Weekend Digital Media Round-Up: Broad Targeting To Precision Marketing: How AI And ML Are Transforming Travel Marketing, Data Vs. Findings Vs. Insights In UX, How Agentic AI and Human Collaboration Are Enhancing CX & More…

By Saima Mujawar

  • May 30, 2025,

1.Broad Targeting To Precision Marketing: How AI And ML Are Transforming Travel Marketing

AI and ML are revolutionizing travel marketing by enabling hyper-personalization at scale. Marketers can now create tailored campaigns based on real-time data, breaking down internal silos and enhancing guest experiences. [Source: Forbes]

2. Data Vs. Findings Vs. Insights In UX

​Data consists of raw observations, findings identify patterns, and insights provide actionable recommendations. UX designers must argue for statistical significance to ensure their insights are reliable and impactful for business strategy. [Source: Smashing Magazine]

3. How Agentic AI and Human Collaboration Are Enhancing CX

Agentic AI is revolutionizing retail by acting autonomously to enhance customer experiences through chatbots and virtual assistants. Retailers are leveraging AI for personalized shopping, automated checkout, and improved customer service, creating a competitive edge and fostering human-AI collaboration. [Source: Total Retail]

4. Marketing to Gen Alpha: How brands can win over the next generation

Gen Alpha, born between 2010 and 2024, is brand-aware and influential. Brands should focus on interest-based content and community-driven discovery to connect with them and their millennial parents. [Source: Marketing Dive]

5. From SEO to Generative Engine Optimization (GEO): Why the new era of search belongs to AI and how to stay visible

Generative Engine Optimization (GEO) is emerging as the new strategy for brand visibility in AI-driven search, replacing traditional SEO. GEO focuses on making content quotable by AI systems like ChatGPT, emphasizing relevance and credibility over keyword rankings. Brands must adapt to this shift to stay influential in the evolving digital landscape. [Source: Tech Startups]

6. Why GenAI And Visual Search Are The Future Of Fashion Retail

Generative AI and visual search are revolutionizing fashion retail by offering personalized shopping experiences and enhancing customer engagement. Glance’s app uses GenAI to suggest clothing based on user selfies, creating a unique and tailored shopping experience. Visual search technology is also gaining popularity for its ability to show visually similar products. [Source: Forbes]

7. Reliably Detecting Third-Party Cookie Blocking In 2025

AI-assisted shopping is transforming retail by enhancing and personalizing the consumer experience through tools like voice assistants, chatbots, and predictive analytics. Brands integrating AI into customer interactions are seeing significant benefits, but they must also address risks such as biased data and trust issues to succeed. [Source: Smashing Magazine]

8. How AI-assisted shopping will shake up the retail landscape

AI-assisted shopping is revolutionizing retail by enhancing personalization and customer experience through tools like voice assistants, visual search, and augmented reality try-ons. Brands integrating AI into their strategies can optimize pricing, predict needs, and create seamless omnichannel experiences, but must also address risks like biased data and trust issues. [Source:  The Drum]

9. How Can You Create Winning Content In The Age Of AI And CX?

Creating winning content in the age of AI and customer experience involves blending machine intelligence with human empathy to produce high-quality, personalized content. Leveraging AI-driven insights and advanced segmentation can help businesses understand their audiences better and build stronger relationships. It’s crucial to start with a clear strategic vision before implementing new technologies. [Source: Forbes]

10. Revolutionizing Business Intelligence: The Role of AI in Shaping the Future of Data Engineering and Data Science

AI is revolutionizing data engineering, data science, and business intelligence by automating data workflows, enhancing predictive modeling, and improving data quality management. These advancements are streamlining processes, reducing manual efforts, and enabling more informed decision-making. [Source: Analytics Insight]

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