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

Google‘s first consolidated AI optimization guide gives guidance on ways to optimize for Google’s generative AI features. View the full article

Executives from Guild and NewRez discussed the steps they are taking as participants in the pilot phase of the roll out of VantageScore 4.0 and FICO 10T. View the full article

In today’s competitive environment, retaining customers is fundamental for long-term success. You can boost customer loyalty through various strategies, such as personalizing interactions and implementing effective loyalty programs. Furthermore, leveraging data analytics can reveal important insights about customer behavior, enabling you to act proactively. Cultivating a sense of community around your brand likewise plays a critical role. These solutions can greatly improve customer experiences, but how do you start integrating them into your business model? Key Takeaways Implement personalized communication strategies using CRM systems to enhance customer engagement and loyalty. Develop effective loyalty programs with tiered rewards and immediate incentives to encourage repeat purchases. Utilize data analytics to identify trends, understand customer preferences, and tailor retention efforts accordingly. Foster community engagement through social media platforms, encouraging user-generated content for authentic brand interaction. Automate routine processes to improve response times and provide proactive support, enhancing overall customer experience and retention. Personalize Every Customer Interaction Personalizing every customer interaction is vital for nurturing loyalty and improving engagement. Studies show that 84% of loyalty program members are more likely to make repeat purchases when they receive customized experiences. Nevertheless, less than 50% of businesses currently provide personalized recommendations within their loyalty programs, presenting a considerable opportunity for advancing customer loyalty and retention. By leveraging Customer Relationship Management (CRM) systems, you can track individual preferences and behaviors, which allows for more pertinent recommendations and personalized service. Recognizing generational differences in communication preferences is also important; younger customers often prefer digital interactions, whereas older customers may favor face-to-face engagement. Furthermore, implementing AI tools can greatly improve personalization efforts by analyzing customer data to provide hyper-specific recommendations. These strategies are key components of effective customer loyalty solutions that can profoundly impact your customer loyalty in marketing and overall business success. Implement Effective Loyalty Programs Implementing effective loyalty programs is imperative for businesses aiming to improve customer retention and drive sales growth. To define customer loyalty, it reflects a customer’s willingness to repeatedly engage with your brand. Your loyalty solutions should align with business goals by incentivizing behaviors like frequent purchases or referrals to maximize customer lifetime value. Research shows over 70% of consumers prioritize discounts and points, so immediate rewards are critical for engagement. Furthermore, incorporating gamification elements can augment customer investment; consider tiered rewards or exclusive access to keep participants motivated. Significantly, only 31% of businesses offer true omnichannel loyalty programs, presenting an opportunity for you to stand out with all-encompassing solutions. Regular tracking and analysis of your loyalty program’s performance is fundamental, allowing you to adjust strategies based on customer preferences and responses, ensuring your program remains relevant and appealing over time. Enhance Customer Communication To improve customer communication, you need to focus on personalized outreach strategies and proactive engagement techniques. By tailoring your messages based on customer behavior and preferences, you can greatly boost engagement and satisfaction. Regular check-ins not only anticipate customer needs but furthermore nurture a sense of trust, leading to stronger loyalty over time. Personalized Outreach Strategies In today’s competitive market, businesses can greatly benefit from adopting personalized outreach strategies to improve customer communication. By tailoring your interactions, you can markedly improve customer engagement and loyalty. Consider these key strategies: Utilize Customer Data: Leverage insights from customer behavior and preferences to send targeted emails with relevant offers. Segment Your Audience: Recognize generational differences in communication preferences, customizing outreach for digital-savvy younger customers and those who prefer in-store interactions. Implement AI Tools: Utilize artificial intelligence to anticipate customer needs, enabling you to deliver personalized recommendations at scale. Proactive Engagement Techniques Proactive engagement techniques are essential for enhancing customer communication and nurturing loyalty in today’s marketplace. Regular check-ins and automated updates about service outages can greatly boost customer satisfaction by addressing potential issues before they escalate. By anticipating customer needs through proactive outreach, you can enjoy higher retention rates, as 87% of customers seek improved service consistency. Utilizing Salesforce systems to provide customized tips based on product usage allows you to create personalized experiences that strengthen customer relationships. Automating routine outreach, like follow-up messages or satisfaction surveys, helps maintain engagement without overwhelming customers. Additionally, addressing concerns with personalized outreach can effectively turn negative experiences into positive outcomes, building trust and driving long-term loyalty. Utilize Data Analytics for Insights Utilizing data analytics offers businesses a potent tool to gain insights into customer behavior and preferences. By tapping into this resource, you can improve your retention strategies and create personalized experiences that resonate with your customers. Here are three key benefits of leveraging data analytics: Identify Trends: You can spot emerging patterns in customer behavior, allowing you to tailor your retention efforts effectively. Measure Effectiveness: Analytics helps you evaluate the success of your retention initiatives, enabling data-driven adjustments to improve outcomes. Spot At-Risk Customers: By recognizing early warning signs, such as reduced purchase frequency, you can proactively intervene to address customer dissatisfaction. Foster a Community Around Your Brand Cultivating a community around your brand can greatly improve customer loyalty, as engaged customers are more likely to advocate for your brand through word-of-mouth referrals. By creating online forums or social media groups, you provide a space for customers to share experiences, ask questions, and connect with others who share their interests. This interaction nurtures deeper relationships and brand attachment. User-generated content from community members considerably boosts brand authenticity, with 79% of consumers stating it impacts their purchasing decisions. Encouraging participation and feedback not only strengthens your community but also offers valuable insights for product and service improvements. When you actively engage with your community—responding to feedback and incorporating suggestions—customer satisfaction and loyalty can increase. In fact, 96% of Voice of Customer and Customer Experience professionals emphasize the importance of collecting and analyzing customer feedback to drive improvements. Building a community around your brand is a strategic approach to improving loyalty and retention. Provide Proactive Customer Support To truly improve customer loyalty, you need to provide proactive customer support that anticipates needs and addresses potential issues before they arise. Regular check-ins and timely updates can transform the customer experience, making individuals feel valued and informed. Anticipate Needs and Issues Proactive customer support plays a crucial role in anticipating needs and addressing potential issues before they escalate. By focusing on this strategy, you can greatly improve customer satisfaction and loyalty. Here are three effective ways to implement proactive support: Automated Updates: Send timely notifications about service outages or potential issues, preventing customer frustration and demonstrating transparency. AI Tools: Utilize artificial intelligence to deliver personalized communications, ensuring customers receive relevant information customized to their specific situations. Address Unhappiness: Reach out to customers showing signs of dissatisfaction to quickly address their concerns, turning negative experiences into positive outcomes. Regular Check-ins Matter Regular check-ins can make a significant difference in how satisfied your customers feel, especially when these interactions are designed to anticipate their needs and address any emerging issues. Engaging in regular communication is preferred by 70% of customers, highlighting the importance of ongoing relationships. Proactive support, particularly for customers showing signs of dissatisfaction, can boost retention rates by 25%. During check-ins, providing helpful resources and tips not just improves the customer experience but also reinforces your brand’s commitment to their success. Automating these check-ins allows you to scale personalized communications effectively, ensuring customers feel valued without overwhelming them. Proactive Communication Strategies Implementing proactive communication strategies can greatly improve your customer support efforts and boost overall satisfaction. By staying ahead of potential issues, you can create a more positive experience for your clients. Consider the following strategies: Automated Updates: Keep customers informed about service outages or delays, minimizing frustration. Regular Check-Ins: Reach out to customers showing signs of dissatisfaction to address concerns before they escalate. Resource Sharing: Provide helpful tips based on product usage to empower customers, improving their experience. Utilizing CRM systems can personalize these communications, tailoring them to individual preferences and behaviors. This proactive approach can lead to a 70% increase in customer loyalty, as clients appreciate brands that anticipate their needs and address them without delay. Automate Routine Processes As businesses endeavor to improve customer loyalty, automating routine processes emerges as an essential strategy for boosting efficiency and responsiveness. By employing AI agents to handle common customer inquiries 24/7, you can considerably reduce wait times, improving the overall customer experience. Automation likewise allows your human agents to focus on more complex issues, which leads to enhanced satisfaction. Here’s how automation can impact your business: Benefit Description Improved Efficiency Automation allows for 24/7 support, reducing response times by up to 70%. Better Customer Experience Freeing up agents for complex issues improves service quality. Increased Retention Rates Timely responses are vital for maintaining customer loyalty. Real-time Insights Automated processes provide data for informed decision-making and engagement. Incorporating automation in your customer service strategy can lead to higher retention rates and encourage repeat business. Actively Seek Customer Feedback To strengthen customer loyalty, you should actively seek feedback through effective surveys and social media engagement. By analyzing customer insights, you can identify pain points and make necessary improvements, ensuring that your services align with their needs. This feedback loop not just improves customer satisfaction but additionally nurtures a deeper connection with your brand, making customers feel valued. Utilize Surveys Effectively Surveys serve as a crucial tool for businesses aiming to improve customer loyalty by actively seeking feedback. By utilizing surveys effectively, you can gain valuable insights into your customers’ experiences and preferences. Consider focusing on these key strategies: Use open-ended questions: These allow customers to express their thoughts in detail, revealing specific areas for improvement. Collect feedback regularly: Consistent feedback helps you adapt your offerings and shows customers you’re committed to continuous improvement. Analyze results thoroughly: By examining survey data, you can identify at-risk customers and implement targeted strategies to boost satisfaction and loyalty. Incorporating these practices will improve customer experiences and ultimately drive retention, making your business more resilient in a competitive market. Engage on Social Media Engaging with customers on social media isn’t just about promoting your products; it’s also a fundamental way to actively seek feedback that can improve customer loyalty. By using these platforms, you can gather real-time insights, as 96% of Voice of Customer professionals utilize surveys to increase comprehension. This engagement cultivates a community atmosphere and encourages user-generated content, which strengthens brand loyalty as it lowers marketing costs. When you respond quickly to feedback, you build trust and credibility, since 87% of customers expect consistent service across all channels. Furthermore, encouraging customers to share their experiences can lead to valuable word-of-mouth marketing, driving new customer acquisitions and further increasing loyalty. Prioritize this interaction to boost retention effectively. Analyze Customer Insights How can businesses effectively understand their customers’ needs and preferences? Actively seeking customer feedback is crucial. By gathering insights through surveys and social media, you can pinpoint pain points and desires. Here’s how to do it: Implement Feedback Programs: Regularly collect and analyze customer feedback to track sentiment over time. This helps you adapt to changing preferences. Engage Customers: Involve customers in the feedback process, demonstrating your commitment to their needs. This nurtures trust and loyalty. Analyze Trends: Regular analysis can reveal insights that inform targeted retention strategies, leading to improved customer experiences. According to research, 96% of professionals in Voice of Customer and Customer Experience prioritize this feedback collection, crucial for enhancing satisfaction and retention. Ensure Transparency and Trust Transparency and trust are foundational elements in building lasting customer loyalty. When you communicate clearly about your policies and any changes, you promote trust, which influences 96% of customers’ purchasing decisions. Openly discussing how you use customer data and addressing rights boosts your credibility; 67% of consumers are more likely to stay loyal to brands that prioritize transparency. It’s vital to respond to customer concerns quickly, as 75% expect a reply within 24 hours, greatly impacting retention rates. Furthermore, honesty in product descriptions and managing customer expectations is key; 82% would switch to a competitor if they feel misled. By building trust through transparency, you can achieve a 25% increase in customer retention, as satisfied customers are more likely to make repeat purchases and advocate for your brand. Prioritizing these principles not just strengthens loyalty but contributes to sustainable business growth. Continuously Improve Customer Experience To nurture customer loyalty, organizations must continuously improve the customer experience across all interactions. This improvement is crucial, as 87% of customers desire improved service consistency across channels. Here are three key strategies to reflect on: Collect Feedback Regularly: Use surveys to gather insights on customer satisfaction. About 96% of Qualtrics and CX professionals rely on this method to identify areas needing attention. Implement Changes: Act on the feedback you receive. For example, The Home Depot have successfully adapted their inventory based on contractor suggestions, demonstrating the value of responsive change. Personalize Responses: Quick, customized replies to inquiries can turn negative experiences into positive ones, greatly boosting retention rates. Frequently Asked Questions How Do Loyalty Programs Affect Customer Spending Habits? Loyalty programs greatly influence your spending habits by encouraging you to make repeat purchases. When you earn rewards, you’re more likely to choose a brand over competitors, as the perceived benefits increase your overall satisfaction. These programs often create a sense of commitment, driving you to spend more to reach reward thresholds. Furthermore, personalized incentives can further motivate you to buy more frequently, in the end enhancing your overall engagement with the brand. What Are the Best Tools for Tracking Retention Metrics? To effectively track retention metrics, you can use tools like Google Analytics, which provides insights into user behavior and retention rates. Customer Relationship Management (CRM) systems, such as Salesforce, allow you to monitor customer interactions and analyze retention trends over time. Furthermore, subscription management platforms like Chargebee help track churn rates. Combining these tools gives you an all-encompassing view of retention, enabling you to make informed decisions to improve customer engagement. How Can I Measure Community Engagement Effectively? To measure community engagement effectively, track metrics like participation rates in discussions, the frequency of content sharing, and feedback on community initiatives. Utilize tools such as surveys to gather direct input from members, and analyze social media interactions for broader insights. Monitor user-generated content and assess overall sentiment through analytics. Regularly review this data to identify trends, adjust strategies, and cultivate a more engaged community, creating a space where members feel valued and heard. What Role Does Social Media Play in Customer Loyalty? Social media plays a significant role in customer loyalty by nurturing direct communication between you and your customers. It allows you to share updates, respond to inquiries, and create a sense of community. By consistently engaging with your audience through posts, comments, and messages, you build trust and familiarity. Additionally, social media platforms enable you to gather feedback, understand customer preferences, and tailor your offerings, ultimately improving customer loyalty and encouraging repeat business. How Often Should I Update My Loyalty Program? You should update your loyalty program at least once or twice a year, but consider more frequent updates based on customer feedback and market trends. Regularly assess the program’s performance, looking for areas to improve engagement and rewards. If you notice a decline in participation or changing customer preferences, it’s a good idea to revise your offerings. Keeping the program fresh and relevant helps maintain interest and encourages continued participation. Conclusion Implementing effective customer loyalty solutions can greatly improve retention rates. By personalizing interactions, creating rewarding loyalty programs, and promoting community engagement, you can strengthen bonds with your customers. Utilizing data analytics allows you to identify trends and proactively address concerns. Automating routine processes guarantees consistent communication, whereas actively seeking feedback builds trust. By continuously improving customer experiences, you not just retain customers but additionally cultivate a loyal base that supports your brand in the long run. Image via Google Gemini and ArtSmart This article, "10 Customer Loyalty Solutions to Boost Retention" was first published on Small Business Trends View the full article

