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There are few things that unite the world like animal videos. There are also few things that are so readily commoditized. Both have occurred in the case of Punch, a baby monkey at the Ichikawa City Zoo in Japan. Punch captured hearts around the world after a viral post showed him hugging a stuffed orangutan toy after being rejected by other monkeys. E-commerce sellers act quickly with monkey merch Now, the young Japanese macaque and his stuffed friend are available as everything from toys on Etsy to a—decide for yourself if it’s AI—children’s book on Amazon. There’s also an “official” Punch Monkey store with products like stickers, shirts, and mugs. Some of the merchandise even contains hopeful sayings, like “Small, but brave,” alongside imagery of the pair. In fact, the original plush orangutan doll is available for $19.99, as it’s one of the Djungelskog soft toys from Ikea. The Swedish retailer has gone so far as to make an advertisement based on Punch and shared to its social channels. In it, a stuffed monkey holds the orangutan while real monkeys appear in the background. The copy reads, “Sometimes, family is who we find along the way.” It then refers to the stuffed toy as “Punch’s comfort orangutan.” Fast Company has reached out to Ikea for more information on the retailer’s orangutan soft toy sales. We will update this post if we hear back. Meanwhile, a new video appears to show Punch having made some progress with his fellow monkeys. But the young creature has already reached the same status as its fellow infamous animals like Moo Deng, the pygmy hippo. View the full article

The February 2026 SEO Update by Yoast is part of our monthly webinar series covering the latest developments in search and AI. In each session, we review the most important news from the past month and explore how it affects your search strategy. Hosted by Carolyn Shelby and Alex Moss, this month’s update focused on AI-driven shifts in search, emerging agentic workflows, and Google’s latest core updates. Below is a recap of the topics discussed and what they mean for your strategy. Watch the full recap on YouTube to hear Carolyn and Alex dive deeper into these topics, answer audience questions, and share real-world examples. SEO and AI news from February 2026 Search engines expand AI reporting and website controls Google and Bing introduced new tools for publishers to manage AI interactions. Bing’s AI Performance Report shows how often Copilot cites your site, including citation counts and queries. Google now allows publishers to control AI access via robots.txt using Google-Extended. Actionable takeaway: Monitor AI citation reports in Bing Webmaster Tools to track visibility Review your robots.txt and AI access settings to align with your strategy Debate over Markdown, AI agents, and machine-readable content OpenAI launched the Codex app, enabling users to manage multiple AI agents for complex tasks. WordPress co-founder Matt Mullenweg proposed making content available in Markdown format to improve AI comprehension, while Cloudflare introduced a Markdown-based approach for AI bots. However, Google’s John Mueller dismissed Markdown files as increasing crawl load. Actionable takeaway: Simplify your site’s structure to make content more accessible to AI agents If your site is overly complex, explore Markdown or structured data alternatives, but prioritize fixing underlying issues first Is Google cracking down on self-promotional listicles? Lily Ray identified a pattern of sites losing visibility due to self-promotional listicles (e.g., “Top 20 SEO Agencies in the US,” with the publisher ranked #1). Google appears to be penalizing manipulative tactics. Actionable takeaway: Avoid self-serving listicles. If creating comparison content, use objective criteria and transparent methodology Microsoft’s vision for a sustainable agentic web Microsoft outlined its approach to agentic search, emphasizing structured data, concise content, and publisher compensation for AI-driven traffic. The shift from human clicks to AI-driven retrieval was highlighted as a major trend. Actionable takeaway: Optimize for machine-readable actions (e.g., structured data, clear CTAs) Prepare for AI-driven monetization models (e.g., compensation for citations) Meta’s Avacado agent and OpenClaw integration Meta is testing Avacado, a new AI agent integrating OpenClaw and Manus for workflow automation. This reflects a broader push toward omnichannel AI interactions. Actionable takeaway: Ensure consistent messaging across all platforms (website, social, email) to reinforce AI comprehension ChatGPT rolls out ads ChatGPT began serving ads to free users, with OpenAI charging advertisers based on ad impressions rather than clicks. The move mirrors traditional search ad models but raises concerns about user experience. Actionable takeaway: Monitor how AI-driven ad placements impact user engagement and brand visibility WebMCP is a new protocol for AI agents Chrome introduced WebMCP, a protocol that enables AI agents to interact with websites via machine-readable actions (e.g., form submissions). Early adoption is limited, but it signals a shift toward agent-first web design. Actionable takeaway: Audit your site’s underlying code for clarity (e.g., semantic HTML, structured data) Proceed cautiously. WebMCP is experimental and could pose security risks if misconfigured Bing Webmaster Tools launches AI Performance Report Bing’s AI Performance Report now shows how often Copilot cites your site, including queries and cited pages. The tool bridges traditional SEO metrics with AI-driven search. Actionable takeaway: Set up Bing Webmaster Tools if you haven’t already Compare Bing’s AI data with Google Search Console to identify gaps Google AI Mode introduces UCP-powered checkout Google’s AI mode now supports UCP-powered checkout, allowing agents to complete purchases on behalf of users. Early adopters include Etsy, Wayfair, and Walmart. Actionable takeaway: If you’re in e-commerce, prioritize structured product data and fast load times to capitalize on agentic commerce OpenClaw, OpenAI, and the future of AI agents The rise of OpenClaw and OpenAI’s advancements underscores a shift toward websites exposing capabilities (not just pages) to AI agents. Early experiments show agents interacting with sites via machine-readable actions. Actionable takeaway: Focus on clear site structure and consistent data to ensure reliable AI interpretation What to focus on in 2026 The February SEO Update by Yoast highlighted four key priorities: Optimize for AI-driven search: Use structured data and markdown to improve AI comprehension Build brand authority across channels: Ensure consistent messaging for AI agents to reinforce Prepare for agentic commerce: Prioritize structured product data and fast load times Avoid low-quality AI content: Google is cracking down on manipulative tactics like self-promotional listicles Sign up for the next SEO Update by Yoast The next SEO Update by Yoast is on March 24, 2026, at 4 PM CET / 10 AM EST. Sign up here to join the live discussion or receive the recording. The post Recap of the February 2026 SEO Update by Yoast appeared first on Yoast. View the full article

Even with the increase in business, Fidelity National Financial reported a net loss for the period, a result of the stock distribution for its life unit. View the full article

The last year has had many of us trying to understand how to report on AI visibility and understand what it takes to be seen and cited by AI. But Rand Fishkin’s latest study on AI response variability has emphasized that LLM outputs aren’t as stable and predictable as search rankings, making this KPI an inconsistent piece of the puzzle. The study found there’s less than a 1 in 100 chance that ChatGPT or Google AI will return the same list of brands across two responses. They analyzed thousands of prompts across multiple LLMs to highlight just how varied they are. This has left some of the SEO community questioning the value of rank tracking at scale. But, rank tracking is far from useless. It’s just misapplied. AI response tracking is an unstable performance KPI in its current state, but it becomes extremely powerful when used as an analysis tool to inform content strategy. Let’s take a look at why you should still be investing in prompt tracking and how it can be used to inform your content strategy. Why AI visibility tracking is unstable (for now) LLMs aren’t deterministic ranking engines. They’re probabilistic language models that can gather and synthesize information from their own training data or live searches. These models use context windows and understanding of intent to serve different answers at any moment. We’ve seen that responses change based on the prompts, and we know that the same question can be written in so many different ways, which opens the door for your CMO to question why you’re not showing up for a specific prompt when they just saw your brand mentioned or cited. Tracking visibility remains an area of uncertainty until there’s greater clarity on user prompting. But it’s still valuable. If prompt response tracking isn’t a stable KPI, then what is it? It’s pattern analysis, something SEOs are very familiar with. Instead of only focusing on whether or not you are cited or listed, you should be trying to understand: How is the prompt response structured? What concepts repeatedly appear? What key phrases or terms are showing up? What level of nuance is typically included? This requires a mental shift. Your customers search everywhere. Make sure your brand shows up. The SEO toolkit you know, plus the AI visibility data you need. Start Free Trial Get started with Dig deeper: 7 hard truths about measuring AI visibility and GEO performance Traditional SEO vs. AI pattern analysis In traditional SEO, we reverse engineer what’s already ranking. With AI search, we can apply the same thinking by reverse engineering the patterns we see in results. Traditional SEOAI pattern analysisMeasures rankingsUnderstanding concept synthesisContent gap analysisTopic associationsFixed results (SERPs)Dynamic responsesDetermined signalsProbability-based responses Analyzing prompt response patterns can help us understand how models synthesize concepts, and not just from the technical level, but at the content level. To define a pattern, you’re not looking for exact response consistency. You’re understanding the structure, themes, and recurring topics. Each LLM model formats its outputs differently, but patterns can still emerge in the structures, despite differences in retrieval methods and how each one functions. I define a pattern by: It appears in 75% or more of outputs. Appears in two different AI models (Like GPT vs. Gemini). Similarities across multiple iterations of the same prompt. The 75% goal felt consistent enough for my sample sizes to highlight a strong pattern versus just randomness. How you define this is truly up to you. There’s no statistical significance in this number. You can adjust this based on your content and space, but for me, this has been the best way to spot consistency over noise. So, say the theme of “pricing transparency” appears in 9 out of 12 responses and across two AI models, that’s not randomness. That’s semantic relevance, and that’s insight. The framework To test this out for yourself, you need a framework that breaks down what you’re looking for. You can break it out into three types of patterns: Structural patterns. Conceptual patterns. Entity patterns. Structural patterns This is where you focus on how the response is organized. You’re looking for: Header/section frequency. List formatting consistency. Order or steps. Pro/con framing. Comparison tables. Decision frameworks. These signals can help show how models organize topics. For example, if the outputs for your prompt show: Definition > Criteria > Tools > Implementation. That’s a structural pattern. You can leverage this to understand what might be helpful to your user, but AI isn’t always right. This is just another tool to identify patterns and decide how it applies to your content. Conceptual patterns These will vary based on your topic focus, but think about the concepts you are targeting. These can be harder to plan for and sometimes take a bit of analysis to start seeing the patterns. For me, I’m focused on “Best domain registrars” as an example, and I’m looking for: Pricing transparency (renewal and purchase). Customer service mentions. Addon inclusions (WHOIS privacy, free emails, free anything). Security features. Bundling options. Transfers. So if I start seeing that renewal prices are commonly discussed across models and variations of this prompt, that signals to me that I need to pay attention to how I frame and discuss it in my articles and product pages. These conceptual patterns help you understand what these models are associated with decision-making. Entity patterns This is where you can view the tools, brands, and other mentions that appear in responses, regardless of their order. This might look like: Brand mentions. Tool mentions. Feature to brand association. Category positioning. Cited sources. In practice, you’d pay attention to how certain features appear with specific brands, or which sites are commonly cited. This helps you evaluate your positioning and identify opportunities with affiliate partners or third-party sites, including which sites you work with and how your brand is positioned on them. Dig deeper: LLM consistency and recommendation share: The new SEO KPI Get the newsletter search marketers rely on. See terms. Building your system You don’t have to invest in prompt-tracking tools to do this, though they make it easier. I handle it manually. It’s not perfect, but it works. If you can’t involve multiple team members, adapt the structure to fit your resources. You may need to track over a longer period or lower your pattern threshold. Instead of 75% consistency, you might set it at 60%. Step 1: Select and cluster your prompts Identify three priority topics you want to track. For each of those topics, come up with 3-5 versions of prompts that would align with that topic. For example, one of my priority topics is finding a domain registrar, so this cluster for me includes: How do I register a domain name? How can I get a domain name? Where can I buy a domain? Step 2: Set up your tracking sheet You’ll need a place to track the responses, like an old-fashioned spreadsheet with the following columns: PromptLLMWeb Search? Y/NDateResponseSources (If Applicable)Is My Brand Mentioned? In the LLM column, note the platform and model to help control for when new versions are released. This is just to start gathering your data. When you know what patterns to look for, add those to the sheet. Consider using Claude or ChatGPT to help with the analysis, so you don’t have to do everything manually. Step 3: Create a tracking plan and start tracking To do this effectively, you need to define: Which models you want to track. Whether search mode is on or off, or left to the model to decide. How many times you want to run each prompt on each model. What frequency you want to track. It’s also helpful to involve other team members, if possible, and use private modes to minimize context influence. Once a week, a handful of my team members run each prompt through ChatGPT, AI Overviews, AI Mode, and Perplexity. Each person tests every prompt across each model, giving me 3-5 responses per prompt, per model, per week. Step 4: Analyze Once you’ve gathered 20–30 responses per prompt, start analyzing. You can use the tool of your choice to streamline this process. From there, identify recurring patterns and map them to relevant pages on your site. Where can you address these themes? Are you answering the right questions, and does your content reflect the patterns you’ve uncovered? This is ongoing work. Track consistently and review patterns quarterly to identify shifts. Over time, this becomes your optimization framework. Dig deeper: How to create answer-first content that AI models actually cite Where AI pattern analysis can mislead you AI is based on probability, and it won’t always be right. This isn’t the only way of optimizing for AI, but it can be part of your playbook. You still run the risk of bias in the training data, inconsistency in whether search or training data was used, and variations in the new “models” launched across the different LLMs. You shouldn’t be blindly aligning with the AI outputs, but you can use your best judgment and understanding of your target audience to understand if it’s the context you want to use for your optimization. How to connect this to performance Now this is the tricky part. We’ve learned just how random AI responses can be, but there are still a few signals you can measure to see how this impacts your content. “Traditional” metrics: Are you seeing more clicks? Better positions in GSC or keyword tracking tools? What about conversions? AI traffic: If you’re able to pull your AI traffic data from Adobe, GA4, or any other analytics tools, you can track to see if there’s any movement on the pages you update. AI tracking tools: And while yes, there’s a lot of variability in this as a KPI, if you’re using AI visibility tools, they will give you an indication of whether your methods are working. You can leverage the same manual tracking outlined here to see if you start noticing your brand emerge as a pattern. See the complete picture of your search visibility. Track, optimize, and win in Google and AI search from one platform. Start Free Trial Get started with Start studying AI outputs There are still many unknowns with LLMs, and it feels like they’re changing every day. But one thing remains consistent: these tools provide answers. If there’s any level of understanding you can get on those answers, you can try to use it. The patterns in the responses can reveal how topics are understood and how brands are discussed, and give you an idea of how to adapt your content strategy. View the full article

