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The trend of tech layoffs is still going strong in 2024. Job losses in tech were significant in 2022 when 1,064 companies laid off about 165,296 employees. Things worsened in 2023, with 1,191 companies cutting 263,180 jobs—a 59% increase from the previous year. As of May  2024, more than 89,000 jobs have been cut by 306 companies, according to the lay-offs tracking website, layoffs.fyi. Big names like Tesla, Google, Amazon, Microsoft, and TikTok laid off thousands of workers in the first months of 2024. Why are there so many layoffs? So why all these layoffs? The problem is rooted in the pandemic, which came with lots of challenges and opportunities. Many companies experienced growth and hired more employees to meet customers' demands, but this caused overstaffing when demands were reduced and economic conditions got tougher. Businesses began to downsize, especially because overhead costs increased due to the economic downturn and inflation, which is why we’re seeing so many layoffs today. Reasons the tech sector has seen layoffs Economic downturn Just like in other industries, tech companies are not immune to economic downturns. When demand drops, profits take a hit, and many tech firms respond by cutting their workforce to save on costs. Over bloated workforce During the pandemic, tech companies went on a hiring spree because of the surge in online activity from people working from home. But now that things are back to normal, these companies realize they hired too many people and are downsizing as a result. Higher Interest rates In July 2023, the Federal Reserve raised interest rates to 5.5% in the U.S., the highest they've been in 22 years. This makes borrowing more expensive for companies, which in turn affects how much investors are willing to put into companies. Sometimes, investors even push companies to lay off employees to cut costs. Inflation Inflation rates drive up the costs of goods and services and for tech companies, the cost of hardware and other items required in production. To cope with these higher expenses, companies often reduce their workforce to save money. Increased AI Adoption and Impact on Jobs Most companies that lay off their staff don’t explicitly say the cuts were due to AI automation, but the signs are there. Jobs in customer service, content writing, and design have been cut. For example, layoffs at Google, which heavily impacted the company's ad sales division, coincided with more deployment of AI across customer care and sales processes. Also, last year, Swedish furniture company IKEA announced that it was training call center employees to become interior design advisors. This was after all routine customer inquiries were delegated to an AI bot named Billie. Job losses to AI in Tech companies are more complicated; tech companies are downsizing to have more money to invest in expensive AI talent and GPU computer chips to train and run AI applications. An example is Salesforce, which laid off 700 employees in January 2024. According to the company’s Chief people officer, it is actively recruiting for positions such as prompt engineers, AI trainers, and AI ethical architects. This suggests that the company has shifted its focus from providing tech sales jobs to hiring AI talent, as part of its investment in Artificial Intelligence. What to do if you have been laid off Finding jobs in a tough economy can be difficult, especially tech jobs. However, getting a new job is not impossible; many tech companies continue to hire despite the layoffs. If you have lost your job in tech and are looking for another job, you want to break into tech, or change your field, here are some of the things you can do. Update your resume While this might sound cliche, your resume is your first introduction to hiring managers and recruiters, so you want it to make that resume stand out and shine. Ensure your resume is in the right format, has details of your professional experience, and showcases your skills and certifications. Write resume sections that align your skills with the different job positions you apply to. A one-size-fits-all resume doesn't work anymore, so it’s better to have multiple versions of your resume saved for applying to different jobs. Recruiters today focus on your core skills, certifications, data skills, and AI skills, so if you have these, emphasize them on your resume. Get the most sought-after skills Today, companies are on the lookout for specific tech skills. These include skills in artificial intelligence, machine learning, data science and analytics, cloud computing, programming languages such as Javascript and Python, virtual and augmented reality, and DevOps methodologies. To get started, explore online courses that teach the basics, some of which are free. You can later proceed to the more comprehensive, paid courses. Another great option is enrolling in a boot camp where you’ll learn about new technologies and tools. Getting these skills can make landing a tech job easier. Focus on companies with recent funding When you’re job hunting, checking job websites such as Indeed, Glassdoor and CareerBuilder can lead you to companies that are hiring. However, here is another thing you can try: “Follow the money”. Look for tech companies that have just secured new funding; they might be hiring soon. You can use Startup databases such as Crunchbase, AngelList and CB Insights for this. Also, keep tabs on News outlets like TechCrunch and Forbes - they often report on funding rounds and investments. That way, you can find companies with open roles and possibly land a job faster. Consider relocation While many tech jobs allow employees to work remotely, you might need to be flexible in a tight job market and consider companies that want employees to work hybrid or fully onsite. Also, think about relocating to places where the available jobs are. Look beyond the cities that are popular as job hubs; there are other cities friendly to job seekers. These cities have high job growth, earnings growth, lower unemployment and more affordable housing. Work with a tech recruiter Teaming up with a recruiter will give you access to a wider range of opportunities when seeking a tech job. The recruiter can offer valuable tips for your interview with the companies and give you an idea of the salaries to expect. You can connect with a recruiter on LinkedIn - just make sure to include a message about why you want to connect. You can also keep an “open to work” badge on your LinkedIn page to attract tech recruiters who may be searching for candidates. Beyond LinkedIn, you can ask your network or do a simple Google search to find recruiters in your area. Don’t stop looking Keep up with the basics of a job search and don’t let news of layoffs discourage you. Keep networking and applying for suitable opportunities. You can create a schedule to allocate time to the job search tasks, such as resume updates, creating a list of companies and roles, and submitting applications. You’ll also need to keep track of your activities on a spreadsheet. Finding a tech job in a tough economy can seem daunting, but it is not impossible. Equip yourself with skills employers are looking for through online courses and position yourself for more opportunities by networking and focusing on companies that are most likely to hire. With the right attitude and determination, you’ll land a role that will be suitable for you within a reasonable time. The trend of tech layoffs is still going strong in 2024. Job losses in tech were significant in 2022 when 1,064 companies laid off about 165,296 employees. Things worsened in 2023, with 1,191 companies cutting 263,180 jobs—a 59% increase from the previous year. As of May  2024, more than 89,000 jobs have been cut by 306 companies, according to the lay-offs tracking website, layoffs.fyi. Big names like Tesla, Google, Amazon, Microsoft, and TikTok laid off thousands of workers in the first months of 2024. The trend of tech layoffs is still going strong in 2024. Job losses in tech were significant in 2022 when 1,064 companies laid off about 165,296 employees. Things worsened in 2023, with 1,191 companies cutting 263,180 jobs—a 59% increase from the previous year. As of May  2024, more than 89,000 jobs have been cut by 306 companies, according to the lay-offs tracking website, layoffs.fyi . Big names like Tesla, Google, Amazon, Microsoft, and TikTok laid off thousands of workers in the first months of 2024. layoffs.fyi layoffs.fyi Why are there so many layoffs? Why are there so many layoffs? So why all these layoffs? The problem is rooted in the pandemic, which came with lots of challenges and opportunities. Many companies experienced growth and hired more employees to meet customers' demands, but this caused overstaffing when demands were reduced and economic conditions got tougher. Businesses began to downsize, especially because overhead costs increased due to the economic downturn and inflation, which is why we’re seeing so many layoffs today. Reasons the tech sector has seen layoffs Reasons the tech sector has seen layoffs Economic downturn Economic downturn Just like in other industries, tech companies are not immune to economic downturns. When demand drops, profits take a hit, and many tech firms respond by cutting their workforce to save on costs. Over bloated workforce Over bloated workforce During the pandemic, tech companies went on a hiring spree because of the surge in online activity from people working from home. But now that things are back to normal, these companies realize they hired too many people and are downsizing as a result. Higher Interest rates In July 2023, the Federal Reserve raised interest rates to 5.5% in the U.S., the highest they've been in 22 years. This makes borrowing more expensive for companies, which in turn affects how much investors are willing to put into companies. Sometimes, investors even push companies to lay off employees to cut costs. Inflation Inflation Inflation rates drive up the costs of goods and services and for tech companies, the cost of hardware and other items required in production. To cope with these higher expenses, companies often reduce their workforce to save money. cost of hardware cost of hardware Increased AI Adoption and Impact on Jobs Increased AI Adoption and Impact on Jobs Most companies that lay off their staff don’t explicitly say the cuts were due to AI automation, but the signs are there. Jobs in customer service, content writing, and design have been cut. For example, layoffs at Google, which heavily impacted the company's ad sales division, coincided with more deployment of AI across customer care and sales processes. Also, last year, Swedish furniture company IKEA announced that it was training call center employees to become interior design advisors. This was after all routine customer inquiries were delegated to an AI bot named Billie. layoffs at Google, layoffs at Google, announced announced Job losses to AI in Tech companies are more complicated; tech companies are downsizing to have more money to invest in expensive AI talent and GPU computer chips to train and run AI applications. An example is Salesforce, which laid off 700 employees in January 2024. According to the company’s Chief people officer , it is actively recruiting for positions such as prompt engineers, AI trainers, and AI ethical architects. This suggests that the company has shifted its focus from providing tech sales jobs to hiring AI talent, as part of its investment in Artificial Intelligence. According to the company’s Chief people officer According to the company’s Chief people officer What to do if you have been laid off Finding jobs in a tough economy can be difficult, especially tech jobs. However, getting a new job is not impossible; many tech companies continue to hire despite the layoffs. If you have lost your job in tech and are looking for another job, you want to break into tech, or change your field, here are some of the things you can do. Update your resume While this might sound cliche, your resume is your first introduction to hiring managers and recruiters, so you want it to make that resume stand out and shine. Ensure your resume is in the right format, has details of your professional experience, and showcases your skills and certifications. Write resume sections that align your skills with the different job positions you apply to. A one-size-fits-all resume doesn't work anymore, so it’s better to have multiple versions of your resume saved for applying to different jobs. Recruiters today focus on your core skills, certifications, data skills, and AI skills, so if you have these, emphasize them on your resume. Get the most sought-after skills Today, companies are on the lookout for specific tech skills. These include skills in artificial intelligence, machine learning, data science and analytics, cloud computing, programming languages such as Javascript and Python, virtual and augmented reality, and DevOps methodologies. To get started, explore online courses that teach the basics, some of which are free . You can later proceed to the more comprehensive, paid courses. Another great option is enrolling in a boot camp where you’ll learn about new technologies and tools. Getting these skills can make landing a tech job easier. free free Focus on companies with recent funding When you’re job hunting, checking job websites such as Indeed, Glassdoor and CareerBuilder can lead you to companies that are hiring. However, here is another thing you can try: “Follow the money”. Look for tech companies that have just secured new funding; they might be hiring soon. You can use Startup databases such as Crunchbase, AngelList and CB Insights for this. Also, keep tabs on News outlets like TechCrunch and Forbes - they often report on funding rounds and investments. That way, you can find companies with open roles and possibly land a job faster. Consider relocation While many tech jobs allow employees to work remotely, you might need to be flexible in a tight job market and consider companies that want employees to work hybrid or fully onsite. Also, think about relocating to places where the available jobs are. Look beyond the cities that are popular as job hubs; there are other cities friendly to job seekers. These cities have high job growth, earnings growth, lower unemployment and more affordable housing. other cities other cities Work with a tech recruiter Teaming up with a recruiter will give you access to a wider range of opportunities when seeking a tech job. The recruiter can offer valuable tips for your interview with the companies and give you an idea of the salaries to expect. You can connect with a recruiter on LinkedIn - just make sure to include a message about why you want to connect. You can also keep an “open to work” badge on your LinkedIn page to attract tech recruiters who may be searching for candidates. Beyond LinkedIn, you can ask your network or do a simple Google search to find recruiters in your area. Don’t stop looking Keep up with the basics of a job search and don’t let news of layoffs discourage you. Keep networking and applying for suitable opportunities. You can create a schedule to allocate time to the job search tasks, such as resume updates, creating a list of companies and roles, and submitting applications. You’ll also need to keep track of your activities on a spreadsheet. Finding a tech job in a tough economy can seem daunting, but it is not impossible. Equip yourself with skills employers are looking for through online courses and position yourself for more opportunities by networking and focusing on companies that are most likely to hire. With the right attitude and determination, you’ll land a role that will be suitable for you within a reasonable time.

