Sequoia Capital has named a new board member for fintech Klarna Bank AB, seeking to quell a conflict that has ensnared the storied venture capital firm, its high-profile former partner and one of the largest startups in Europe.

Sequoia Klarna Bank Spat Rooted in Tension Between Startup’s Founders - Bloomberg

Sequoia Capital has picked partner Andrew Reed to replace Matthew Miller on the board of ‘buy now, pay later’ company Klarna, according to people familiar with the matter.Miller had joined the board in January and recently led a failedeffort to remove former Sequoia leader Michael Moritz ...

Sequoia to Appoint Andrew Reed to Klarna Board After Failed Moritz Ouster — The Information

Sequoia seeks to oust Klarna chairman Michael Moritz from the board ahead of a possible float by the “buy now, pay later” lender

Sequoia seeks to oust Klarna chairman Michael Moritz

Plus, a UK electronics retailer sparks a cross-border bidding war and meet the auditors that signed off on China Evergrande

Friends now, foes later: Sequoia’s drama at Klarna

Sequoia Capital has retracted its request to remove Michael Moritz as chairman of Klarna’s board, according to emails reviewed by The Information. Sequoia partner Matthew Miller, the director who in recent days had asked Klarna shareholders to remove Moritz, is instead leaving the Klarna board.

Sequoia Retracts Request to Oust Moritz From Klarna Board — The Information

Silicon Valley VC firm says it fully supports its former partner ‘upon a fuller assessment’

Sequoia abandons effort to oust Michael Moritz from Klarna board

The unicorn round was led by Wellington Management with participation from Bluepool Capital in addition to existing investors.

Buy now, pay later platform Tabby nabs $200M in Series D funding at $1.5B valuation

Ongoing negotiations over collective bargaining agreements at some of Sweden’s largest tech companies have led to unions issuing strike warnings for some members. What are your rights if this affects you?

How do strikes work in Sweden and what are your rights?

Run: What's interesting to me is that John Perez is not a developer. Nor is he an advocate for the homeless, although he is certainly dipping a toe into both ponds.

CBF Morning Run: Tiny houses and a big idea to combat homelessness

Buy now, pay later is a way for holiday travelers to finance their flights and hotels this season, but is it a good idea?

Buy now, pay later for holiday travel is on the rise: Should you use it?

