MARKETING
Generative Engine Optimization Framework Introduced in New Research
There are several AI chatbot-like features available in the current search engines, including Bing Copilot, Google, Bard, and Gemini. They help to optimize the content visibility in the search results with the help of an AI-powered Search engine known as a Generative engine or AI Search.
A traditional search engine like Bing, Google, or Yahoo ranks and displays information in the SERPs based on the search terms a user inputs. 🔎
The generative engine, on the other hand, generates comprehensive, accurate, and relevant results and information with the help of Generative AI or Large Language Models (LLMs) such as chatGPT, Gemini, and Claude. They understand and integrate information from various sources for the user’s queries.
In this blog, We will discuss the GEO that is introduced in the new research, its framework, and how it can change traditional Search engine optimization (SEO) practices and optimize content for visibility.
The Key Components of the GEO Framework and How They Transform Traditional SEO Practices
GEO is described in the research paper as: “A novel paradigm to aid content creators in improving the visibility of their content in Generative Engine responses through a black-box optimization framework for optimizing and defining visibility metrics. We facilitate systematic evaluation in this new paradigm by introducing GEO-bench, a benchmark of diverse user queries across multiple domains, coupled with sources required to answer these queries.”
Traditional SEO depends upon the keyword volume, difficulty, and optimization for the specific search terms, which focus less on an interpretation relationship between the concepts of keywords or user queries. SEO practices prioritize text-based source content over other sources of content format where regular updating of fresh content is not a primary focus. Also, metrics like impression and click rates affect ranking system results in traditional methods.
GEO encourages detailed information over just the keyword, addressing the related main queries by creating depth content and potential subtopics with the understanding of concept and relationship, encouraging the other formats, such as visual, audio, and images, not just text-based. Moreover, it emphasizes the latest updated content information with continuous accuracy and relevance to provide the most accurate and up-to-date details.
The Impact of Introducing GEO on Website Ranking and Content Relevancy
A generative engine relies on traditional SEO practices like user intent and algorithms for ranking to a degree, such as keyword stuffing. Although it focuses on keywords, it tries to find connections and meanings beyond the keywords in order to create high-quality content.
GEO doesn’t directly indicate the web visibility or page ranking in the Search Engine Result Page. However, it can optimize the overall website visibility and indirectly drive user traffic to your websites through generated responsive data and information.
GEO-optimized content provides the AI Search or a Large Language Model (LLM) with reliable and completely detailed information, enabling them to generate the most accurate and relevant information for responses to user questions or inputs.
These AI-powered engines can deliver a vibrant user experience using optimized content for user engagement and interactive experiences. Furthermore, It also builds trust with a user as it relies on renowned and credible sources, which enhances the effectiveness and reliability of the generated response data and provides synthesizing information.
Comparison with Existing SEO Models: Why GEO Stands Out in Enhancing Search Engine Performance
GEO utilizes auto-generative algorithms for content generation based on predetermined objectives and standards where generated content can cover a broader range of keywords and related topics in various formats like image and visual.
A generative search engine uses modern optimization techniques that involve cognitive SEO, NLP (natural language processing), and structured data markup to maintain and improve content leverage, relevancy, and search engine visibility. In addition, it introduces new methods for determining citations’ importance and website visibility, as well as improving user-centric content by using impression metrics.
Traditional SEO models rely upon and use specific keywords to optimize and rank manually in search results. It uses traditional optimization techniques like link building, meta tags, and URLs.
In traditional search optimization, content creation and optimization can be slow and have low content scalability compared to AI-powered, requiring manual effort for generation and optimization. Constant monitoring and adaptation to platform algorithms are needed to produce the latest and updated information for dynamic user behavior.
Both are equally responsible for improving the brand or website’s online visibility; traditional SEO models require the manual touch for content creation and optimization. GEO tends to use generative responses automatically for content generation as per user queries, making it more effective for user-centric content creation, optimization, and stability in related topics or keywords.
9 Test research findings to improve the website content in GEO
The researchers from Princeton University, Georgia Tech, Allen Institute for AI, and IIT Delhi tested nine various GEO approaches to improve site content for generative engines. Techniques that have been tried and tested over 10,000 search queries, nine optimization strategies were tested on something that “closely resembles the design of BingChat”:
- Authoritative: The content was altered to be more compelling while conveying definitive claims.
- Keyword Stuffing: More keywords were added to match the query.
- Statistics Addition: Instead of a qualitative conversation, quantitative statistics were included.
- Sources: Relevant citations have been added. Like quotes statistics
- Quotation Addition: Quotations from reliable sources have been included.
- Easy-to-understand: Simplified the language.
- Fluency Optimisation: Improved fluency.
- Unique Words: Used in the text whenever possible.
- Technical terms: Technical terms have been incorporated into the content.
The data set for search queries was obtained from Google, Microsoft Bing, and Perplexity. Sources include AI Discover, GPT-4, and others.
So, focus on creating detailed and comprehensive blogs or articles by defining the relation and highlighting the context for deeper meaning. Utilize the various formats for content creation to enrich information and diversify the learning perspective.
Also, update your content with the latest information and trends to maintain regular effectiveness and relevancy in the generative engines.