How to Land a Tech Job in a Post-Layoff Market

The digital world is changing, and e-commerce is leading this change. In a few years, you could be walking around a virtual shopping center, checking out all your favorite brands – trying on clothes on your avatar, and earning rewards for browsing. This isn’t sci-fi: it’s the future of shopping in the metaverse. Statista predicts that spending in this space will jump from $30.6bn in 2024 to $210.3bn by 2030, driven by artificial intelligence (AI). By the end of this decade, there could be 1.3 billion people regularly shopping in virtual worlds. In this article, we’ll look at how AI is changing metaverse shopping – and everything from finding products to engaging customers. The Rise of Metaverse Shopping From crowded aisles to endless scrolling through product pages online, our shopping habits have come a long way. E-commerce changed retail; now the metaverse is taking things up another level. Historical Context: **1968:**The movie 2001: A Space Odyssey showed early ideas of immersive digital experiences.1973:Maze War, the first virtual world, let users interact in a digital environment, setting the stage for future virtual worlds.1992: Sci-fi writer Neal Stephenson coined the term “metaverse” in his novel Snow Crash, describing a fully immersive virtual reality. 2009: The Introduction of blockchain technology with Bitcoin laid the foundation for digital ownership and transactions, a crucial part of the metaverse economy. Key Milestones in Metaverse Development: 2012: Oculus raised $2.5 million for its VR headset, a big step towards consumer-accessible VR. Facebook bought Oculus in 2014. 2016: Oculus releases the Rift, the first widely available VR headset, bringing immersive experiences closer to reality. 2020: Over 5 million VR and AR headsets are shipped annually, showing growing consumer interest in immersive technology. 2021: Facebook rebrands as Meta, investing $10 billion and hiring 10,000 people in Europe to develop the metaverse. AI's Transformative Impact on the Shopping Experience Artificial Intelligence (AI) has more to give than self-driving vehicles or virtual assistants. It is transforming the metaverse shopping experience into something personal, efficient, and fun. Here’s how: Your Virtual Shopping Assistant Your unique preferences are understood by AI through analysis of your purchase behavior, browsing history, and even social media engagements. Mallconomy for example can offer personalized product recommendations that guide you towards items that you will most likely love. See it Before You Buy It With virtual try-on tools powered by AI, you can try clothes on your avatar or place furniture in your digital space to see how it looks and fits. Behind the Scenes Efficiency SRetailers are helped with demand predictions, hence products being made available when customers need them most at their desired locations resulting in reduced stock outs coupled with quicker deliveries. Shopping as an Adventure Engagement forms within the metaverse also get powered by artificial intelligence that is new as well as exciting. Some examples include the “Browse To Earn” model endorsed by different brands where users’ time spent onsite attracts rewards for them. Here's a quick comparison of traditional vs. AI-powered shopping: Feature