Klarna

klarna

Hey everyone! and just like you, I’ve been fascinated with the recent progress of artificial intelligence. Realizing that I needed to stay abreast with all the developments happening, I decided to embark on a personal journey of learning, thus was born! With this series, I will be learning about LLMs and share ideas, experiments, opinions, trends & learnings through my blog posts. You can follow along the journey on HackerNoon or my personal website . In today’s article, we’ll be looking at the case study of how Klarna leveraged Gen AI and what other companies can learn from it. , I’m Nataraj 100 days of AI here here How is Klarna using generative AI? There are lots of ways generative AI is being used by companies, and we have explored some of them . In case of Klarna, they launched an AI customer support assistant that is available 24*7 and supports 30+ languages. They launched the assistant using Open AI as their LLM provider. If we have to guess, they have finetuned it using their own customer support interaction data set that they have collected over the years. in a previous post What features does the Klarna AI assistant support? The assistant is designed to handle wide range of questions related to refunds, returns, payment-related issues, cancellations, disputes, and invoice inaccuracies, ensuring swift and effective solutions. Like any tech feature, its available 24*7 across all geographies. For customers who want to know what they can afford and not afford, it also provides info related to purchase power, spending limits and the reasons why it exists. All the info of a customer account is with the assistant to help any questions related to it. Is the new AI assistant effective? Its cool to say that we have used gen AI and see the stock pop up, but the real question is, does the feature deliver value to the company. And from the numbers, it looks like it is beating expectations. Here are some of the numbers. Two-thirds of all customer service chats are handled by the assistant, which is an impressive 2.3M conversations. Customer satisfaction same as human conversations. 25% drop in repeat inquiries Estimated to drive $40M in profit improvements to the company. What are the takeaways from Klarna’s gen AI assistant? Its clear that generative AI is being adopted and creating value for companies at all stages. Customer support will get more effective and cheaper. If the new assistant is handling two-thirds of all conversations, it means the next set of companies which start their customer support teams will not require the same number of people in their teams. And this also means the existing customer support teams might be larger than needed. We will see a lot of growth stage startups follow Klarna to boost their profitability and reduce expenses. For companies like Klarna whose stocks are down due to the concern of profitability, companies with similar concerns will start to look at leveraging AI to cut costs and boost profitability. Open AI’s board drama didn’t effect its trust among companies to use them for production scale deployments. When Sam Altman was fired and re-hired at Open Ai, one of the concerns was that this would reduce the trust among developers to use Open Ai to create AI applications. When a large public company publishes such a case study its clear that the board drama at Open Ai didn’t effect Open AI’s abilities. Expect more companies to announce improvements because of using gen AI technology in fields outside customer support. More case studies using generative AI will come from more public companies showing how they are leveraging Ai. This is a function of every company wanting to get a piece of the AI narrative and the fact that gen AI features are actually delivering on value. If you are a company with large data set related to customer support, you should explore creating an AI assistant for support interactions. That’s it for Day 19 of 100 Days of AI. I write a newsletter called Above Average where I talk about the second order insights behind everything that is happening in big tech. If you are in tech and don’t want to be average, . subscribe to it Follow me on , or for latest updates on 100 days of AI or . If you are in tech you might be interested in joining my community of tech professionals . Twitter LinkedIn HackerNoon bookmark this page here

100 Days of AI, Day 19: Klarna’s AI Chatbot and The Rise of Generative AI in Startups

এআই-এর 100 দিন, 19তম দিন: ক্লারনার এআই চ্যাটবট এবং স্টার্টআপে জেনারেটিভ এআইয়ের উত্থান

100 Tage KI, Tag 19: Klarnas KI-Chatbot und der Aufstieg der generativen KI in Startups

100 días de IA, día 19: El chatbot de IA de Klarna y el auge de la IA generativa en las empresas emergentes

100 jours d'IA, jour 19 : le chatbot IA de Klarna et l'essor de l'IA générative dans les startups

एआई के 100 दिन, दिन 19: क्लार्ना का एआई चैटबॉट और स्टार्टअप्स में जनरेटिव एआई का उदय

100日間のAI、19日目: KlarnaのAIチャットボットとスタートアップにおける生成AIの台頭

AI 100일, 19일차: Klarna의 AI 챗봇과 스타트업에서 생성 AI의 부상

100 dias de IA, dia 19: AI Chatbot de Klarna e a ascensão da IA generativa em startups

100 дней искусственного интеллекта, день 19: чат-бот Klarna с искусственным интеллектом и рост генеративного искусственного интеллекта в стартапах

100 Gün Yapay Zeka, 19. Gün: Klarna'nın Yapay Zeka Sohbet Robotu ve Yeni İşletmelerde Üretken Yapay Zekanın Yükselişi

100 ngày của AI, Ngày 19: Chatbot AI của Klarna và Sự trỗi dậy của AI sáng tạo trong các công ty khởi nghiệp