In today’s competitive environment, retaining customers is fundamental for long-term success. You can boost customer loyalty through various strategies, such as personalizing interactions and implementing effective loyalty programs. Furthermore, leveraging data analytics can reveal important insights about customer behavior, enabling you to act proactively. Cultivating a sense of community around your brand likewise plays a critical role. These solutions can greatly improve customer experiences, but how do you start integrating them into your business model? Key Takeaways Implement personalized communication strategies using CRM systems to enhance customer engagement and loyalty. Develop effective loyalty programs with tiered rewards and immediate incentives to encourage repeat purchases. Utilize data analytics to identify trends, understand customer preferences, and tailor retention efforts accordingly. Foster community engagement through social media platforms, encouraging user-generated content for authentic brand interaction. Automate routine processes to improve response times and provide proactive support, enhancing overall customer experience and retention. Personalize Every Customer Interaction Personalizing every customer interaction is vital for nurturing loyalty and improving engagement. Studies show that 84% of loyalty program members are more likely to make repeat purchases when they receive customized experiences. Nevertheless, less than 50% of businesses currently provide personalized recommendations within their loyalty programs, presenting a considerable opportunity for advancing customer loyalty and retention. By leveraging Customer Relationship Management (CRM) systems, you can track individual preferences and behaviors, which allows for more pertinent recommendations and personalized service. Recognizing generational differences in communication preferences is also important; younger customers often prefer digital interactions, whereas older customers may favor face-to-face engagement. Furthermore, implementing AI tools can greatly improve personalization efforts by analyzing customer data to provide hyper-specific recommendations. These strategies are key components of effective customer loyalty solutions that can profoundly impact your customer loyalty in marketing and overall business success. Implement Effective Loyalty Programs Implementing effective loyalty programs is imperative for businesses aiming to improve customer retention and drive sales growth. To define customer loyalty, it reflects a customer’s willingness to repeatedly engage with your brand. Your loyalty solutions should align with business goals by incentivizing behaviors like frequent purchases or referrals to maximize customer lifetime value. Research shows over 70% of consumers prioritize discounts and points, so immediate rewards are critical for engagement. Furthermore, incorporating gamification elements can augment customer investment; consider tiered rewards or exclusive access to keep participants motivated. Significantly, only 31% of businesses offer true omnichannel loyalty programs, presenting an opportunity for you to stand out with all-encompassing solutions. Regular tracking and analysis of your loyalty program’s performance is fundamental, allowing you to adjust strategies based on customer preferences and responses, ensuring your program remains relevant and appealing over time. Enhance Customer Communication To improve customer communication, you need to focus on personalized outreach strategies and proactive engagement techniques. By tailoring your messages based on customer behavior and preferences, you can greatly boost engagement and satisfaction. Regular check-ins not only anticipate customer needs but furthermore nurture a sense of trust, leading to stronger loyalty over time. Personalized Outreach Strategies In today’s competitive market, businesses can greatly benefit from adopting personalized outreach strategies to improve customer communication. By tailoring your interactions, you can markedly improve customer engagement and loyalty. Consider these key strategies: Utilize Customer Data: Leverage insights from customer behavior and preferences to send targeted emails with relevant offers. Segment Your Audience: Recognize generational differences in communication preferences, customizing outreach for digital-savvy younger customers and those who prefer in-store interactions. Implement AI Tools: Utilize artificial intelligence to anticipate customer needs, enabling you to deliver personalized recommendations at scale. Proactive Engagement Techniques Proactive engagement techniques are essential for enhancing customer communication and nurturing loyalty in today’s marketplace. Regular check-ins and automated updates about service outages can greatly boost customer satisfaction by addressing potential issues before they escalate. By anticipating customer needs through proactive outreach, you can enjoy higher retention rates, as 87% of customers seek improved service consistency. Utilizing Salesforce systems to provide customized tips based on product usage allows you to create personalized experiences that strengthen customer relationships. Automating routine outreach, like follow-up messages or satisfaction surveys, helps maintain engagement without overwhelming customers. Additionally, addressing concerns with personalized outreach can effectively turn negative experiences into positive outcomes, building trust and driving long-term loyalty. Utilize Data Analytics for Insights Utilizing data analytics offers businesses a potent tool to gain insights into customer behavior and preferences. By tapping into this resource, you can improve your retention strategies and create personalized experiences that resonate with your customers. Here are three key benefits of leveraging data analytics: Identify Trends: You can spot emerging patterns in customer behavior, allowing you to tailor your retention efforts effectively. Measure Effectiveness: Analytics helps you evaluate the success of your retention initiatives, enabling data-driven adjustments to improve outcomes. Spot At-Risk Customers: By recognizing early warning signs, such as reduced purchase frequency, you can proactively intervene to address customer dissatisfaction. Foster a Community Around Your Brand Cultivating a community around your brand can greatly improve customer loyalty, as engaged customers are more likely to advocate for your brand through word-of-mouth referrals. By creating online forums or social media groups, you provide a space for customers to share experiences, ask questions, and connect with others who share their interests. This interaction nurtures deeper relationships and brand attachment. User-generated content from community members considerably boosts brand authenticity, with 79% of consumers stating it impacts their purchasing decisions. Encouraging participation and feedback not only strengthens your community but also offers valuable insights for product and service improvements. When you actively engage with your community—responding to feedback and incorporating suggestions—customer satisfaction and loyalty can increase. In fact, 96% of Voice of Customer and Customer Experience professionals emphasize the importance of collecting and analyzing customer feedback to drive improvements. Building a community around your brand is a strategic approach to improving loyalty and retention. Provide Proactive Customer Support To truly improve customer loyalty, you need to provide proactive customer support that anticipates needs and addresses potential issues before they arise. Regular check-ins and timely updates can transform the customer experience, making individuals feel valued and informed. Anticipate Needs and Issues Proactive customer support plays a crucial role in anticipating needs and addressing potential issues before they escalate. By focusing on this strategy, you can greatly improve customer satisfaction and loyalty. Here are three effective ways to implement proactive support: Automated Updates: Send timely notifications about service outages or potential issues, preventing customer frustration and demonstrating transparency. AI Tools: Utilize artificial intelligence to deliver personalized communications, ensuring customers receive relevant information customized to their specific situations. Address Unhappiness: Reach out to customers showing signs of dissatisfaction to quickly address their concerns, turning negative experiences into positive outcomes. Regular Check-ins Matter Regular check-ins can make a significant difference in how satisfied your customers feel, especially when these interactions are designed to anticipate their needs and address any emerging issues. Engaging in regular communication is preferred by 70% of customers, highlighting the importance of ongoing relationships. Proactive support, particularly for customers showing signs of dissatisfaction, can boost retention rates by 25%. During check-ins, providing helpful resources and tips not just improves the customer experience but also reinforces your brand’s commitment to their success. Automating these check-ins allows you to scale personalized communications effectively, ensuring customers feel valued without overwhelming them. Proactive Communication Strategies Implementing proactive communication strategies can greatly improve your customer support efforts and boost overall satisfaction. By staying ahead of potential issues, you can create a more positive experience for your clients. Consider the following strategies: Automated Updates: Keep customers informed about service outages or delays, minimizing frustration. Regular Check-Ins: Reach out to customers showing signs of dissatisfaction to address concerns before they escalate. Resource Sharing: Provide helpful tips based on product usage to empower customers, improving their experience. Utilizing CRM systems can personalize these communications, tailoring them to individual preferences and behaviors. This proactive approach can lead to a 70% increase in customer loyalty, as clients appreciate brands that anticipate their needs and address them without delay. Automate Routine Processes As businesses endeavor to improve customer loyalty, automating routine processes emerges as an essential strategy for boosting efficiency and responsiveness. By employing AI agents to handle common customer inquiries 24/7, you can considerably reduce wait times, improving the overall customer experience. Automation likewise allows your human agents to focus on more complex issues, which leads to enhanced satisfaction. Here’s how automation can impact your business: Benefit Description Improved Efficiency Automation allows for 24/7 support, reducing response times by up to 70%. Better Customer Experience Freeing up agents for complex issues improves service quality. Increased Retention Rates Timely responses are vital for maintaining customer loyalty. Real-time Insights Automated processes provide data for informed decision-making and engagement. Incorporating automation in your customer service strategy can lead to higher retention rates and encourage repeat business. Actively Seek Customer Feedback To strengthen customer loyalty, you should actively seek feedback through effective surveys and social media engagement. By analyzing customer insights, you can identify pain points and make necessary improvements, ensuring that your services align with their needs. This feedback loop not just improves customer satisfaction but additionally nurtures a deeper connection with your brand, making customers feel valued. Utilize Surveys Effectively Surveys serve as a crucial tool for businesses aiming to improve customer loyalty by actively seeking feedback. By utilizing surveys effectively, you can gain valuable insights into your customers’ experiences and preferences. Consider focusing on these key strategies: Use open-ended questions: These allow customers to express their thoughts in detail, revealing specific areas for improvement. Collect feedback regularly: Consistent feedback helps you adapt your offerings and shows customers you’re committed to continuous improvement. Analyze results thoroughly: By examining survey data, you can identify at-risk customers and implement targeted strategies to boost satisfaction and loyalty. Incorporating these practices will improve customer experiences and ultimately drive retention, making your business more resilient in a competitive market. Engage on Social Media Engaging with customers on social media isn’t just about promoting your products; it’s also a fundamental way to actively seek feedback that can improve customer loyalty. By using these platforms, you can gather real-time insights, as 96% of Voice of Customer professionals utilize surveys to increase comprehension. This engagement cultivates a community atmosphere and encourages user-generated content, which strengthens brand loyalty as it lowers marketing costs. When you respond quickly to feedback, you build trust and credibility, since 87% of customers expect consistent service across all channels. Furthermore, encouraging customers to share their experiences can lead to valuable word-of-mouth marketing, driving new customer acquisitions and further increasing loyalty. Prioritize this interaction to boost retention effectively. Analyze Customer Insights How can businesses effectively understand their customers’ needs and preferences? Actively seeking customer feedback is crucial. By gathering insights through surveys and social media, you can pinpoint pain points and desires. Here’s how to do it: Implement Feedback Programs: Regularly collect and analyze customer feedback to track sentiment over time. This helps you adapt to changing preferences. Engage Customers: Involve customers in the feedback process, demonstrating your commitment to their needs. This nurtures trust and loyalty. Analyze Trends: Regular analysis can reveal insights that inform targeted retention strategies, leading to improved customer experiences. According to research, 96% of professionals in Voice of Customer and Customer Experience prioritize this feedback collection, crucial for enhancing satisfaction and retention. Ensure Transparency and Trust Transparency and trust are foundational elements in building lasting customer loyalty. When you communicate clearly about your policies and any changes, you promote trust, which influences 96% of customers’ purchasing decisions. Openly discussing how you use customer data and addressing rights boosts your credibility; 67% of consumers are more likely to stay loyal to brands that prioritize transparency. It’s vital to respond to customer concerns quickly, as 75% expect a reply within 24 hours, greatly impacting retention rates. Furthermore, honesty in product descriptions and managing customer expectations is key; 82% would switch to a competitor if they feel misled. By building trust through transparency, you can achieve a 25% increase in customer retention, as satisfied customers are more likely to make repeat purchases and advocate for your brand. Prioritizing these principles not just strengthens loyalty but contributes to sustainable business growth. Continuously Improve Customer Experience To nurture customer loyalty, organizations must continuously improve the customer experience across all interactions. This improvement is crucial, as 87% of customers desire improved service consistency across channels. Here are three key strategies to reflect on: Collect Feedback Regularly: Use surveys to gather insights on customer satisfaction. About 96% of Qualtrics and CX professionals rely on this method to identify areas needing attention. Implement Changes: Act on the feedback you receive. For example, The Home Depot have successfully adapted their inventory based on contractor suggestions, demonstrating the value of responsive change. Personalize Responses: Quick, customized replies to inquiries can turn negative experiences into positive ones, greatly boosting retention rates. Frequently Asked Questions How Do Loyalty Programs Affect Customer Spending Habits? Loyalty programs greatly influence your spending habits by encouraging you to make repeat purchases. When you earn rewards, you’re more likely to choose a brand over competitors, as the perceived benefits increase your overall satisfaction. These programs often create a sense of commitment, driving you to spend more to reach reward thresholds. Furthermore, personalized incentives can further motivate you to buy more frequently, in the end enhancing your overall engagement with the brand. What Are the Best Tools for Tracking Retention Metrics? To effectively track retention metrics, you can use tools like Google Analytics, which provides insights into user behavior and retention rates. Customer Relationship Management (CRM) systems, such as Salesforce, allow you to monitor customer interactions and analyze retention trends over time. Furthermore, subscription management platforms like Chargebee help track churn rates. Combining these tools gives you an all-encompassing view of retention, enabling you to make informed decisions to improve customer engagement. How Can I Measure Community Engagement Effectively? To measure community engagement effectively, track metrics like participation rates in discussions, the frequency of content sharing, and feedback on community initiatives. Utilize tools such as surveys to gather direct input from members, and analyze social media interactions for broader insights. Monitor user-generated content and assess overall sentiment through analytics. Regularly review this data to identify trends, adjust strategies, and cultivate a more engaged community, creating a space where members feel valued and heard. What Role Does Social Media Play in Customer Loyalty? Social media plays a significant role in customer loyalty by nurturing direct communication between you and your customers. It allows you to share updates, respond to inquiries, and create a sense of community. By consistently engaging with your audience through posts, comments, and messages, you build trust and familiarity. Additionally, social media platforms enable you to gather feedback, understand customer preferences, and tailor your offerings, ultimately improving customer loyalty and encouraging repeat business. How Often Should I Update My Loyalty Program? You should update your loyalty program at least once or twice a year, but consider more frequent updates based on customer feedback and market trends. Regularly assess the program’s performance, looking for areas to improve engagement and rewards. If you notice a decline in participation or changing customer preferences, it’s a good idea to revise your offerings. Keeping the program fresh and relevant helps maintain interest and encourages continued participation. Conclusion Implementing effective customer loyalty solutions can greatly improve retention rates. By personalizing interactions, creating rewarding loyalty programs, and promoting community engagement, you can strengthen bonds with your customers. Utilizing data analytics allows you to identify trends and proactively address concerns. Automating routine processes guarantees consistent communication, whereas actively seeking feedback builds trust. By continuously improving customer experiences, you not just retain customers but additionally cultivate a loyal base that supports your brand in the long run. Image via Google Gemini and ArtSmart This article, "10 Customer Loyalty Solutions to Boost Retention" was first published on Small Business Trends View the full article

