred to as ‘parasite SEO’ – leads to a bad search experience for people, and today’s policy update helps to crack down on this behavior.”

Policy Clarification

The updated policy states that using third-party content to exploit a site’s ranking signals violates Google’s guidelines, regardless of first-party involvement or oversight.

This clarification comes after Google’s review of various business arrangements, including white-label services, licensing agreements, and partial ownership structures.

The updated policy language states:

“Site reputation abuse is the practice of publishing third-party pages on a site in an attempt to abuse search rankings by taking advantage of the host site’s ranking signals.”

Policy Details

What’s A Violation?

Google outlines several examples of policy violations, including:

  • Educational sites hosting third-party payday loan reviews
  • Medical sites publishing unrelated content about casino reviews
  • Movie review sites featuring content about social media services
  • Sports websites hosting third-party supplement reviews without editorial oversight
  • News sites publishing coupon content from third parties without proper involvement

What’s Not A Violation?

Google acknowledges there’s a difference between abusive practices and legitimate third-party content.

Acceptable examples include:

  • Wire service and syndicated news content
  • User-generated content on forum websites
  • Editorial content with close host site involvement
  • Properly disclosed advertorial content
  • Standard advertising units and affiliate links

Background

Enforcement of the site reputation abuse policy began in May.

The rollout is having a notable impact in the news and publishing industry, as documented by Olga Zarr.

Major organizations including CNN, USA Today, and LA Times were among the first to receive manual penalties, primarily for hosting third-party coupon and promotional content.

Glenn Gabe shared early observations:

The recovery process has shown clear patterns: sites that removed offending content or implemented noindex tags on affected sections have started seeing their manual actions lifted. However, ranking recovery takes time as Google’s crawlers need to process these changes.

Looking Ahead

While enforcement relies on manual actions, Google has indicated plans for algorithmic updates to automate the detection and demotion of site reputation abuse, though no specific timeline has been announced.

Site owners found in violation will receive notifications through Search Console and can submit reconsideration requests.

Featured Image: JarTee/Shutterstock

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Nov 19 2024
Google Rolls Out AI-Powered In-Store Shopping Tools via @sejournal, @MattGSouthern

Google announced new features for in-store shopping and expanded payment options, marking changes to its retail technology offerings.

Key Updates

Google Lens

Google Lens, which reportedly processes 20 billion searches monthly, will enable users to photograph products in stores to find price comparisons and reviews.

The system uses Google’s product database of over 45 billion listings and its Gemini AI models.

Google announcement states:

“This new update is made possible by major advancements in our AI image recognition technology. It’s powered by the Shopping Graph’s 45 billion+ product listings, in-stock inventory data from a range of retailers and our Gemini models to bring you an entirely new way to shop in-store.”

Internal research cited by the company suggests that 72% of Americans use smartphones while shopping in physical stores.

The feature will initially be launched for beauty products, toys, and electronics at participating retailers in the United States.

Users must opt into location sharing through the Google app on Android or iOS to access the functionality.

In a related development, Google Maps will incorporate product search capabilities, allowing users to locate specific items at nearby stores.

Security Measures

Google also announced plans to test new fraud detection services for merchants.

The system aims to identify fraudulent transactions better while reducing false positives that may block legitimate purchases.

Google explains:

“We’re always working to protect consumers and businesses from fraud, which is forecasted to grow substantially in the coming years. Soon we’ll begin piloting a service to help merchants better identify fraudulent transactions and help prevent fraudsters from using stolen financial information. This will also help unblock good transactions that may be mistaken as fraud.”

Looking Ahead

The announcements come as retailers prepare for increased holiday shopping activity.

According to company statements, the features are expected to roll out gradually over the coming weeks.

The timing coincides with broader industry efforts to integrate AI technology into retail experiences while addressing growing concerns about payment security.

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Nov 18 2024
Google Analytics 4 (GA4) Users Report Data Collection Issues via @sejournal, @MattGSouthern

Google Analytics 4 (GA4) users report data collection issues affecting websites globally, with many experiencing up to 50% drops in reported traffic since November 13.

The problem has sparked discussions across Google’s support forums and social media platforms.

Key Issues

Multiple website owners have documented discrepancies between GA4 reports and actual traffic levels.

While GA4 shows reduced numbers, cross-referencing with Google Search Console and other analytics platforms confirms normal traffic levels.

One user explained the severity of the issue:

“The incomplete data is there since 13th November which shows only 4445 users when in actual (looking at Search and Discover in GSC), I am calculating more than 13,000 users (at least).”

Real-time tracking appears unaffected, suggesting the issue impacts historical data.

Technical Details

Investigations reveal that data flows to BigQuery for users with connected accounts.

However, this only provides a partial solution, as many GA4 users don’t utilize BigQuery integration.

The timing coincides with Google’s mid-November attribution system updates, though no direct connection has been confirmed.

