News 2/25/25
- Scott Davis
- Feb 25
- 3 min read
Music:
News:
"Customer Data Platform: Figures and Market Trends" by Olivier Renard, Feb 24, 2025
"How to restructure your website using personas: A guide for better results" by Adam Heitzman, Feb 20, 2025
"Salesforce and Google Bring Gemini to Agentforce, Enable More Customer Choice in Major Partnership Expansion" Feb 24, 2025
"How AI Agents Are Starting to Automate the Enterprise" by Richard MacManus, Feb 24, 2025
"Cross-Channel Marketing" by David Joosten, May 17, 2024
Worthwhile:
Blink:
How to Take Smart Notes (2017) is exactly that – an explanation of how and why to take smart notes. It explains how this simple, little-known, and often misunderstood technique can aid your thinking, writing, and learning. With the help of smart notes, you may never face the horror of a blank page again.
About the author
Sönke Ahrens is a writer and researcher in education and social science. He’s also the author of the award-winning book Experiment and Exploration: Forms of World-Disclosure.
Word of the Day:
LLM - Large Language Model, is a type of artificial intelligence designed to understand and generate human-like text. It's trained on vast amounts of written data, allowing it to predict and produce coherent responses, translate languages, summarize information, or even write strories. Think of it as a super-smart text processor that excels at conversations and language tasks.
LAM - Large Action Model, is a newer concept that goes beyond just language. It's built to not only understand text but also to take actions in the real world based on that understanding. Imagine an AI that can read your request, figure out what needs to be done, and then, say, book a flight or control a smart device - acting as an agent rather than just a talker. While LLMs are about mastering language, LAMs aim to bridge the gap between comprehension and practice execution.
In short, LLMs chat, LAMs do.
Multichannel Marketing - Engages customers across multiple channels, however, data from each customer interaction on a channel is siloed, meaning data and insights are not shared with other channels. Teams are also siloed in a multichannel marketing strategy, meaning, for example, paid media, email marketing, and product marketing teams operate independently.
Cross-channel Marketing - Engages customers across multiple sales and marketing channels and shares data across the channels. Cross-channel marketing addresses a single topic, goal, or campaign.
Omnichannel Marketing - Engages customers across multiple channels, shares data across the channels, and constantly seeks to belend experiences across channels in more personalized ways. Omnichannel marketing allows customers to interact across multiple platforms and devices simultaneously, with their data from all interactions affecting their experience in real-time.
Multichannel marketing is the least advanced strategy, as it does not integrate the entire journey, and it is quickly becoming outdated based on customer expectations. Omnichannel marketing is the most advanced strategy, however, it also presents unique technical challenges (sometimes involving hundreds of integrations). Cross-channel marketing is attainable for most organizations and serves as a stepping stone to omnichannel marketing.
Metric of the Day:
An AI Literacy metric isn’t a single, universally defined standard, but rather a conceptual framework used to assess an individual’s or group’s understanding and ability to effectively interact with artificial intelligence systems. It typically measures a combination of knowledge, skills, and critical thinking related to AI technologies.
At its core, an AI Literacy metric might evaluate:
Understanding of AI Concepts: Grasping basics like machine learning, neural networks, or natural language processing at a level appropriate to the context—whether it’s a layperson recognizing AI in everyday tools or a developer knowing how algorithms function.
Practical Application: The ability to use AI-driven tools (e.g., chatbots, recommendation systems, or data analysis platforms) efficiently and purposefully.
Critical Evaluation: Recognizing AI’s limitations, biases, and ethical implications, such as spotting when an algorithm might be unfair or misleading.
Adaptability: Comfort with evolving AI technologies and the capacity to learn new systems as they emerge.
Tool/Company of the Day:
Orby - AI Agent Platform company with a proprietary Large Action Model. The platform observes, learns, automates, and adapts to enterprise work. Case Studies Here
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