Traditional Shopping

AI-Powered Shopping in the Metaverse



Recommendations

Limited, often generic

Personalized based on individual preferences



Try-ons

Physical fitting rooms only

Virtual try-on and styling options



Customer service

Limited hours, potential wait times

24/7 support from chatbots/virtual assistants



Inventory management

Less efficient, potential for stockouts

AI-optimized for better availability



Engagement

Primarily transactional

Gamified, interactive experiences Mallconomy: A Case Study in AI-Powered Metaverse Shopping Mallconomy is an example of the growing field of AI-powered metaverse shopping. It’s a virtual space where users can shop, enjoy entertainment, and socialize with other users. The “Browse-to-earn” model which is one of the key features of Mallconomy, uses AI to reward users for exploring and shopping within the virtual mall. AI-Powered Features Mallconomy leverages AI in several key areas: Personalized Recommendations: Mallconomy analyzes customer data like browsing habits or purchase history through artificial intelligence systems. Using this data the platform suggests items most relevant to each user on their platform based on their interests, making shopping more entertaining for them. ChatGPT-Powered Avatars: Sales assistants become intelligent thanks to Mallconomy’s integration with Ready Player Me avatars which are supported by ChatGPT too. This makes it possible for the round-the-clock support service to answer any questions about products or help with choosing and buying things. Browse-to-Earn Model: Mallconomy also has a gamified system called “Browse-to-Earn” which rewards users with $WOOT tokens for looking through different categories as well as purchasing goods. Community-Driven Commerce and NFTs The goal behind Mallconomy was community building and creative empowerment. To achieve this, artists can display their work on its marketplace. In this way, they can earn money when people buy NFTs from them. With the adoption of non-fungible tokens, there's a global trend towards digital ownership within metaverses, complemented by collectibility. MallCard NFTs and Staking MallCard NFTs work in two ways. First, they act as membership cards that grant various access privileges such as Launchpad. Second, they can be staked for higher rewards thereby making the experience more game-like and encouraging users to stay involved with the Mallconomy ecosystem. Roadmap and Future Developments As part of their roadmap, Mallconomy plans to introduce land sales in metaverses where users can buy virtual spaces to run their stores while still operating them digitally. There is also an upcoming Launchpad feature that will democratize the investment process for metaverse projects hosted on this platform. What Other Brands Are Up To in the Metaverse Brands are jumping into the metaverse to create exciting shopping experiences. Here are some examples: Disney - Entertainment In 2022, Disney started selling "Golden Moments" NFTs with VeVe and is planning a virtual theme park. This lets fans enjoy Disney magic in a whole new way. Nike - Sports and Fashion Nike launched "Nikeland" in Roblox in 2021. Over 26 million visitors have explored, played games, and bought digital Nike items. Nike also sold digital sneakers as NFTs, making a big splash in the metaverse. Gucci - Luxury Gucci opened the "Gucci Garden" on Roblox in 2021. Here, users can explore a virtual Gucci store and buy digital items. This space gives a luxurious shopping experience online. The Metaverse Awaits The future of shopping is immersive, personal, and rewarding – it’s about stepping into the world. This is where you connect with communities who provide incentives for your involvement beyond buying items. This process driven largely thanks to AI systems makes retail shopping more enjoyable than ever before while creating additional touchpoints between brands and consumers. However, there is still much to explore within metaverse space that requires us to not only embrace but also further explore these unknown areas waiting for discovery. By doing so, we will redefine our understanding of shopping patterns and socializing habits when dealing with physical rather than digital outlets. From pixels to profits, let us start on a journey that will change forever how we see markets today! The digital world is changing, and e-commerce is leading this change. In a few years, you could be walking around a virtual shopping center, checking out all your favorite brands – trying on clothes on your avatar, and earning rewards for browsing. This isn’t sci-fi: it’s the future of shopping in the metaverse. Statista predicts that spending in this space will jump from $30.6bn in 2024 to $210.3bn by 2030, driven by artificial intelligence (AI). By the end of this decade, there could be 1.3 billion people regularly shopping in virtual worlds. Statista Statista In this article, we’ll look at how AI is changing metaverse shopping – and everything from finding products to engaging customers. The Rise of Metaverse Shopping The Rise of Metaverse Shopping From crowded aisles to endless scrolling through product pages online, our shopping habits have come a long way. E-commerce changed retail; now the metaverse is taking things up another level. Historical Context: Historical Context: **1968:**The movie 2001: A Space Odyssey showed early ideas of immersive digital experiences. 1973: Maze War, the first virtual world, let users interact in a digital environment, setting the stage for future virtual worlds. 1992: Sci-fi writer Neal Stephenson coined the term “metaverse” in his novel Snow Crash, describing a fully immersive virtual reality. 1973: 1992: 2009: The Introduction of blockchain technology with Bitcoin laid the foundation for digital ownership and transactions, a crucial part of the metaverse economy. 2009: Key Milestones in Metaverse Development: Key Milestones in Metaverse Development: 2012: Oculus raised $2.5 million for its VR headset, a big step towards consumer-accessible VR. Facebook bought Oculus in 2014. 2012: 2016: Oculus releases the Rift, the first widely available VR headset, bringing immersive experiences closer to reality. 2016: 2020: Over 5 million VR and AR headsets are shipped annually, showing growing consumer interest in immersive technology. 2020: 2021: Facebook rebrands as Meta, investing $10 billion and hiring 10,000 people in Europe to develop the metaverse. 2021: AI's Transformative Impact on the Shopping Experience Artificial Intelligence (AI) has more to give than self-driving vehicles or virtual assistants. It is transforming the metaverse shopping experience into something personal, efficient, and fun. Here’s how: Your Virtual Shopping Assistant Your unique preferences are understood by AI through analysis of your purchase behavior, browsing history, and even social media engagements. Mallconomy for example can offer personalized product recommendations that guide you towards items that you will most likely love. See it Before You Buy It With virtual try-on tools powered by AI, you can try clothes on your avatar or place furniture in your digital space to see how it looks and fits. Behind the Scenes Efficiency SRetailers are helped with demand predictions, hence products being made available when customers need them most at their desired locations resulting in reduced stock outs coupled with quicker deliveries. Shopping as an Adventure Engagement forms within the metaverse also get powered by artificial intelligence that is new as well as exciting. Some examples include the “Browse To Earn” model endorsed by different brands where users’ time spent onsite attracts rewards for them. Here's a quick comparison of traditional vs. AI-powered shopping: Feature

Traditional Shopping

AI-Powered Shopping in the Metaverse



Recommendations

Limited, often generic

Personalized based on individual preferences



Try-ons

Physical fitting rooms only

Virtual try-on and styling options



Customer service

Limited hours, potential wait times

24/7 support from chatbots/virtual assistants



Inventory management

Less efficient, potential for stockouts

AI-optimized for better availability



Engagement

Primarily transactional

Gamified, interactive experiences Feature

Traditional Shopping

AI-Powered Shopping in the Metaverse Recommendations

Limited, often generic

Personalized based on individual preferences Try-ons

Physical fitting rooms only

Virtual try-on and styling options Customer service

Limited hours, potential wait times

24/7 support from chatbots/virtual assistants Inventory management

Less efficient, potential for stockouts

AI-optimized for better availability Engagement

Primarily transactional

Gamified, interactive experiences Feature Traditional Shopping AI-Powered Shopping in the Metaverse Feature Feature Traditional Shopping Traditional Shopping AI-Powered Shopping in the Metaverse AI-Powered Shopping in the Metaverse Recommendations Limited, often generic Personalized based on individual preferences Recommendations Recommendations Limited, often generic Limited, often generic Personalized based on individual preferences Personalized based on individual preferences Try-ons Physical fitting rooms only Virtual try-on and styling options Try-ons Try-ons Physical fitting rooms only Physical fitting rooms only Virtual try-on and styling options Virtual try-on and styling options Customer service Limited hours, potential wait times 24/7 support from chatbots/virtual assistants Customer service Customer service Limited hours, potential wait times Limited hours, potential wait times 24/7 support from chatbots/virtual assistants 24/7 support from chatbots/virtual assistants Inventory management Less efficient, potential for stockouts AI-optimized for better availability Inventory management Inventory management Less efficient, potential for stockouts Less efficient, potential for stockouts AI-optimized for better availability AI-optimized for better availability Engagement Primarily transactional Gamified, interactive experiences Engagement Engagement Primarily transactional Primarily transactional Gamified, interactive experiences Gamified, interactive experiences Mallconomy: A Case Study in AI-Powered Metaverse Shopping Mallconomy is an example of the growing field of AI-powered metaverse shopping. It’s a virtual space where users can shop, enjoy entertainment, and socialize with other users. The “Browse-to-earn” model which is one of the key features of Mallconomy, uses AI to reward users for exploring and shopping within the virtual mall. Mallconomy Mallconomy AI-Powered Features Mallconomy leverages AI in several key areas: Personalized Recommendations: Mallconomy analyzes customer data like browsing habits or purchase history through artificial intelligence systems. Using this data the platform suggests items most relevant to each user on their platform based on their interests, making shopping more entertaining for them. Personalized Recommendations : Mallconomy analyzes customer data like browsing habits or purchase history through artificial intelligence systems. Using this data the platform suggests items most relevant to each user on their platform based on their interests, making shopping more entertaining for them. Personalized Recommendations ChatGPT-Powered Avatars: Sales assistants become intelligent thanks to Mallconomy’s integration with Ready Player Me avatars which are supported by ChatGPT too. This makes it possible for the round-the-clock support service to answer any questions about products or help with choosing and buying things. ChatGPT-Powered Avatars : Sales assistants become intelligent thanks to Mallconomy’s integration with Ready Player Me avatars which are supported by ChatGPT too. This makes it possible for the round-the-clock support service to answer any questions about products or help with choosing and buying things. ChatGPT-Powered Avatars Browse-to-Earn Model: Mallconomy also has a gamified system called “Browse-to-Earn” which rewards users with $WOOT tokens for looking through different categories as well as purchasing goods. Browse-to-Earn Model: Mallconomy also has a gamified system called “Browse-to-Earn” which rewards users with $WOOT tokens for looking through different categories as well as purchasing goods. Browse-to-Earn Model: Community-Driven Commerce and NFTs The goal behind Mallconomy was community building and creative empowerment. To achieve this, artists can display their work on its marketplace. In this way, they can earn money when people buy NFTs from them. With the adoption of non-fungible tokens, there's a global trend towards digital ownership within metaverses, complemented by collectibility. MallCard NFTs and Staking MallCard NFTs work in two ways. First, they act as membership cards that grant various access privileges such as Launchpad. Second, they can be staked for higher rewards thereby making the experience more game-like and encouraging users to stay involved with the Mallconomy ecosystem. Roadmap and Future Developments As part of their roadmap, Mallconomy plans to introduce land sales in metaverses where users can buy virtual spaces to run their stores while still operating them digitally. There is also an upcoming Launchpad feature that will democratize the investment process for metaverse projects hosted on this platform. What Other Brands Are Up To in the Metaverse Brands are jumping into the metaverse to create exciting shopping experiences. Here are some examples: Disney - Entertainment In 2022, Disney started selling "Golden Moments" NFTs with VeVe and is planning a virtual theme park. This lets fans enjoy Disney magic in a whole new way. Nike - Sports and Fashion Nike launched "Nikeland" in Roblox in 2021. Over 26 million visitors have explored, played games, and bought digital Nike items. Nike also sold digital sneakers as NFTs, making a big splash in the metaverse. Gucci - Luxury Gucci opened the "Gucci Garden" on Roblox in 2021. Here, users can explore a virtual Gucci store and buy digital items. This space gives a luxurious shopping experience online. The Metaverse Awaits The future of shopping is immersive, personal, and rewarding – it’s about stepping into the world. This is where you connect with communities who provide incentives for your involvement beyond buying items. This process driven largely thanks to AI systems makes retail shopping more enjoyable than ever before while creating additional touchpoints between brands and consumers. However, there is still much to explore within metaverse space that requires us to not only embrace but also further explore these unknown areas waiting for discovery. By doing so, we will redefine our understanding of shopping patterns and socializing habits when dealing with physical rather than digital outlets. From pixels to profits, let us start on a journey that will change forever how we see markets today!

