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For ChatGPT ads, behavior matters more than targeting

William Gu
William Gu
Marketing AI Team @ MKTGAGENT.AI

Understanding the Shift to Behavior-Driven Ads in ChatGPT

The recent integration of advertisements within ChatGPT signals a transformative change in how brands approach advertising. Unlike traditional platforms that rely heavily on keyword targeting, advertising on ChatGPT demands a deep understanding of user behavior. This shift underscores the importance of recognizing that users engage with ChatGPT in a focused and task-driven manner, rather than passively scrolling through content.

From Keyword Targeting to Behavioral Insights

Traditional ad platforms typically depend on keywords to target potential customers, aiming to catch users based on search queries or browsing habits. However, ChatGPT users behave differently; they turn to the AI for problem-solving, decision-making, and specific tasks. Advertisers must now interpret different behavioral modes such as exploring options, simplifying complex decisions, confirming choices, or taking direct action.

This behavioral context matters more than just keywords because it allows ads to be functional and genuinely helpful within the flow of the user’s task. Ads that seamlessly assist users without disrupting the experience have greater potential to succeed.

Measuring Success in a New Advertising Environment

Given this new landscape, traditional metrics like click-through rates may no longer offer a full picture of ad effectiveness. Success on ChatGPT will require innovative metrics that measure brand recall, influence on decision-making, and how well ads integrate with the user’s intent and task.

Key Insights

  • Why does behavior matter more than targeting keywords? Because ChatGPT users engage with the platform actively in task-oriented ways, focusing on solving problems rather than casual browsing.

  • How should advertisers adapt? By identifying motivational contexts such as exploration, simplification, confirmation, and action, and providing ads that support these behaviors.

  • What challenges lie ahead in measuring ad effectiveness? Traditional metrics must evolve to capture the nuances of influence and trust rather than mere clicks.

Conclusion

The evolution of advertising into behavior-centric models within AI environments like ChatGPT presents exciting opportunities for brands. Success hinges on understanding how users utilize AI tools for real-life problem-solving and decisions, creating ads that are helpful, trustworthy, and contextually relevant. Advertisers who embrace this shift will be better positioned to connect with users meaningfully and measure true impact beyond conventional advertising metrics.

Source: https://martech.org/for-chatgpt-ads-behavior-matters-more-than-targeting/

How to structure pages for AEO and answer engines: A quick-start guide

William Gu
William Gu
Marketing AI Team @ MKTGAGENT.AI

How to Structure Pages for AEO and Answer Engines: A Quick-Start Guide

Introduction

The evolution of search engines has brought a shift from traditional SEO to Answer Engine Optimization (AEO). Unlike classic SEO, which focuses on ranking pages for keywords, AEO aims to optimize content so AI-powered answer engines like ChatGPT can efficiently analyze and extract precise information in real time. This quick-start guide walks you through practical strategies to design web pages that boost visibility and performance in AEO-driven environments.

What is Answer Engine Optimization?

Answer Engine Optimization is the practice of structuring content to enhance its accessibility to large language models (LLMs) and AI answer engines. These technologies prioritize clear, concise answers and logically organized information to provide users with immediate and relevant responses. AEO requires a rethink of how web pages are laid out to highlight key information and facilitate swift AI comprehension.

Key Strategies for Structuring Your Pages

  • Use Clear, Question-Based Headings: Organize your content with headings phrased as questions to match user queries that answer engines encounter.
  • Front-Load Critical Information: Provide the most important answers early in the content to ensure answer engines capture the key details quickly.
  • Incorporate TL;DR Sections: Summaries help both users and AI to grasp the main points without wading through lengthy text.
  • Add FAQ Modules: Frequently Asked Questions sections respond directly to common queries, increasing the chance of your content being featured in rich answers.
  • Logical Information Flow: Structure your narrative in a coherent sequence so that each piece of information builds upon the previous, enhancing clarity.
  • Leverage Schema Markup: Use structured data standards to label your content, making it easier for AI systems to understand and extract relevant information.

Common Pitfalls to Avoid

Many pages fail in AEO because they are cluttered, have vague headings, or bury key facts too deep inside paragraphs. Overuse of jargon without explanation can also reduce comprehension by answer engines. Avoid ambiguous language and ensure your content maintains a user-friendly tone.

Key Insights

  • Why is core content structure so vital for AEO? Clear, well-structured content enables AI to quickly identify and extract answers, improving your visibility in answer-driven search results.
  • How can question-based headings improve performance? They align directly with natural language queries users pose, boosting the likelihood that AI will pick them as featured snippets.
  • What role does schema markup play in AEO? It provides machine-readable tags that help AI discern the context and importance of page elements.