人工智能 100 天，第 19 天：Klarna 的人工智能聊天机器人和初创企业中生成式人工智能的兴起

Today's digital shoppers love to shop online instead of visiting a brick-and-mortar store. Daily marketing tactics make brands think of something new and strategic to beat the competition. Buy Now Pay Later is one of the marketing strategies and a convenient payment option that drives customers to buy the products. Let’s understand in detail about Buy Now, Pay Later. What does Buy Now Pay Later, or BNPL, mean? "Buy now, pay later" means you can buy something and get it right away, but you pay for it later, usually in several installments. Recent growth in buy now, pay later (BNPL) doesn't look like it will slow down. Source: eMarketer Even though this kind of payment plan has been around for a long time, it took off during the pandemic when more people started shopping online. You can now use a "buy now, pay later" plan at most significant stores, but whether you should use the BNPL method for purchasing a product as a consumer depends on your financial capacity. On the other hand, whether you should offer this method of purchase as a business owner should be determined by your business model and objective. Your purchase from BNPL is split into equal payments, the first of which is due at checkout. Your debit or credit card is charged for the rest of the costs until your purchase is paid for in full. Source When you shop online, you'll often see BNPL payment plans. There are also BNPL sites for travel and health care. How Do Buying Now and Paying Later Work? For an example of BNPL operation, let's look at Meg, a shopper who wants the best air purifier for her house and pay for it later. She looks for one online and finds the one she wants at one  site for $200. It costs more than other models that fit her budget of $125, but it has all the features she wants. The following steps show how she got to the point where she could buy the item in installments. Meg finds out that she can pay over time. On the product page for the air purifier, she sees a $200 price tag and a message that says she could pay $50 each month for four months and own the item. This 4-way split is attractive to her because she can't afford to pay $200 immediately. But it's easier to handle if you spend $50 every two weeks. The pricing information also links to a BNPL service, so she can apply to see if she is eligible. She applies by clicking the link. Then, she gives four short pieces of information (name, birthdate, phone number, and email address). Affirm, an app used for BNPL uses an underwriting technology that looks at the data and lets her know within seconds if she has been approved or not. Meg buys an air cleaner. At checkout, she chooses to Affirm as her payment method. She can see exactly how her payments will work and agrees to them. This finishes the deal. (In this case, she doesn't have to pay any interest. Other BNPL purchases may have longer terms and fixed monthly payments with interest. For these kinds of deals, you might also need to get more information.) The store gets paid right away. Generic Air doesn't have to collect Meg's payments because of the tie-up with Affirm. Meg gets the air cleaner she's been wanting. Meg gives money to Affirm. In this case, she pays her bills every two weeks, which is what she agreed to do. How does BNPL Help Business Owners? Lots of sales Customers are more likely to buy more products from stores that offer more ways to pay, like BNPL. Customer usually only buys things when they are comfortable with how to pay for them. When customers can choose how to pay, they are more likely to buy more than one product, even if it is a bit more expensive. For example, you are a Magento store owner, and you want to provide better payment options to your customer. You can consider adding the buy now, pay later option to your store. You can purchase the extension from the marketplace, or you can take help from who can develop this custom solution for you. expert Magento developers Fewer people are giving up on their carts A found that using BNPL at checkout can cut the number of carts that are left empty by 35%. Most shoppers go to their carriages to look around, but that's not always the case. These problems can be solved well by BNPL. For example, suppose shoppers abandon their carts because they couldn't find a good way to pay or because the products were too expensive. In that case, BNPL will be able to reject every reason because customers can deliver when it's convenient for them. The price of expensive products can be split up into more affordable payments. Mastercard study Increase the customer's value over time When a customer buys from an online platform that accepts BNPL payments repeatedly, the merchant has met the customer's needs. Also, returning to a platform to buy something shows that the merchant has earned the customers' trust and loyalty by giving them flexible payment terms. Customer Lifetime Value can be easily raised when customers return to the same shopping platform to buy things. Raise the average value of each order When customers order products one at a time, the total amount is higher, which is good for a business. Customers can shop more by choosing BNPL at checkout because they can make the payment later and also in installments despite having low cash at the time of purchase.  Paying overtime and not having to pay anything upfront makes it easier to buy more. Paying overtime and not having to pay anything upfront makes it easier to buy more. When customers know how BNPL can help them and shop more, the average order value increases, and merchants get more from each customer. Why Millennials and Gen Zs are jumping on the buy now pay later trend? In 2018 when apps like Affirm, Klarna, and Afterpay were still establishing themselves as the key players in the BNPL, the primary users of the payment method were Gen X and the millennials. These two groups will continue to be the driving force in the BNPL method, where last year, 40 percent of the users were millennials, and in the next four years, Gen Z will eat into the share. it was reported Millennials and Gen Z are jumping into this trend because they find it a financially flexible payment solution with a limited cash flow. It is often used for larger ticket items like clothing, fashion items, jewelry, digital leisure goods, and appliances. All of these are the most frequently purchased items by the younger cohort. Apart from the financial convenience it offers, certain other benefits that make the younger generation buy into the trend are: Items can be bought even if they don’t have credit cards; it is a perfect alternative to credit cards. Alternative To Credit Cards: The easy integration of the BNPL apps into the stores makes purchasing easy for these two generations. Often the service is integrated into the buyer’s e-wallet, giving the consumer a wider choice for purchases. E-Wallet Integration: The refunds are accessible with the method, where the money is transferred back to the wallet immediately when a payment is made when the customer cancels the purchase. Whereas in case of payment is made through net banking or debit/credit cards, the refund can happen after a couple of days. Easy Refund: The easy EMI feature offered by BNPL makes the method a hit among the millennials and Gen Z. They can repay the amount in small EMIs without interest, allowing them to buy items that would otherwise be out of their reach. Easy EMI: Moreover, Buy Now Pay Later (BNPL) is a significant change in alternative financing and payments for online shopping. The pandemic sped up this trend; it became trendy very quickly. BNPL has become a digital lifestyle solution as more merchants start to use it. The most recent report showed that e-commerce in Western Europe will grow at a CAGR of 10%, bringing the size of the market to EUR 715 billion by 2026. Why shouldn't you buy now and pay later? Buy now, pay later. Financing can give you more money to spend and help your credit score simultaneously. But each loan offer is different. The three main things that should make you not want to join the BNPL are: You might be tempted to spend more than you can afford With BNPL, you only have to pay for a portion of your purchase up front. The rest can be paid off over several months. Buy now, pay later E-commerce you can't pay for immediately makes it much more manageable. But that could lead you to spend more than you can afford. Say you want to buy something that costs $500, but you only have $50. If you can pay for it over time, you might be more likely to buy it. But in reality, if you only have $50 left over in your budget, it may be a sign that you shouldn't buy a $500 item unless you need it in an emergency (like if you're a freelance writer and that $500 item is a laptop you need to do your job). If you don't pay on time, you might have to pay fees and penalties BNPL plans are better than credit cards because they don't start charging interest right away. But if you don't make your payments on time, you might have to pay penalties and fees that make your purchases much more expensive. It could hurt your credit score You don't have a good credit score to get a BNPL plan. These services don't check your credit report as thoroughly as other loans. But if you got into your BNPL payments, your credit score could take a big hit, just like it would if you didn't make the minimum payment on your credit card when it was due. The Impact Of Buy Now Pay Later Buy Now, Pay Later sounds attractive to customers. It can also be risky for the brands to go for this. It’s advisable no matter which marketing strategy you opt to attract customers. So, make sure to plan out smartly to offer buy now and pay later to customers. In the short run, this tactic can show you a good number of product sales. For more interesting information about the latest eCommerce trends, visit our blog and stay up to date.