Conclusion:
In the end, Generative Engine Optimization (GEO) provides a more automated, scalable, and adaptive method of content creation and optimization than traditional Search Engine Optimization (SEO) approaches, which need manual and constant work for the optimization and ranking. Compared to traditional search engines, generative engines give instant and detailed personalized information to users’ queries for improved engagement.
Conventional SEO uses metrics like impression, session duration, and click-through rate (CTR), whereas GEO proposes new metrics to measure the relevance and visibility of citations within generative engine responses, making users eliminate the need to visit individual websites for information as it generates the responses on users queries from the reliable, relevant, and various sources.
AI-powered search optimization is still developing and becoming popular since most users and business owners are using generative AI as their source of information and improved visibility with universally applicable diverse content formats.
MARKETING
YouTube Ad Specs, Sizes, and Examples [2024 Update]
Introduction
With billions of users each month, YouTube is the world’s second largest search engine and top website for video content. This makes it a great place for advertising. To succeed, advertisers need to follow the correct YouTube ad specifications. These rules help your ad reach more viewers, increasing the chance of gaining new customers and boosting brand awareness.
Types of YouTube Ads
Video Ads
- Description: These play before, during, or after a YouTube video on computers or mobile devices.
- Types:
- In-stream ads: Can be skippable or non-skippable.
- Bumper ads: Non-skippable, short ads that play before, during, or after a video.
Display Ads
- Description: These appear in different spots on YouTube and usually use text or static images.
- Note: YouTube does not support display image ads directly on its app, but these can be targeted to YouTube.com through Google Display Network (GDN).
Companion Banners
- Description: Appears to the right of the YouTube player on desktop.
- Requirement: Must be purchased alongside In-stream ads, Bumper ads, or In-feed ads.
In-feed Ads
- Description: Resemble videos with images, headlines, and text. They link to a public or unlisted YouTube video.
Outstream Ads
- Description: Mobile-only video ads that play outside of YouTube, on websites and apps within the Google video partner network.
Masthead Ads
- Description: Premium, high-visibility banner ads displayed at the top of the YouTube homepage for both desktop and mobile users.
YouTube Ad Specs by Type
Skippable In-stream Video Ads
- Placement: Before, during, or after a YouTube video.
- Resolution:
- Horizontal: 1920 x 1080px
- Vertical: 1080 x 1920px
- Square: 1080 x 1080px
- Aspect Ratio:
- Horizontal: 16:9
- Vertical: 9:16
- Square: 1:1
- Length:
- Awareness: 15-20 seconds
- Consideration: 2-3 minutes
- Action: 15-20 seconds
Non-skippable In-stream Video Ads
- Description: Must be watched completely before the main video.
- Length: 15 seconds (or 20 seconds in certain markets).
- Resolution:
- Horizontal: 1920 x 1080px
- Vertical: 1080 x 1920px
- Square: 1080 x 1080px
- Aspect Ratio:
- Horizontal: 16:9
- Vertical: 9:16
- Square: 1:1
Bumper Ads
- Length: Maximum 6 seconds.
- File Format: MP4, Quicktime, AVI, ASF, Windows Media, or MPEG.
- Resolution:
- Horizontal: 640 x 360px
- Vertical: 480 x 360px
In-feed Ads
- Description: Show alongside YouTube content, like search results or the Home feed.
- Resolution:
- Horizontal: 1920 x 1080px
- Vertical: 1080 x 1920px
- Square: 1080 x 1080px
- Aspect Ratio:
- Horizontal: 16:9
- Square: 1:1
- Length:
- Awareness: 15-20 seconds
- Consideration: 2-3 minutes
- Headline/Description:
- Headline: Up to 2 lines, 40 characters per line
- Description: Up to 2 lines, 35 characters per line
Display Ads
- Description: Static images or animated media that appear on YouTube next to video suggestions, in search results, or on the homepage.
- Image Size: 300×60 pixels.
- File Type: GIF, JPG, PNG.
- File Size: Max 150KB.
- Max Animation Length: 30 seconds.
Outstream Ads
- Description: Mobile-only video ads that appear on websites and apps within the Google video partner network, not on YouTube itself.
- Logo Specs:
- Square: 1:1 (200 x 200px).
- File Type: JPG, GIF, PNG.
- Max Size: 200KB.
Masthead Ads
- Description: High-visibility ads at the top of the YouTube homepage.
- Resolution: 1920 x 1080 or higher.
- File Type: JPG or PNG (without transparency).
Conclusion
YouTube offers a variety of ad formats to reach audiences effectively in 2024. Whether you want to build brand awareness, drive conversions, or target specific demographics, YouTube provides a dynamic platform for your advertising needs. Always follow Google’s advertising policies and the technical ad specs to ensure your ads perform their best. Ready to start using YouTube ads? Contact us today to get started!
MARKETING
Why We Are Always ‘Clicking to Buy’, According to Psychologists
Amazon pillows.
MARKETING
A deeper dive into data, personalization and Copilots
Salesforce launched a collection of new, generative AI-related products at Connections in Chicago this week. They included new Einstein Copilots for marketers and merchants and Einstein Personalization.