From Pixels to Profits: The Impact of AI-Powered Metaverse Shopping on the Future of Commerce

পিক্সেল থেকে লাভ পর্যন্ত: বাণিজ্যের ভবিষ্যতের উপর এআই-চালিত মেটাভার্স শপিংয়ের প্রভাব

Von Pixeln zu Gewinnen: Die Auswirkungen des KI-gestützten Metaverse-Shoppings auf die Zukunft des Handels

De los píxeles a las ganancias: el impacto de las compras metaversas impulsadas por IA en el futuro del comercio

Des pixels aux profits : l'impact du shopping métaverse alimenté par l'IA sur l'avenir du commerce

पिक्सेल से मुनाफ़े तक: वाणिज्य के भविष्य पर AI-संचालित मेटावर्स शॉपिंग का प्रभाव

ピクセルから利益へ: AI を活用したメタバース ショッピングが商取引の未来に与える影響

픽셀에서 이익으로: AI 기반 메타버스 쇼핑이 미래 상거래에 미치는 영향

Dos pixels aos lucros: o impacto das compras no metaverso alimentadas por IA no futuro do comércio

От пикселей к прибыли: влияние покупок в метавселенной с использованием искусственного интеллекта на будущее коммерции

Piksellerden Kârlara: Yapay Zeka Destekli Metaverse Alışverişinin Ticaretin Geleceği Üzerindeki Etkisi

Từ điểm ảnh đến lợi nhuận: Tác động của việc mua sắm Metaverse được hỗ trợ bởi AI đối với tương lai của thương mại

从像素到利润：人工智能驱动的元宇宙购物对未来商业的影响

Many organizations that we’ve spoken to are in the exploration phase of using for AI-powered personalization, recommendations, semantic search and anomaly detection. The recent and astronomical improvements in accuracy and accessibility of large language models (LLMs) including BERT and OpenAI have made companies rethink how to build relevant search and analytics experiences. vector search In this blog, we capture engineering stories from 5 early adopters of vector search- Pinterest, Spotify, eBay, Airbnb and Doordash- who have integrated AI into their applications. We hope these stories will be helpful to engineering teams who are thinking through the full lifecycle of vector search all the way from generating embeddings to production deployments. What is vector search? Vector search is a method for efficiently finding and retrieving similar items from a large dataset based on representations of the data in a high-dimensional space. In this context, items can be anything, such as documents, images, or sounds, and are represented as vector embeddings. The similarity between items is computed using distance metrics, such as or , which quantify the closeness of two vector embeddings. cosine similarity Euclidean distance The vector search process usually involves: : Where relevant features are extracted from the raw data to create vector representations using models such as , or Generating embeddings word2vec BERT Universal Sentence Encoder : The vector embeddings are organized into a data structure that enables efficient search using algorithms such as or Indexing FAISS HNSW : Where the most similar items to a given query vector are retrieved based on a chosen distance metric like cosine similarity or Euclidean distance Vector search To better visualize vector search, we can imagine a 3D space where each axis corresponds to a feature. The time and the position of a point in the space is determined by the values of these features. In this space, similar items are located closer together and dissimilar items are farther apart. GITHUB JULIE-MILLS ^
  |          x Item 1
  |         /
  |        /
  |       /x Item 2
  |      /
  |     /
  |    /x Item 3
  |   /
  |  /
  | /
  +-------------------> Given a query, we can then find the most similar items in the dataset. The query is represented as a vector embedding in the same space as the item embeddings, and the distance between the query embedding and each item embedding is computed. The item embeddings with the shortest distance to the query embedding are considered the most similar. Query item:         x
                    |
  ^                 |
  |          x Item 1|
  |         /       |
  |        /        |
  |       /x Item 2 |
  |      /          |
  |     /           |
  |    /x Item 3    |
  |   /             |
  |  /              |
  | /               |
  +-------------------> This is obviously a simplified visualization as vector search operates in high-dimensional spaces. In the next sections, we’ll summarize 5 engineering blogs on vector search and highlight key implementation considerations. The full engineering blogs can be found below: by Jinfeng Zhuang at Pinterest PinText: A Multitask Text Embedding System in Pinterest by Alexandre Tamborrino at Spotify Introducing Natural Language Search for Podcast Episodes by Senthilkumar Gopal, Shubhangi Tandon, Christopher Miller, Deepika Srinivasan, Rui Kong, Selcuk Kopru and Srinivas Bhagavathula at eBay How eBay’s New Search Feature Was Inspired By Window Shopping by Mihajlo Grbovic at AirBnb Listing Embeddings in Search Ranking by Mitchell Koch, Aamir Manasawala, Raghav Ramesh at Doordash Personalized Store Feed with Vector Embeddings Pinterest: Interest search and discovery Pinterest uses for image search and discovery across multiple areas of its platform, including recommended content on the home feed, related pins and search using a multitask learning model. vector search A multi-task model is trained to perform multiple tasks simultaneously, often sharing underlying representations or features, which can improve generalization and efficiency across related tasks. In the case of Pinterest, the team trained and used the same model to drive recommended content on the homefeed, related pins and search. Pinterest trains the model by pairing a users search query (q) with the content they clicked on or pins they saved (p). Here is how Pinterest created the (q,p) pairs for each task: : Word embeddings are derived from the selected subject (q) and the pin clicked on or saved by the user (p). Related Pins : Word embeddings are created from the search query text (q) and the pin clicked on or saved by the user (p). Search : Word embeddings are generated based on the interest of the user (q) and the pin clicked on or saved by the user (p). Homefeed To obtain an overall entity embedding, Pinterest averages the associated word embeddings for related pins, search and the homefeed. Pinterest created and evaluated its own supervised Pintext-MTL (multi-task learning) against unsupervised learning models including GloVe, word2vec as well as a single-task learning model, PinText-SR on precision. PinText-MTL had higher precision than the other embedding models, meaning that it had a higher proportion of true positive predictions among all positive predictions. Pinterest also found that multi-task learning models had a higher recall, or a higher proportion of relevant instances correctly identified by the model, making them a better fit for search and discovery. To put this all together in production, Pinterest has a multitask model trained on streaming data from the homefeed, search and related pins. Once that model is trained, vector embeddings are created in a large batch job using either Kubernetes+Docker or a map-reduce system. The platform builds a search index of vector embeddings and runs a K-nearest neighbors (KNN) search to find the most relevant content for users. Results are cached to meet the performance requirements of the Pinterest platform. Spotify: Podcast search Spotify combines keyword and to retrieve relevant podcast episode results for users. As an example, the team highlighted the limitations of keyword search for the query “electric cars climate impact”, a query which yielded 0 results even though relevant podcast episodes exist in the Spotify library. To improve recall, the Spotify team used Approximate Nearest Neighbor (ANN) for fast, relevant podcast search. semantic search The team generates vector embeddings using the as it is multilingual, supporting a global library of podcasts, and produces high-quality vector embeddings. Other models were also evaluated including , a model trained on a big corpus of text data, but found that BERT was better suited for word embeddings than sentence embeddings and was pre-trained only in English. Universal Sentence Encoder CMLM model BERT Spotify builds the vector embeddings with the query text being the input embedding and a concatenation of textual metadata fields including title and description for the podcast episode embeddings. To determine the similarity, Spotify measured the cosine distance between the query and episode embeddings. To train the base Universal Sentence Encoder CMLM model, Spotify used positive pairs of successful podcast searches and episodes. They incorporated in-batch negatives, a technique highlighted in papers including and , to generate random negative pairings. Testing was also conducted using synthetic queries and manually written queries. Dense Passage Retrieval for Open-Domain Question Answering (DPR) Que2Search: Fast and Accurate Query and Document Understanding for Search at Facebook To incorporate vector search into serving podcast recommendations in production, Spotify used the following steps and technologies: : Spotify indexes the episode vectors offline in batch using , a search engine with native support for ANN. One of the reasons that Vespa was chosen is that it can also incorporate metadata filtering post-search on features like episode