In the comments on a recent post, someone mentioned that a boss once sent them home because they’d forgotten to wear a belt that day (“I wasn’t showing butt cleavage, but he wasn’t having it.”) Someone else mentioned a boss who expected people to rise whenever he entered the office (?!). Let’s discuss managers and offices with weirdly outdated expectations who appear to be from a far-off era. The post let’s discuss throwback bosses: managers with outdated work expectations appeared first on Ask a Manager. View the full article

Yet another powerful person has stepped down after being named in the Epstein files. Børge Brende, president and CEO of the World Economic Forum (WEF), best known for hosting an annual summit of world leaders in Davos, Switzerland, has stepped down after an internal investigation into his ties to convicted sex offender Jeffrey Epstein. In a statement released Thursday, Brende announced that after eight years in his role, he’d be resigning in the wake of the latest batch of files released from the federal investigation into Epstein. “I am grateful for the incredible collaboration with my colleagues, partners, and constituents, and I believe now is the right moment for the Forum to continue its important work without distractions,” Brende said. WEF co-chairs André Hoffmann and Larry Fink also released a statement on behalf of the Board of Trustees, thanking Brende for his years of service and respecting his choice to step down. “His dedication and leadership have been instrumental during a pivotal period of reforms for the organization, leading to a successful annual meeting in Davos,” they said. They also noted that the WEF’s investigation into Brende found “no additional concerns beyond what has been previously disclosed.” Though Brende had previously claimed he “was completely unaware of [Epstein’s] criminal acts and past” in statements to the Norwegian media, the newly released collection of Epstein files tell a different story. Epstein and Brende stayed in contact long after Epstein was convicted of soliciting a minor for prostitution in 2008, with messages between the two continuing through at least mid-2019, just months before Epstein died in jail. In one text exchange, Epstein appears to have sent Brende a letter by his lawyers that was published in the The New York Times, which included the claim, “The number of young women involved in the investigation has been vastly exaggerated.” Brende replied to the letter with a thumbs-up emoji. Brende’s resignation comes less than a year after the last shakeup at the WEF. In April 2025, founder Klaus Schwab stepped down as chair of its board, and a month later in May, the board opened an investigation into Schwab after an anonymous letter accused him of misusing funds and making inappropriate comments toward women. Between the two scandals, the WEF’s reputation as a mecca for world leaders has taken a massive hit. In Brende’s absence, the WEF’s managing director Alois Zwinggi will serve as interim president and CEO. Brende is far from the only executive to step down after appearing in the Epstein files. Since the newest batch of files released on January 30, business leaders including Hollywood agent Casey Wasserman and former general counsel for Goldman Sachs Kathryn Ruemmler have resigned from their positions, while political figures including Britain’s Andrew Mountbatten-Windsor, formerly known as Prince Andrew, and Peter Mandelson, the country’s ambassador to Washington, have been arrested for their ties to Epstein. View the full article

When you look at successful franchises, it’s clear they set new business standards through innovative practices. Chick-fil-A emphasizes customer service and quality, whereas Anytime Fitness offers a flexible, semi-absentee model. Dunkin’ uses advanced technology for efficiency, Jersey Mike’s focuses on fresh ingredients and community ties, and 7-Eleven streamlines operations with innovative back-office solutions. Each franchise presents unique methods that could inform your own business strategy, revealing important lessons for aspiring entrepreneurs. Key Takeaways Innovative technology integration streamlines operations, enhancing efficiency and franchisee satisfaction in successful franchises. Customer-centric approaches focus on high-quality ingredients and exceptional service, fostering loyalty and growth. Semi-absentee business models empower franchisees to manage multiple locations while maintaining operational standards. Continuous improvement cultures enable franchises to adapt to market changes and sustain growth effectively. Strong brand recognition and transparent financial performance attract and retain profitable franchisees. Innovative Practices Driving Success Innovative practices are essential for driving success in the franchise industry, as they enable brands to stay competitive and responsive to market demands. Successful franchises often develop robust support systems, ensuring franchisees feel respected and supported. For instance, Sport Clips boasts an 86% respect rate among its franchisees, enhancing operational efficiency. Incorporating technology, like the advanced back-office solutions from Window Genie, helps streamline processes, leading to improved service delivery and customer satisfaction. Moreover, innovative business models, such as the semi-absentee approach from Salons by JC, empower franchisees to manage multiple locations, maximizing revenue potential. Ongoing training and marketing support, seen with Payroll Vault, further enable franchisees to navigate challenges effectively, contributing to a profitable franchise business. Customer-Centric Approaches in Franchising Customer satisfaction stands as a cornerstone in the franchise industry, shaping the strategies of successful brands. For instance, franchises like Kona Ice report that 99% of franchisees enjoy their business operations, showcasing the importance of a customer-centric approach. Wingstop focuses on high-quality ingredients, enhancing customer loyalty and franchisee growth. Payroll Vault’s low overhead allows franchisees to prioritize exceptional service, contributing to its status among the most profitable franchises of all time. The Franchise Satisfaction Index (FSI) highlights the relationship between franchisee engagement and customer-centric practices. Brands like Sport Clips implement a manager-run model, enabling franchisees to concentrate on customer service, further establishing themselves as some of the best franchises to own in Texas during promoting operational excellence and growth. Leveraging Technology for Operational Efficiency How can franchises effectively utilize technology to improve operational efficiency? By leveraging innovative solutions, top restaurant McDonald’s franchises and the most successful food franchises streamline their operations. Here’s how you can improve your franchise’s efficiency: Use advanced back-office technology for better operational management. Implement digital training programs to quickly adapt best practices. Employ appointment scheduling software for optimized staffing. Utilize digital analytics for targeted marketing campaigns. Improve customer management systems to enhance service delivery. These strategies not just reduce costs but additionally boost profitability and return on investment. As you integrate technology into your operations, you’ll notice an increase in customer satisfaction, leading to repeat business and a solid competitive edge in the market. Cultivating a Culture of Continuous Improvement Cultivating a culture of continuous improvement is essential for franchises aiming to sustain growth and adapt to ever-changing market conditions. The highest earning franchise brands prioritize feedback from franchisees, implementing changes that lead to high satisfaction scores, like Kona Ice’s 99%. Many top food franchises invest in ongoing training and development, ensuring franchisees stay updated on industry trends. This commitment boosts long-term success and resilience. Extensive operational support, exemplified by Sport Clips’ manager-run model, allows franchisees to focus on growth rather than daily tasks. By leveraging technology and marketing innovations, franchises increase brand visibility and efficiency, nurturing environments where franchisees thrive. In the end, these strategies contribute to significant growth and strong market positioning for successful franchises like Window Genie. Inspiring Business Strategies for Aspiring Entrepreneurs Franchises that embody a culture of continuous improvement often set a strong example for aspiring entrepreneurs looking to establish their own business ventures. Learning from the hottest restaurant franchises and top grossing franchises can provide valuable insights. Here are some strategies to reflect upon: Leverage strong brand recognition to attract customers. Prioritize franchisee satisfaction for better retention and profitability. Tap into emerging industries, like health and wellness, to meet market demands. Guarantee transparency in financial performance to build trust. Offer thorough training resources to help new franchisees succeed. Frequently Asked Questions Why Is It Only $10,000 to Open a Chick-Fil-A? Chick-fil-A’s franchise fee is only $10,000 since the company retains ownership of the restaurant and land, allowing franchisees to concentrate on operations without property costs. This low entry fee, combined with substantial training and support, encourages franchisee success. Nevertheless, applicants must meet strict financial criteria, including a net worth of approximately $1 million and at least $500,000 in liquid assets, ensuring they can effectively manage the business. What Is the Most Profitable Franchise to Own? The most profitable franchise to own often depends on various factors, including location and personal interests. McDonald’s leads with a 20% ROI because of its brand strength and efficient operations. Dunkin’ offers around $1.2 million in annual revenue, thanks to a loyal customer base. Furthermore, Wingstop franchises report average sales exceeding $1.5 million. Each option presents unique advantages, so it’s essential to evaluate your circumstances and goals before deciding. What Is the Most Successful Franchise of All Time? The most successful franchise of all time is McDonald’s, operating over 39,000 locations worldwide and generating annual revenues exceeding $46 billion as of 2022. Its franchise model offers a proven system, extensive training, and a globally recognized brand, which improves the success rate of franchisees. What Is the 7 Day Rule for Franchise? The 7-Day Rule for franchises requires franchisors to provide the Franchise Disclosure Document (FDD) to prospective franchisees at least 14 days before any agreements or payments. This timeframe allows you to thoroughly review the information, consult with advisors, and understand the franchise’s terms. Adhering to this rule is essential for compliance with Federal Trade Commission regulations, ensuring transparency and protecting your interests as a potential franchisee from misleading practices and unforeseen obligations. Conclusion In summary, these five franchises exemplify how innovative practices, customer-centric approaches, and technology can redefine business standards in the franchising industry. By promoting a culture of continuous improvement and implementing effective strategies, they set a benchmark for aspiring entrepreneurs. Comprehending these successful models can guide you in making informed decisions if you’re considering entering the franchise world. Embracing these principles can improve your chances of creating a thriving business in today’s competitive environment. Image via Google Gemini and ArtSmart This article, "5 Successful Franchises That Redefine Business Standards" was first published on Small Business Trends View the full article

