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xpln.ai Launches in U.S. with CRO, Gina Cavallo, to Capture Demand for Next Gen Attention Solutions

Jan 29, 2026

xpln.ai Expands into the U.S. Market with New Leadership to Revolutionize Attention Measurement

xpln.ai, a trailblazer in the field of attention measurement technology, has officially launched its operations in North America. The company has appointed Gina Cavallo as Chief Revenue Officer (CRO) to lead its growth initiatives across the region. This strategic expansion comes on the heels of xpln.ai’s successful partnerships with major brands like AXA, Levi’s, and General Motors in Europe and the Asia-Pacific (APAC) markets.

Introducing Next-Generation Attention Measurement Solutions

xpln.ai’s platform offers cutting-edge, research-grade insights that go beyond conventional viewability metrics. In the advertising world, viewability typically measures if an ad was simply visible to a user. xpln.ai enhances this by providing a comprehensive understanding of how users actually engage with creative content across a multitude of channels such as social media platforms and connected TV (CTV).

Driving Advertising Effectiveness with Privacy-Safe Insights

As privacy regulations grow stricter worldwide, xpln.ai distinguishes itself by delivering privacy-safe data collection and analysis. Advertisers can obtain detailed attention metrics without compromising user privacy. These insights empower brands to optimize campaign planning and media buying in an increasingly cluttered and competitive advertising landscape.

Why xpln.ai’s Expansion Matters

The North American market represents a significant opportunity for next-generation marketing tools, particularly those that provide deeper insight into consumer attention and engagement. By appointing an experienced leader like Gina Cavallo, who brings industry knowledge and drive, xpln.ai is well-positioned to meet the demand for advanced attention measurement solutions in the region.

Key Insights

What sets xpln.ai apart from traditional advertising metrics? xpln.ai offers research-grade, privacy-compliant insights into actual viewer attention and engagement, not just ad visibility.
How will this expansion impact advertisers in North America? Advertisers will gain access to more precise, actionable data to improve campaign effectiveness and media strategy.
Why is attention measurement critical today? In a saturated and noisy media environment, understanding what truly captures consumer attention can drive better ROI.
What role does Gina Cavallo play in this launch? As CRO, Cavallo will spearhead growth, forging new partnerships and expanding xpln.ai’s footprint.

Conclusion

xpln.ai’s entry into the U.S. market marks a major step forward in evolving how advertisers measure audience engagement. As brands seek more meaningful metrics amid increasing privacy constraints, solutions like xpln.ai’s provide a valuable competitive edge. Looking ahead, this expansion promises to enhance advertising effectiveness, enabling smarter media investment decisions and ultimately delivering better outcomes for advertisers navigating today’s complex media environment.

Source: https://martechseries.com/sales-marketing/programmatic-buying/xpln-ai-launches-in-u-s-with-cro-gina-cavallo-to-capture-demand-for-next-gen-attention-solutions/

AI Won’t Shop For You – Yet

Jan 28, 2026

William Gu

Marketing AI Team @ MKTGAGENT.AI

AI Won’t Shop For You – Yet: Understanding the Evolution of AI in Commerce

Artificial intelligence (AI) continues to reshape many aspects of daily life and business, but its role in autonomous shopping remains in its infancy. Recently, LiveRamp CEO Scott Howe shared insights on the evolving landscape of AI within commerce that temper expectations for fully autonomous AI shopping agents. While AI’s influence is undeniable, most consumers are expected to maintain control over their purchasing decisions for the foreseeable future.

The Current State of AI in the Shopping Experience

According to Howe in a recent AdExchanger Talks episode, AI is set to enhance the shopping journey rather than replace human decision-making. From personalized recommendations to improved customer service interactions, AI tools assist consumers in making informed choices. Notably, AI is increasingly integrated into search chatbots like ChatGPT and Perplexity, which now feature embedded advertising designed to be contextual and relevant without disrupting the user experience.

The Rise of Contextual Advertising in AI Chatbots

The integration of advertisements into AI-driven chatbots represents a significant shift in marketing strategies. These chatbots aim to deliver non-intrusive, contextually relevant ads during search interactions, offering brands new channels to reach consumers at critical moments. Howe emphasizes the importance for companies to pinpoint ideal points in the consumer journey where AI can enhance satisfaction while respecting privacy norms.

Key Insights

Will AI replace human shoppers? No, most consumers prefer to retain control over their purchases despite AI’s support.
How does AI assist shoppers today? By providing tailored information and enhancing customer support through smart recommendations.
What role do chatbots play in marketing? They serve as platforms for contextual advertising that aligns ads with user search intent.
Why is strategic integration important? Because timely AI enhancements improve consumer experience without compromising privacy.