popularity. Index episode vectors Vespa : Spotify uses to generate a query vector. Vertex AI was chosen for its support for GPU inference, which is more cost effective when using large transformer models to generate embeddings, and for its query cache. After the query vector embedding is generated, it is used to retrieve the top 30 podcast episodes from Vespa. Online inference Google Cloud Vertex AI Semantic search contributes to the identification of pertinent podcast episodes, yet it is unable to fully supplant keyword search. This is due to the fact that semantic search falls short of exact term matching when users search an exact episode or podcast name. Spotify employs a hybrid search approach, merging semantic search in Vespa with keyword search in , followed by a conclusive re-ranking stage to establish the episodes displayed to users. Elasticsearch eBay: Image search Traditionally, search engines have displayed results by aligning the search query text with textual descriptions of items or documents. This method relies extensively on language to infer preferences and is not as effective in capturing elements of style or aesthetics. eBay introduces to help users find relevant, similar items that meet the style they’re looking for. image search eBay uses a multi-modal model which is designed to process and integrate data from multiple modalities or input types, such as text, images, audio, or video, to make predictions or perform tasks. eBay incorporates both text and images into its model, producing image embeddings utilizing a Convolutional Neural Network (CNN) model, specifically , and title embeddings using a text-based model such as . Every listing is represented by a vector embedding that combines both the image and title embeddings. Resnet-50 BERT Once the multi-modal model is trained using a large dataset of image-title listing pairs and recently sold listings, it is time to put it into production in the site search experience. Due to the large number of listings at eBay, the data is loaded in batches to HDFS, eBay’s data warehouse. eBay uses Apache Spark to retrieve and store the image and relevant fields required for further processing of listings, including generating listing embeddings. The listing embeddings are published to a columnar store such as HBase which is good at aggregating large-scale data. From HBase, the listing embedding is indexed and served in Cassini, a search engine created at eBay. The pipeline is managed using Apache Airflow, which is capable of scaling even when there is a high quantity and complexity of tasks. It also provides support for Spark, Hadoop, and Python, making it convenient for the machine learning team to adopt and utilize. Visual search allows users to find similar styles and preferences in the categories of furniture and home decor, where style and aesthetics are key to purchase decisions. In the future, eBay plans to expand visual search across all categories and also help users discover related items so they can establish the same look and feel across their home. AirBnb: Real-time personalized listings Search and similar listings features drive 99% of bookings on the AirBnb site. AirBnb built a to improve similar listing recommendations and provide real-time personalization in search rankings. listing embedding technique AirBnb realized early on that they could expand the application of embeddings beyond just word representations, encompassing user behaviors including clicks and bookings as well. To train the embedding models, AirBnb incorporated over 4.5M active listings and 800 million search sessions to determine the similarity based on what listings a user clicks and skips in a session. Listings that were clicked by the same user in a session are pushed closer together; listings that were skipped by the user are pushed further away. The team settled on the dimensionality of a listing embedding of d=32 given the tradeoff between offline performance and memory needed for online serving. https://youtu.be/aWjsUEX7B1I?si=GREROVrWxWqtqL-m&embedable=true AirBnb found that certain listings characteristics do not require learning, as they can be directly obtained from metadata, such as price. However, attributes like architecture, style, and ambiance are considerably more challenging to derive from metadata. Before moving to production, AirBnb validated their model by testing how well the model recommended listings that a user actually booked. The team also ran an A/B test comparing the existing listings algorithm against the vector embedding-based algorithm. They found that the algorithm with vector embeddings resulted in a 21% uptick in CTR and 4.9% increase in users discovering a listing that they booked. The team also realized that vector embeddings could be used as part of the model for real-time personalization in search. For each user, they collected and maintained in real time, using Kafka, a short-term history of user clicks and skips in the last two weeks. For every search conducted by the user, they ran two similarity searches: based on the geographic markets that were recently searched and then the similarity between the candidate listings and the ones the user has clicked/skipped Embeddings were evaluated in offline and online experiments and became part of the real-time personalization features. Doordash: Personalized store feeds Doordash has a wide variety of stores that users can choose to order from and being able to surface the most relevant stores using personalized preferences improves search and discovery. Doordash wanted to apply to its store feed algorithms using vector embeddings. This would enable Doordash to uncover similarities between stores that were not well-documented including if a store has sweet items, is considered trendy or features vegetarian options. latent information Doordash used a derivative of word2vec, an embedding model used in natural language processing, called store2vec that it adapted based on existing data. The team treated each store as a word and formed sentences using the list of stores viewed during a single user session, with a maximum limit of 5 stores per sentence. To create user vector embeddings, Doordash summed the vectors of the stores from which users placed orders in the past 6 months or up to 100 orders. As an example, Doordash used vector search to find similar restaurants for a user based on their recent purchases at popular, trendy joints 4505 Burgers and New Nagano Sushi in San Francisco. Doordash generated a list of similar restaurants measuring the cosine distance from the user embedding to store embeddings in the area. You can see that the stores that were closest in cosine distance include Kezar Pub and Wooden Charcoal Korean Village BBQ. Doordash incorporated store2vec distance feature as one of the features in its larger recommendation and personalization model. With vector search, Doordash was able to see a 5% increase in click-through-rate. The team is also experimenting with new models like , model optimizations and incorporating real-time onsite activity data from users. seq2seq Key considerations for vector search Pinterest, Spotify, eBay, Airbnb and Doordash create better search and discovery experiences with vector search. Many of these teams started out using text search and found limitations with fuzzy search or searches of specific styles or aesthetics. In these scenarios, adding vector search to the experience made it easier to find relevant, and often personalized, podcasts, pillows, rentals, pins and eateries. There are a few decisions that these companies made that are worth calling out when implementing vector search: : Many started out using an off-the-shelf model and then trained it on their own data. They also recognized that language models like word2vec could be used by swapping words and their descriptions with items and similar items that were recently clicked. Teams like AirBnb found that using derivatives of language models, rather than image models, could still work well for capturing visual similarities and differences. Embedding models : Many of these companies opted to train their models on past purchase and click through data, making use of existing large-scale datasets. Training : While many companies adopted ANN search, we saw that Pinterest was able to combine metadata filtering with KNN search for efficiency at scale. Indexing : Vector search rarely replaces text search. Many times, like in Spotify’s example, a final ranking algorithm is used to determine whether vector search or text search generated the most relevant result. Hybrid search : We’re seeing many teams use batch-based systems to create the vector embeddings, given that these embeddings are rarely updated. They employ a different system, frequently Elasticsearch, to compute the query vector embedding live and incorporate real-time metadata in their search. Productionizing Rockset, a real-time search and analytics database, recently added support for . Give vector search on Rockset a try for real-time personalization, recommendations, anomaly detection and more by starting a with $300 in credits today. vector search free trial