When you look at successful franchises, it’s clear they set new business standards through innovative practices. Chick-fil-A emphasizes customer service and quality, whereas Anytime Fitness offers a flexible, semi-absentee model. Dunkin’ uses advanced technology for efficiency, Jersey Mike’s focuses on fresh ingredients and community ties, and 7-Eleven streamlines operations with innovative back-office solutions. Each franchise presents unique methods that could inform your own business strategy, revealing important lessons for aspiring entrepreneurs. Key Takeaways Innovative technology integration streamlines operations, enhancing efficiency and franchisee satisfaction in successful franchises. Customer-centric approaches focus on high-quality ingredients and exceptional service, fostering loyalty and growth. Semi-absentee business models empower franchisees to manage multiple locations while maintaining operational standards. Continuous improvement cultures enable franchises to adapt to market changes and sustain growth effectively. Strong brand recognition and transparent financial performance attract and retain profitable franchisees. Innovative Practices Driving Success Innovative practices are essential for driving success in the franchise industry, as they enable brands to stay competitive and responsive to market demands. Successful franchises often develop robust support systems, ensuring franchisees feel respected and supported. For instance, Sport Clips boasts an 86% respect rate among its franchisees, enhancing operational efficiency. Incorporating technology, like the advanced back-office solutions from Window Genie, helps streamline processes, leading to improved service delivery and customer satisfaction. Moreover, innovative business models, such as the semi-absentee approach from Salons by JC, empower franchisees to manage multiple locations, maximizing revenue potential. Ongoing training and marketing support, seen with Payroll Vault, further enable franchisees to navigate challenges effectively, contributing to a profitable franchise business. Customer-Centric Approaches in Franchising Customer satisfaction stands as a cornerstone in the franchise industry, shaping the strategies of successful brands. For instance, franchises like Kona Ice report that 99% of franchisees enjoy their business operations, showcasing the importance of a customer-centric approach. Wingstop focuses on high-quality ingredients, enhancing customer loyalty and franchisee growth. Payroll Vault’s low overhead allows franchisees to prioritize exceptional service, contributing to its status among the most profitable franchises of all time. The Franchise Satisfaction Index (FSI) highlights the relationship between franchisee engagement and customer-centric practices. Brands like Sport Clips implement a manager-run model, enabling franchisees to concentrate on customer service, further establishing themselves as some of the best franchises to own in Texas during promoting operational excellence and growth. Leveraging Technology for Operational Efficiency How can franchises effectively utilize technology to improve operational efficiency? By leveraging innovative solutions, top restaurant McDonald’s franchises and the most successful food franchises streamline their operations. Here’s how you can improve your franchise’s efficiency: Use advanced back-office technology for better operational management. Implement digital training programs to quickly adapt best practices. Employ appointment scheduling software for optimized staffing. Utilize digital analytics for targeted marketing campaigns. Improve customer management systems to enhance service delivery. These strategies not just reduce costs but additionally boost profitability and return on investment. As you integrate technology into your operations, you’ll notice an increase in customer satisfaction, leading to repeat business and a solid competitive edge in the market. Cultivating a Culture of Continuous Improvement Cultivating a culture of continuous improvement is essential for franchises aiming to sustain growth and adapt to ever-changing market conditions. The highest earning franchise brands prioritize feedback from franchisees, implementing changes that lead to high satisfaction scores, like Kona Ice’s 99%. Many top food franchises invest in ongoing training and development, ensuring franchisees stay updated on industry trends. This commitment boosts long-term success and resilience. Extensive operational support, exemplified by Sport Clips’ manager-run model, allows franchisees to focus on growth rather than daily tasks. By leveraging technology and marketing innovations, franchises increase brand visibility and efficiency, nurturing environments where franchisees thrive. In the end, these strategies contribute to significant growth and strong market positioning for successful franchises like Window Genie. Inspiring Business Strategies for Aspiring Entrepreneurs Franchises that embody a culture of continuous improvement often set a strong example for aspiring entrepreneurs looking to establish their own business ventures. Learning from the hottest restaurant franchises and top grossing franchises can provide valuable insights. Here are some strategies to reflect upon: Leverage strong brand recognition to attract customers. Prioritize franchisee satisfaction for better retention and profitability. Tap into emerging industries, like health and wellness, to meet market demands. Guarantee transparency in financial performance to build trust. Offer thorough training resources to help new franchisees succeed. Frequently Asked Questions Why Is It Only $10,000 to Open a Chick-Fil-A? Chick-fil-A’s franchise fee is only $10,000 since the company retains ownership of the restaurant and land, allowing franchisees to concentrate on operations without property costs. This low entry fee, combined with substantial training and support, encourages franchisee success. Nevertheless, applicants must meet strict financial criteria, including a net worth of approximately $1 million and at least $500,000 in liquid assets, ensuring they can effectively manage the business. What Is the Most Profitable Franchise to Own? The most profitable franchise to own often depends on various factors, including location and personal interests. McDonald’s leads with a 20% ROI because of its brand strength and efficient operations. Dunkin’ offers around $1.2 million in annual revenue, thanks to a loyal customer base. Furthermore, Wingstop franchises report average sales exceeding $1.5 million. Each option presents unique advantages, so it’s essential to evaluate your circumstances and goals before deciding. What Is the Most Successful Franchise of All Time? The most successful franchise of all time is McDonald’s, operating over 39,000 locations worldwide and generating annual revenues exceeding $46 billion as of 2022. Its franchise model offers a proven system, extensive training, and a globally recognized brand, which improves the success rate of franchisees. What Is the 7 Day Rule for Franchise? The 7-Day Rule for franchises requires franchisors to provide the Franchise Disclosure Document (FDD) to prospective franchisees at least 14 days before any agreements or payments. This timeframe allows you to thoroughly review the information, consult with advisors, and understand the franchise’s terms. Adhering to this rule is essential for compliance with Federal Trade Commission regulations, ensuring transparency and protecting your interests as a potential franchisee from misleading practices and unforeseen obligations. Conclusion In summary, these five franchises exemplify how innovative practices, customer-centric approaches, and technology can redefine business standards in the franchising industry. By promoting a culture of continuous improvement and implementing effective strategies, they set a benchmark for aspiring entrepreneurs. Comprehending these successful models can guide you in making informed decisions if you’re considering entering the franchise world. Embracing these principles can improve your chances of creating a thriving business in today’s competitive environment. Image via Google Gemini and ArtSmart This article, "5 Successful Franchises That Redefine Business Standards" was first published on Small Business Trends View the full article

Everyone who has tried to code with Anthropic’s Claude Code AI agents runs into the same usability problem: If you run two or three concurrent artificial intelligence sessions—say, one rewriting your server code, another generating tests, a third doing background research—you are forced to manually hunt through separate terminal tabs, each one generating a relentless stream of machine-readable log entries, just to figure out what each program is actually doing at any given moment. Not only is it hard to follow what’s really going on, but not checking constantly can also lead to problems, as agents might stop to ask you something and you won’t notice it for minutes or hours. Developer Pablo De Lucca thought there had to be another way: What if you could create a control panel and alert system that bridges the AI coding agents with your brain in an intuitive way, allowing you to control at a glance what’s going on? That’s how Pixel Agents was born. Pixel Agents is an extension that runs inside Visual Studio Code, the most popular code editor on the planet. If you have no idea what I’m talking about, that’s okay. The important thing to know here is that the UX of agentic coding could someday soon look a lot different. While it looks like an adorable 8-bit video game, Pixel Agents is not something you can play. Rather, it transforms the user experience of coding with Anthropic’s Claude Code agentic AIs by turning them into sprite characters who live, work, and interact in an office doing your bidding. The extension draws directly from the language of video games because it’s something everyone understands. “I envision a future where agent-based user interfaces resemble a video game more than a traditional IDE,” he said in the Reddit thread introducing his tool. “Projects like AI Town have demonstrated the appeal of visualizing agents as characters within a tangible space, which I find much more engaging than just viewing endless lines of terminal text.” How Pixel Agents worksThe extension achieves this transformation by acting as a silent observer. Think of Anthropic’s Claude Code as a worker who keeps a detailed, timestamped diary of every action it takes: every file it opens, every command it runs, every moment it waits. These diaries are stored in a format called JSONL transcript files, essentially a structured log that records the machine’s activity in real time. Pixel Agents reads these logs continuously, without touching or modifying Claude Code itself, and uses the entries as triggers to update the state of the corresponding character, animating them on screen and making them “talk” using speech bubbles when needed. Developers can customize the virtual office where these characters live to better suit their needs. A built-in layout editor lets them design their own workspace on a grid that can be expanded to up to 64 by 64 tiles, with furniture, walls, and floors arranged to taste. Then, each concurrent Claude Code session spawns one of six distinct animated pixel art character designs into that space. The layout persists across VS Code windows so the office retains its configuration between work sessions. The result is a spatial map of your entire active workload. “Each character moves around, takes a seat at a desk, and visually represents the actions of the agent,” De Lucca describes on Reddit. “For instance, when coding, the character types; when searching for files, it appears to read; and if it’s waiting for input, a speech bubble appears.” Love them bubblesOne of the most persistent frustrations in AI-assisted development is the blocked agent. That’s when a program that has paused its work to request human authorization (for example, permission to execute a potentially destructive system command) sits completely idle. It’s usually invisible inside a minimized terminal tab until the developer happens to notice it. Pixel Agents converts that invisible pause into a visual and audio event: an amber bubble over the character’s head, with an optional sound notification. The extension also tackles a second, subtler problem: the spawning of sub-agents. Modern AI coding tools routinely break large tasks into smaller pieces, launching temporary child processes to handle discrete sub-problems before terminating. In a text terminal, the birth and death of these ephemeral processes is nearly invisible and cognitively taxing to follow. Inside the Pixel Agents office, each sub-agent physically materializes as a separate character visually linked to its parent, then disappears with a dedicated exit animation the moment its job is complete. De Lucca says that the sub-agents “enter and exit with neat animations reminiscent of the Matrix.”​ That way, the workload hierarchy becomes something you can see rather than something you have to infer from logs. The extension is free but the furniture and office tile graphics come from a commercial asset pack called ‘Office Interior Tileset (16×16)’ by an artist named Donarg, which is available on itch.io for $2. De Lucca has publicly called for community contributions of public domain art assets to fully open and extend the visual ecosystem. Hopefully people will contribute. Pixel Agents is one of those happy ideas that solve a real problem in a fun way, making the invisible visible and turning the annoying into entertainment. Translating the abstract, parallel labor of multiple autonomous machines into a spatial, ambient picture that a human brain can monitor at a glance is definitely something to admire. Whether that constitutes the beginning of a broader shift in how we design interfaces for AI tools remains to be seen, but as a proof of concept, it is hard to argue with.​ View the full article