Buy Now, Pay Later: What is it, and Should You Offer it to the Customers?

Turbocharging Profitability: Growth Hacking Your Customer Retention to Drive Growth For 2025

Profitabilnost turbo punjenja: Hakiranje rasta za zadržavanje vaših kupaca kako biste potaknuli rast do 2025.

Impulsando la rentabilidad: cómo retener a sus clientes para impulsar el crecimiento en 2025

Boostez votre rentabilité : Growth Hacking pour fidéliser vos clients afin de stimuler la croissance d'ici 2025

収益性を加速させる: 顧客維持率をグロースハックして 2025 年の成長を促進

Turbocharging Profitability: Bokoli Hacking Retention ya Client na yo mpo na ko diriger croissance Pona 2025

Профитабилност со турбо полнење: Хакирање на растот на задржувањето на вашите клиенти за да се поттикне растот за 2025 година

Turbocharged Profitability: Growth Hacking van uw klantenbehoud om groei te stimuleren voor 2025

Turbocarga Rentabilidad: Wiñay Hacking Cliente Retenciónniyki Wiñay 2025 kaqpaq purichinapaq

Përfitueshmëria e turbocharging: Growth Hacking Mbajtja e klientit tuaj për të nxitur rritjen për 2025

Профитабилност турбо пуњења: раст Хаковање задржавања ваших купаца како би подстакао раст до 2025.

Faida ya Turbocharging: Ukuaji Kuingilia Uhifadhi wa Wateja Wako ili Kuendeleza Ukuaji wa 2025

Turbocharging Profitability: Ku Kula Ku Hacking Ku Hlayisa Ka Khasimende Ya Wena Ku Fambisa Ku Kula Ka 2025