Conclusion

Optimizing for Answer Engine Optimization requires a deliberate focus on clarity, structure, and directness. By applying the suggested strategies—question-based headings, TL;DR summaries, FAQ modules, and schema markup—webmasters can significantly enhance the discoverability and impact of their content in AI-driven answer ecosystems. As AI technologies evolve, embracing AEO principles today positions websites for sustained visibility and user engagement tomorrow.

Source: https://blog.hubspot.com/marketing/aeo-page-structure

Inspiring examples of responsible and realistic vibe coding for SEO

William Gu
William Gu
Marketing AI Team @ MKTGAGENT.AI

Inspiring Examples of Responsible and Realistic Vibe Coding for SEO

Introduction

In the fast-evolving world of software development and digital marketing, vibe coding is emerging as a groundbreaking new approach. Recognized by Collins Dictionary as the “word of the year,” vibe coding enables users to create software applications using everyday language rather than traditional programming skills. This innovation is especially transformative for SEO professionals looking to automate tasks and prototype ideas efficiently.

What is Vibe Coding?

Vibe coding simplifies the complex process of writing software by leveraging AI-powered tools that interpret natural language commands. Rather than delving into technical programming languages, users can instruct AI to generate code that serves their specific needs. This approach not only democratizes software development but also accelerates the innovation cycle.

Variations of Vibe Coding

There are several forms of vibe coding currently in use:

  • AI-Assisted Coding: Users receive suggestions and code completions based on their natural language input.
  • No-Code Platforms: Completely remove the need for users to write any code, relying on graphical interfaces.
  • Low-Code Platforms: Combine minimal manual coding with automation features to speed up development.

Each variation presents unique benefits, allowing SEOs and developers to choose the method best suited to their expertise and project requirements.

Practical Use Cases for SEO

Vibe coding enables SEO specialists to automate repetitive tasks like keyword research, backlink analysis, and content audits. It also allows quick prototyping of SEO tools, which can be tailored precisely to a team’s workflow. This capability fosters experimentation and rapid iteration, crucial in the dynamic environment of search engine optimization.

How to Implement Vibe Coding Responsibly

While vibe coding opens many doors, it requires mindful use to avoid common pitfalls. A step-by-step guide emphasizes the importance of validating AI-generated code rigorously and understanding its limitations. Responsible developers and SEOs should:

  1. Clearly define the task and desired outcomes.
  2. Iteratively review and test the generated code.
  3. Combine AI suggestions with human expertise to ensure accuracy and reliability.

This careful approach helps maintain the quality and security of applications created through vibe coding.

Inspiring Examples of SEO Tools Built with Vibe Coding

Several innovative SEO tools have already been developed using vibe coding techniques. These tools showcase a broad range of functionalities—from automating data analysis to enhancing user interfaces—demonstrating the versatility and enormous potential of this technique.

Key Insights

  • What makes vibe coding a game changer for SEO? It allows individuals without deep programming knowledge to create automation and prototype tools quickly, enhancing productivity.
  • How can SEOs ensure the reliability of AI-generated code? Through careful validation, iterative testing, and combining AI-generated outputs with human oversight.
  • What are the different vibe coding approaches available today? AI-assisted coding, no-code platforms, and low-code platforms each offer varying levels of involvement and accessibility.

Conclusion

Vibe coding represents a promising frontier in both software development and SEO automation. By adopting responsible and realistic practices, professionals can unlock significant efficiencies and creative possibilities. As this approach matures, it will likely redefine how software and SEO solutions are developed, making the process more accessible, agile, and innovative for users across disciplines.

Source: https://searchengineland.com/vibe-coding-for-seo-467865

Introducing the Agentic Customer Platform

William Gu
William Gu
Marketing AI Team @ MKTGAGENT.AI

Unlocking Business Potential with the Agentic Customer Platform

In today’s fast-evolving business landscape, artificial intelligence (AI) promises a lot but often falls short in delivering clear, actionable outcomes that truly benefit companies. This is the challenge the new Agentic Customer Platform is designed to overcome. By providing a richer, more contextual understanding of customer data, this innovative platform aims to bridge the gap between AI’s potential and real-world business results.

What Is the Agentic Customer Platform?

The Agentic Customer Platform is developed by HubSpot to address a frequently heard complaint among business leaders: AI tools generate impressive outputs, but these often don’t translate into meaningful business outcomes. Unlike many existing AI solutions, this platform emphasizes the critical importance of context—something that is often missing.

At its core, the platform integrates multiple layers of information: detailed customer data, relevant business context, and the dynamics of internal teams. This comprehensive approach allows businesses to harness both human expertise and AI efficiency in a coordinated manner.