Conclusion

AI’s role in commerce is growing but remains supportive rather than substitutive when it comes to shopping decisions. Companies should focus on deploying AI strategically to amplify customer satisfaction and comply with privacy expectations. This balanced approach ensures AI becomes a valuable partner in the shopping experience, laying groundwork for more advanced applications in the future.

Source: https://www.adexchanger.com/adexchanger-talks/ai-wont-shop-for-you-yet/

Bake AI, Automation and Copilots — Without Sacrificing CSAT

Jan 28, 2026

William Gu

Marketing AI Team @ MKTGAGENT.AI

Baking AI, Automation, and Copilots into Contact Centers Without Sacrificing CSAT

As contact centers face growing demand to handle increasing volumes of customer interactions, many are turning to artificial intelligence (AI) to boost efficiency. Yet, this shift comes with a critical challenge: How can contact centers leverage AI automation without sacrificing customer satisfaction (CSAT)? The key might lie in a “copilot-first” approach that supports human agents rather than replacing them.

The Promise and Pitfalls of AI in Contact Centers

AI promises to streamline operations by automating repetitive tasks and managing high call volumes. However, an efficiency-driven implementation risks alienating customers if it diminishes the personal touch or generates dissatisfaction due to rigid automation. These issues emerge when AI operates without balance, undermining trust and potentially lowering CSAT scores.

Introducing the Copilot-First Strategy

Instead of positioning AI as a replacement for human agents, the copilot-first strategy envisions AI as an assistant that enhances agent performance. By providing timely, contextual insights, AI acts as a supportive copilot that helps agents deliver better customer experiences.

The strategy emphasizes four core principles:

Agent-First AI: Prioritize human agents in the design and deployment of AI tools to enhance—not replace—their capabilities.
CSAT as a Guardrail: Use real-time customer satisfaction metrics to guide AI decisions and adjust assistance dynamically.
Workflow-Centric Design: Build AI features that align with agents’ actual workflows for seamless integration.
Incremental Rollouts: Deploy AI tools gradually with controlled pilots to ensure effectiveness and user acceptance.

Why This Approach Matters

By keeping the agent at the center and continuously measuring customer satisfaction, contact centers can harness AI’s efficiency boosts without diminishing service quality. This approach fosters trust, preserves the human element in customer interactions, and helps organizations meet high-volume demand effectively.

Key Insights

What is the copilot-first approach? It’s a method where AI supports human agents by providing contextual help rather than replacing them.
How does CSAT guide AI? Real-time customer satisfaction metrics act as a control measure to ensure AI assistance enhances rather than harms the customer experience.
Why design AI around workflows? Aligning AI tools with actual agent workflows ensures smoother adoption and better utility.
What are the benefits of incremental AI rollouts? Controlled pilots allow organizations to assess impact, refine tools, and encourage agent buy-in.

Conclusion

When contact centers bake AI, automation, and copilots into their operations with a focus on customer satisfaction and agent support, they can achieve better efficiency without compromising service quality. This balanced integration sets the stage for more effective, trusted customer service solutions that adapt to evolving demands and expectations.

Source: https://www.cmswire.com/contact-center/bake-ai-automation-and-copilots-without-sacrificing-csat/?utm_source=cmswire.com&utm_medium=web&utm_campaign=cm&utm_content=all-articles-rss

How CMOs should think about discovery in an AI-first world

Jan 28, 2026

William Gu

Marketing AI Team @ MKTGAGENT.AI

How CMOs Should Navigate Discovery in an AI-First Marketing World

Introduction

Marketing is evolving rapidly as AI-driven discovery reshapes how buyers find and engage with brands. Traditional search rankings are being replaced by AI-generated summaries that tailor information to individual needs, creating a new paradigm for brand visibility. For Chief Marketing Officers (CMOs), this transformation requires fresh strategies to remain relevant and effectively measure marketing success.

The Shift to AI-Powered Discovery

In an AI-first world, discovery means more than just appearing on the first page of search results. Buyers now expect synthesized, concise information tailored to their unique questions. AI systems prioritize inclusion in their responses, which means brands must optimize not just for clicks, but for how often and how accurately they are referenced by AI.

This shift changes the metrics CMOs use to evaluate performance. Instead of focusing mainly on click-through rates, metrics like synthetic visibility—how frequently a brand shows up in AI-generated answers—and narrative control—the brand’s influence on the AI’s story—are becoming critical.

Aligning Content Strategy with AI Discovery

To thrive, content must be designed with AI referencing in mind. This means producing clear, detailed, and actionable materials that AI systems can easily understand and cite. Rather than broad content aimed at attracting clicks, CMOs need to emphasize quality and relevance that corresponds directly with buyer intent.

