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Klaviyo Launches AI Agents that Work Together to Drive Revenue for Consumer Brands

William Gu
William Gu
Marketing AI Team @ MKTGAGENT.AI

Klaviyo Introduces Innovative AI Agents to Boost Consumer Brand Revenue

In a significant leap for marketing technology, Klaviyo has launched two advanced AI agents—Composer and Customer Agent—designed to enhance revenue generation for consumer brands. These new tools integrate seamlessly within Klaviyo’s CRM platform, using real-time customer data to personalize marketing and service interactions more effectively than ever before.

Revolutionizing Marketing with AI

The newly introduced AI agents are engineered to work in tandem to create a more connected and personalized customer experience. Composer, currently in public beta, automates the discovery of marketing opportunities by analyzing patterns and building targeted campaigns with greater efficiency. This allows marketers to focus on strategy and creativity while Composer handles the heavy lifting of campaign construction.

Meanwhile, the Customer Agent leverages comprehensive, unified customer profiles to provide actionable insights and recommendations. By understanding the context and behavior of each customer, it helps brands engage more intelligently and responsively, leading to improved customer satisfaction and loyalty.

Leveraging Real-Time Data for Personalization

A core advantage of Klaviyo’s AI agents lies in their ability to synthesize data across both marketing and customer service touchpoints. This interconnected approach ensures that marketing messages are not only timely but deeply relevant to each individual, maximizing the chances for conversions and repeat business.

By harnessing this real-time data, brands can better anticipate customer needs, deliver tailored offers, and create a consistent omnichannel experience that resonates deeply with consumers.

Key Insights

  • What makes Klaviyo’s AI agents unique? Their integration across marketing and customer service allows a holistic view of the customer journey, enabling context-rich AI-driven actions.
  • How does Composer improve marketing campaigns? It automates opportunity detection and campaign building, saving time and enhancing targeting precision.
  • What role does Customer Agent play? It uses detailed customer profiles to generate personalized recommendations and optimize engagement.
  • Why is real-time data integration important? It ensures that brand communications are timely and relevant, increasing revenue opportunities.

Conclusion

Klaviyo’s launch of Composer and Customer Agent symbolizes a strategic shift toward AI-powered marketing that is both smarter and more connected. By combining comprehensive customer data with sophisticated AI, consumer brands can now deliver personalized experiences that drive substantial revenue growth. As AI continues to reshape marketing, tools like these highlight the critical value of context and data integration for staying competitive and meeting evolving customer expectations.

Source: https://martechseries.com/sales-marketing/crm/klaviyo-launches-ai-agents-that-work-together-to-drive-revenue-for-consumer-brands/

New Zeta Global Research Signals the Rise of Agentic Commerce

William Gu
William Gu
Marketing AI Team @ MKTGAGENT.AI

The Rise of Agentic Commerce: How AI is Shaping Consumer Shopping Behavior

Introduction

The landscape of retail and consumer behavior is evolving with the advancement of artificial intelligence (AI). New research by Zeta Global reveals a significant shift toward “agentic commerce,” a trend where consumers are increasingly trusting AI systems to make shopping and purchasing decisions on their behalf. In this article, we explore the implications of this development and what it means for brands aiming to stay ahead in a changing marketplace.

Understanding Agentic Commerce

Agentic commerce refers to a growing consumer willingness to delegate the decision-making process for shopping to AI-powered systems. This shift is driven by the convenience and efficiency that AI provides in tasks such as automatic reordering of products and personalized product discovery.

According to a survey of 2,000 U.S. adults conducted by Zeta Global, parents are leading this trend by showing greater trust in AI tools to manage and simplify their shopping responsibilities. These individuals value AI’s capabilities in anticipating needs and streamlining the purchasing process, though they still prefer to complete their transactions directly with the brands themselves.

Key Implications for Brands

This emerging consumer behavior highlights important considerations for brands and marketers. To capture the attention of consumers and their AI shopping agents, brands must:

  • Optimize their presence for both human shoppers and AI recommendation systems.
  • Offer personalized AI-driven experiences that foster consumer trust and engagement.
  • Ensure seamless and trustworthy transaction processes as consumers like to finalize purchases directly with brands.

By aligning marketing strategies with this dual engagement approach, brands can increase their visibility and relevance in an AI-influenced shopping environment.