Customized product recommendations are customized suggestions created to improve your shopping experience. They analyze your browsing history, past purchases, and search queries using advanced algorithms. There are two main techniques: collaborative filtering, which finds similarities between users, and content-based filtering, which focuses on the characteristics of items you’ve liked. Comprehending how these systems work can lead to more relevant suggestions. But what implications do these recommendations have for businesses and consumers alike? Key Takeaways Personalized product recommendations tailor suggestions to individual users by analyzing their behavior, including browsing history and past purchases. Recommendation engines use algorithms, including collaborative filtering and content-based filtering, to generate tailored product suggestions. Collaborative filtering identifies patterns in similar users’ buying behaviors, while content-based filtering focuses on specific product attributes. Hybrid systems combine both approaches for improved accuracy and relevance in recommendations. Implementing personalized recommendations increases customer engagement, conversion rates, and overall shopping satisfaction. Understanding Personalized Product Recommendations Grasping customized product recommendations is essential for enhancing the online shopping experience. These recommendations leverage algorithms that analyze your behavior, such as your search queries, browsing history, and past purchases, to generate personalized suggestions. By utilizing collaborative filtering, which looks at similarities in purchasing behaviors among users, and content-based filtering, which focuses on product attributes you’ve previously liked, e-commerce recommendations become more relevant. Studies show that 55% of return customers who engage with these suggestions are more likely to make a purchase, demonstrating their effectiveness. Additionally, personalized recommendations can lead to a 150% increase in order rates and a 20% rise in items added to shopping carts. This seamless shopping experience reduces decision fatigue, nurturing deeper customer loyalty, as 62% of shoppers prefer personalized suggestions over generic ones. Comprehending how these recommendations work enables you to make better buying decisions and enjoy a more satisfying shopping experience. The Technology Behind Recommendation Engines Recommendation engines are essential tools in e-commerce, utilizing sophisticated algorithms to analyze user behavior and preferences for generating personalized product suggestions. They employ various strategies, such as the Amazon recommendation algorithm, to improve the shopping experience. Here’s how they function: Collaborative filtering: Analyzes data from multiple users to find similar purchasing behaviors. Content-based filtering: Focuses on individual user preferences and item characteristics. Hybrid systems: Combine both collaborative and content-based approaches for accuracy. Machine learning models: Continuously improve recommendations by training on user interactions and demographics. Data sources: Utilize search queries, browsing history, and social media interactions to boost relevance. Types of Recommendation Systems In terms of recommendation systems, two primary types stand out: collaborative filtering and content-based filtering. Collaborative filtering analyzes user activities to suggest items based on the preferences of similar users, whereas content-based filtering recommends products based on features and similarities to items you’ve previously liked. Comprehending these systems can help you see how personalized recommendations improve your shopping experience. Collaborative Filtering Systems Collaborative filtering systems play a crucial role in modern ecommerce by analyzing user activities and preferences to make customized product recommendations. These systems identify patterns among similar users, enhancing the shopping experience. They can be classified into: Memory-Based Collaborative Filtering: Groups users with shared interests, predicting preferences based on past interactions. Model-Based Collaborative Filtering: Utilizes machine learning to forecast future preferences from historical data. User-Based Filtering: Focuses on the similarities between users. Item-Based Filtering: Looks at similarities between products themselves. Sales Boost: Approximately 80% of businesses see a 38% increase in average order value through these ecommerce recommendations, underscoring their effectiveness in driving sales and improving customer engagement. Content-Based Filtering Systems Content-based filtering systems offer a customized approach to product recommendations by focusing on the specific characteristics of items that users have previously liked or purchased. These systems analyze product features, such as color, size, and style, to create personalized ecommerce product recommendations that align closely with your preferences. By evaluating similarities between products, they improve the relevance of suggestions, making it easier for you to discover items that match your tastes. Moreover, content-based filtering is particularly beneficial for new customers since it generates personalized ai product recommendations without requiring extensive historical data from similar users. This targeted approach effectively caters to niche interests, ensuring that every recommendation feels uniquely suited to your individual shopping experience. Benefits of Personalized Recommendations Customized recommendations greatly improve your shopping experience by providing personalized product suggestions that align with your preferences and browsing habits. This not just makes it easier for you to find what you’re looking for but additionally increases the likelihood of making further purchases, driving up sales potential for retailers. Enhanced Shopping Experience Enhancing your shopping experience is essential in today’s competitive e-commerce environment, especially when customized recommendations guide you toward products that truly match your needs. Personalized product recommendations, driven by an efficient ecommerce recommendation engine, help you navigate vast catalogs seamlessly. Consider these benefits: Increased likelihood of purchase, with 70% of new customers engaging with recommendations. Higher engagement, leading to a 150% rise in order rates. Reduced cart abandonment, addressing the 67.49% average in retail. Boosted average order value, with increases of up to 38%. Enhanced customer loyalty, as 56% are likely to repurchase after a customized experience. These factors highlight how personalized recommendations transform your shopping experience into a more satisfying and efficient endeavor. Increased Sales Potential As you explore the domain of e-commerce, comprehension of how personalized recommendations can improve your shopping experience is crucial. Utilizing an AI recommendation engine, businesses can customize product suggestions based on your browsing history and preferences. This personalization greatly boosts sales potential. In fact, customers exposed to these customized recommendations are 70% more likely to make a purchase, leading to a 150% increase in order rates. Additionally, personalized product suggestion engines can elevate average order value by up to 38%, with 80% of businesses reporting this improvement. By providing relevant recommendations, cart abandonment rates decrease, making it easier for you to find and buy desired items, ultimately nurturing customer loyalty and encouraging repeat purchases. Best Practices for Implementing Recommendations To effectively implement personalized recommendations in ecommerce, it’s essential to adopt best practices that optimize customer interaction and improve sales performance. Here are some strategies to take into account: A/B Testing: Continuously evaluate placements and content to find what engages customers best. Data Utilization: Leverage customer demographics, browsing history, and real-time search queries for customized suggestions. Strategic Placement: Position recommendations during checkout or on 404 error pages to encourage purchases and reduce cart abandonment. Algorithm Updates: Regularly refine your recommendation algorithms based on new consumer data and trends to keep suggestions relevant. Quality Over Quantity: Curate a limited volume of recommendations to improve user experience without overwhelming customers. Real-World Examples of Personalized Recommendations Personalized recommendations play a significant role in enhancing the customer experience across various e-commerce platforms. For instance, Amazon employs a recommendations engine using collaborative filtering to suggest products based on similar customers’ buying behaviors, displaying sections like “Customers who bought this likewise bought.” Netflix utilizes content-based filtering by analyzing your viewing history, recommending shows and movies that align with your preferences. Spotify combines user listening patterns with content information in a hybrid recommendation system, creating personalized playlists like “Discover Weekly.” Online retailers, such as Kylie Cosmetics, recommend complementary products, suggesting lipstick shades that pair well with your previous purchases. Furthermore, brands often use customized email campaigns, including abandoned cart reminders with AI recommendations for items you’ve viewed, encouraging you to complete purchases. These real-world examples demonstrate how effective personalized recommendations can drive engagement and conversion across diverse platforms. Frequently Asked Questions How Do Personalized Recommendations Work? Customized recommendations work by analyzing your browsing history, purchase patterns, and demographic data. Algorithms, like collaborative and content-based filtering, identify products that align with your preferences and past behaviors. These systems adapt over time, improving suggestions based on real-time interactions. How Do Product Recommendations Work? Product recommendations work by analyzing your behavior, including search queries, browsing history, and past purchases. Algorithms, like collaborative filtering and content-based filtering, compare your preferences with those of similar users or suggest items based on previously liked features. This data helps the system generate customized suggestions, enhancing your shopping experience. In the end, these recommendations guide you to products that align with your interests, making it easier to discover items you’re likely to purchase. What Is the Main Benefit of Personalized Recommendations? The main benefit of customized recommendations lies in their ability to improve the shopping experience. By analyzing your browsing history and preferences, these recommendations suggest products designed for your interests, making it easier to discover relevant items. This not just saves time but furthermore increases the likelihood of making a purchase. As a result, personalized recommendations can greatly boost sales, with businesses experiencing higher average order values and improved customer retention rates. Which Technology Is Most Commonly Used for Personalized Product Recommendations? The most commonly used technology for personalized product recommendations involves machine learning algorithms. These algorithms analyze user behavior, preferences, and purchase history to generate customized suggestions. Collaborative filtering systems leverage data from multiple users, whereas content-based filtering focuses on individual characteristics and past interactions. Hybrid systems combine both approaches for improved accuracy. Real-time data analysis, including browsing history and user events, is essential for delivering relevant recommendations that increase engagement and conversion rates. Conclusion In conclusion, personalized product recommendations play an essential role in enhancing online shopping experiences. By utilizing collaborative and content-based filtering techniques, businesses can effectively suggest products customized to individual preferences. Implementing these systems can lead to increased customer satisfaction and higher conversion rates. As you explore the realm of e-commerce, comprehending how these recommendation engines function will help you appreciate the customized experiences designed to meet your needs and preferences. Image via Google Gemini This article, "What Are Personalized Product Recommendations and How Do They Work?" was first published on Small Business Trends View the full article

Customized product recommendations are customized suggestions created to improve your shopping experience. They analyze your browsing history, past purchases, and search queries using advanced algorithms. There are two main techniques: collaborative filtering, which finds similarities between users, and content-based filtering, which focuses on the characteristics of items you’ve liked. Comprehending how these systems work can lead to more relevant suggestions. But what implications do these recommendations have for businesses and consumers alike? Key Takeaways Personalized product recommendations tailor suggestions to individual users by analyzing their behavior, including browsing history and past purchases. Recommendation engines use algorithms, including collaborative filtering and content-based filtering, to generate tailored product suggestions. Collaborative filtering identifies patterns in similar users’ buying behaviors, while content-based filtering focuses on specific product attributes. Hybrid systems combine both approaches for improved accuracy and relevance in recommendations. Implementing personalized recommendations increases customer engagement, conversion rates, and overall shopping satisfaction. Understanding Personalized Product Recommendations Grasping customized product recommendations is essential for enhancing the online shopping experience. These recommendations leverage algorithms that analyze your behavior, such as your search queries, browsing history, and past purchases, to generate personalized suggestions. By utilizing collaborative filtering, which looks at similarities in purchasing behaviors among users, and content-based filtering, which focuses on product attributes you’ve previously liked, e-commerce recommendations become more relevant. Studies show that 55% of return customers who engage with these suggestions are more likely to make a purchase, demonstrating their effectiveness. Additionally, personalized recommendations can lead to a 150% increase in order rates and a 20% rise in items added to shopping carts. This seamless shopping experience reduces decision fatigue, nurturing deeper customer loyalty, as 62% of shoppers prefer personalized suggestions over generic ones. Comprehending how these recommendations work enables you to make better buying decisions and enjoy a more satisfying shopping experience. The Technology Behind Recommendation Engines Recommendation engines are essential tools in e-commerce, utilizing sophisticated algorithms to analyze user behavior and preferences for generating personalized product suggestions. They employ various strategies, such as the Amazon recommendation algorithm, to improve the shopping experience. Here’s how they function: Collaborative filtering: Analyzes data from multiple users to find similar purchasing behaviors. Content-based filtering: Focuses on individual user preferences and item characteristics. Hybrid systems: Combine both collaborative and content-based approaches for accuracy. Machine learning models: Continuously improve recommendations by training on user interactions and demographics. Data sources: Utilize search queries, browsing history, and social media interactions to boost relevance. Types of Recommendation Systems In terms of recommendation systems, two primary types stand out: collaborative filtering and content-based filtering. Collaborative filtering analyzes user activities to suggest items based on the preferences of similar users, whereas content-based filtering recommends products based on features and similarities to items you’ve previously liked. Comprehending these systems can help you see how personalized recommendations improve your shopping experience. Collaborative Filtering Systems Collaborative filtering systems play a crucial role in modern ecommerce by analyzing user activities and preferences to make customized product recommendations. These systems identify patterns among similar users, enhancing the shopping experience. They can be classified into: Memory-Based Collaborative Filtering: Groups users with shared interests, predicting preferences based on past interactions. Model-Based Collaborative Filtering: Utilizes machine learning to forecast future preferences from historical data. User-Based Filtering: Focuses on the similarities between users. Item-Based Filtering: Looks at similarities between products themselves. Sales Boost: Approximately 80% of businesses see a 38% increase in average order value through these ecommerce recommendations, underscoring their effectiveness in driving sales and improving customer engagement. Content-Based Filtering Systems Content-based filtering systems offer a customized approach to product recommendations by focusing on the specific characteristics of items that users have previously liked or purchased. These systems analyze product features, such as color, size, and style, to create personalized ecommerce product recommendations that align closely with your preferences. By evaluating similarities between products, they improve the relevance of suggestions, making it easier for you to discover items that match your tastes. Moreover, content-based filtering is particularly beneficial for new customers since it generates personalized ai product recommendations without requiring extensive historical data from similar users. This targeted approach effectively caters to niche interests, ensuring that every recommendation feels uniquely suited to your individual shopping experience. Benefits of Personalized Recommendations Customized recommendations greatly improve your shopping experience by providing personalized product suggestions that align with your preferences and browsing habits. This not just makes it easier for you to find what you’re looking for but additionally increases the likelihood of making further purchases, driving up sales potential for retailers. Enhanced Shopping Experience Enhancing your shopping experience is essential in today’s competitive e-commerce environment, especially when customized recommendations guide you toward products that truly match your needs. Personalized product recommendations, driven by an efficient ecommerce recommendation engine, help you navigate vast catalogs seamlessly. Consider these benefits: Increased likelihood of purchase, with 70% of new customers engaging with recommendations. Higher engagement, leading to a 150% rise in order rates. Reduced cart abandonment, addressing the 67.49% average in retail. Boosted average order value, with increases of up to 38%. Enhanced customer loyalty, as 56% are likely to repurchase after a customized experience. These factors highlight how personalized recommendations transform your shopping experience into a more satisfying and efficient endeavor. Increased Sales Potential As you explore the domain of e-commerce, comprehension of how personalized recommendations can improve your shopping experience is crucial. Utilizing an AI recommendation engine, businesses can customize product suggestions based on your browsing history and preferences. This personalization greatly boosts sales potential. In fact, customers exposed to these customized recommendations are 70% more likely to make a purchase, leading to a 150% increase in order rates. Additionally, personalized product suggestion engines can elevate average order value by up to 38%, with 80% of businesses reporting this improvement. By providing relevant recommendations, cart abandonment rates decrease, making it easier for you to find and buy desired items, ultimately nurturing customer loyalty and encouraging repeat purchases. Best Practices for Implementing Recommendations To effectively implement personalized recommendations in ecommerce, it’s essential to adopt best practices that optimize customer interaction and improve sales performance. Here are some strategies to take into account: A/B Testing: Continuously evaluate placements and content to find what engages customers best. Data Utilization: Leverage customer demographics, browsing history, and real-time search queries for customized suggestions. Strategic Placement: Position recommendations during checkout or on 404 error pages to encourage purchases and reduce cart abandonment. Algorithm Updates: Regularly refine your recommendation algorithms based on new consumer data and trends to keep suggestions relevant. Quality Over Quantity: Curate a limited volume of recommendations to improve user experience without overwhelming customers. Real-World Examples of Personalized Recommendations Personalized recommendations play a significant role in enhancing the customer experience across various e-commerce platforms. For instance, Amazon employs a recommendations engine using collaborative filtering to suggest products based on similar customers’ buying behaviors, displaying sections like “Customers who bought this likewise bought.” Netflix utilizes content-based filtering by analyzing your viewing history, recommending shows and movies that align with your preferences. Spotify combines user listening patterns with content information in a hybrid recommendation system, creating personalized playlists like “Discover Weekly.” Online retailers, such as Kylie Cosmetics, recommend complementary products, suggesting lipstick shades that pair well with your previous purchases. Furthermore, brands often use customized email campaigns, including abandoned cart reminders with AI recommendations for items you’ve viewed, encouraging you to complete purchases. These real-world examples demonstrate how effective personalized recommendations can drive engagement and conversion across diverse platforms. Frequently Asked Questions How Do Personalized Recommendations Work? Customized recommendations work by analyzing your browsing history, purchase patterns, and demographic data. Algorithms, like collaborative and content-based filtering, identify products that align with your preferences and past behaviors. These systems adapt over time, improving suggestions based on real-time interactions. How Do Product Recommendations Work? Product recommendations work by analyzing your behavior, including search queries, browsing history, and past purchases. Algorithms, like collaborative filtering and content-based filtering, compare your preferences with those of similar users or suggest items based on previously liked features. This data helps the system generate customized suggestions, enhancing your shopping experience. In the end, these recommendations guide you to products that align with your interests, making it easier to discover items you’re likely to purchase. What Is the Main Benefit of Personalized Recommendations? The main benefit of customized recommendations lies in their ability to improve the shopping experience. By analyzing your browsing history and preferences, these recommendations suggest products designed for your interests, making it easier to discover relevant items. This not just saves time but furthermore increases the likelihood of making a purchase. As a result, personalized recommendations can greatly boost sales, with businesses experiencing higher average order values and improved customer retention rates. Which Technology Is Most Commonly Used for Personalized Product Recommendations? The most commonly used technology for personalized product recommendations involves machine learning algorithms. These algorithms analyze user behavior, preferences, and purchase history to generate customized suggestions. Collaborative filtering systems leverage data from multiple users, whereas content-based filtering focuses on individual characteristics and past interactions. Hybrid systems combine both approaches for improved accuracy. Real-time data analysis, including browsing history and user events, is essential for delivering relevant recommendations that increase engagement and conversion rates. Conclusion In conclusion, personalized product recommendations play an essential role in enhancing online shopping experiences. By utilizing collaborative and content-based filtering techniques, businesses can effectively suggest products customized to individual preferences. Implementing these systems can lead to increased customer satisfaction and higher conversion rates. As you explore the realm of e-commerce, comprehending how these recommendation engines function will help you appreciate the customized experiences designed to meet your needs and preferences. Image via Google Gemini This article, "What Are Personalized Product Recommendations and How Do They Work?" was first published on Small Business Trends View the full article