Google's information gain patent is real. Commodity content can't beat it. Here's the practical content framework for search that still delivers value. The post Creating ‘Non-Commodity’ Content That Cuts Through The Noise appeared first on Search Engine Journal. View the full article

We may earn a commission from links on this page. Deal pricing and availability subject to change after time of publication. The Asus ROG Ally is a Windows 11-based handheld gaming console, and right now, a refurbished unit is down to $389.99 at Woot—the lowest price ever recorded for it, according to price trackers. For context, the same device (used) costs $665 on Amazon, and the lowest it's ever been is $449.99. This deal runs for another four days or until the stock runs out. Shipping is free for Prime members, and Woot only ships within the lower 48 states. Asus ROG Ally $389.99 at Woot $665.00 Save $275.01 Shop Now Shop Now $389.99 at Woot $665.00 Save $275.01 The Ally runs a full, unmodified version of Windows 11 Home on an AMD Ryzen Z1 Extreme processor with 16GB of RAM and a 512GB SSD, so you’re not locked into one storefront or ecosystem. That means you can install Steam, Xbox Game Pass, Epic Games Store, Ubisoft Connect, GOG—anything that runs on a Windows PC runs here. That flexibility is a big part of the appeal for people who already have large PC game libraries. Asus also did a decent job of simplifying the experience with Armoury Crate, which pulls your various game libraries into a single interface, so you spend less time navigating Windows menus on a tiny screen. Battery life can drain pretty fast during demanding games, so this works best near a charger during longer sessions. You’ll also end up tweaking graphics settings fairly often if you want smoother performance in newer AAA games. Windows itself can occasionally feel clunky on a handheld, and while the RGB rings around the joysticks look fun at first, they can get distracting during darker games. Still, PCMag gave the ROG Ally an "excellent" review, and at this price—$275 below the current Amazon listing—it's a solid entry point into PC handheld gaming for someone who's been on the fence. Our Best Editor-Vetted Tech Deals Right Now Apple AirPods Pro 3 Noise Cancelling Heart Rate Wireless Earbuds — $199.00 (List Price $249.00) Apple Watch Series 11 [GPS 46mm] Smartwatch with Jet Black Aluminum Case with Black Sport Band - M/L. Sleep Score, Fitness Tracker, Health Monitoring, Always-On Display, Water Resistant — $329.00 (List Price $429.00) Apple iPad 11" A16 128GB Wi-Fi Tablet (Silver, 2025) — $299.00 (List Price $349.00) Fire TV Stick 4K Plus Streaming Player With Remote (2025 Model) — $29.99 (List Price $49.99) Deals are selected by our commerce team View the full article

AI is saving workers more than two hours a day. That sounds like an unqualified win, and in many ways, it is. But beneath the productivity headlines, something more complicated is happening. Employees are getting faster, but some are also getting less confident, less skilled, and less certain they can do their jobs without a machine doing much of the thinking for them. That tension is the defining workforce challenge of 2026, and most companies aren’t prepared to address it. New research from GoTo, conducted in partnership with Workplace Intelligence, surveyed 2,500 global employees and IT leaders on AI use and sentiment. The findings tell a story about a workforce caught between the tools that help them and the habits those tools are forming. Fifty percent of employees now say they rely on AI too much. Thirty percent say they can no longer function without it. And 39% believe their overreliance on AI is actively eroding their skills and making them less intelligent, a number that climbs to 46% among Gen Z workers. These aren’t fringe opinions. They are the quiet consensus of a workforce that adopted AI fast and is now reckoning with the consequences. The Pressure to Use AI Is Outrunning the Guardrails to Use It Well One of the clearest findings in the research is how much external pressure is shaping AI behavior at work. Sixty percent of employees say they feel pressured to use AI tools to boost productivity regardless of whether the task calls for it. That pressure, absent the right training and policies, is a setup for misuse. The numbers bear this out. Seventy percent of employees (up from 54% just a year ago) admit they’ve used AI for sensitive or high-stakes tasks, including legal or compliance work, decisions requiring emotional intelligence, and actions involving confidential information. These are exactly the domains where human judgment is most irreplaceable, and where AI errors carry the highest cost. The fact that this number jumped 16 percentage points in a single year suggests the problem isn’t slowing down on its own. Compounding this is an “AI workslop” problem that’s starting to tax the entire workforce. Forty-three percent of employees say they’ve submitted AI-generated content despite suspecting it was low quality or contained errors. With that in mind, it’s unsurprising that 77% percent say reviewing AI-generated work takes more time than reviewing human work. And 66%sixty-six percent say wading through other people’s AI output creates extra work for them. The efficiency gains from AI are real, but they’re being partially offset by a flood of under-reviewed, unreliable output that everyone else must spend time, energy, and resources to clean up. The Leadership Gap Is Where the Real Risk Lives What makes these findings particularly striking is the disconnect between employees and the leaders responsible for guiding them. Eighty-four percent of employees say their company could do more to encourage responsible AI use, however only 48% of IT leaders agree. That gap of 36 points is a signal that IT leadership is significantly underestimating the extent of the problem. The policy picture is just as concerning. Only 44% of IT leaders say their company has an AI policy in place at all. And among those that do, 77% of employees say the policy needs improvement. Meanwhile, 80% of employees and 60% of IT leaders acknowledge that most workers aren’t being properly trained to use AI tools. The infrastructure for responsible AI use, including the policies, the training, and the role-specific guidance hasn’t kept pace with how fast employees have adopted these tools. This is not a technology issue, not a generational issue, and not something that will self-correct as AI matures. Employees are not misusing AI out of laziness or bad faith; they’re doing it because they’ve been handed powerful tools without the context and enablement to use them well, and told implicitly or explicitly to produce results. When organizations reward output without asking how it was produced, they get exactly what they incentivize. What Companies That Get This Right Will Do Differently The same research that surfaces these problems also points toward solutions, and they’re not complicated. They require organizational commitment, not technological breakthroughs. The priority is building AI policies that work. That means policies employees understand, see as relevant to their daily work, and feel equipped to follow, not compliance documents that live on an intranet page. Given that 65% of employees say their employers have not equipped them with the skills they need as AI takes over more work, this must be paired with genuine training investment, including role-specific guidance on where AI adds value and where it doesn’t belong. The second priority is deliberate investment in human skills. Workers themselves identified the capabilities they believe will matter most in an AI-driven workplace: creative thinking, emotional intelligence, sound judgment, and the ability to know when to trust AI outputs and when to override them. These aren’t soft skills in the dismissive sense; they are the hard-to-automate competencies that determine whether AI amplifies a workforce or quietly hollows it out. They’re also the foundation of effective human-AI collaboration. The employees who will create the most value aren’t those who use AI the most, but the ones who know how to work alongside it. Workers should focus on contributing the judgment, context, and creativity that AI cannot supply, while letting AI handle the volume, speed, and synthesis it does well. Companies that train employees to operate in that partnership model, rather than simply handing them tools and expecting results, will be better positioned when the next wave of AI capabilities arrives. The third is cultural: leaders need to model what responsible AI use looks like, not just mandate it. Employees who see their managers using AI thoughtfully, knowing when to rely on it, when to push back on its outputs, and when to set it aside entirely are more likely to develop the same instincts. Policy shapes behavior at the edges; culture shapes it at the center. Eighty-eight percent of employees say AI has benefited them. That number should give every business leader confidence that the technology is working. But the same research makes clear that productivity gains alone are not a strategy. The companies that will win the next decade of work aren’t the ones who pushed AI adoption hardest. They’re the ones who built the organizational discipline to use it wisely, and kept their people capable, confident, and trusted in the process. View the full article