A Look Into 5 Use Cases for Vector Search from Major Tech Companies

প্রধান প্রযুক্তি কোম্পানি থেকে ভেক্টর অনুসন্ধানের জন্য 5টি ব্যবহারের ক্ষেত্রে একটি নজর৷

Ein Blick auf 5 Anwendungsfälle für die Vektorsuche bei großen Technologieunternehmen

Una mirada a cinco casos de uso de búsqueda de vectores de las principales empresas tecnológicas

Un aperçu de 5 cas d'utilisation de la recherche de vecteurs par de grandes entreprises technologiques

प्रमुख टेक कंपनियों द्वारा वेक्टर सर्च के लिए 5 उपयोग मामलों पर एक नज़र

大手テクノロジー企業によるベクトル検索の 5 つのユースケースを検証

주요 기술 회사의 벡터 검색에 대한 5가지 사용 사례 살펴보기

Uma análise de 5 casos de uso para pesquisa vetorial de grandes empresas de tecnologia

Взгляд на 5 вариантов использования векторного поиска от крупных технологических компаний

Büyük Teknoloji Şirketlerinden Vektör Aramaya Yönelik 5 Kullanım Durumuna Bir Bakış

Xem xét 5 trường hợp sử dụng tìm kiếm vectơ từ các công ty công nghệ lớn

了解主要科技公司矢量搜索的 5 个用例

HackerNoon launches our long-awaited offering: Technology Press Releases with HackerNoon! You can now leverage HackerNoon’s distribution engine to share your press releases! Leveraging prominent onsite and newsletter distribution, owned and paid social media, traditional media press release pickup, and machine learning-generated translations, HackerNoon now averages 5k readers per press release. Visit our Technology Press Releases home page to learn more. The introductory prices start at $750 per release, and bulk discounts are available. There is no expiration date for your Press Release credits. Avail the Introductory Pricing for Your Press Release Now. Why Publish Your Company’s Technology Press Releases with HackerNoon? Great question. Here are not one but 8 reasons why: Technical Achievements Are Best Hosted with a Technical Audience HackerNoon is the go-to tech news and learning platform for millions of technologists. Our library of 100k+ technology stories attracts 3M+ monthly visitors. See below the data gathered from 1k+ job titles entered via HackerNoon.com in Q1 2024. Accessibility: Translated into 12 Languages, 2 Audio Files, and Backed Up on the Blockchain HackerNoon makes content more accessible to increase audience size. Via machine learning models from OpenAI, Google, & others, we translate your story into 12+ languages, 2 audio files and back it up on the Arweave blockchain for good measure. Distribution Across HackerNoon and Around the Web Via owned and paid social media, traditional media like press releases, machine learning generated translations, and the creation and distribution of the audio story, HackerNoon maximizes technology readership. Creating an Average of 5k Readers Per Press Release We’ve used the traditional press release services, and they usually get just a couple hundred readers per story. Sometimes even less. When you publish your technology press release on HackerNoon, you should expect ~5k readers. Republished on the Wire to 100+ Traditional Media Sites Just like the traditional press release wire services, we put your story on the wire. You should expect your technology press release to be re-published to ~100 more sites, such as Benzinga, Digital Journal, Yahoo Finance, and The AP. Prominent Distribution Across the HackerNoon Site, App, and Newsletter Technology Press Releases on HackerNoon are featured on the Homepage, the Category Page, 8 Tagged Pages, 12 Translation Homepages, the Mobile App, the Terminal App, the Lite Version, and the Noonification Newsletters (250k+ subscribers). Easy Content Uploading and Formatting with Staff Support We hate how difficult it is to upload content to archaic press release distribution services. The HackerNoon uploader and story editor are smooth AF. We also accept your submissions via unlisted link like Google doc or even a URL like your pressroom. 1 Business Day Turnaround Time (TAT) and Professional Editorial Support Technology press releases are timely content. Our editorial and support staff guarantee one business day turnaround time. HackerNoon is here to make sure your story gets distributed on time. Book a meeting with us to learn how Octopus Network, KuCoin Community Chain, Avalanche, ICON Foundation, and other tech companies publish their press releases with HackerNoon. HackerNoon launches our long-awaited offering: Technology Press Releases with HackerNoon! You can now leverage HackerNoon’s distribution engine to share your press releases! Leveraging prominent onsite and newsletter distribution, owned and paid social media, traditional media press release pickup, and machine learning-generated translations, HackerNoon now averages 5k readers per press release. Visit our Technology Press Releases home page to learn more. Technology Press Releases home page The introductory prices start at $750 per release , and bulk discounts are available. There is no expiration date for your Press Release credits. $750 per release Avail the Introductory Pricing for Your Press Release Now. Avail the Introductory Pricing for Your Press Release Now. Avail the Introductory Pricing for Your Press Release Now. Why Publish Your Company’s Technology Press Releases with HackerNoon? Great question. Here are not one but 8 reasons why: Technical Achievements Are Best Hosted with a Technical Audience HackerNoon is the go-to tech news and learning platform for millions of technologists. Our library of 100k+ technology stories attracts 3M+ monthly visitors. See below the data gathered from 1k+ job titles entered via HackerNoon.com in Q1 2024. HackerNoon.com Accessibility: Translated into 12 Languages, 2 Audio Files, and Backed Up on the Blockchain HackerNoon makes content more accessible to increase audience size. Via machine learning models from OpenAI, Google, & others, we translate your story into 12+ languages, 2 audio files and back it up on the Arweave blockchain for good measure. Distribution Across HackerNoon and Around the Web Via owned and paid social media, traditional media like press releases, machine learning generated translations, and the creation and distribution of the audio story, HackerNoon maximizes technology readership. Creating an Average of 5k Readers Per Press Release We’ve used the traditional press release services, and they usually get just a couple hundred readers per story. Sometimes even less. When you publish your technology press release on HackerNoon, you should expect ~5k readers. Republished on the Wire to 100+ Traditional Media Sites Just like the traditional press release wire services, we put your story on the wire. You should expect your technology press release to be re-published to ~100 more sites, such as Benzinga, Digital Journal, Yahoo Finance, and The AP. Prominent Distribution Across the HackerNoon Site, App, and Newsletter Technology Press Releases on HackerNoon are featured on the Homepage, the Category Page, 8 Tagged Pages, 12 Translation Homepages, the Mobile App, the Terminal App, the Lite Version, and the Noonification Newsletters (250k+ subscribers). Easy Content Uploading and Formatting with Staff Support We hate how difficult it is to upload content to archaic press release distribution services. The HackerNoon uploader and story editor are smooth AF. We also accept your submissions via unlisted link like Google doc or even a URL like your pressroom. 1 Business Day Turnaround Time (TAT) and Professional Editorial Support Technology press releases are timely content. Our editorial and support staff guarantee one business day turnaround time. HackerNoon is here to make sure your story gets distributed on time. Book a meeting with us to learn how Octopus Network , KuCoin Community Chain , Avalanche, ICON Foundation , and other tech companies publish their press releases with HackerNoon. Book a meeting with us Book a meeting with us Octopus Network Octopus Network KuCoin Community Chain KuCoin Community Chain ICON Foundation ICON Foundation

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B2B Tools You Need To Have in Your Arsenal in 2024

Welcome back business owners, marketing professionals, and anyone interested in harnessing digital analytics for business growth! In the first part of this series, we discussed: The importance of digital analytics for online businesses. How Google Analytics 4 collects and processes data. The basic structure of a GA4 account, and The role of dimensions and metrics in GA4. : Catch up here Google Analytics 4 (GA4) for Beginners—Part 1: Data Collection, Processing, and Account Structure In this part, we’ll: Take a closer look at the GA4 user interface. Learn the reports available in analytics and how to gain insights from them. Ready? Let’s get started! Understanding the Google Analytics 4 User Interface When you log into your GA4 account, the first thing you see is your Home Page. This contains a snapshot of insights relevant to your business. Suppose you have already played around with the tool. In that case, you will also see recently accessed reports and content suggestions that become more personalized as you continue to use Google Analytics. If the dimensions and metrics in the image above seem confusing, don’t worry; they’ll become much less so as we proceed. To the left of the homepage, you’ll find a navigation bar with 4 icons in addition to the Home Page: Reports Explore Advertising, & Admin Let’s take a quick peek at these sections. The GA4 Reports Page The Reports page contains…Reports! Seriously, though, reports help you understand user activity data on your website and apps. The orange rectangle in the image above encases general and more specific ready-made reports about how your users behave, from the moment they encounter your website or app and engage with it till they navigate away from it. \ The GA4 Explore Page The Explore page offers more sophisticated approaches for analyzing data with powerful audience discovery and comparison tools. At the top of your Explore page, you will find a template gallery containing select data visualization techniques such as: Free form Funnel exploration Path exploration Segment overlap Cohort exploration, & User lifetime These can be applied to your business for deeper insights into user behavior on your website or app. The GA4 Advertising Page This section helps you understand the ROI of your marketing spend and advertising efforts across channels. The GA4 Admin Page The admin section allows you to manage Google accounts, properties, events, and data streams. You can customize events, dimensions & metrics and manage who can access your data. \At the Top of your GA4 screen, you’ll find the account and property toggle; which lets you easily switch between all your properties, and a search bar. Using the GA4 Search Bar The search bar in GA4 is one of my favorite parts of the entire tool because it has many useful features - reports, educational materials, and, best of all, specific insights about user interaction/engagement. Suppose I want to find out how many users have visited from Nigeria in the last month. I can ask that question directly in the search bar. HackerNoon Voila! A detailed response will appear in the right-hand panel of your GA4 screen. Suppose, instead, I forgot how to switch accounts or properties in GA4. I can ask as well. Continue exploring the search bar to discover more business-related questions you can answer. Now that we’re done skimming through the Google Analytics 4 user interface, let’s dive a little deeper into how reports are put together! Understanding Reports in Google Analytics 4 Reports allow you to investigate your data, monitor traffic, and understand user activity. Once you’re on the reports page in GA4, you’ll first see your data as ‘Realtime Reports.’ This displays a quick view of traffic coming to your site and/or app over the past 30 minutes. After that, your data is processed into more detailed reports. The Reports navigation menu can be broadly grouped into 2 default collections: Life cycle - contains info about acquisition, monetization, and onsite behavior. User - contains reports about the tech that users access your website and/or app with, as well as their geographical information. the default reports available to you depend on some of the information you provided during setup. Not to worry; you can always tweak this in admin. Note: There are two main types of reports in GA4: - These summarize information about a specific topic into cards with different objectives. Overview Reports - This zooms in on 1 or 2 dimensions and shows you a more detailed view of your data. Detailed Reports Let’s explore this further with . Acquisition reports GA4 Acquisition Reports Once you click on Acquisition, you’ll see a list of available reports underneath, the first being Acquisition Overview. In this report, you’ll see cards that display intuitive summaries of your user and traffic acquisition data. \At the bottom of each card, you’ll find a clickable link that takes you deeper into the insights that the card summarizes. Clicking on this will load a new page with more information on the dataset. Next, let’s look at the User Acquisition report. GA4 User Acquisition Report This report offers insights into how users find your website or app for the first time. By default, the user acquisition report will have ‘First Channel’ as its primary dimension. \At the top of the table, you can see a dimension/metric toggle that lets you change your primary dimension depending on your unique business needs. Each dimension contains the text “First User” to demonstrate that the data is collected from when the user initially found your website. Suppose you want to know how old your users are, their genders, or what countries they find your website from. You can learn all these and more by adding a secondary dimension. Once you select a secondary dimension, a new column is added to the table like so: Let’s get some useful information from this table. Taking with the , and columns, we can see that the sample site has gotten from in the over the selected date range. Row 3 First User, Country New Users 2178 new users organic search United States Next, let’s look at the traffic acquisition report. GA4 Traffic Acquisition Report This report is self-explanatory. It displays information on how traffic reaches your website or app. In this report, each dimension contains “Sessions,” a session that groups user interactions based on when they engaged with your website or app. For instance, a web session starts when a user opens their browser and navigates to your website. It ends when a user closes the browser tab on your website. The traffic acquisition report is concerned with where new sessions come from regardless of whether the user is new or returning. This is unlike the user acquisition report, which focuses on only the first session from a user and nothing else beyond that. You can toggle your primary and secondary dimensions in the Traffic acquisition report the same way you did in the User acquisition report. Google Analytics will show data for the last 28 days. However, this can easily be adjusted to several preset date ranges or custom date ranges. This brings us to the end of this post! In this guide, we’ve: Made the GA4 user interface seem a lot less obscure/confusing. Played around with reports a little and learned how they’re curated and how to gain business-specific insights from them. In the next post, we’ll go further into reports, compare report data, use the explore page for deeper analysis, and more! To ensure you don’t miss any updates, on HackerNoon. Subscribe to me See you soon! Lead image source.