In my previous article, I explored the evolution of Point of Sale (POS) and Buy Now, Pay Later (BNPL) services, focusing on how these options influence customer choice. That discussion was from the customer's perspective. Now, let’s look at it from the viewpoint of businesses, specifically merchants.This article will explore what’s most important for merchants when they integrate financial solutions like POS and BNPL. We’ll look at how these solutions enhance the customer experience and boost sales.Both POS and BNPL services operate in a B2B2C (Business-to-Business-to-Consumer) model, where merchants play a key role in connecting the service provider with the end customer. In this model, merchants are not just the middlemen — they are vital to attracting customers and increasing sales. By offering flexible payment options, merchants create a smoother purchasing experience, which in turn increases customer satisfaction and conversion rates.From a technical perspective, the better the integration between financial services and merchants, the more appealing the product becomes to customers. This includes not just competitive terms, but also the speed of processing, ease of use, and how quickly issues are resolved.The success of POS and BNPL services depends not only on how appealing they are to customers, but also on the willingness of merchants to adopt and integrate them. Without strong partnerships with merchants, these services wouldn’t reach their full potential. So, what exactly matters most to merchants when considering these financial solutions?Flexibility and Customization Square is a great example of a company that offers flexibility and customization to merchants. Square provides a range of payment solutions that can be tailored to fit the needs of businesses of all sizes. For instance, Square’s point-of-sale system is modular, allowing merchants to choose and pay for only the features they need. This flexibility extends to their financing options, where merchants can offer different payment plans to customers based on their preferences. Square’s ability to adapt to the specific needs of a business—whether it's a small café or a large retail chain—has made it a popular choice for merchants looking for customizable solutions.On the other hand, Oracle’s legacy POS systems have been criticized for their lack of customization options. Many merchants found that the system couldn’t be easily adapted to their specific business processes, leading to frustration and even abandonment of the platform. When systems are too rigid, they can prevent merchants from optimizing their operations, which ultimately affects both their business and customer satisfaction.Merchant and Customer Support A good example of strong merchant and customer support comes from Shopify. Shopify has built its reputation not only on its eCommerce platform but also on the extensive support it provides to its merchants. Shopify offers 24/7 customer support through various channels such as chat, email, and phone. They also provide comprehensive online resources, including tutorials and webinars, to help merchants fully utilize their platform. This type of support reassures merchants that they can resolve issues quickly, reducing downtime and ensuring that their businesses run smoothly.On the flip side, eBay faced criticism for its slow and ineffective customer support in the past. Sellers on the platform complained about long wait times and unhelpful responses, which led some merchants to switch to other platforms like Etsy or Amazon. This example illustrates how poor support can lead to a loss of merchant confidence and ultimately, business. Competitive Rates and Terms Klarna, a leading BNPL provider, is an excellent example of a company offering competitive terms to merchants. Klarna charges merchants a fee per transaction, but these fees are often offset by the increase in sales that Klarna helps generate. Klarna's flexibility in structuring merchant fees based on volume or other business-specific factors has helped them win over large retail clients like H&M and ASOS. These competitive terms are appealing to merchants because they see a clear return on investment through increased sales and customer retention.In contrast, PayPal has been criticized by some merchants for its high fees, especially for international transactions. This has led some businesses to limit their use of PayPal or seek alternatives like Stripe or Square, which offer more favorable terms depending on transaction types and volumes.Speed and Ease of Integration Stripe is often cited as one of the best examples of a payment processor that offers fast and easy integration. Stripe’s APIs are developer-friendly, and their documentation is comprehensive, making it easy for businesses to integrate payment processing quickly. The simplicity and speed of integration have attracted major clients like Lyft and Slack, allowing these companies to focus on their core business operations without getting bogged down by complex payment integrations. This fast setup enables businesses to offer Stripe’s