By now, you’ve heard the doom and gloom. In April, Verizon CEO Dan Schulman warned that AI could push U.S. unemployment to 20%-30% over the next two to five years. Anthropic CEO Dario Amodei has warned that AI could wipe out half of entry-level white-collar jobs within five years. Ford CEO Jim Farley has said AI could replace “literally half” of white-collar workers in the U.S. SEO is a white-collar job. So does that mean our jobs will be eliminated, too? The answer isn’t as obvious as you might think. Yes, the world is changing. But if you’ve been doing SEO for a while, you should be used to that by now. SEOs have always been forced to wear strange combinations of hats: part technical analyst, part content strategist, part UX researcher, part marketer, and part analyst. I don’t think AI will make SEO expertise obsolete. But it will make shallow SEO obsolete. The people who thrive will be the ones who understand search behavior, business outcomes, technical systems, content strategy, analytics, and how to turn all of that into better decisions. The old version of SEO stopped working years ago I’ve been doing SEO since before there was a word for “SEO.” Every few years, there’s a viral article declaring that “SEO is dead.” One of the first to catch fire was a 2005 article by Jeremy Schoemaker, repeating something he’d heard from Jason Calacanis. Then, in 2009, Danny Sullivan wrote an article on this site reacting to a blog post by Robert Scoble declaring that “SEO isn’t important anymore.” We know the reality. SEO never died. But over the years, it’s changed a lot. Look at this screenshot of a Google search for [flowers] in 2007 versus the same search in 2026. Google’s “flowers” SERP in 2007, when a No. 1 organic ranking controlled most of the visible page. Google’s “flowers” SERP in 2026, where organic listings compete with ads, shopping results, local packs, AI features, and other search elements. This example is near and dear to my heart because I wrote that title tag in 2007. I was fortunate enough to lead SEO at 1-800-Flowers at a time when a No. 1 organic ranking meant significant traffic and revenue. Twenty years later, their team has maintained the No. 1 organic ranking. However, today it’s so buried on the SERP that I wonder whether it gets any clicks at all. This phenomenon isn’t limited to searches for “flowers.” Search for any competitive head term these days, and chances are you’ll see the organic result buried. Is SEO “dead”? That really depends on your definition of “SEO.” If your definition is “getting to the top of Google organic search” by spending your whole day writing title tags, then yeah, SEO is pretty much dead. It has been for a long time. If your definition of SEO is understanding that people are looking for your goods and services, understanding their needs, answering their questions, and meeting them wherever they go to find information, then your journey as an SEO expert — or whatever you eventually decide to call yourself — is only beginning. Dig deeper: Could AI eventually make SEO obsolete? 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 Why true SEO experts are uniquely positioned to thrive There’s one phenomenon I’ve noticed with AI, not just in SEO, but across every industry. You might have noticed it too. On social media, you’ll see a lot of AI-generated videos. The vast majority are silly “look what I can do with AI” videos. You see them, maybe press “Like,” and then forget about them. But the ones with staying power are made by people who understand filmmaking: pacing, framing, lighting, composition, camera movement, editing, sound design, and how to build toward an emotional payoff. In other words, even though everyone can generate videos with AI now, the differentiator is no longer how “cool” the visuals are. It’s how skillfully creators use AI as a tool to achieve their vision. There’s an analogous situation happening with SEO and AI. I’ve noticed a lot of people typing simplistic prompts and, like Neo in “The Matrix,” declaring, “I know SEO.” What these folks don’t realize is that SEO is a lot more than title tags, and it was never just about reverse-engineering search engines. It was always about reverse-engineering the human brain, drawing on knowledge and experience across keyword lists, user behavior, content strategy, technical systems, analytics, persuasion, UX, and business outcomes. When others are typing simplistic prompts into their LLMs, SEO experts will be having deep conversations with their LLMs, teaching them, challenging them, and finding ways to get the best out of them. Those who excel in this new world won’t be the ones who have all the answers. They’ll be the ones who have the right questions. While it’s still early, and I’m convinced we haven’t even scratched the surface of ways to use LLMs in SEO, here are just a few ways I’ve been using AI in my SEO work to make it more efficient and effective than ever. 1. Performing SEO basics with unprecedented efficiency and effectiveness I’m generally not a fan of AI-generated long-form writing. You end up with generic, inauthentic slop that, in the words of Shakespeare, is “full of sound and fury, signifying nothing.” I predict that a year from now, most people will be able to spot the clear signs of AI-generated copy: not just obvious tells like excessive use of em dashes and repetitive phrasing (“That’s not X … it’s Y!”), but a lack of authentic personality and stories. Metadata is one of the places where I don’t mind AI assistance because its job isn’t to invent original thought. It’s to compress the page’s value, intent, and positioning into the right format for the right surface. The big mistake I see people making with AI-generated metadata is that their prompts are far too generic: “Write a title tag for this page.” A seasoned SEO knows the goal isn’t to create a “pretty title tag.” It’s to create the most effective title tag possible for human, search engine, and AI discovery. It takes into account various search intents, brand positioning, competitor gaps, conversion drivers, and practical space limitations. AI opens up new opportunities that weren’t practical before. Not many people know that ideally, your title tag, Open Graph tag, and Twitter card should be distinct from one another because they’ll be shown to different audiences on Google, Facebook, and X. And it took me a few tries to remind AI that title tag length isn’t based on character count, but on pixel width. Those “in the know” will start using AI to generate everything: title tags, meta description tags, OG tags, Twitter cards, and the right structured data. Someone without SEO experience will write generic prompts and wonder why their perfectly polished title tags aren’t doing anything for them a year from now. Dig deeper: The AI writing tics that hurt engagement: A study 2. Turning SEO recommendations into dev-ready tickets One “edge” I’ve had throughout my career is the ability to translate vague marketing goals into precise technical requirements developers can actually execute. But as technology has become more complex, I found myself hitting my own limits. I understood the principles of coding, but had a hard time articulating exactly what I needed developers to do. Googling hardly ever helped because I’d just find high-level articles written by consultants, some of whom clearly didn’t understand it either. A practical example is modern React or single-page app architecture, where a page may look complete to users while key SEO content is assembled after load from JavaScript rather than appearing as crawlable HTML. In the past, I might’ve written a vague recommendation like “we need more crawlable content on this page,” forcing my poor developer to figure out what that means. With AI, I can turn that into a real implementation ticket: grounding the LLM in the site’s tech stack, translating the SEO need into concepts like server-side rendering, hydration, DOM content, and crawlable links, and adding examples, test cases, edge cases, and acceptance criteria. The point isn’t to become a React engineer. It’s to communicate SEO requirements in a way that developers can execute without forcing them to think too much about it. Trust me, your developer will thank you. 3. Mining GSC, GA4, and Semrush or Ahrefs data for actual user needs Treating AI optimization as long-tail SEO done right has been one of the game-changers for me when it comes to my own productivity. The holy grail of SEO has always been to read your users’ minds and create content that meets their needs. Anyone who’s spent a lot of time with SEO data knows that there are enormous amounts of insights locked within this data. The first problem is unlocking them. The second problem is getting them into a format that will get people to pay attention. In the past, I would literally lock myself in a room with a giant spreadsheet open on my screen. I’d go through search terms one by one, categorizing and clustering them, and, if I was lucky, end up with a handful of insights days later. I might start with a list of 30,000 keywords and get through maybe a few hundred before getting completely exhausted. And when I’d present my insights, along with my giant pivot table, to stakeholders, they’d nod their heads, and then everyone would forget about them. LLMs are changing the game. You can simply upload data from GSC, GA4, and Semrush and Ahrefs, along with your own business and market insights, and then simply ask your LLM questions. Here are just a few recent examples of analyses I’ve done for my clients. These would once have taken days or weeks. Now I can get to a strong first pass in minutes. Analyze our GSC keyword data and organize the keywords into topical clusters. Which topics do we clearly have a “right to own” in Google’s eyes? Review our top competitors and uncover keywords within this topical neighborhood that they rank for but we don’t. What kind of content do we need to “break in”? Surface GSC queries that get lots of impressions but few clicks. What improvements can we make to our titles, snippets, or positioning to drive more clicks? Examine organic landing pages that attract a lot of traffic but fail to convert. What is the search intent behind the keywords driving traffic to these pages, and how can we improve conversion? Find keywords where we’re in “striking distance” of stronger rankings. What additional content do we need to create or adjust to push us to the top? Analyze the queries people type into our on-site search. What are examples of searches they might perform on Google or prompts they might use in LLMs when looking for this information? There are literally an endless number of questions you can ask. I didn’t present these as sample prompts because they’re thought starters. While you’ll probably get a decent answer, the real value from AI comes only when you: Dig deep into specific concepts, pages, and keywords. Validate the LLM’s responses. Challenge it as necessary. Recognize hallucinations or context drift. Put your findings into immediate action. Dig deeper: How to use AI to diagnose and improve search intent alignment Get the newsletter search marketers rely on. See terms. 