Key Insights

  • What is driving the rise of agentic commerce? Increased consumer trust in AI’s ability to handle specific purchasing tasks, especially among parents, is a key factor.
  • How do consumers want to interact with AI in commerce? Consumers prefer AI to handle routine tasks like reordering but want to complete purchases directly with brands.
  • What should brands prioritize? Developing personalized AI experiences that connect authentically with consumers while optimizing for AI system discovery.

Conclusion

Agentic commerce is reshaping how consumers shop by integrating AI as an assistant in purchasing decisions. Brands that recognize and adapt to this trend by creating trustworthy AI interactions and balancing automated and human shopping experiences are likely to thrive. As AI becomes increasingly entwined in commerce, personalized and seamless AI experiences will be crucial in capturing and maintaining consumer loyalty.

Source: https://martechseries.com/predictive-ai/ai-platforms-machine-learning/new-zeta-global-research-signals-the-rise-of-agentic-commerce/

Perception Graph: How AI Models See Brands Before They Answer

William Gu
William Gu
Marketing AI Team @ MKTGAGENT.AI

Understanding the Perception Graph: How AI Models View Brands Before Responding

In the rapidly evolving world of artificial intelligence, how AI models perceive brands can significantly influence the way they generate responses. The concept of the Perception Graph introduces a new framework designed to analyze and understand this crucial aspect of AI behavior. This article explores what the Perception Graph is, its implications for brand representation, and the technological tools that help bridge perception gaps.

What is the Perception Graph?

The Perception Graph is a structured framework that delves into how AI models “see” or interpret brands before providing answers or generating content. Unlike traditional AI visibility metrics that merely note where brands appear in data, the Perception Graph assesses how brands are framed or perceived. This distinction is vital because perception ultimately shapes the AI’s responses and recommendations.

The Challenge of Perception Gaps

A key issue in AI brand interpretation is the presence of perception gaps. These occur when AI models misrepresent brands—confusing brands with their products or overlooking critical brand attributes. Such misinterpretations can lead to inaccurate or incomplete AI outputs, which in turn affects brand reputation and effectiveness in AI-driven interactions.

The Role of the Signal Graph and AOOE

To address these challenges, the article highlights two important concepts: the Signal Graph and Agent-Oriented Ontology Engineering (AOOE). The Signal Graph serves as an evidence layer, providing grounded data points that support and clarify how brands are understood by AI models. Meanwhile, AOOE helps improve the structuring of knowledge so AI agents can better interpret brand information, reducing confusion and enriching AI responses.

Practical Applications for Brands

By utilizing the Perception Graph alongside tools like the Signal Graph and AOOE, brands can actively manage and improve their representation in AI systems. This proactive approach ensures not only visibility but accurate understanding, enabling brands to shape how AI agents view them and interact with consumers in meaningful ways.

Key Insights

  • How does the Perception Graph differ from traditional brand visibility tracking? It focuses on perception framing rather than just appearance.
  • Why are perception gaps problematic? They cause AI models to misrepresent or misunderstand brand attributes.
  • What does the Signal Graph contribute? It offers concrete evidence aiding accurate brand perception in AI.
  • How does AOOE help AI models? It structures knowledge for AI to interpret brand information more precisely.
  • What benefits do brands gain from these frameworks? Enhanced accuracy in AI-driven brand representation and interaction.

Conclusion

The Perception Graph represents a significant step forward in understanding and improving the interplay between AI models and brand identity. Brands equipped with these insights and tools can close perception gaps, ensuring that AI not only notices them but understands them accurately. As AI continues to shape digital interaction, mastering these frameworks will be critical for sustaining impactful brand presence and engagement.

Source: https://wordlift.io/blog/en/perception-graph/

Profound Launches the Profound Index at Zero Click New York, Creating the Definitive Benchmark for AI Search Visibility

William Gu
William Gu
Marketing AI Team @ MKTGAGENT.AI

Profound Unveils the Profound Index at Zero Click New York: Setting a New Benchmark for AI Search Visibility

Introduction

In a groundbreaking move at the Zero Click New York conference, Profound has introduced the Profound Index, a pioneering tool designed to revolutionize how marketers measure and understand AI-generated search visibility. This new index arrives as the digital marketing landscape faces rapid shifts, with traditional SEO metrics losing relevance amid the rise of AI-driven recommendations.

Understanding the Profound Index

The Profound Index leverages an extensive dataset comprising over 1.5 billion real-user prompts, allowing it to benchmark brand performance across multiple AI platforms. Unlike conventional search engine rankings, which rely heavily on keywords and backlinks, this index focuses on the visibility of brands within AI-generated answers and recommendations—a growing area as consumers increasingly depend on AI for discovering products and services.