services to their customers without delays, preventing any potential sales loss.On the other hand, SAP’s complex ERP solutions have been known for their lengthy and resource-intensive integration processes. Many businesses, especially smaller ones, have struggled with the time and cost of integrating SAP systems, which can take months or even years to fully implement. This example highlights how a slow or complex integration can deter merchants from adopting a solution, even if it offers other benefits.Ongoing Merchant Support A great example of ongoing merchant support comes from Amazon Web Services (AWS). AWS provides extensive resources to its clients even after initial setup, offering regular updates, analytics tools, and personalized account management to ensure merchants are maximizing the platform’s potential. AWS also hosts events, webinars, and workshops to educate users on new features and best practices. This constant engagement helps merchants grow and adapt, fostering long-term loyalty and improving the merchant's overall experience.In contrast, Facebook has struggled with ongoing support for small and medium-sized businesses (SMBs) using its ad platform. Many SMBs have complained about a lack of communication and support after their ads were flagged or accounts were suspended, often without clear explanations or resolutions. This lack of ongoing support has driven some businesses to other platforms like Google Ads, which provides more proactive assistance and account management services.These real-world examples illustrate the importance of flexibility, support, competitive terms, fast integration, and ongoing assistance in maintaining successful merchant partnerships. Companies that excel in these areas, such as Square, Shopify, Klarna, Stripe, and AWS, foster long-term merchant relationships, driving mutual growth. In contrast, companies like Oracle, eBay, PayPal, SAP, and Facebook have lost merchants due to a lack of customization, poor support, high fees, slow integration, or inadequate follow-up assistance.In a future article, I’ll dive deeper into the technical aspects of integration, exploring how a seamless connection between merchants and financial services can drive further growth. if you ever want to get in touch with me Tg: https://t.me/LinaI LinkedIn: https://www.linkedin.com/in/lina-ivanova23/ In my previous article , I explored the evolution of Point of Sale (POS) and Buy Now, Pay Later (BNPL) services, focusing on how these options influence customer choice. That discussion was from the customer's perspective. Now, let’s look at it from the viewpoint of businesses, specifically merchants. This article will explore what’s most important for merchants when they integrate financial solutions like POS and BNPL. We’ll look at how these solutions enhance the customer experience and boost sales. Both POS and BNPL services operate in a B2B2C (Business-to-Business-to-Consumer) model, where merchants play a key role in connecting the service provider with the end customer. In this model, merchants are not just the middlemen — they are vital to attracting customers and increasing sales. By offering flexible payment options, merchants create a smoother purchasing experience, which in turn increases customer satisfaction and conversion rates. From a technical perspective, the better the integration between financial services and merchants, the more appealing the product becomes to customers. This includes not just competitive terms, but also the speed of processing, ease of use, and how quickly issues are resolved. The success of POS and BNPL services depends not only on how appealing they are to customers, but also on the willingness of merchants to adopt and integrate them. Without strong partnerships with merchants, these services wouldn’t reach their full potential. So, what exactly matters most to merchants when considering these financial solutions? Flexibility and Customization previous article Flexibility and Customization Square is a great example of a company that offers flexibility and customization to merchants. Square provides a range of payment solutions that can be tailored to fit the needs of businesses of all sizes. For instance, Square’s point-of-sale system is modular, allowing merchants to choose and pay for only the features they need. This flexibility extends to their financing options, where merchants can offer different payment plans to customers based on their preferences. Square’s ability to adapt to the specific needs of a business—whether it's a small café or a large retail chain—has made it a popular choice for merchants looking for customizable solutions. On the other hand, Oracle’s legacy POS systems have been criticized for their lack of customization options. Many merchants found that the system couldn’t be easily adapted to their specific business processes, leading to frustration and even abandonment of the platform. When systems are too rigid, they can prevent merchants from optimizing their operations, which ultimately affects both their business and customer satisfaction. Merchant