4. Prototyping page layouts, content modules, and more Something else I’ve found LLMs can do really well is generate a solid wireframe of a page or page module that you can pass on to your web designer and developer. But this is another area where the quality of the output depends almost entirely on the quality of your prompt and the context you provide the LLM. Most people will simply type “design me a web page,” perhaps with a few “wish list” items they’d like to see. AI may produce something that looks “complete” on the surface, perhaps a hero section, a list of benefits, some FAQs, and a call to action (CTA). But when executed, it’ll feel lifeless, generic, and disconnected from the actual business problem. The better approach is to ground the LLM with as much background information as possible. This doesn’t need to include every SEO report, but rather the ones that provide the highest-quality signals, such as the ones we discussed above: topic clusters, competitor gaps, conversion data, and on-site search data. Add other useful information like sales objections, customer reviews, your brand’s unique value propositions, and a clear explanation of what the page needs to accomplish. With proper context, AI can help lay out something that transcends a generic landing page. For example, it can propose a strong hero section with suggested wording, recommendations for CTAs, section order, comparison tables, proof blocks, FAQs based on real questions, trust elements, and paths for different stages of intent. Remember that it works in reverse, too. Upload a screenshot of an existing page, either yours or your competitor’s, tell the LLM what your goals are for the page, and ask it to critique the page. AI can also open up other SEO opportunities that have previously been roadblocks. Want to do A/B testing? Tell the LLM the hypothesis you want to test, and have it come up with variants for you. Want to prototype a simple interactive tool? Provide your requirements, provide the underlying data, and see what your LLM can do. In some cases, it can go beyond a static mockup and produce a working prototype that a developer can evaluate, harden, and turn into production code. Your edge as an SEO is knowing what information to feed the model, what problems the page actually needs to solve, and which ideas are strategically useful versus just AI-generated decoration. The one thing that I haven’t seen AI do very well yet is generate professional-quality design and production-quality code. But everything up to that point is at your fingertips now. 5. Making analytics useful again As I’m sure it was for many of you, July 1, 2024, was a dark day for me. That’s when Google shut down Universal Analytics and forced us all onto GA4. Since it was called Urchin, I’d all but mastered UA. Then one day, all of my reports and dashboards were simply gone. And I had no interest in spending another decade on a learning curve just to recreate reports that they’d once given me by default. But with the arrival of LLMs, you can simply ask the LLM to walk you through building whatever report you want. The first report I had to re-create was the on-site search report, one that’s inexplicably missing from GA4. I wrote my own prompt to walk me through creating this, but for the purposes of this article, I had ChatGPT write the prompt: Act as a senior GA4 analytics consultant. I want to rebuild a useful onsite search report in GA4/Looker Studio. GA4 does not provide the same dedicated Site Search report that Universal Analytics had, but I can use the `view_search_results` event, the `search_term` parameter, and any custom parameters needed. Create a practical, implementation-ready plan that covers: 1. How to confirm onsite search tracking is working. 2. Recommended event name and parameters, including which should be registered as custom dimensions. 3. How to track searches when the site does not use URL query parameters. 4. The most useful report sections, including: - total searches - unique searchers - top search terms - zero-result searches - refined or repeated searches - searches followed by exits - searches followed by conversions - searches by page, device, and user type 5. Step-by-step instructions for building the report in GA4 Explore and Looker Studio. 6. A QA checklist to make sure the data is accurate. Keep the answer concise, practical, and usable by both a marketer and a developer. The key to writing these prompts, or prompts that generate prompts, is including the phrase “step by step.” One of the nice things about AI is that it doesn’t judge. Take as long as you need, ask it to break the setup down into steps as granular as you like, and feel free to ask “dumb” questions. It’ll oblige enthusiastically. You can imagine what this opens up. One of the classic issues with SEO analytics is that all too often, they’re merely vanity metrics. Conversions, clicks, impressions, and rankings may look impressive at first, but eventually the dreaded “so what” question will arise. Who really cares if you see impressions and rankings growing like wildfire if your revenue isn’t increasing? This is where you want to ask your AI to help you tie data to business performance. Which unbranded keywords are actually driving revenue? Which are leading to soft conversion goals like email signup, account creation, or pricing page visits? Which search queries bring in engaged visitors who come back later through brand search, direct traffic, or email? Again, the sky’s the limit. You can build a report or dashboard to answer just about any question your stakeholders have, provided you’re collecting the right data, and if you’re not, AI can help you create tickets for your web developer to collect that data. Dig deeper: SEO analytics: How to interpret SEO data & anomalies 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 The work is changing. The need for expertise isn’t. Like I said, this is only scratching the surface of how AI can help transform the work we do as SEOs. But let’s get to the question everyone is really asking: Is your job safe? I don’t have a crystal ball. But one thing is pretty clear to me. Not every SEO job will survive unchanged. Big companies will likely cut roles. Teams will likely get smaller. A lot of tactical work that used to require specialists may be done faster, cheaper, or “good enough” by someone using AI. If your value is limited to tasks that AI can perform on command, there may be challenges ahead. But if your value is understanding customers, interpreting search behavior, connecting data to business outcomes, translating strategy into execution, and helping companies become more findable, useful, and trusted, then AI isn’t the end of your career. It may be the best leverage you’ve ever had. And there’s another reason I’m optimistic. The same AI disruption hitting SEO is hitting every other white-collar profession, too. If large companies do lay off significant numbers of talented people, many of those people aren’t just going to disappear from the economy. Some will start businesses. Some will finally pursue ideas they’ve had in their heads for years. Some will use AI to build prototypes, launch products, test markets, and create companies in ways that would have required far more capital and staff just a few years ago. That should give us hope. Many of the great companies we know today started with little more than a few people, an idea, and the willingness to figure things out as they went. Steve Jobs and Steve Wozniak, Bill Gates and Paul Allen, Mark Zuckerberg, Jeff Bezos, Larry Page and Sergey Brin, Michael Dell, and many others did not begin with massive corporations behind them. They began with ideas, persistence, and the tools available to them at the time. If they were able to accomplish what they did with their tools, imagine what a new generation of entrepreneurs will be able to do with AI. Maybe you’ll be one of those entrepreneurs. Or maybe your role will be helping one of them turn their ideas into businesses people can actually discover, understand, trust, and choose. Either way, the products, services, brands, and businesses built with AI will still need to be found. They will still need to explain why they matter. They will still need to earn attention, authority, and trust. SEO is dead. Long live SEO. View the full article