Why Traditional SEO Metrics Fall Short

Traditional SEO has long been the foundation for online visibility, centered on optimizing for search engines like Google. However, as AI tools become the primary source of user queries and recommendations, these metrics struggle to accurately reflect true brand presence. The Profound Index addresses this gap by providing real-time insights into how brands perform in AI-generated search results, which often diverge from traditional ranking fluctuations.

Early Findings and Market Implications

Initial reports from the Profound Index reveal significant shifts in how answers are generated by AI, emphasizing the need for brands to rethink their marketing strategies. Brands that excel in traditional SEO might find their visibility diminished in AI contexts if they do not adapt. The index highlights trends and competitive dynamics that marketers must monitor to maintain a strong AI search presence.

Key Insights

  • What makes the Profound Index unique? It taps into over 1.5 billion user prompts, offering unmatched insights into AI search visibility that go beyond conventional SEO.
  • How does AI search visibility differ from traditional SEO? It focuses on brand presence within AI-generated answers and recommendations rather than keyword rankings.
  • Why is this shift important for marketers? As consumer behavior moves toward AI recommendations, real-time data on AI visibility becomes crucial for optimizing marketing strategies.
  • What should brands do in response? They must adapt their approach to include AI search visibility metrics to stay competitive.

Conclusion

The launch of the Profound Index marks a significant evolution in digital marketing analytics. As AI-generated search gains momentum, marketers need tools that accurately measure brand presence within this new ecosystem. The Profound Index not only shines a light on emerging visibility trends but also equips brands to navigate and succeed in an AI-driven marketplace. Embracing these insights will be essential for brands aiming to thrive amid the ongoing transformation of search and discovery.

Source: https://martechseries.com/predictive-ai/ai-platforms-machine-learning/profound-launches-the-profound-index-at-zero-click-new-york-creating-the-definitive-benchmark-for-ai-search-visibility/

The new SEO stack: What replaces your old toolset

William Gu
William Gu
Marketing AI Team @ MKTGAGENT.AI

The New SEO Stack: What Replaces Your Old Toolset?

In the constantly evolving landscape of digital marketing, SEO tools have long been essential for shaping strategies and measuring success. However, the traditional SEO toolkit is undergoing a significant transformation. The rise of AI and automation is driving a new, more agile SEO stack that promises to enhance efficiency and improve analytical depth.

From Old to New: Why Change Is Essential

Conventional SEO tools, such as rank trackers and keyword research applications, are slowly losing their impact due to fundamental shifts in search behavior and how users engage with content. Today’s SEO professionals need more than static tools; they require flexible solutions that can adapt to rapidly changing market dynamics.

Components of the New AI-Driven SEO Stack

The emerging SEO stack centers around powerful technologies like Large Language Models (LLMs), including ChatGPT and Claude, which facilitate natural language understanding and content generation. APIs play a crucial role in this new environment by enabling seamless data integration and management across platforms.

Automation is another cornerstone—lightweight scripts help automate repetitive, time-consuming tasks, freeing professionals to focus on strategic initiatives. Additionally, digital notebooks offer consistent environments for data management and experimentation, supporting more reliable workflows.

Embracing a Hybrid Workflow

Rather than discarding old tools entirely, the forward-thinking approach suggests combining the best of both worlds. Hybrid SEO stacks maintain proven methods while integrating innovative AI-driven tools, creating optimized workflows that respond effectively to evolving marketing needs.

Key Insights

  • What drives the shift toward AI in SEO? Rapid changes in search engine algorithms and user engagement patterns require tools that can adapt quickly and handle more complex data analysis.
  • How do Large Language Models improve SEO? LLMs enhance keyword research, content creation, and competitive analysis by understanding context and generating human-like text.
  • Why is automation critical now? Automation reduces manual workload and increases accuracy in repetitive tasks, boosting overall productivity.
  • What is the benefit of a hybrid stack? A hybrid approach leverages the strengths of traditional and AI tools, enabling a more comprehensive and flexible SEO strategy.

Conclusion

The SEO field is entering a new era where artificial intelligence and automation reshape how professionals work. Adopting an AI-driven SEO stack, complemented by existing tools, offers a competitive advantage by improving analysis, streamlining workflows, and addressing modern marketing challenges more effectively. Businesses that embrace this hybrid, flexible approach will be better positioned to succeed as search behavior and technology continue to evolve.

Source: https://searchengineland.com/new-seo-stack-481277