and Customer Support Merchant and Customer Support A good example of strong merchant and customer support comes from Shopify. Shopify has built its reputation not only on its eCommerce platform but also on the extensive support it provides to its merchants. Shopify offers 24/7 customer support through various channels such as chat, email, and phone. They also provide comprehensive online resources, including tutorials and webinars, to help merchants fully utilize their platform. This type of support reassures merchants that they can resolve issues quickly, reducing downtime and ensuring that their businesses run smoothly. On the flip side, eBay faced criticism for its slow and ineffective customer support in the past. Sellers on the platform complained about long wait times and unhelpful responses, which led some merchants to switch to other platforms like Etsy or Amazon. This example illustrates how poor support can lead to a loss of merchant confidence and ultimately, business. Competitive Rates and Terms Competitive Rates and Terms Klarna, a leading BNPL provider, is an excellent example of a company offering competitive terms to merchants. Klarna charges merchants a fee per transaction, but these fees are often offset by the increase in sales that Klarna helps generate. Klarna's flexibility in structuring merchant fees based on volume or other business-specific factors has helped them win over large retail clients like H&M and ASOS. These competitive terms are appealing to merchants because they see a clear return on investment through increased sales and customer retention. In contrast, PayPal has been criticized by some merchants for its high fees, especially for international transactions. This has led some businesses to limit their use of PayPal or seek alternatives like Stripe or Square, which offer more favorable terms depending on transaction types and volumes. Speed and Ease of Integration Speed and Ease of Integration Stripe is often cited as one of the best examples of a payment processor that offers fast and easy integration. Stripe’s APIs are developer-friendly, and their documentation is comprehensive, making it easy for businesses to integrate payment processing quickly. The simplicity and speed of integration have attracted major clients like Lyft and Slack, allowing these companies to focus on their core business operations without getting bogged down by complex payment integrations. This fast setup enables businesses to offer Stripe’s services to their customers without delays, preventing any potential sales loss. On the other hand, SAP’s complex ERP solutions have been known for their lengthy and resource-intensive integration processes. Many businesses, especially smaller ones, have struggled with the time and cost of integrating SAP systems, which can take months or even years to fully implement. This example highlights how a slow or complex integration can deter merchants from adopting a solution, even if it offers other benefits. Ongoing Merchant Support Ongoing Merchant Support A great example of ongoing merchant support comes from Amazon Web Services (AWS). AWS provides extensive resources to its clients even after initial setup, offering regular updates, analytics tools, and personalized account management to ensure merchants are maximizing the platform’s potential. AWS also hosts events, webinars, and workshops to educate users on new features and best practices. This constant engagement helps merchants grow and adapt, fostering long-term loyalty and improving the merchant's overall experience. In contrast, Facebook has struggled with ongoing support for small and medium-sized businesses (SMBs) using its ad platform. Many SMBs have complained about a lack of communication and support after their ads were flagged or accounts were suspended, often without clear explanations or resolutions. This lack of ongoing support has driven some businesses to other platforms like Google Ads, which provides more proactive assistance and account management services. These real-world examples illustrate the importance of flexibility, support, competitive terms, fast integration, and ongoing assistance in maintaining successful merchant partnerships. Companies that excel in these areas, such as Square, Shopify, Klarna, Stripe, and AWS, foster long-term merchant relationships, driving mutual growth. In contrast, companies like Oracle, eBay, PayPal, SAP, and Facebook have lost merchants due to a lack of customization, poor support, high fees, slow integration, or inadequate follow-up assistance. In a future article, I’ll dive deeper into the technical aspects of integration, exploring how a seamless connection between merchants and financial services can drive further growth. if you ever want to get in touch with me Tg: https://t.me/LinaI Tg: https://t.me/LinaI Tg: https://t.me/LinaI https://t.me/LinaI LinkedIn: https://www.linkedin.com/in/lina-ivanova23/ LinkedIn: https://www.linkedin.com/in/lina-ivanova23/ LinkedIn: https://www.linkedin.com/in/lina-ivanova23/ https://www.linkedin.com/in/lina-ivanova23/