The Platform’s Innovative Architecture

The architecture of the Agentic Customer Platform is structured around three main layers:

  • Context Layer: Consolidates full customer profiles and gathers insights from all previous interactions. This ensures that every AI recommendation or action is informed by a holistic view rather than isolated data points.

  • Action Layer: Equipped with advanced hubs and AI agents designed to carry out tasks intelligently and automate workflows, this layer transforms insights into tangible actions.

  • Coordination Layer: Facilitates seamless collaboration between human teams and AI tools, ensuring that each complements the other and fostering productive synergy.

Why Context Matters in AI

AI systems traditionally rely heavily on raw data but often miss what surrounds that data—the context. Context includes the nuances of customer preferences, past interactions, team objectives, and broader business goals. The Agentic Customer Platform’s focus on context means smarter, more relevant AI-driven recommendations, leading to improved customer experiences and stronger business results.

Key Insights

  • How does the Agentic Customer Platform improve AI outcomes? It integrates customer context, business factors, and team dynamics to create a unified environment where AI and humans work smarter together.

  • What makes this platform different from other AI tools? Its layered structure that explicitly addresses the gap between raw outputs and real-world outcomes by embedding complete customer and business context.

  • Who will benefit most from this platform? Business leaders and teams seeking to leverage AI to drive measurable results and enhance customer interactions meaningfully.

  • What is the broader impact on business operations? It encourages more informed decision-making, greater collaboration between people and machines, and more personalized customer engagement.

Conclusion

The Agentic Customer Platform represents a significant step forward in how businesses can leverage AI—not just for automation or data processing but as a truly intelligent partner in decision-making and customer engagement. By uniting comprehensive customer data with business context and team collaboration, it empowers organizations to achieve better, measurable outcomes and foster deeper, more tailored customer relationships moving forward.

Source: https://blog.hubspot.com/marketing/introducing-the-agentic-customer-platform

LinkedIn: AI-powered search cut traffic by up to 60%

William Gu
William Gu
Marketing AI Team @ MKTGAGENT.AI

LinkedIn Adapts to AI-Powered Search Changes: Traffic Drops and New Marketing Strategies

In the evolving landscape of digital marketing and search engine technology, LinkedIn has encountered a significant challenge that is reshaping how businesses attract and engage audiences. Recently reported data reveals that LinkedIn’s non-brand B2B awareness traffic has dropped by up to 60% due to changes brought by Google’s AI-powered search features. This steep decline has prompted LinkedIn to rethink its marketing approach and prioritize new strategies adapted to AI-driven search ecosystems.

The Impact of AI-Powered Search on LinkedIn Traffic

Google’s introduction of the Search Generative Experience (SGE), which has evolved into AI Overviews, is transforming how users access information. These AI-generated summaries provide direct answers to queries, reducing the need for click-throughs to external websites. For LinkedIn, this means fewer organic visits from non-brand search terms despite their stable search rankings. The diminished click-through rate (CTR) reflects a broader challenge where traditional ‘search, click, website’ methods no longer guarantee visibility or traffic.

Shifting Marketing Focus: From Clicks to Consideration

In response, LinkedIn is shifting its marketing paradigm from counting on search clicks to focusing on brand presence and customer perception. This new model is summarized as ‘be seen, be mentioned, be considered, be chosen.’ The emphasis is now on creating fresh, authoritative content that stands out in an AI-dominated search landscape. LinkedIn aims to be more than a search result; it wants to be part of the narrative that AI shares with users.

Strategic Initiatives and Challenges Ahead

To respond effectively, LinkedIn is optimizing its owned content for generative AI visibility and has launched an AI Search Taskforce. This team is dedicated to combating misinformation and enhancing LinkedIn’s authority and relevance in search results influenced by AI. However, with limited data available on the effectiveness of these measures, the company faces uncertainty in measuring how well these new approaches will perform.

Key Insights

  • What caused the traffic decline on LinkedIn? Google’s AI-powered search features, specifically the SGE and AI Overviews, are reducing click-through traffic by providing direct answers.
  • How is LinkedIn adapting its marketing strategy? By moving from a traditional search-click approach to focusing on brand visibility and authoritative content.
  • What role does content play in this new era? Fresh, reliable, and optimized content is critical to being featured in AI-generated summaries.
  • What are LinkedIn’s next steps? Enhancing owned content and combating misinformation through a dedicated AI Search Taskforce.

Conclusion

LinkedIn’s experience underscores a broader shift in digital marketing caused by AI in search engines. As AI continues to reduce organic search traffic by offering instant answers, businesses must adapt by prioritizing brand presence and content quality over traditional SEO tactics. This evolution presents both challenges and opportunities for marketers to rethink how they engage audiences and measure success in an AI-influenced digital world.

Source: https://searchengineland.com/linkedin-ai-powered-search-cut-traffic-468187