To better understand, not only the potential impact of the new products, but the evolving Salesforce architecture, we sat down with Bobby Jania, CMO, Marketing Cloud.
Dig deeper: Salesforce piles on the Einstein Copilots
Salesforce’s evolving architecture
It’s hard to deny that Salesforce likes coming up with new names for platforms and products (what happened to Customer 360?) and this can sometimes make the observer wonder if something is brand new, or old but with a brand new name. In particular, what exactly is Einstein 1 and how is it related to Salesforce Data Cloud?
“Data Cloud is built on the Einstein 1 platform,” Jania explained. “The Einstein 1 platform is our entire Salesforce platform and that includes products like Sales Cloud, Service Cloud — that it includes the original idea of Salesforce not just being in the cloud, but being multi-tenancy.”
Data Cloud — not an acquisition, of course — was built natively on that platform. It was the first product built on Hyperforce, Salesforce’s new cloud infrastructure architecture. “Since Data Cloud was on what we now call the Einstein 1 platform from Day One, it has always natively connected to, and been able to read anything in Sales Cloud, Service Cloud [and so on]. On top of that, we can now bring in, not only structured but unstructured data.”
That’s a significant progression from the position, several years ago, when Salesforce had stitched together a platform around various acquisitions (ExactTarget, for example) that didn’t necessarily talk to each other.
“At times, what we would do is have a kind of behind-the-scenes flow where data from one product could be moved into another product,” said Jania, “but in many of those cases the data would then be in both, whereas now the data is in Data Cloud. Tableau will run natively off Data Cloud; Commerce Cloud, Service Cloud, Marketing Cloud — they’re all going to the same operational customer profile.” They’re not copying the data from Data Cloud, Jania confirmed.
Another thing to know is tit’s possible for Salesforce customers to import their own datasets into Data Cloud. “We wanted to create a federated data model,” said Jania. “If you’re using Snowflake, for example, we more or less virtually sit on your data lake. The value we add is that we will look at all your data and help you form these operational customer profiles.”
Let’s learn more about Einstein Copilot
“Copilot means that I have an assistant with me in the tool where I need to be working that contextually knows what I am trying to do and helps me at every step of the process,” Jania said.
For marketers, this might begin with a campaign brief developed with Copilot’s assistance, the identification of an audience based on the brief, and then the development of email or other content. “What’s really cool is the idea of Einstein Studio where our customers will create actions [for Copilot] that we hadn’t even thought about.”
Here’s a key insight (back to nomenclature). We reported on Copilot for markets, Copilot for merchants, Copilot for shoppers. It turns out, however, that there is just one Copilot, Einstein Copilot, and these are use cases. “There’s just one Copilot, we just add these for a little clarity; we’re going to talk about marketing use cases, about shoppers’ use cases. These are actions for the marketing use cases we built out of the box; you can build your own.”
It’s surely going to take a little time for marketers to learn to work easily with Copilot. “There’s always time for adoption,” Jania agreed. “What is directly connected with this is, this is my ninth Connections and this one has the most hands-on training that I’ve seen since 2014 — and a lot of that is getting people using Data Cloud, using these tools rather than just being given a demo.”
What’s new about Einstein Personalization
Salesforce Einstein has been around since 2016 and many of the use cases seem to have involved personalization in various forms. What’s new?
“Einstein Personalization is a real-time decision engine and it’s going to choose next-best-action, next-best-offer. What is new is that it’s a service now that runs natively on top of Data Cloud.” A lot of real-time decision engines need their own set of data that might actually be a subset of data. “Einstein Personalization is going to look holistically at a customer and recommend a next-best-action that could be natively surfaced in Service Cloud, Sales Cloud or Marketing Cloud.”
Finally, trust
One feature of the presentations at Connections was the reassurance that, although public LLMs like ChatGPT could be selected for application to customer data, none of that data would be retained by the LLMs. Is this just a matter of written agreements? No, not just that, said Jania.
“In the Einstein Trust Layer, all of the data, when it connects to an LLM, runs through our gateway. If there was a prompt that had personally identifiable information — a credit card number, an email address — at a mimum, all that is stripped out. The LLMs do not store the output; we store the output for auditing back in Salesforce. Any output that comes back through our gateway is logged in our system; it runs through a toxicity model; and only at the end do we put PII data back into the answer. There are real pieces beyond a handshake that this data is safe.”
-
SEO6 days ago
Google’s AI Overviews Avoid Political Content, New Data Shows
-
SEARCHENGINES5 days ago
Google Shopping Researched with AI
-
WORDPRESS6 days ago
5 Most Profitable Online Businesses You Can Start Today for Free!
-
WORDPRESS5 days ago
8 Best Banks for ECommerce Businesses in 2024
-
SEARCHENGINES7 days ago
Google AI Overview Ads, New Link Format, AI Organized Search Results & Plus More
-
AFFILIATE MARKETING7 days ago
Learn a New Language with This Fresh Approach
-
AFFILIATE MARKETING5 days ago
How to Choose Your Battles Wisely at Work
-
SEARCHENGINES4 days ago
Google Showing Competitor Ads Above Local Reviews