Operationally, this involves constantly monitoring where and how a brand appears in AI-driven discovery channels. CMOs should invest in tools and processes to track synthetic visibility and adjust content accordingly.

Cross-Team Collaboration for Consistent Messaging

As AI discovery channels grow, cross-departmental alignment becomes essential. Marketing, product, and content teams must collaborate closely to ensure messaging consistency and prepare for rapidly evolving AI capabilities. This integrated approach helps maintain a coherent brand narrative, which supports stronger presence within AI recommendations.

Key Insights

What defines discovery in an AI-first world? Discovery is now about inclusion and frequency of brand mentions in AI-generated summaries rather than traditional search rankings.
What new metrics should CMOs adopt? Synthetic visibility and narrative control are key metrics to gauge brand impact within AI-powered discovery.
How should content strategies evolve? Content must be clear, detailed, and aligned with buyer intent to be effectively referenced by AI.
Why is cross-team collaboration critical? It ensures consistent messaging and readiness for AI’s fast-changing landscape.

Conclusion

The rise of AI-driven discovery is redefining marketing visibility and forcing CMOs to rethink KPIs and content strategy. By embracing synthetic visibility, refining content for AI referencing, and fostering cross-team coordination, brands can secure a competitive edge. As AI technology continues to advance, adaptive and proactive marketing leadership will be essential to navigate this evolving landscape successfully.

Source: https://martech.org/how-cmos-should-think-about-discovery-in-an-ai-first-world/

Is your account ready for Google AI Max? A pre-test checklist

Jan 28, 2026

William Gu

Marketing AI Team @ MKTGAGENT.AI

Is Your Account Ready for Google AI Max? A Pre-Test Checklist

Google’s AI Max is revolutionizing the advertising landscape by moving beyond traditional keyword targeting. This new technology leverages multiple signals to display ads more intelligently, aiming to improve ad performance significantly. However, its effectiveness depends heavily on certain prerequisites and careful preparation.

Understanding Google AI Max

AI Max is designed to optimize ad delivery by analyzing a broad set of signals rather than relying purely on keywords. It integrates automated bidding strategies and requires precise conversion tracking to function correctly. Unlike conventional campaigns that use standard keyword targeting, AI Max utilizes a complex algorithm that learns from campaign performance over time.

The Essential Pre-Test Checklist

Before enabling AI Max on your account, there are critical factors to evaluate to ensure success:

Sufficient Conversion Volume: AI Max needs a steady stream of conversions to learn and optimize effectively. Without enough data points, the system cannot make accurate predictions.
Budget Stability: Ensure your campaigns are not losing impression share due to budget constraints. Running out of budget can hinder AI Max’s ability to gather performance data.
Proven Broad Match Success: Previous success with broad match keyword strategies is essential. AI Max builds on this experience to extend reach and improve conversions.
Accurate Conversion Tracking: Robust tracking mechanisms must be in place to feed reliable data into AI Max’s algorithms.
Careful Handling of Automated Assets: AI Max may automatically generate assets and expand URLs, which can lead to suboptimal results if not monitored closely.
Focus on Non-Brand Campaigns: Initial tests should prioritize non-brand campaigns that have documented conversion histories to better gauge AI Max’s impact.

Potential Challenges to Monitor

While AI Max promises enhanced efficiency, advertisers should be aware of certain potential pitfalls:

Automated Asset Creation: Automatically generated ad components may not always align with brand tone or strategy.
URL Expansion Issues: Expanded URLs can sometimes lead to less relevant landing pages, affecting conversion quality.
Data Reliance: The algorithm depends heavily on historical data. Inaccurate or incomplete data can degrade performance.

Key Insights

What makes AI Max different from traditional keyword targeting? It uses multiple signals and machine learning rather than relying solely on keywords.
Why is conversion volume crucial? AI Max needs data to optimize effectively; insufficient conversions limit its learning capability.
How can advertisers prepare for AI Max? By ensuring solid conversion tracking, budget stability, and experience with broad match keywords.
What should be monitored post-activation? Automated assets, URL expansions, and campaign performance metrics closely.
Who benefits most from AI Max? Advertisers with established, data-rich campaigns looking to improve efficiency.

Conclusion

Google AI Max offers exciting opportunities for advertisers to enhance their campaign performance through advanced machine learning. However, success depends on meeting key prerequisites such as sufficient conversion data, budget stability, and proven keyword strategies. Advertisers must exercise caution with automated features and conduct thorough testing, especially in non-brand campaigns. By following this pre-test checklist, marketers can position their accounts for a smoother transition to AI Max and better advertising outcomes in the evolving digital landscape.

Source: https://searchengineland.com/google-ai-max-checklist-467929