OpenAI has emerged as one of the government’s leading providers of artificial intelligence. According to the company, 37 federal agencies now have access to its tech, and about 80,000 government employees are now using it regularly. This makes OpenAI a frontrunner in the race between the top AI companies to get their tech in front of government users. These workers are just a small fraction of these frontier labs’ total customer bases, but they’re symbolically valuable. Wooing the U.S. government is important enough to these companies that they’re offering their technology at a steep discount. And, in another bid to speed up the administration’s use of the tech, several of those labs—OpenAI, Perplexity, and Google—have now earned a fast-track to offer their AI on a government-approved cloud. Of course, working with the U.S. government brings a host of logistical challenges. Between arduous cybersecurity requirements and arcane procurement rules, getting technology to federal agencies can be a real chore. Federal agencies also operate on far tighter budgets than the commercial sector, and are slow to adapt to new tech, which is why OpenAI, like other companies, is offering them access to ChatGPT for basically nothing. Government contracting can also put tech companies under a microscope. Working for government agencies, particularly more polarizing ones (like the Department of Homeland Security) has become politically toxic—not just to the broader public, but also to tech workers. And as Anthropic is learning in real-time, the government can be a troublesome customer. The Pentagon, which has grown highly reliant on Claude, is now threatening to deem Anthropic a “supply chain risk,” should the company not accede to its demands for essentially unlimited usage terms. Felipe Millon, who leads government sales at OpenAI, spoke with Fast Company about why the AI giant wants to work with the U.S. government, and its progress in getting federal employees to use its tech. This interview has been edited for length and clarity. I can’t imagine that government sales are determinative for the success of OpenAI’s business model. Why do this? Why work with the government, if it’s so hard and there are all these extra complications involved with it. I joined two years ago as our first government hire before we had anything here. It is absolutely very hard. It is also—I won’t say not material—but we don’t ever expect government sales to be a very large percentage of OpenAI’s revenue. If you want to think of it purely from a financial perspective, the reason is very mission-aligned, right? OpenAI has a mission as a public benefit corporation now, that is to ensure this technology called AGI, Artificial General Intelligence, benefits all of humanity. And what we have discussed internally with our leadership team is that . . . creating a technology, AGI, that is better than humans at most economically viable tasks and deploying that to the world will not happen without the U.S. government being involved. They can’t understand it unless they’re users of the technology, right? The best way to understand what’s happening in AI is to be a user and to see it for yourself, whether that’s a chatbot, coding, or other tools. We’re ready to start seeing where it can add value. And so part of our mission is really to ensure that the U.S. government understands what is coming by being able to unlock that for government use cases. If our mission is to ensure AGI benefits all humanity, one of the ways that [humanity] is benefited is by the delivery of citizen services—whether it be someone who is reliant on food stamps or someone who is getting housing support from Housing and Urban Development, or whether they are paying their taxes in an effective way with the IRS. So you’re now able to host your own AI as a cloud service. Why does that matter, and how does it impact government users? With the advent of cloud computing, a lot of government tools have moved to the cloud and so off a government-hosted computer. Previously, government [agencies] would host their own mainframes and their servers and their own personal data in their own data centers. . . . Business models emerged with cloud computing, where large hyperscalers, mainly Amazon, Microsoft, Google, Oracle. [They] said, “Hey, we can run this at scale, and you can just use this capacity from us on demand as a service.” So rather than owning your server, you get compute and storage and things like that . . . and you pay for it. We use cloud-based services to host our tools, whether that be the models we operate and provide in an API service to developers, or as ChatGPT Enterprise. We would like to use that enterprise version of ChatGPT, at, for example, the Treasury or at HHS or at the State Department. But in order to do so, we need to be compliant with these cybersecurity rules. This accreditation means that now the government agencies are allowed to use our tools with real data and are able to really start getting value. I understand that you don’t work on the defense side of OpenAI’s government business. Obviously we’ve seen in the news, there can be tensions between AI companies or any software company selling to the government what the government wants to do, and what you know a company might be interested or comfortable with. Can you talk a little bit about weighing that when you’re thinking about selling to the civilian side of the government? I’m not going to cover a lot of the national security side that is outside of my specific purview. I focus on the civilian and state and local side. On the civilian side, we rarely encounter these things. It’s rare that these things will come up at places like the Treasury, etc. If they do come up, really, I think it’s just a good faith discussion and negotiation with the government. I’m wondering about the penetration of OpenAI technology in the government right now, particularly after the OneGov deal, which saw you offer ChatGPT to the government at a major discount. We have a commercial tool that is available . . . and anyone can download it on their phone. We saw that over 100,000 people had a government email address in ChatGPT, before we even launched an enterprise product. We also have a relationship with Microsoft. It’s a very complicated relationship, but they . . . deploy their own products called Azure OpenAI, which is our model hosted and run by Microsoft. But that’s a Microsoft product, and that product has been used in government for some time, because Microsoft has a very large and established government business. We want to work directly with the government as well. There’s two main barriers that have blocked government adoption of AI: authorization, which we’re just getting with FedRAMP, and then the other one is procurement and budgeting. HHS, for example, is a very large user of ChatGPT Enterprise. They have tens of thousands of users. The U.S. Treasury also has tens of thousands of users through ChatGPT Enterprise. I would say around 50 or so federal agencies have taken advantage of our OneGov deal and have used it. It has been painful because they have to provide agency level authorization. So their authorizing officials and their security have to do their own cybersecurity review—either that or they don’t use the tool. We actually have our only on-premises deployment with Los Alamos, which was kind of a separate work that we had done. The majority of the national labs are enterprise customers. View the full article

A recent report from the National Federation of Independent Business (NFIB) underscores a pressing concern for small business owners: rising energy costs and their impact on operations. The NFIB’s newly released Small Business Energy Survey reveals that energy expenditures significantly affect nearly every aspect of running a business, from hiring practices to overall financial stability. Small businesses are particularly vulnerable to fluctuations in energy costs, with about 80% of owners reporting that these costs have a significant impact on their operations. Holly Wade, Executive Director of NFIB’s Research Center, highlighted the gravity of the situation, stating, “Small businesses are highly exposed to energy cost increases, have limited flexibility to reduce costs, and experience direct operational and financial impacts as a result. As owners work to absorb the impact of energy costs into their business, it can often limit their ability to hire, retain talent, and grow.” The survey reveals that the most common method small business owners employ to manage rising costs is by lowering profits—cited by 58% of respondents—or increasing prices for customers, reported by 52%. Only a small fraction, just 8%, reported no increase in costs over the past three years, showcasing the common struggle to remain financially viable in a climate of escalating energy prices. Key Findings Among the report’s significant findings are: Sources of Energy Costs: Heating and cooling overwhelmingly account for the highest energy costs, followed by operating equipment and vehicular expenses. Limited Management Options: Many respondents find that they lack viable strategies to mitigate energy expenses, leading to stark reductions in profit margins. Energy Efficiency in Transportation: Two-thirds of small business owners consider energy efficiency important when replacing vehicles. However, the actual adoption of electric and hybrid alternatives remains low, indicating a gap between awareness and implementation. Given the realities of these findings, small business owners may want to explore energy efficiency upgrades, such as low-energy lighting or high-efficiency appliances. In fact, 23% of those who did not experience cost increases reported that upgrading equipment helped manage expenses. However, navigating these energy concerns isn’t without its challenges. Upgrading to more efficient solutions often requires upfront capital, which may be scarce for some small business owners, particularly those grappling with tighter cash flows. Insights on Grid Reliability The stability of electricity supply is another crucial concern, with two-thirds of small business owners experiencing power outages in the past year. The main triggers for these disruptions? Equipment failure rather than environmental factors. This raises questions about the reliability of local utility services and potential investments in backup systems like generators. Interestingly, the survey also illuminated a negligible perception that local data centers contribute to increased electricity costs, with 42% of owners asserting that their operations are unaffected by such entities. Recycling and Sustainability Practices A strong commitment to sustainability is evident, with most small business owners engaging in recycling initiatives—often voluntarily rather than by regulatory demand. Typical materials collected for recycling include paper, cardboard, metal, and glass. This not only reflects a growing commitment to environmental responsibility but may also present opportunities for cost savings. Practical Considerations Ahead As the landscape of energy use and costs continues to evolve, small business owners should remain proactive in strategizing around their energy consumption. With energy expenses impacting hiring and growth strategies, the survey serves as a wake-up call to evaluate energy practices proactively. Investing in efficiency may be an avenue to explore, but sustainable practices will require a robust assessment of cash flow, operational flexibility, and a willingness to adapt business models. The trade-offs involved in these decisions need careful scrutiny to ensure that actions taken lead to meaningful long-term benefits in both costs and sustainability. Small business owners can access the full findings of the NFIB’s Small Business Energy Survey for a more in-depth understanding and possible strategies for their specific situations through the NFIB website here. As energy costs remain a formidable challenge, staying informed and adaptable will be critical for maintaining operational health and fostering growth in the ever-competitive marketplace. Image via Google Gemini This article, "Small Business Owners Struggle with Rising Energy Costs, New Survey Finds" was first published on Small Business Trends View the full article