Google Analytics 4 (GA4) for Beginners—Part 2: Mastering Reports and User Interface Navigation

নতুনদের জন্য Google Analytics 4 (GA4) — পার্ট 2: মাস্টারিং রিপোর্ট এবং ইউজার ইন্টারফেস নেভিগেশন

Google Analytics 4 (GA4) für Anfänger – Teil 2: Berichte und Benutzeroberflächennavigation meistern

Google Analytics 4 (GA4) para principiantes: Parte 2: Dominar los informes y la navegación en la interfaz de usuario

Google Analytics 4 (GA4) pour les débutants – Partie 2 : Maîtriser les rapports et la navigation dans l'interface utilisateur

शुरुआती लोगों के लिए Google Analytics 4 (GA4)—भाग 2: रिपोर्ट और उपयोगकर्ता इंटरफ़ेस नेविगेशन में महारत हासिल करना

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面向初学者的 Google Analytics 4 (GA4) - 第 2 部分：掌握报告和用户界面导航

108 Stories To Learn About Content Strategy

Let's learn about via these 70 free stories. They are ordered by most time reading created on HackerNoon. Visit the to find the most read stories about any technology. Faagm /Learn Repo 1. Tech Hiring Cultures Analyzed via Glassdoor Interview Data During the process of working on my book on machine learning interviews, one question that I’ve been asked many times by candidates is the onsite-to-offer ratio, e.g. what percentage of people who interview at a company eventually get an offer. Another number that recruiters and hiring managers are keen to know is the yield rate on offers of different companies – e.g. what percentage of people who are extended an offer by a company accept that offer? 2. Amazon Monopoly. The Rise of Amazon Private Label Brands in 2019 Just look at these 2 names: 3. Lessons from a Product Manager at Google Quick background 4. Why the Apple Watch Outsells the Entire Swiss Watch Industry The Apple Watch outsold the entire Swiss watch industry in 2019. Now let this reality sink in: five years ago, technology watchers said the Apple Watch was a flop and a disappointment. 5. What Does Apple’s Stock Split Mean? A stock split is not a new thing. Hundreds if not thousands of companies have appealed to it to keep the price in the desired trading range and, theoretically, to make the stock more affordable. One of the most recent examples was Apple, announcing the division of its share price by four. It will be achieved by giving three additional shares to each investor. 6. A Step Towards Innovation: The Story Of Apple's AirPod God, have you looked at the new AirPods which has been released? You must be craving to have that right? But have you ever thought how AirPods came to be? The story behind it and why even being so expensive is a demanding object? Well in this article we will relinquish the era of apple and tell you why Apple's AirPods are most desired item ever. 7. You are not Facebook. You are not Google. You are also not Spotify, Amazon, Apple, Microsoft, Netflix or one of several other big tech companies I could mention here. 8. Responding to Online War Propaganda on LinkedIn: Who Do the Rules Protect? I am from Ukraine, and I’m trying to use my LinkedIn in order to spread the truth about the war that is going on right now. 9. How Did Uber Grow 4000% Through Their Referrals In India? When I say the word cab or ride-sharing, very often, the first word that pops up would either be Uber or in some cases Ola. Today I am going to tell you about how the former cemented itself such a prominent position in your mind. This is solidified by the fact that they have a presence in over 50 cities in India and carry out over 11 MILLION rides per WEEK! 10. Processing 350K Requests Per Month via Three Free ETA Services Instead of One Paid Google Service This is a story on how to not spend even a penny by using three ETA (estimated time of arrival) services instead of one. Everything is based on my personal experience working as a back-end developer at GoDee project. GoDee is a start-up project that offers booking seats on a bus online. You could find more information about this project here. 11. On the Strategies Netflix Leveraged to Crush Blockbuster Blockbuster 12. Google Will Stop Tying Search Ranking To Accelerated Mobile Pages In 2021 The end of web publishers being handcuffed to Accelerated Mobile Pages (AMP) is in sight. In May of 2021, Google will roll out an algorithm update using a new set of page experience signals to help determine rankings. these new guidelines will no longer give preferential treatment to sites using Google’s AMP format. 13. Can You Tell What Products On This List Are Prohibited On Amazon? Amazon is the largest retailer in the world, but a majority of the sales on the site come from third-party sellers. Along with toasters and mops, the site’s a target for people trying to sell dangerous and illicit products. 14. A Student Is Reviewing New Microsoft Surface Laptop 3 Microsoft comes with new Surface lineup, which is sale in market in 22th October. The large 15 inch size comes with AMD Processors. The other surface laptop come this Intel Processors. 15. The Inconvenient Truth about Facebook: It's Growing Despite Scandal With its latest quarterly earnings report, Facebook once again confounds the naysayers by succeeding financially and growing its global user base amid scandal. Here's what the Q3 2019 numbers say: 16. How Facebook tracks you on Android (even if you don't have a Facebook account) Suppose you’ve picked up an Android phone on the street and you saw the 4 apps above. Can you guess the profile of the phone user? 17. How to Drive Organic Traffic from Google Discover Marketers have been catching up with updates and tweaks made by Google over the years. 18. Dear Tim Cook, Apple Should Support Open Systems Apple used to feel like something more innovative. Now it seems like lots have changed. When you look at the history of Apple, it went from pioneers like Wozniak promoting an Open-Architecture design to Apple suing independent electronic repair shops. Now I don't want to reminisce about old history, but a lot needs to be said about open systems, the right to repair, and innovation. 19. Facebook is Beta Testing a More Spacious New Newsfeed Design Saw this on Facebook.com desktop today (flow follows): 20. Google Will Slow Hiring For The Rest of 2020 Google parent Alphabet Inc is easing back recruiting for the rest of the year, the most exceptional activity by the web search mammoth since the Covid-19 pandemic started battering its promoting business half a month prior. 21. Learn About Google DeepMind – DeepMind may allude to two things: the innovation behind Google’s man-made reasoning (AI) venture, and the organization that is liable for it. The organization DeepMind is an auxiliary of Alphabet, the parent organization of Google. 22. Privacy Challenges And Improper Use of User Profile Data Improper use of user-profiles data by third parties is probably the most serious potential risk with regard to personal data contained 23. Why Vigilance is Our Best Weapon Right Now When news broke that a firm was able to illegally harvest, analyze and use our own data against us, we were horrified. We couldn’t believe that a scheme like this could run for so long undetected. It wasn’t just terrifying but infuriating. The bodies to which we had entrusted power i.e Big Tech had failed us. It was becoming increasingly apparent that these tech gods couldn’t handle such a responsibility. This was the tipping point. People finally had enough. The whole Cambridge Analytica data scandal pointed to a larger issue – corporate accountability. 24. The Markup Investigation: Congressman Says that Google engineered a “Walled Garden” Comment comes as part of big tech CEO grilling by antitrust committee 25. Whether You Think You're The Next FAANG Or Not, You're Right In the words of Henry Ford, “whether you think you can, or you think you can't– you're right.” 26. Top Exciting Features of Android 10 You Should Know while Developing Your Android App There were speculations around the market regarding the world’s most popular operating system Android. In March, this year, Google released the beta version of Android 10 as they’re considering this latest version close to their heart. And undoubtedly, it is the 10th version of Android, and so Google is all set to make it revolutionary. Moreover, even customers are eagerly waiting for the release of Android 10. 27. Why Amazon Won’t Dominate in Groceries More than two years after Whole Foods buy, grocery has yet to be “Amazoned.” And here’s why. 28. How To Manage Your Privacy Online Think carefully before publishing your personal data (especially name, address, telephone number) in a user profile. 29. A Chance for Emerging Video-Sharing Apps: What Happens If TikTok Is Banned in the US? So far, 800 million people worldwide use the TikTok video streaming app for entertainment and connection, and 12.5% of them, 100 million, are Americans. After President Donald Trump said on July 31 that he planned to bar the Chinese-owned social media app from operating in the US, he reversed course, saying he would allow TikTok to keep operating if it were sold to an American owner. Microsoft is one of the companies most likely to buy the app. The possible acquisition will allow the software company to compete with Facebook and Google-owned YouTube and attract a younger audience. 30. Five Recent FAAGM Statistics That Underline the Tech Industry's Longer Term Trends 30% fee Apple charges developers for App Store transactions 31. The Great Polarisation When you look at the trends of the last few decades, it’s difficult to miss the signs, overt and covert, of there being an increasing polarization in the business world. Even in the last decade, firms have been seeing extremely high profit rates primarily driven by the lack of competition. But this hides the underlying fragmentation of a previously solid market, which is likely to be the beginning of the next phase of exploration vs exploitation. To take a few examples: 32. Are Tech Giants Building Walled Gardens (Again)? In the last few years, I've been dissecting business models of any type, and companies of any size. At the same time, I've been talking, interviewing, and discussing business models and business model innovation with dozens of entrepreneurs, and practitioners. 