After filing for bankruptcy several weeks ago, a large franchisee that operates dozens of Carl’s Jr. restaurants in California is planning to cut loose some of its underperforming locations, according to newly filed court documents. Sun Gir Incorporated, the lead debtor in a group of affiliated Chapter 11 cases that were filed in early April, has asked for court permission to reject the leases on at least three Carl’s Jr. locations in the Los Angeles area. As of this week, the restaurants appeared to still be open. But they have been operating at a substantial negative cashflow for the franchisee, as documented in three separate dockets filed in federal court for California’s Central District. Sun Gir says the underperforming restaurants are burdensome, and that they impose financial losses on the franchisee “without providing sufficient economic benefit,” the filings reveal. The filings do not explicitly say that the restaurants will close, although that would be the typical outcome for a court-approved lease rejection. The franchisee has stated in the filings that it wants to focus on its more profitable locations as part of a restructuring. In a separate filing, Sun Gir said that it has hired National Franchise Sales (NFS), a business brokerage firm, to help it sell some of its Carl’s Jr. locations, but it did not specify which ones. The details of that process are still being worked out, with bids expected to be due in July and an auction potentially scheduled for August. It’s unclear how many jobs could be lost as part of the restructuring or any resulting closures. Sun Gir and its affiliates own 59 Carl’s Jr. restaurants in California. Together, they employ roughly 1,000 employees. The debtors are all affiliated with Friendly Franchisees Corporation (FFC), in La Palma, California, which is not directly named in the bankruptcy cases. FFC and its general counsel did not respond to requests for comment about the fate of the Carl’s Jr. stores. Why did this Carl’s Jr. franchise go bankrupt? In court documents, Sun Gir Incorporated cited a number of factors that have contributed to its Chapter 11 bankruptcy. Carl’s Jr. restaurants within its portfolio have faced increased competition, rising operating costs, and diminishing sales, all of which have added up to “financial distress.” Sun Gir is also among the restaurant companies that have blamed its precarious financial situation in part on California’s two-year-old minimum wage policy, which requires $20 an hour for workers at fast food chains. Tellingly, Sun Gir’s bankruptcy filings include detailed financial breakdowns of restaurant operating losses that begin on April 1, 2024—the day the minimum wage policy took effect. The bankruptcy cases were filed the following day. Recent research on the impact of that policy, including one March study led by an economist at UC Santa Cruz, has found that while fast food wages did indeed increase, some restaurant operators have reduced their work shifts as a result. Is Carl Jr.’s in trouble? The bankruptcy filings concern restaurants owned by a single franchisee and do not necessarily reflect the health or appeal of the Carl’s Jr. brand. Founded in 1941, Carl’s Jr. is known for its charbroiled burgers and other indulgent menu items. The fast food brand is owned by Tennessee-based CKE Restaurants Holdings, the privately held company that also owns Hardee’s. CKE declined to comment about the franchisee’s bankruptcy or any potential store closures. Carl’s Jr. has more than 1,000 U.S. locations, mostly in western states, with California being the state with the most Carl’s Jr. locations. How many restaurants are at stake in the bankruptcy? Friendly Franchisees Corporation says on its website that it operates 65 Carl’s Jr. locations, but its affiliated bankruptcy cases have stated 59 locations: 52 in Southern California and 7 in Northern California. It’s not entirely clear what accounts for the discrepancy. Sun Gir said in a court filing that one of its locations in North Hollywood closed two years before its bankruptcy petition. It’s possible that others have closed in recent years. Which Carl’s Jr. locations are closing or being sold? Sun Gir told a court that it wants to reject the leases on three underperforming locations. It did not respond to questions about whether the locations will be permanently closed or sold to another entity. The addresses are as follows: 19400 Ventura Blvd, Tarzana, CA 91356 165 E Duarte Rd Arcadia, CA, 91006 573 N Azusa Ave Covina, CA 91722 All three of these stores have been around for many years. The oldest of the leases, for the Arcadia store, dates back to the year 2000. However, that store suffered a net operating lose of $403,003 over a two-year period between April 2024 and March 2026, a court filing reveals. That makes it the biggest lossmaker of the three locations. For now, it’s unclear if additional locations could be impacted by future lease rejections. We’ve asked FFC for more details and will update this story if we hear back. This story is developing . . . View the full article

It used to be that my friend Kristin had a vague sense of how her husband’s day went. He’d come home with a story to share or sometimes he didn’t. Sometimes he seemed annoyed, and when he was in one of those moods, she didn’t press. They’d kick their feet up, pour some wine, and talk about the upcoming weekend. Now they both work remote and all of a sudden, she knows a lot more about her husband’s day. “I know how many times he’s opened the fridge,” she told me recently. “Seven times. Seven times before lunch.” She wasn’t angry when she said it. “I love him,” she said. “But I don’t know that I was meant to know this much.” You’re seeing too much I’ve been thinking about Kristin and her fridge for weeks. Working from home hasn’t just changed the way we work. It has also changed some marriages in ways no one expected. Couples used to have built-in distance. Before you got home, there was space to think about your partner, miss them, and feel grateful. These days, couples are with each other all day. They see each other’s stress spirals, doom scrolling breaks, things they said in meetings that bothered them, emails that didn’t make sense, phone calls they wish they could re-do. It’s a level of intimacy we never asked for. For some, it’s endearing. For others, it’s a lot. You used to get the best version of your partner when you walked through the door. But now you get the full, unedited version all the time. Little annoyances you never knew about build up because you’re around to hear them. Your partner is everywhere you are and it’s absorbed into your day. It can change how you see them. The who does what debate And when you are both home all day, you will need to renegotiate who does what. When one of you used to leave for work, a lot of things were just decided by that dynamic. One person handled what was happening at home. It wasn’t always fair, but it was clear. Now, it’s not clear at all. You both are there, both have jobs, and both look busy. So, all day long, there’s this unspoken conversation. Do they look more slammed than me? Who’s dealing with the laundry? Should I figure out dinner, or will they? You lose the space between you There’s another issue that is harder to name. You lose a little bit of mystery. When you worked in different places, you didn’t know the details of each other’s day. You asked about it and shared stories. That back and forth was a kind of connection. Now, you already know that important meeting went badly because you heard it through the wall. You know they are overwhelmed because you are watching it in real time. There’s less to share at the end of the day, less curiosity, and fewer moments to discover things about each other. And that matters more than we think. Research on relationships shows that small moments of curiosity and having genuine interest in someone’s day help keep a couple feeling close. Feeling consistently cared for isn’t about big gestures. What matters more is the daily habit of turning to your partner and saying, “Tell me what happened,” and waiting to hear the answer. When you already know everything, those moments may start to disappear. Create the break So, the question is, how do we still show up for each other when nothing feels new? The answer is: you have to create a little distance on purpose. Work in different rooms if you can; Take solo breaks to go outside; try not to eavesdrop; occasionally make plans to have lunch or take a coffee break away from home. And when the workday comes to a close, take a walk together, shut the laptop when chatting, ask about each other’s day even if you think you know the answer. It’s not really about knowing what happened. It’s the act of sharing and creating moments of connection. View the full article