Affected metrics Include:

  • Overall traffic volumes
  • Channel attribution data
  • Landing page metrics
  • Event tracking

Site owners from multiple countries, including Taiwan and various European regions, report identical patterns of data loss beginning November 13:

“Taiwan is experiencing the same issue. On 11/13, there was a sudden drop in traffic, and from 11/14 to 11/17, it decreased by 20-30% compared to the same period last month.”

People note that while their real-time analytics show expected traffic levels, historical data since November 13 reflects only about half of their actual visitor numbers:

“I usually track the data from the day before yesterday on the current day. However, there’s only nearly 50% traffic on my website. Just want to know is there anyone with the same situation as me?”

Why This Matters

This disruption poses challenges for organizations relying on GA4 for business intelligence and reporting.

Many companies face difficulties in performance analysis and decision-making processes without accurate historical data.

Despite numerous support threads and community discussions, Google hasn’t officially addressed the situation or indicated whether the missing data will be retroactively restored to affected accounts.

We will continue to monitor this situation and provide updates as information becomes available.

Featured Image: MacroEcon/Shutterstock

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Nov 16 2024
Google’s AI Search Experiment: “Learn About” via @sejournal, @martinibuster

Google has quietly introduced a new AI Search experiment called Learn About, which summarizes content and offers navigational menus to explore related subtopics. This new way of exploring content uses drill-down navigational menus called Interactive Lists and if the user scrolls down far enough they will eventually find links to human created content.

This new way of searching encourages exploration with an interface that continually presents additional summaries and links to human-created content. The experience resembles a children’s “choose your story” book, where the narrative shifts based on the reader’s decisions.

Google’s Learning Initiative

The Learn About AI Search is offered as part of Google Labs. It’s also a part of Google’s Learning Initiative. The Learning Initiative page offers links to Google Labs projects that are related to learning.

The Learning Initiative contains links to various projects:

  • Learn About
  • Shiffbot
  • Illuminate
  • NotebookLM

Pilot Program (early access to AI products for 12 and higher education)

Experiments for Learning (AI learning tools that students can use to create songs or travel virtually to Mars)

The Google Learning Initiative page describes Learn About:

“Learn About
Grasp new topics and deepen understanding with this adaptable, conversational, AI-powered learning companion.”

Interactive List User Interface

Learn About’s Interactive List exploration menus are illustrated with images, which is appealing because humans are visually oriented. That makes it faster to comprehend the written content because the image reinforces the text.

The images in the interactive menu appear to be licensed from stock image providers like Shutterstock, Adobe, and Alamy. None of the images appear to be sourced from creator websites.

Screenshot Of Interactive List Navigational Menu

Questions trigger a summary and a drill down navigational menu that’s called an Interactive List. These search results lead to related topics and progressively granular summaries, more Interactive Lists.

Beneath the Interactive Lists is a section called “Explore related content” that offers links to actual human created content like YouTube videos and website content.

Beneath the links to creator content is a group of buttons labeled with options to Simplify, Go deeper, or Get images. Beneath those three choices are speech balloons with additional search queries on related topics.

Screenshot Of Explore Related Content Section

There is also a left-hand navigational menu with an invitation to explore using Interactive List menu.

Screenshot Of Left-Hand Navigation

Availability Of Learn About

Learn About is only available to users who are 18 or older in the United States and is available in in English.

Interestingly, it also answers questions in Spanish but then quickly erases the Spanish answer and replaces it with a statement that it doesn’t speak that language yet. But if you ask it a question in English followed by another question in Spanish then it may answer the question in English and provide links to Spanish language human created content. As shown in the image below, Google Learn About will not only understand and answer a Spanish language query.

Learn about will also understand it when the query contains a typo. The query below contains a typo of the word “comer” which is missing the letter “r.”

The Spanish language query I tried was “Es posible a comer el ojo de un pescado” which means, “is it possible to eat the eye of a fish?”

Screenshot Of Spanish Language Query In Learn About

Privacy Controls

Google’s Learn About has privacy controls that are explained in a consent form that must be agreed to before using Learn About.

It contains information about how Google handles questions, a warning to not ask questions of a personal and private nature and details about managing the information saved by Learn About. It also says that human reviewers may access information shared with Learn About but that it will be stripped of identifying information.

The consent agreement explains:

“Google stores your Learn About activity with your Google Account for up to 18 months.

You can choose to delete your Learn About data any time by clicking the settings button next to your Google account profile photo in Learn About and then choosing “Delete activity”.

To help with quality and improve our products (such as generative machine-learning models that power Learn About), human reviewers read, annotate, and process your Learn About conversations. We take steps to protect your privacy as part of this process. This includes disconnecting your conversations with Learn About from your Google Account before reviewers see or annotate them.

Please don’t enter confidential information in your conversations or any data you wouldn’t want a reviewer to see or Google to use to improve our products, services, and machine-learning technologies.”

Google Learn About And SEO

There is no hint about whether this will eventually be integrated into Google Search. Given that it’s a part of Google’s Learning Initiative it’s possible that it could become a learning-only tool.

Try Learn About, an experimental project of Google Labs.

Featured Image by Shutterstock/Cast Of Thousands

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