A recent report from the National Federation of Independent Business (NFIB) underscores a pressing concern for small business owners: rising energy costs and their impact on operations. The NFIB’s newly released Small Business Energy Survey reveals that energy expenditures significantly affect nearly every aspect of running a business, from hiring practices to overall financial stability. Small businesses are particularly vulnerable to fluctuations in energy costs, with about 80% of owners reporting that these costs have a significant impact on their operations. Holly Wade, Executive Director of NFIB’s Research Center, highlighted the gravity of the situation, stating, “Small businesses are highly exposed to energy cost increases, have limited flexibility to reduce costs, and experience direct operational and financial impacts as a result. As owners work to absorb the impact of energy costs into their business, it can often limit their ability to hire, retain talent, and grow.” The survey reveals that the most common method small business owners employ to manage rising costs is by lowering profits—cited by 58% of respondents—or increasing prices for customers, reported by 52%. Only a small fraction, just 8%, reported no increase in costs over the past three years, showcasing the common struggle to remain financially viable in a climate of escalating energy prices. Key Findings Among the report’s significant findings are: Sources of Energy Costs: Heating and cooling overwhelmingly account for the highest energy costs, followed by operating equipment and vehicular expenses. Limited Management Options: Many respondents find that they lack viable strategies to mitigate energy expenses, leading to stark reductions in profit margins. Energy Efficiency in Transportation: Two-thirds of small business owners consider energy efficiency important when replacing vehicles. However, the actual adoption of electric and hybrid alternatives remains low, indicating a gap between awareness and implementation. Given the realities of these findings, small business owners may want to explore energy efficiency upgrades, such as low-energy lighting or high-efficiency appliances. In fact, 23% of those who did not experience cost increases reported that upgrading equipment helped manage expenses. However, navigating these energy concerns isn’t without its challenges. Upgrading to more efficient solutions often requires upfront capital, which may be scarce for some small business owners, particularly those grappling with tighter cash flows. Insights on Grid Reliability The stability of electricity supply is another crucial concern, with two-thirds of small business owners experiencing power outages in the past year. The main triggers for these disruptions? Equipment failure rather than environmental factors. This raises questions about the reliability of local utility services and potential investments in backup systems like generators. Interestingly, the survey also illuminated a negligible perception that local data centers contribute to increased electricity costs, with 42% of owners asserting that their operations are unaffected by such entities. Recycling and Sustainability Practices A strong commitment to sustainability is evident, with most small business owners engaging in recycling initiatives—often voluntarily rather than by regulatory demand. Typical materials collected for recycling include paper, cardboard, metal, and glass. This not only reflects a growing commitment to environmental responsibility but may also present opportunities for cost savings. Practical Considerations Ahead As the landscape of energy use and costs continues to evolve, small business owners should remain proactive in strategizing around their energy consumption. With energy expenses impacting hiring and growth strategies, the survey serves as a wake-up call to evaluate energy practices proactively. Investing in efficiency may be an avenue to explore, but sustainable practices will require a robust assessment of cash flow, operational flexibility, and a willingness to adapt business models. The trade-offs involved in these decisions need careful scrutiny to ensure that actions taken lead to meaningful long-term benefits in both costs and sustainability. Small business owners can access the full findings of the NFIB’s Small Business Energy Survey for a more in-depth understanding and possible strategies for their specific situations through the NFIB website here. As energy costs remain a formidable challenge, staying informed and adaptable will be critical for maintaining operational health and fostering growth in the ever-competitive marketplace. Image via Google Gemini This article, "Small Business Owners Struggle with Rising Energy Costs, New Survey Finds" was first published on Small Business Trends View the full article

If you don’t know, that’s a problem. ChatGPT influences millions of product decisions every day—and unlike Google, it gives you zero impressions data, no Search Console, and no built-in analytics. In this guide, I’ll show you how to monitor your…Read more ›View the full article

Iran and the United States were holding indirect negotiations Thursday in Geneva as talks over Tehran’s nuclear program hang in the balance following Israel’s 12-day war on the country in June and the Islamic Republic carrying out a bloody crackdown on nationwide protests. U.S. President Donald The President has kept up pressure on Iran, moving an aircraft carrier and other military assets to the Persian Gulf and suggesting the U.S. could attack Iran over the killing of peaceful demonstrators or if Tehran launches mass executions over the protests. A second aircraft carrier now is in the Mediterranean Sea. The President has pushed Iran’s nuclear program back into the frame as well after the June war disrupted five rounds of talks held in Rome and Muscat, Oman, last year. Two rounds of talks so far have yet to reach a deal, though. Mideast nations fear a collapse in diplomacy could spark a new regional war. U.S. concerns also have gone beyond Iran’s nuclear program to its ballistic missiles, support for proxy networks across the region and other issues. Iran has said it wants talks to focus solely on the nuclear program. Iranian President Masoud Pezeshkian has insisted that his nation was “not seeking nuclear weapons. … and are ready for any kind of verification.” However, the United Nations’ nuclear watchdog — the International Atomic Energy Agency — has been unable for months to inspect and verify Iran’s nuclear stockpile. The President began the diplomacy initially by writing a letter last year to Iran’s 86-year-old Supreme Leader Ayatollah Ali Khamenei to jump start these talks. Khamenei has warned Iran would respond to any attack with an attack of its own, particularly as the theocracy he commands reels following the protests. Here’s what to know about Iran’s nuclear program and the tensions that have stalked relations between Tehran and Washington since the 1979 Islamic Revolution. The President writes letter to Khamenei The President dispatched the letter to Khamenei on March 5, 2025, then gave a television interview the next day in which he acknowledged sending it. He said: “I’ve written them a letter saying, ‘I hope you’re going to negotiate because if we have to go in militarily, it’s going to be a terrible thing.'” Since returning to the White House, the president has been pushing for talks while ratcheting up sanctions and suggesting a military strike by Israel or the U.S. could target Iranian nuclear sites. A previous letter from The President during his first term drew an angry retort from the supreme leader. But The President’s letters to North Korean leader Kim Jong Un in his first term led to face-to-face meetings, though no deals to limit Pyongyang’s atomic bombs and a missile program capable of reaching the continental U.S. Oman mediated previous talks Oman, a sultanate on the eastern edge of the Arabian Peninsula, has mediated talks between Araghchi and U.S. Mideast envoy Steve Witkoff. The two men have met face to face after indirect talks, a rare occurrence due to the decades of tensions between the countries. It hasn’t been all smooth, however. Witkoff at one point made a television appearance in which he suggested 3.67% enrichment for Iran could be something the countries could agree on. But that’s exactly the terms set by the 2015 nuclear deal struck under former U.S. President Barack Obama, from which The President unilaterally withdrew America. Witkoff, The President and other American officials in the time since have maintained Iran can have no enrichment under any deal, something to which Tehran insists it won’t agree. The first attempt at negotiations ended, however, with Israel launching the war in June on Iran. A new effort has seen two new rounds of talks in Oman and Geneva so far. The 12-day war and nationwide protests Israel launched what became a 12-day war on Iran in June that included the U.S. bombing Iranian nuclear sites. Iran later acknowledged in November that the attacks saw it halt all uranium enrichment in the country, though inspectors from the IAEA, the U.N. nuclear watchdog, have been unable to visit the bombed sites. Half a year later, Iran saw protests that began in late December over the collapse of the country’s rial currency. Those demonstrations soon became nationwide, sparking Tehran to launch a bloody crackdown that killed thousands and saw tens of thousands detained by authorities. Iran’s nuclear program worries the West Iran has insisted for decades that its nuclear program is peaceful. However, its officials increasingly threaten to pursue a nuclear weapon. Iran now enriches uranium to near weapons-grade levels of 60%, the only country in the world without a nuclear weapons program to do so. Under the original 2015 nuclear deal, Iran was allowed to enrich uranium up to 3.67% purity and to maintain a uranium stockpile of 300 kilograms (661 pounds). The last report by the IAEA on Iran’s program put its stockpile at some 9,870 kilograms (21,760 pounds), with a fraction of it enriched to 60%. The agency for months has been unable to assess Iran’s program, raising nonproliferation concerns. U.S. intelligence agencies assess that Iran has yet to begin a weapons program, but has “undertaken activities that better position it to produce a nuclear device, if it chooses to do so.” Iranian officials have threatened to pursue the bomb. Israel, a close American ally, believes Iran is pursuing a weapon. It wants to see the nuclear program scrapped, as well as a halt in its ballistic missile program and support for anti-Israel militant groups such as Hezbollah in Lebanon and Hamas. Decades of tense relations between Iran and the US Iran was once one of the U.S.’s top allies in the Mideast under Shah Mohammad Reza Pahlavi, who purchased American military weapons and allowed CIA technicians to run secret listening posts monitoring the neighboring Soviet Union. The CIA had fomented a 1953 coup that cemented the shah’s rule. But in January 1979, the shah, fatally ill with cancer, fled Iran as mass demonstrations swelled against his rule. The Islamic Revolution followed, led by Grand Ayatollah Ruhollah Khomeini, and created Iran’s theocratic government. Later that year, university students overran the U.S. Embassy in Tehran, seeking the shah’s extradition and sparking the 444-day hostage crisis that saw diplomatic relations between Iran and the U.S. severed. The Iran-Iraq war of the 1980s saw the U.S. back Saddam Hussein. The “Tanker War” during that conflict saw the U.S. launch a one-day assault that crippled Iran at sea, while the U.S. later shot down an Iranian commercial airliner that the U.S. military said it mistook for a warplane. Iran and the U.S. have seesawed between enmity and grudging diplomacy in the years since, with relations peaking when Tehran made the 2015 nuclear deal with world powers. But The President unilaterally withdrew the U.S. from the accord in 2018, sparking tensions in the Mideast that persist today. The Associated Press receives support for nuclear security coverage from the Carnegie Corporation of New York and Outrider Foundation. The AP is solely responsible for all content. —Jon Gambrell, Associated Press View the full article

Student-Led Conversations With Arpan Grewal and Harshita Multani Center for Accounting Transformation Go PRO for members-only access to more Center for Accounting Transformation. View the full article

Student-Led Conversations With Arpan Grewal and Harshita Multani Center for Accounting Transformation Go PRO for members-only access to more Center for Accounting Transformation. View the full article