Shark Tank investor Kevin O’Leary doubled down on his belief that true wealth requires at least $5 million in liquid assets. “You’d be amazed, how many wealthy people that say they’re rich do not have liquidity,” O’Leary said on Fox Business. O’Leary said he practices what he preaches, keeping at least $5 million of his own wealth in Treasury bills—short-term U.S. government securities that can be quickly converted to cash. The Canadian businessman argues that true financial security means being able to access your wealth at a moment’s notice, be it to weather an emergency or to seize an investment opportunity. A house, a private business, or illiquid assets may look impressive on paper, but in his view, they don’t count toward real wealth. Financial experts say the strategy has merit. Tech entrepreneur and FinlyWealth co-founder Abid Salahi told GOBankingRates, “Our data shows that clients with a higher liquidity ratio—typically 20 percent to 30 percent of their total assets—are better equipped to handle financial emergencies and capitalize on investment opportunities.” O’Leary acknowledges that hitting that number is no small task. “It’s very hard to get five million liquid because in this market that makes you $250,000 a year pretax,” he said. “You have a family of four and poo-poo hits the fan in your world and everybody loses their job, you can sustain a family on 250 pretax. That’s why it’s the magic number.” This isn’t the first time O’Leary’s made this claim. He had the same sentiments back in November. Even when you are tempted to spend or loan the money, he advises people not to. “That is not what it’s for,” O’Leary continued. “It’s there to guarantee your financial freedom and that of your family for the rest of your life.” O’Leary is not alone in that thinking. His former Shark Tank co-star Mark Cuban said that the first step to getting rich is having cash available. “You aren’t saving for retirement. You are saving for the moment you need cash,” he wrote on his blog. In 2020, billionaire investor and Bridgewater Associates founder Ray Dalio declared that “cash is trash,” but by 2023, he had walked back on that position, stating, “Cash offers a good return without price risk. It also keeps my money as dry powder, so cash looks ‘pretty good’ to me.” O’Leary sees the $5 million threshold not as a finish line, but as a foundation: “I tell all my entrepreneurs, ‘That’s your goal.’ ” —Amaya Nichole This article originally appeared on Fast Company’s sister website, Inc.com. Inc. is the voice of the American entrepreneur. We inspire, inform, and document the most fascinating people in business: the risk-takers, the innovators, and the ultra-driven go-getters that represent the most dynamic force in the American economy. View the full article

As large language models seep into everyday life, some worry the technology could trigger a mass political realignment. Chatbots, the theory goes, can be shaped by training data and system instructions to privilege certain worldviews, and users who interact with them daily may gradually absorb those biases at scale. But Dartmouth College political scientist Brendan Nyhan cautions against assuming such a future is inevitable. LLMs may be powerful, he says, but that doesn’t mean they’ll influence people in the ways we expect, or even in the ways their creators intend. There are several reasons an AI-driven political shift may be harder to engineer than it sounds. Most people don’t closely follow political news, and it’s unclear how often they use AI tools for political guidance in the first place. And while chatbots can sound persuasive, and in some cases have encouraged disturbing behavior, there’s little evidence that they are fundamentally reshaping most users’ core beliefs. There’s also a practical tension at play. Companies may face pressure to steer AI systems toward certain viewpoints, but they are simultaneously competing on qualities like accuracy and reasonableness. It’s difficult to optimize for both at once. The dawn of the social media age was instructive, says Nyhan, who—along with coauthors—recently published a preprint chapter explicating some of the challenges of studying AI’s impact on politics. As many of us remember, the outcome of the 2016 election prompted serious concerns that social media platforms like Facebook had caused political polarization through biased algorithms and fake news. Still, a decade after that election, social science research is still open about whether social media actually had this kind of impact. Fast Company spoke with Nyhan about how, while technology can be transformative, human behavior can also be quite sticky. This interview has been edited for length and clarity. We did have this whole big discourse about whether social media had sort of caused massive political polarization. What were the lessons learned from that era as we think about AI? It’s important to recognize that we often hear new technologies and seize on claims about the harms that they’re going to create before the evidence is strong enough to really justify what’s being claimed. In this case, the evidence is pretty thin. Social media platforms are hard to study—but to the extent that we can evaluate it—it’s not obvious that social media has made our politics more polarized. They may have contributed in certain specific ways, but in a lot of cases, they’re reflecting the polarization of our politics back to us. I was one of the authors of a study that randomized exposure to like-minded sources on social media, which is one of the most frequently cited mechanisms by which social media could make people more polarized. When we reduced that exposure to like-minded sources, it had no effect on the polarization of people’s attitudes or vote choice. There have also been a number of studies that pay people to stop using social media for a period of time. Those similarly have quite modest effects at best. Though not necessarily zero, there’s certainly no evidence that social media is the primary cause of polarization. The fear is that these companies have a lot of control and have become funnels or information, particularly as more people switch from search engines to LLM platforms. There’s this fear that they’re going to sort of make us all Republican or Democrat. They do exercise a lot of power. [We talk] about the fear that authoritarian countries will influence the content of LLMs in problematic ways . . . I do think there’s reason to worry about the content on which elements are trained. At the same time, it turns out to be a lot harder to persuade people at scale than is typically assumed. AI chatbots can be pretty persuasive when people interact with them about controversial topics, but most people aren’t asking AIs what they should believe about climate change or who to vote for. Is there any evidence that these large language models do actually seem to exhibit some values internally that swing one way or the other in terms of left and right? People have administered various questionnaires to the LLMs to benchmark them against the attitudes they express against humans. When they are asked questions in that format, they tend to give answers that, on average, lean to the left. That’s likely reflecting the balance of the information that they’ve been trained on. It may also reflect, in part, the way the companies are developing them. Increasing model performance has tended to drive LLMs towards more accurate answers. What I mean by that is that AI companies are obviously in this race to develop better models against each other, and we’ve generally seen that models that perform better on the benchmarks they compete on are generally performing better at providing accurate, evidence-based information. Right? Of course, not always, and not perfectly. But the improvement has been quite rapid, and it’s actually so far proven to be pretty hard to have a frontier model that just gives you political output that you find appealing. Grok has really fallen off the cutting edge, and you can even see it reverting back to more standard types of answers when Elon Musk stops paying as close attention to it and badgering his engineers to manipulate it. It tends to revert back to saying things like climate change is real. View the full article

A flock of chickens living in a coop near Dallas, Texas, are ordinary birds. But they hatched inside 3D-printed artificial eggs in a lab at Colossal Biosciences, the Dallas-based “de-extinction” company. Colossal designed a new system that functions essentially like a natural egg. One of the company’s goals: to use it to bring back the South Island giant moa, a bird that went extinct in the 15th century. But the technology could also be used to help breed currently endangered birds. It’s not the first time that scientists tried to raise birds outside a natural shell. But previous systems, first developed in the 1980s, required a flow of oxygen and other interventions for the embryo to survive. (The oxygen also sometimes damages the birds’ DNA.) The new shell can sit inside an ordinary incubator. “We want to make sure that it is as close to an existing egg as possible,” says Ben Lamm, Colossal’s CEO. R&D took nearly two years. The new design uses a rigid titanium lattice, shaped like a partial egg, lined with a permeable membrane that can hold an embryo. The shell was initially “more egg-like,” Lamm says. “But then we thought if we’re going to be reimagining the egg, how do we reengineer it in a way that we get the most flexibility out of it?” Leaving the top open means that it can be attached to a microscope, for example, and easily monitored as the embryo grows. To test the system, the team carefully moved chicken embryos from regular chicken eggs to the new shell. When the chick is ready to hatch, it can pop through a thin membrane at the top; staff also monitor them to help them get out. Every chick that made it to term is now a healthy chicken, Lamm says. To raise a giant moa, the company would need to build a much larger version—the bird was as tall as 12 feet, with eggs as much as 80 times larger than a chicken egg. The company’s controversial process to bring back extinct species involves sequencing surviving fragments of DNA, comparing it with living relatives, and using gene editing to modify related species to produce embryos that are raised by a surrogate. (When Colossal announced that it had “brought back” dire wolves, many scientists argued that they were wolves with a handful of dire wolf traits, not actually dire wolves.) In the case of the giant moa, since no living bird is large enough to act as a surrogate for the egg, an artificial system is necessary. You might ask: why bring back this particular bird? Lamm’s argument is that we need the tools of de-extinction to deal with the current crisis; the moa is a way to learn. “If you look at the trend line, it’s forecasted that we could lose half of biodiversity in the next 25 years,” he says. “It’s better to have a de-extinction toolkit and not need it and not have it. Unfortunately, I do think you’re going to need some of these technologies.” For birds that are currently endangered, conservation organizations could use it to breed birds that are difficult to breed in captivity, and that don’t have readily available surrogates to raise eggs. Scientists could genetically modify other birds to produce the endangered species, which could be raised inside artificial eggs tailored to the right size for each bird. Of course, it doesn’t solve the bigger problem: if species are going extinct because forests are plowed down for farming or development, or because climate change is fundamentally reshaping ecosystems like the Amazon, raising more birds won’t mean that they can survive in the wild. Global governments need to deal with those issues, Lamm says, “but I think that giving some of these countries and some of these different NGO partners the ability to have the animals both in sanctuaries and in captive breeding locations is a solid start.” View the full article

Google has added markdown files, .md.txt files, to the Google Search help documents. But John Mueller from Google said that these are not being used for Search or generative AI responses in Search.View the full article

Today is Google I/O, where Google announces many of its new features across its products, including Google Search. It is not uncommon to see Google search ranking volatility spike around Google I/O, and that is exactly what we are seeing today - the morning of Google I/O.View the full article

OpenAI's ChatGPT web search feature does offer a web cache, an offline, locally stored version of web pages that have been previously crawled. This should not come as a surprise, as all search engines store cached versions of web pages (even if they don't show it to us).View the full article

Your lift study came in low. Should you cut the channel? Not before reading how MER, incrementality, and attribution work together as a stack. The post Why Incrementality Testing Alone Won’t Fix Your Paid Media Budget – The Missing Metric appeared first on Search Engine Journal. View the full article

Microsoft is running a number of tests and experiments for Bing Search and the Copilot answers within Bing Search. These tests include font changes, link updates, product results and more. View the full article

Microsoft, not to be outdone by Google, added AI citations to Microsoft Clarity, its web analytics tool. This report shows how your content is referenced in AI-generated answers.View the full article