33. Social Media Deplatforming: Big Tech’s Gag Order Ever since the likes of Alex Jones and Milo Yiannopoulos were kicked out of their dominant platforms, debate has begun on whether tech companies should be armed with the power of deplatforming. 34. The Pros and Cons of Selling on Amazon and eBay Selling your products on Amazon and eBay seems like an irresistible deal at first. You have their entire customer base at your disposal, and these sites get a hell lot of traffic; what could be a more profitable way to start an eCommerce business? 35. Business Lessons To Learn From The Apple-Next Deal It was the year 1997. Apple was experiencing a sharp sales decline. 36. Is Big Tech Too Big? A couple of weeks ago Alphabet shares dropped 6%, Facebook tanked 7.5%, and Amazon shed 4.5% of its value. Why did this happen? An anti-trust probe was called into life regarding Alphabet’s Google Search business and investors fear a similar fate for its tech brethren, seemingly rightfully so: This news story unearths a public debate that has been discussed intensively since the 2016 US elections concerning Big Tech’s power and the need for anti-trust legislation. A debate, which appears to have the nearly unanimous agreement that these companies should be broken up. Logically, therefore, these firms should already be broken up, yet they are not. Why is this? Aside from the strawman “The government is corrupt/incompetent” reasoning, there actually exist multiple genuine arguments for keeping Big Tech intact, which we’ll be examining in the following. 37. Who Has the Power to Break Up Big Tech? The Big Five tech companies — Amazon, Apple, Facebook, Microsoft and Google owner Alphabet — have come under serious scrutiny in the past few years. 38. Amazon's 'Essential Item' Policy Upsets Everyone Currently that appears to include a 10-pack of rubber chickens 39. Is Amazon Affiliate Marketing Dead? The question of whether Amazon Associates Affiliate marketing is dead has been raised many times. Why? Because of the recently announced cuts in the commission fees. If you were not aware, Amazon announced a reduction in commission fees from 8% to 3% for some of the categories of products. 40. How Facebook Changed Their Homepage Every Year for the Last 17 Years 2004 Feb 12. TheFacebook.com 41. About Keen: Google’s AI-powered Answer to Pinterest Google’s potential rival to image-sharing platform Pinterest was quietly launched this summer. So, let’s take a closer look at the new app and explore where it sits in the already vast market of social media platforms. 42. Wake Up and Smell These #TopTechStories 🍏 43. The Era of Attention Economy and Its Destructive Impact: Reasoning by Examples Google, LinkedIn, Facebook, Instagram, Snapchat, Twitter, Gmail, Amazon, Apple, Discord, WhatsApp, Telegram, Medium, Spotfiy. Everyone wants our attention today on every platform possible. 44. What is Broken About the Internet? This Slack discussion with Utsav Jaiswal, richard-kubina, Natasha, Limarc, Linh, Adrian, Dane, Anna Bleker and me occurred in hackernoon's official #slogging-beta channel. 45. Apple and Google Have Too Much Power On Their App Stores, But Kicking Out Parler is Justified Op Ed: Why Apple and Google have too much power, but their decision about Parler was justified. 46. Are Your Photos Exposing You? You may be accidentally sharing personal information in your photos 47. Facebook Portal TV Reviewed It’s Saturday morning, and my phone has been burning for the past few hours. Just after I wake up, I stare at the screen and I have several messages from my parents, who live on the other side of the world, telling me that they miss me and that they are ready to talk. 48. Shelf Labels In Dynamics 365 For Finance and Operations Shelf label is nothing but labels to put on a rack or a shelf where an item is stored. These labels are printed and put on store rack or shelf.  For example, when you go to a supermarket you can see labels are displayed with various information where the product is stored. 49. Should Apple Buy DuckDuckGo? Or Create Its Own Search Engine? An Analyst suggested that should Apple buy DuckDuckGo, a privacy-focused search engine. Although, Apple most likely won’t buy DuckDuckGo, it might still be interested in creating a search engine. 50. The Unintended Consequences of Facebook Ad Boycott Campaign The burgeoning campaign by over a thousand large and small businesses, and civil rights organizations  to boycott ads  on Facebook so as to force the social media giant to take more drastic measures to curb hate speech is misguided. 51. Nextiva Takes Huge Step Growing Sales Channel With Addition of Microsoft’s Former GM, Eric Martorano For six-months (and counting) COVID-19 has accelerated the need for SMBs to leverage UCaaS (unified communications as a service), forcing executives to jump into one of the fastest-growing segments in the supply chain. 52. While We're All Super Into Conspiracy Theories: Let's Talk AI Ethics and Google With more development and implementation of AI, many pundits and experts have been very vocal of its potential and have called for a standstill in development or at least some regulations. These calls of action have even penetrated the heavy armor that Big Tech hides behind. Imagine letting just a single company toying with such powerful technology 53. Reputation Doesn’t Have Quarantine: How to Maintain Brand Health During a Pandemic The role of a PR specialists is to stay sober and level-headed. To me, a crisis is nothing but a crush test for business. I adopt new communication tactics, new strategies, and totally reinvent the processes. 54. How Tech Companies are Helping in Times of Covid-19 Crisis Since the pandemic COVID-19 is shaking all over the world, several people are dying and facing issues to earn for their livelihood. Well, big tech companies have responded with unusual alacrity and they are helping people during this crisis. 55. The Issue of Privacy on Social Networks Personal data becomes publicly accessible in ways and in quantities hitherto unknown; above all, this happens concerning an enormous quantity of images 56. Uber’s voice recording feature creates more problems than it fixes For those who don’t know, Uber has come out with a new initiative to install a voice recording feature on their application. This feature will allow both the driver and the passenger to agree on Uber’s app to gain access to each of their microphones and record the whole ride until it’s over. 57. Don't Fall For These Ads With Misinformation Even Though Facebook Approves Them While vowing to police COVID-19 misinformation on its platform, Facebook let advertisers target users interested in “pseudoscience”. 58. What to Expect in 2019 From Google’s Knowledge Graph Updates To those folks who are accustomed doing search engine optimization (SEO), we’ve been viewing URLs stuffed with content, and links between that content, however algorithms like PageRank (based upon links pointed between pages) and data retrieval scores primarily based upon the connection of that content are determinative how well pages rank in search engines and ends up in response to queries entered into search boxes by searchers. Websites connected by links are seen as info points connected by nodes. This was the primary generation of SEO. Chances are pretty high that several of the strategies that we've been exploiting and trying to do SEO can stay the identical as new options seem in search, like knowledge panels, rich results, featured snippets, structured snippets, search by photos, and enlarged schema covering more industries and options then it does at the moment. 59. Tell Me Your Secrets: How Many Workers Review Posts on Amazon? We found banned items for sale on Amazon.com 60. 25 Products Google Sent To Their Graveyard: A Brief History Google is generally at innovation’s forefront, and a large number of its items and administrations have become industry pioneers. Be that as it may, not all things google conjures up goes to gold. The tech goliath has created a couple of clunkers, yet Google doesn’t stop for a second to cut its misfortunes. Here’s a voyage through the Google Graveyard, with a glance at certain items and administrations Google has executed off as the years progressed. 61. 6 Steps to Grow Into A Lead Engineer At FAANG If you are a software engineer in one of the FAANG companies or similar you are probably one among the thousands of engineers  at the company. How do you go from starting fresh as a junior engineer to leading a team in say 5 years? 62. Identifying Fake Reviews On Amazon Spotting the fakes isn’t always possible, but here are some tips 63. Tech Titans Hammered By Congress At Antitrust Hearing Lawmakers allege abuse of monopoly power and political bias 64. Can You Speak The Google Language, Please? The Markup obtained internal documents that coach new employees to avoid creating “very real legal risks” in using words like “market” and “networkeffects” 65. Big Tech Brief: What You Need to Know About Congress’ High-Profile Tech CEO Hearings The House Antitrust subcommittee grilled the CEOs of four of the largest tech companies in a display that showed the preliminary results of their investigation that began in 2019. 66. Big Tech's Response to the Coronavirus Outbreak It’s not just about Google and what coronavirus website 67. There's Something Wrong With Google Search Results The search engine dedicated almost half of the first page of results in our test to its own products, which dominated the coveted top of the page 68. New Report Finds That Facebook Still Runs Discriminatory Ads We found discriminatory ads can still appear, despite Facebook's efforts 69. Governments Pressuring Tech Giants Isn't Going to End Well (for Governments) The world has come a long way in terms of its relationship with technology and knowledge. It’s almost universal that people are often afraid of new inventions that are supposed to improve their lives. We feel threatened by what’s out of our intellectual grasp. 70. Breaking Down Apple’s Services Growth Strategy: The Case Against TV+ Introduction Thank you for checking out the 70 most read stories about Faagm on HackerNoon. to find the most read stories about any technology. Visit the /Learn Repo

70 Stories To Learn About Faagm

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