As snow piled up in front of bus stops and fire hydrants during New York City’s second winter storm of the year, city workers have tried to move fast to remove it before snow hardened into ice. A new internal tool makes that job easier to track. The city’s Department of Sanitation (DSNY) now tags infrastructure that’s been plowed in a mobile mapping tool that employees can update on the go. “We have started the work of geotagging every single bus shelter and crosswalk,” Mayor Zohran Mamdani said Monday, and overnight, he said the city cleared more than 1,600 crosswalks, 419 fire hydrants, and nearly 900 bus stops. DSNY handles trash collection, but it’s also tasked with snow removal from city streets and bike lanes, areas within its legal obligation. DSNY sometimes provides supplemental services too, plowing pedestrian infrastructure like curb ramps, unsheltered bus stops, and fire hydrants that property owners are responsible for. In the past, this supplemental work was done piecemeal, but under Mamdani, the amount of supplemental service has “vastly increased,” says Joshua Goodman, a DSNY deputy commissioner. “That necessitated a need to formally track this work,” he says. Cities from Bellevue, Washington, to Syracuse, New York, use digital maps to show residents when streets get plowed, and New Yorkers can track when their streets were last plowed on PlowNYC, a public site launched in 2013. DSNY needed its own PlowNYC, but for bus stops and more. “We developed an internal mapping tool, and Sanitation Supervisors make live updates from the field when one of these locations in their assigned section is complete,” Goodman tells Fast Company. “So maybe it’s a bit simpler than the terminology implies—it’s essentially someone making updates to a central database on their work cell phone—but it’s a big development for us, especially so quickly.” “This is our first storm using it, but it is allowing greater efficiency around clearing these important areas,” he adds. Preparations began following the snowstorm in January, when sites were surveyed for the mapping tool. The interface looks like a typical maps app, and while perhaps simpler than what the idea of “geotagging” might conjure, the database of information the tool stores is vast. New York City has about 13,000 bus stops and about 83,000 crosswalks in commercial corridors. The tool was designed by the DSNY operations management division, which is its data and analytics team. To handle snow from the latest storm, DSNY has delayed trash and recycling collection so its workers can prioritize snow removal, and it’s hired hundreds of emergency temporary snow shovelers for $30 per hour. That’s a pop-up snow shoveling army with tens of thousands of sites and miles of ground to cover. Tracking this work with clipboards wouldn’t be efficient. By developing an internal tool to better monitor their job, DSNY found a quick solution to solve a pressing problem. View the full article

If you’ve been paying attention to AI at all lately, you’ve certainly seen the “Something Big Is Happening” essay by Matt Shumer, or at least some of the reaction to it. In it, Shumer describes how coding, for him, has completely transitioned from manually writing code to simply prompting and approving the near-flawless work done by AI. The piece was meant as a warning to all knowledge workers, essentially saying: AI has taken over my job, and it’s coming for yours next. There have been countless thought pieces on the merits and flaws of Shumer’s argument, and I have no intention of adding to the pile. But journalism is knowledge work, too, and the field had its own, slightly less viral, moment of AI existential crisis this past week. The editor of Cleveland.com, Chris Quinn, wrote a column this week, describing how a college student who had applied for a reporting job withdrew their application when they found out how the publication uses AI. Besides using AI to help generate story ideas, the newsroom developed an “AI rewrite specialist” to write stories based on the material that reporters gather. By ditching writing, according to Quinn, their reporters have been able to reclaim an extra workday each week. The backlash was predictably vicious. On X, Axios reporter Sam Allard earned a lot of likes by comparing what Cleveland.com is doing to being an “AI content farmer,” while various veteran journalists on Substack expressed various degrees of outrage and dismay. Most of the reaction was along the lines of this piece from journalist Stacey Woelfel: “Writing is an integral part of the reporting process.” The AI newsroom split That’s true, but I think what Quinn describes isn’t so easily dismissed. After all, reporters often work in teams on single articles, with one of them taking the lead on the draft. Did the others then . . . not report? And I’ve certainly been in breaking-news situations where a reporter would text, email, or call in their notes to an editor or writer who would put together the piece. It’s generally recognized that writing and reporting are different skills, and what Quinn and Cleveland.com appear to have done is use AI to fully separate them. The conventional wisdom on the “correct” way to use AI is to let it take over the tasks that it can do faster and better than humans, freeing them up to do the things that absolutely require human engagement and judgment. In the case of a reporter, that’s talking to sources, learning new things, and earning their trust. Well, at long last, AI is actually very good at writing. Certainly, much of the text that’s come out of AI systems over the past few years hasn’t done much for its literary reputation (yes, we’re all tired of the rampant em-dashes and the “it’s not X—it’s Y” bits). But if you use the most powerful models with a modest amount of deliberate prompting, they can produce highly competent prose. And if we’re being honest, highly competent prose is all that’s needed for a large amount of reported stories. Many, if not most, news reports are meant to convey basic information about what happened, with little judgment or opinion, and typically written in AP style, which is essentially a formula. It’s not quite code, but it’s a very functional way of writing. The most important thing is conveying the facts, accurately and with context, as quickly as possible. Again, it’s important to understand that the reporter is not removed from the process, but their role changes significantly. Just as Shumer found himself becoming a supervisor to an AI building machine, reporters may become operators of writing bots, ensuring they’re crafting stories properly out of the raw material they’ve been given. In the case of Quinn’s newsroom, reporters have final say over the copy. Bleeding between the lines None of this is to say this approach will result in a perfect future. There are writers who aren’t great at reporting, and there are reporters who aren’t skilled at writing, but there are plenty who are good at both. Will they need to pick a side—either become a feature or opinion writer, or settle for just doing the reporting part? And what about skill building? Even if this approach is as successful as Quinn says, how will junior staff become better writers without the day-in, day-out act of writing stories? When Woelfel says writing is integral to reporting, I think he means it’s integral to storytelling, which is an act of curation, prioritization, and expression—all with an audience in mind. This is what Ben Affleck meant when he famously drew a distinction between AI as a craftsman and AI as an artist. But how do you become an artist if AI is doing all the crafting? The irony of Shumer’s piece is that, while he makes a solid case that AI will soon disrupt most knowledge work—and even name-checks journalism as one of the areas in the crosshairs—he did it with an essay with a distinctly human voice. I honestly don’t know if he used AI to fully or partially write the piece, but I’m certain that if he did, he also was meticulous about every word. I think that’s a hopeful sign that, even if we relegate some of the craft of writing to AI, that we might not lose as much as we might think. Audiences will always demand a human touch, so that touch will need to manifest in some form. It’s true that no one wants to read AI slop. But it might turn out that the most valuable reporting skill in the future will be the ability to turn slop into stories. View the full article

Here is a recap of what happened in the search forums today...View the full article

At least 25 million people have had their personal information stolen in a major hack on business services company Conduent. The data breach itself isn't new—it was initially disclosed in January 2025, and Conduent has already notified millions of individuals whose data was compromised in the incident. However, the breach is now believed to be larger in scale than previously reported, possibly among the largest to affect healthcare. Who is Conduent? Conduent is a New Jersey-based business processing outsourcing (BPO) company that provides services like printing, payment, and document and claims processing to state and federal government agencies as well as large commercial and transportation organizations. According to the company's 2025 annual report, these offerings include disbursement of benefits, such as food assistance and child support, and administration of government healthcare programs (like Medicaid). For large corporations, services include workplace and unemployment benefits management. Conduent was spun off from Xerox in 2017 and now employs around 51,000 people worldwide. What happened with the Conduent breach?In January 2025, Conduent suffered an outage that was later confirmed to be the result of a "cybersecurity incident." The disruption lasted several days, during which agencies across the U.S. were unable to process some benefit payments. While the breach was discovered in January, hackers reportedly gained access to Conduent's systems months earlier on October 21, 2024. The Safepay ransomware gang later took credit for the attack. While Conduent confirmed in April 2025 that client information had been stolen in the breach, it didn't begin notifying affected individuals until October. According to those notices, the compromised data included names, Social Security numbers, dates of birth, health insurance policy information, and medical information. How many people were impacted by the breach? The scope of the breach continues to grow, but the total number of individuals affected currently sits around 25 million. The greatest impact appears to be in Texas and Oregon, though residents in California, Delaware, Maine, Massachusetts, New Hampshire, and New Mexico have also received notices. (For reference, the total number of users impacted by the 2024 ransomware attack on Change Healthcare is now estimated at 190 million.) What to do if you were affectedIf you receive a notice saying your information was compromised, you should take every precaution to secure your identity: At a minimum, ensure your credit is frozen, and set up a one-year fraud alert on your credit files to prevent someone from applying for credit using your information. None of the notices we've seen have offered any type of credit monitoring or identity theft protection services to affected individuals, but you could utilize these services as well. At this point—given the ubiquity of data breaches and information compromise—you should be keeping a close eye on your credit report and financial accounts at all times to quickly catch anything suspicious. If you do find fraudulent activity, report it to your bank and/or credit issuer immediately, and file an identity theft report. View the full article