Large language models (LLMs) excel at synthesizing enormous amounts of information into personalized responses to plain-language prompts. These responses draw on massive training datasets and are often enhanced with internet searches. The fastest way to influence what LLMs say about your brand is to influence the content they retrieve through those searches. At Evertune Research, we use the Evertune AI marketing platform to track hundreds of brands across 250 categories across every major LLM. This gives us clear insight into which content AI models cite most often, especially when users ask for brand or product recommendations across industries. For this analysis, we reviewed the 6,000 most-cited URLs per model across ChatGPT, Copilot, Gemini, Google AI Mode, Google AI Overview, and Perplexity for March and April. We found that these models share a key behavior: they heavily cite listicles. Half of LLMs’ most-cited URLs are listicles Of the roughly 25,000 unique URLs we reviewed, half were listicles. Across nearly 400 million citations from all models, 63% pointed to listicles. Listicles have many qualities that make them ideal for models’ consumption. They’re tightly focused on a single topic, like “best laptops for gamers,” which makes them highly relevant to user prompts. Their structured format also makes the content easy for models to parse and reproduce. For brand-related queries, listicles do much of the work for LLMs by comparing products head-to-head on features, price, materials, and more—a format ChatGPT now features prominently in its shopping widget. Listicles were pervasive across every model we reviewed. They accounted for 40–65% of the most-cited URLs, with Copilot at the low end and Gemini at the high end. The vast majority of listicles in our analysis featured ranked lists, such as “Top 5 CRM Tools.” Depending on the model, these made up 71% to 86% of listicles. Unranked lists, such as “7 Ways to Save on Groceries,” were a distant second. Institutional rankings (e.g., data-heavy lists like U.S. News & World Report’s Best Colleges rankings) accounted for just 1.4% to 4.7% of listicles. Corporate, earned media, and affiliate domains were the top sources of listicles in our analysis. It’s worth noting, however, that individual pages may contain affiliate content even when the broader domain does not. For example, Forbes.com is an earned media domain, but it includes affiliate segments such as Forbes Advisor and Forbes Vetted. It ranked among the top three sources on every model for listicles in our URL dataset. A word of warning before making listicles the foundation of a GEO strategy: Google has already signaled its intent to crack down on promotional listicles. Simply ranking your own brand No. 1 alongside competitors could also run afoul of a Federal Trade Commission rule that “prohibits a business from misrepresenting that a website or entity it controls provides independent reviews or opinions about a category of products or services that includes its own products or services,” among other prohibitions. URLs that thrive on multiple models We reviewed the 6,000 most-cited URLs across six LLMs, which in theory produced a pool of 36,000 URLs. In practice, the dataset contained about 25,000 unique URLs, since many appeared among the most-cited results across multiple models. Among the models, the three Google Gemini-powered models — Gemini, AI Mode, and AI Overviews — showed the highest overlap. More than half of Google AI Mode’s most-cited URLs also appeared among Google AI Overviews’ most-cited URLs. Gemini likewise shared a large portion of its top-cited URLs with both Google AI Mode and Google AI Overview. The remaining models also shared the most URLs with Google AI Mode and Google AI Overviews, though the overlap was much smaller. Perplexity shared more than 20% of its URLs with both models, while ChatGPT shared more than 15% with each. Given the thousands of URLs models cite on any topic, that still represents meaningful overlap. Copilot, by contrast, shared just 4% to 6% of its URLs with any other model. The URLs that models cite most deviate for many reasons, including model training, sites’ crawl permissions and other factors. Traditional SEO that moves content higher in search results, no matter if the search is by a bot or a person, also plays a role, especially for Google AI Mode and Google AI Overview. Page components of heavily cited URLs Our review of the roughly 25,000 URLs heavily cited by LLMs found that these pages typically ranged from 1,000 to 2,000 words, averaged 18 words per sentence, linked frequently, and used structured headings (H2s and H3s) throughout. Copilot favored the most concise content, typically citing pages with 964 words and 24 paragraphs. Gemini skewed more verbose, typically citing pages with 1,977 words and 53 paragraphs. Although there’s no cookie-cutter formula for success in AI visibility, we found that the most-cited pages typically included the following components: GEO takeaways Each LLM has its own preferences and quirks, and a strong GEO strategy accounts for them. But our analysis of more than 25,000 URLs suggests that some GEO best practices can improve brand visibility and sentiment across models. All LLMs cite large volumes of highly structured, hyper-specific content, which listicles exemplify. Avoid spammy, self-promotional listicles that Google penalizes, but otherwise aim to create and appear in lists where relevant. Traditional SEO supports GEO. Pages that perform well in human search results also tend to perform well in bot-driven searches. This is especially true for Gemini-based models. Pay attention to the page structures most often cited by the model you want to target. Copilot tends to favor brevity, while Gemini responds better to more expansive content. In general, keep pages under 2,000 words, use frequent links, apply strong structure, and include images and lists when relevant. View the full article

The President’s excessive faith in military power is squarely within the US traditionView the full article

Writing formulaic SEO content, updating old articles, reporting on blog performance, even running complicated performance analyses… these are all things Agent A does for me. Here are some of our favorite Agent A use cases for content marketers. Agent A…Read more ›View the full article

Fashion chain boss Isak Andic died while on a hiking trip with his son Jonathan two years agoView the full article

After losing a boardroom power struggle with Apple CEO John Sculley, Steve Jobs was exiled to a small building across the street from Apple’s headquarters. It was May 1985. He and his colleagues called his new office “Siberia.” Corporate reports stopped flowing to his desk, and executives stopped calling, leaving him bored and lonely. “It was amazing to see how ostracized he was in the Valley,” recalled Susan Barnes, a Macintosh financial controller who had previously reported to him. “It was really cruel.” Jobs is remembered as the visionary who returned to Apple, the company he cofounded, in 1997, and saved it from near-bankruptcy. But before the comeback, he made a series of leadership decisions that destabilized the company and left it drifting toward death. An overlooked truth: the instincts that made Jobs extraordinary, his perfectionism, his force of will, his refusal to compromise, also nearly destroyed Apple in its early years. After he left, Jobs spent twelve years failing at a company called NeXT, and those failures laid the foundation for Apple’s resurgence with the iPod, iPhone, and iPad. Here are five times Steve was wrong and learned from his mistakes: 1. He made himself the center of every decision By early 1985, Apple had splintered into warring factions. Jobs undermined Sculley to colleagues and challenged his every decision. “I am the board,” Jobs, Apple’s chairman, told one executive. Sculley’s supporters stormed the human resources department to complain. As one executive observed, no one knew who was really running the company. The civil war paralyzed Apple at the worst moment. Macintosh sales were declining, IBM and its clones were eating market share, and for the first time in its history Apple laid off employees, more than 1,200 of them, and announced its first-ever quarterly loss. The company secretly entered talks to sell itself to General Electric. By the time the board sided with Sculley and stripped Jobs of his authority, the internal war had already cost Apple months of progress. That autumn, Jobs left Apple and started a new computer company, NeXT. The pattern followed him. Ignoring the warnings of his cofounders, Jobs rushed out the first NeXT computer, called the Cube, in October 1988 with an unfinished operating system. The price was more than double what its target customers said they could pay. Selling only a few dozen computers a month, the company eventually laid off half its workforce and abandoned hardware entirely. When the founder becomes the only voice in the room, the company has nothing to fall back on when the voice is wrong. 2. He built for his own taste instead of the market Between the Super Bowl commercial, the famous keynote, and the promise of a “computer for the rest of us,” the Macintosh launch in January 1984 became one of the most mythologized product launches in American business. For the first hundred days, shipments were strong and the Mac looked poised to succeed. But the machine had no hard drive, extremely limited functionality, and a price tag of $2,495, almost $8,000 in today’s dollars. The first wave of buyers loved it. At that price, there was no second wave. The Mac was a beautiful machine that regular customers simply couldn’t justify buying. The commercial disappointment helped trigger the power struggle with Sculley, Jobs’s ouster, and twelve years of strategic drift that nearly killed Apple. 3. He shipped before products were ready and blamed his team when they fell apart In early 1985, Jobs pushed Apple to release the Macintosh Office, a version of the Mac aimed at corporate buyers. Its technical heart, a device for sharing files across office computers, was severely delayed and not ready to ship. The product landed to weak sales, accelerating the internal crisis that would end with Jobs’s removal months later. At NeXT, he repeated the pattern. After the Cube was released, NeXT cofounder Dan’l Lewin presented Jobs with a list of problems piling up. Rather than fix them, Jobs blamed the sales team. “We’re so far away from selling anybody anything right now,” Lewin pushed back. “You don’t want to hear it, but this is not a problem in sales.” So Jobs demoted Lewin and announced it in an email to the entire company. 4. He couldn’t kill what wasn’t working When Gil Amelio became Apple’s CEO in 1996, he kept hearing the same phrase from engineers: “Steve Jobs can get away with whatever he wants, so I’m going to do whatever I want.” By then, Apple had lost all focus. The company had released more than seventy products in a single year, including a $6,500 laptop that caught fire and had to be recalled. Apple had poured $500 million into a new operating system called Copland that never shipped. Nobody could decide when to cut their losses. Jobs spent a decade at NeXT making the same mistake, refusing to abandon his hardware business long after his advisors told him it was finished. But when he returned to Apple in 1997, he killed 70 percent of the product portfolio. The visionary who once couldn’t let go of the beautiful black Cube had learned, at enormous cost, that survival sometimes means letting go of the product you love. 5. He treated the people he needed as obstacles On Super Bowl Sunday in January 1985, Apple aired a follow-up to its iconic “1984” commercial. Called “Lemmings,” the ad depicted blindfolded businesspeople marching off a cliff. The message to corporate customers: you’re idiots if you don’t buy our product. At NeXT, Jobs called his distribution partner’s stores “ugly.” He blew off lucrative meetings arranged by his biggest investor, Ross Perot, the Texas billionaire and soon-to-be presidential candidate. So Perot delivered the lesson himself. At a dinner with NeXT executives and corporate customers in San Francisco, Perot asked all the customers to stand. Then he turned to everyone still sitting, Jobs included: “Now, everybody who’s sitting down, applaud these people who are standing up, because that’s why we’re here.” It took twelve years of humbling for Jobs to absorb these lessons. By 1997, he had learned to step back, delegate, and let go. He chose his battles instead of fighting every one. The tantrums that had defined his management style ebbed, and instead he listened to his lieutenants in Monday morning staff meetings, implemented their advice, and built an executive team at Apple that held together for eight years. “Sometimes I go for ‘best’ when I should go for ‘better,’” he later admitted, “and end up going nowhere or backwards.” It was the kind of admission the younger Steve could never have made. View the full article