Google’s AI Overviews now appear across search results with varying frequency. However, in certain categories, they dominate entirely. According to Adthena: Finance queries see AI Overviews on 79% of longer searches with five or more words. Retail shows 84% visibility for comparison and product discovery queries in the 9-10 word range. Healthcare also triggers high AI Overview penetration even when users are searching short medical questions of 1-3 words. You know organic traffic faces headwinds. What you might underestimate is how severe the downstream impact on paid search can be. Here’s what that looks like in practice. AI Overviews’ impact on paid search AI Overviews are systematically changing paid search, affecting everything from click volume to auction dynamics and conversion behavior. They are accelerating structural trends that are already reshaping search, including SERP saturation, automated bidding, Performance Max adoption, and broad match expansion. What makes AI Overviews significant is the speed of the rollout. In many verticals, Google had compressed what would have normally been a multi-year transition into mere months. Understanding the impact on your own paid search efforts requires examining how AI answers have reshaped each component of your campaign performance. Your customers search everywhere. Make sure your brand shows up. The SEO toolkit you know, plus the AI visibility data you need. Start Free Trial Get started with AI Overviews drive lower response rates So, how much have response rates been impacted by AI Overviews? Recent data from Seer Interactive reveals the scale of the decline. Paid CTR on queries featuring AI Overviews plummeted by 68%, dropping from 19.7% to 6.34% between June 2024 and September 2025. At the same time, we saw organic CTR fall 61% on the same queries, but the steeper paid decline suggests AI Overviews reshape where paid ads appear and who clicks them, not simply their overall presence. The trend accelerated sharply in July 2025 when paid CTR collapsed from approximately 11% to 3% in a single month. One month. This happened as Google expanded AI Overviews more aggressively into commercial and navigational queries, demonstrating AI Overviews’ direct impact on paid search response rates. What we’re finding is that these declines are the most severe for non-branded informational queries. But it’s not all bad news. Branded search and high-intent transactional queries are showing greater resilience, with many advertisers seeing minimal impact on their core conversion-driving terms. AI Overviews contribute to higher CPCs through inventory compression We’re also finding a direct correlation between AI Overviews and the cost of paid search campaigns. That’s because the response rate decline is directly driving cost-per-click (CPC) inflation through supply and demand mechanics. Google Search spending grew 9% year-over-year in Q1 2025, but click growth was only 4%. That 5% gap represents more dollars chasing fewer clicks across many industries. AI Overviews amplify this CPC inflation through several mechanisms. Some of that has to do with ad positioning. Research on ad positioning shows that ads that appear above an AI Overview still perform reasonably well. But the ads below are seeing a dramatic reduction in impression share and CTR. At the same time, double-serving policies are concentrating impression share among larger advertisers, which is forcing smaller ones to bid more aggressively. Automated bidding systems optimize toward conversion predictions rather than cost efficiency, which means campaigns are paying premium CPCs as the click inventory shrinks. AI Overviews collapse the consideration phase We’re also seeing a dip in the consideration phase of the buyer’s journey. Customer journeys that used to take up to a few days, AI Overviews can now compress into minutes by handling the research and comparison activities that traditionally occurred across multiple search sessions. For example, think back to how in, say, 2023 a search for [best project management software for remote teams] would have triggered a multi-day sequence for users who would first, perhaps, click through to organic results, then read some comparison articles, then perhaps visit some vendor websites, and, finally, after maybe 7-14 days, they might finally convert. Today, when you search for [best project management software for remote teams], you could convert in a single session. An AI Overview can give users everything they need at once: a comparison table with features, pricing, and use cases, then refined recommendations for two or three options. People could decide in hours instead of weeks. This compression reshapes campaign performance in three ways: Smaller retargeting pools: Retargeting pools shrink dramatically because fewer clicks during research means there are fewer users entering remarketing audiences. While Google has lowered audience minimums from 1,000 to 100 users, the shift is meant to help boost small business campaigns, but it still means that a campaign that historically would have built up a 10,000-user pool from informational traffic might now capture only 3,000 users. Less brand awareness: Brand awareness suffers when users never visit your site during research, entering the purchase decision having consumed AI-generated comparisons rather than experiencing your messaging directly. AI Overviews mentions are a must: AI citation creates a winner-take-all dynamic. Being mentioned in AI Overviews becomes a primary determinant of visibility. Brands that appear in the AI answer capture disproportionate traffic, while those excluded lose ground entirely. AI Overviews create a quality-over-quantity trade-off The journey compression caused by AI Overviews is producing a counterintuitive economic outcome. As click volume declines, conversion rates improve. A benchmark analysis of 16,446 campaigns confirms the pattern. While overall click volume declined across nearly all query types in 2025, 65% of industries actually saw improved conversion rates. For many of those industries, the jump was substantial. For example, education and instruction saw conversion rates jump 43.87% year-over-year, while sports and recreation climbed 42.43%. So why is this happening? The improved conversion rates are reflective of AI Overviews pre-qualifying users by answering their basic questions before they click ads. This filters out a lot of the users who are simply seeking general information without any intention to convert and leaving only high-intent prospects. These improved conversion rates could also potentially partially offset CPC inflation in many scenarios. For example, let’s say a business software campaign is generating 1,000 clicks at $2.00 CPC. The campaign generated a 5% conversion rate, resulting in 50 conversions at a $40 CPA. Then, let’s say, Google rolled out AI Overviews for their keywords, and it compressed the customer journey. The same campaign might then generate fewer clicks, say 700, at $2.90 CPC and a higher 7% conversion rate, producing 49 conversions at $41.43 CPA. The effective cost increase is only 3.6% despite 45% CPC inflation and 30% volume decline. Get the newsletter search marketers rely on. See terms. 4 strategic pivots for the AI search era Paid search still offers opportunities for advertisers who adapt quickly. Let’s look at four strategies you can incorporate into your own campaigns that align with the new realities of AI-mediated search. 1. Monitor informational intent performance and optimize accordingly Since AI Overviews are fundamentally changing the economics of informational queries, they require extra scrutiny from you. Implement systematic monitoring rather than blanket exclusions of informational keywords to identify which keywords still deliver value and which have become budget drains. Begin by understanding which informational keywords still hold value. Informational keywords like “what is,” “how to,” and “guide to” are being cannibalized by AI Overviews at substantial rates. In finance, AI Overviews appear on 79% of longer queries, while in retail they show up on 84% of comparison searches. However, transactional keywords like “buy,” “best,” “compare,” and “near me” maintain higher CTRs because AI typically doesn’t complete transactions. The user needs to click away from AI Overviews to complete their transaction. We’re still seeing 69% of transactional searches in AI Mode result in clicks to websites. Branded search remains largely intact, with AI Overviews primarily affecting non-branded informational queries. To identify which informational keywords still perform, follow these steps: Start by pulling 90 days of Google Ads query data. Next, you’ll want to flag queries that contain informational trigger words. Then, cross-reference that data with Google Search Console, since GSC now tags these in performance reports, to identify which queries trigger AI Overviews. Finally, you can calculate CTR and conversion rate for informational versus transactional queries to establish your baselines. For the informational queries that show less than 1% CTR and less than 50% of your average conversion rate, you have three options: Test whether you can improve performance by focusing on creative optimization for unique offers rather than information. Reduce your bids on those queries to maintain presence at a lower cost while continuing to monitor for changes. Shift your budget toward transactional and navigational keywords that are performing better, while maintaining minimal informational presence to bolster brand visibility. Note: An important exception applies for brands that are consistently being cited in AI Overviews. Since cited brands are seeing a 91% paid CTR lift, this suggests that these informational keywords could become strategic assets. If your brand appears in AI Overviews for informational queries like “best accounting software for freelancers,” it may warrant maintaining or increasing bids on those terms. You’ll also want to scrutinize for any uncited queries more aggressively to see if you’re missing any opportunities. 2. Prioritize feed quality Yes, generative AI can summarize and compare, but it can’t invent price, inventory, or availability from thin air. This creates a structural advantage if you have robust product feeds in Google Shopping, Hotel Ads, and local inventory. Google’s AI Mode shopping experience, powered by the Shopping Graph with 50 billion product listings refreshed hourly, relies entirely on structured product data from Merchant Center feeds. When users search, for example, for “breathable bamboo crib sheets under $40,” the AI can only surface products whose feeds include that level of attribute specificity. Shopping ads now appear directly within AI Overviews for queries with commercial intent, powered by existing Shopping and Performance Max campaigns. Feed optimization requires four priorities: Attribute enrichment must include contextual details like “waterproof for rainy commutes” or “red couch for small apartment” that match natural language queries. Real-time accuracy matters as Google updates listings hourly and outdated data filters products out of AI Mode entirely. Structured data completeness determines visibility. Google’s AI prioritizes products with rich, complete attribute data over listings with minimal information. Rich media assets have become table stakes. Google’s AI prioritizes listings with five or more product images and video content, with virtual try-on features integrated across Search, Shopping, and Images, driving visual discovery. 3. Craft creative that differentiates Since users have already learned about the features and benefits they were querying in AI Overviews before clicking, your ad must answer why they should choose you and why they should choose you now. Lead with unique value propositions instead of generic benefits. For example: “Project Management Software for Teams” is generic and would convert less often than a specific offering like “14-Day Free Trial + Free Migration from Asana/Monday.” An overly-general value prop like “Tax Preparation Services” would be expected to underperform something much more specific and unique like “Same-Day CPA Review | $50 Off Filing This Week.” You’ll also want to leverage ad extensions aggressively. Research shows that ads can appear above or below AI Overviews depending on query type and industry. When AI Overviews pushes everything down the page, extensions are your way to stay visible. Ads that use all available sitelinks, callouts, and structured snippets can occupy 2-3 times the SERP real estate of basic ads. Taking up that extra space is critical as ads now appear within AI Overviews themselves for commercial intent queries. You can use responsive search ads to test value proposition hypotheses at scale. Start by loading Responsive Search Ads (RSAs) with diverse headlines that test: Urgency (i.e., “Limited Availability”). Risk reversal (i.e., “No Credit Card Required”). Social proof (i.e., “4.9 Stars, 5,000+ Reviews”). Differentiation (i.e., “Only Platform with Native Zapier Integration”). Then let Google’s machine learning identify which messages resonate with high-intent users who’ve already completed their research. If your brand is cited in AI Overviews for specific use cases, reference those directly. For example, if AI Overview consistently recommends your accounting software for “freelancers,” you’ll want to include “Built for Freelancers” in headlines to align with the recommendation users just consumed. 4. Embrace audience data These days, it’s all about the data. As keyword-based targeting becomes less reliable in an AI-dominated search environment, first-party audience data is becoming more and more your sustainable competitive advantage. When AI answers queries without regard to keyword precision, your existing customer relationships represent what AI can’t disintermediate. What we mean is that you know your audience already. Take advantage of that. Customer Match lists allow you to upload email lists, phone numbers, and CRM data, with Google lowering the minimum from 1,000 to 100 users in 2025. Remember, users who’ve already engaged with your brand will convert at significantly higher rates than cold traffic and search with intent to re-engage rather than research. It’s also important to build granular website visitor segments based on the behaviors that signal purchase intent. You want to represent all prospects who have moved beyond research: Product page viewers who didn’t convert. Abandoned cart users. Visitors to pricing and comparison tools. Users with 10+ minute sessions. Target these audiences with messaging that assumes they’ve already completed their evaluation through AI-powered search. Use similar audiences and lookalikes to help Google’s AI identify users who match your highest-value customer profiles. Performance Max and Demand Gen campaigns work best when fed customer lists and purchase history, which allows for identifying intent patterns beyond keywords. In the AI Overview environment, shift your budget from old-school, keyword-heavy Search campaigns to audience-driven Performance Max and Demand Gen formats that prioritize first-party data. Build email capture mechanisms through gated content and progressive profiling. Then, integrate your CRM with Google Ads to activate customer data for targeting and bidding. A good place to start is by reallocating an underperforming informational query budget to audience-based campaigns, and then scaling based on results. First-party data provides higher signal quality than behavioral targeting alone, which gives advertisers with robust data infrastructure measurable advantages in conversion rates and customer acquisition costs. See the complete picture of your search visibility. Track, optimize, and win in Google and AI search from one platform. Start Free Trial Get started with Adaptation is the key to today’s search success AI Overviews are changing paid search. There’s no doubt about it. And the data shows the real pressure paid search is facing. But there’s good news: you can still succeed if you adapt your strategy to match how search works now — not how it worked two years ago. Start by monitoring which of your informational queries are still working, rather than excluding them all. Then, prioritize feed quality for Shopping campaigns. Make sure you write ads that differentiate rather than inform. And definitely build first-party audience lists before your competitors do. View the full article

ChatGPT ecommerce traffic converted 31% higher than non-branded organic search across 94 ecommerce sites in 2025, but it still drove a small share of revenue. That’s based on a 12-month GA4 analysis by Visibility Labs covering January through December 2025. Why we care. This data shows that AI referral traffic converts at a higher rate than traditional non-branded search traffic, but the volume remains small. This signals emerging value, not a replacement channel. Higher conversion rate. ChatGPT traffic converted at 1.81% vs. 1.39% for non-branded organic (31% higher). It outperformed organic in 10 of 12 months. Visibility Labs attributes the higher rate to intent compression. Users often refine product needs in ChatGPT before clicking. By the time they reach a product page, they may be closer to purchase than a typical search visitor still comparing options. Key findings. ChatGPT’s conversion advantage is clear, but growth is slowing and volume remains small. Massive traffic growth: ChatGPT visits grew 1,079%, from 1,544 in January to 18,202 in December. Non-branded organic grew 17% over the same period. Lower AOV: Average order value was $204 for ChatGPT vs. $238 for organic, a 14.3% gap. Higher revenue per session: Despite lower AOV, ChatGPT generated $3.65 per session vs. $3.30 for organic (10.3% higher). Small revenue share: ChatGPT drove $474,000 in revenue vs. $32.1 million from non-branded organic — 1.48% of organic revenue, rising to 2.2% in the second half of 2025. Growth tied to product updates: Visibility Labs links the first-half spike to shopping carousel features introduced in April 2025. Growth began flattening around August. Still dwarfed by organic: Non-branded organic traffic was 70x larger than ChatGPT overall, narrowing to 47x in Q4. Early 2025 volatility included months with just 15 to 37 ChatGPT-attributed conversions, limiting statistical confidence until midyear. The attribution gap. GA4 referral data likely understates ChatGPT’s influence. According to Visibility Labs: Many users get product recommendations from ChatGPT, then search for the brand or product on Google before purchasing. Those conversions are typically attributed to branded organic search. Set up post-purchase surveys to better capture AI-influenced revenue. About the data. Visibility Labs analyzed 12 months of GA4 data (January to December 2025) from 94 seven- and eight-figure ecommerce brands, comparing 9.46 million non-branded organic sessions to 135,000 ChatGPT referral sessions. The study excluded homepage and blog traffic to focus on commercial-intent visits more likely to evaluate and purchase products. The report. ChatGPT Traffic Converts 31% Better than Non-Branded Organic Search (94 eCommerce Sites Analyzed) View the full article

We’re bringing the SEJ newsroom to a screen near you. On March 11 from 12–3pm ET, we’ve gathered our own search experts, alongside some very special guests, to help you master AI search visibility this year. This is SEJ Live, a new series we’ve been building behind the scenes, and I couldn’t be more excited to see it come to life. Here’s why this matters right now: I’m seeing a huge disconnect between leadership and the marketing teams doing the work. Leadership wants performance yesterday, but those with their feet on the ground know that customer behaviors have changed. The […] The post We’re Bringing The SEJ Newsroom To You, Live [Free Event] appeared first on Search Engine Journal. View the full article