What Merchants Care About: How POS and BNPL Improve Customer Experience and Boost Sales

I don't want to alarm anyone, but the entire SaaS industry is about to implode. Or explode. Or both, simultaneously. That’s because AI is making software creation so cheap and fast that current SaaS pricing models are starting to look like a particularly unfunny joke. Remember when people paid actual money for calculators? It's going to be like that, but for entire software suites. Let me take you to this new reality for a moment. The rise of the solo product visionary Large Language Models have quickly become superhuman at generating correct code for entire applications. It's like giving everyone a team of world-class developers in their pocket. For instance, GitHub's Copilot has already shown that developers using AI are 2.2x faster at completing tasks. What could possibly go wrong? Imagine the following: a random person with good product sense and an AI sidekick decides, "You know what? I could make a better version of Salesforce." And then they just... do it. Over the weekend. This may sound crazy, but it’s almost already possible. The YouTube-ification of software distribution Thanks to YouTube, your neighbour's cat can become a viral sensation overnight. Well, soon, your neighbour's hastily cobbled together AI-assisted app could be the next unicorn. There will be product discovery platforms very similar to the App Store that will make or break businesses, just like Youtube’s algorithm makes or breaks content creators. Instead of star ratings and likes, we'll have publicly available user numbers and retention statistics. But many of the most popular apps will be fully open source and remixable, so you will be able to not only tell if an app is good before you download it, but also tweak it yourself to match your preferences. That also means that the era of everyone accepting that most software is bad is over soon, too. We're entering a different kind of era, where most software, from recipe apps to sophisticated crypto indexes, might have a user experience that’s actually good. I firmly believe that those with the best ideas will win this competition - those with the right vision to use technology effectively. Product taste and domain expertise will become more crucial than ever. The other impact of this is that developers will need to pick up new skills, or stay in niches where a human touch will stay important. What about SaaS companies? You might be thinking, "This all sounds great for consumers, but what about the poor SaaS companies?". Let me be blunt: in 2025, SaaS companies without a clear moat will start noticing churn to fresh competition and to internal tools built with AI to serve the same need. It’s already starting: Klarna recently announced it is replacing Salesforce with internally built AI. It’ll be like watching a slow-motion train wreck, except the train is made of bits and bytes, and the wreck is your business model. This shift won't hit everyone equally. Regulated industries will naturally be less impacted. Segments like designers and developers, who are always ready to jump on the best value proposition, will be the canaries in the coal mine, signalling the changes to come. My advice to current SaaS companies is therefore to adopt AI in development to speed up their iteration process, keep zooming into verticals where domain expertise is key, and not to be afraid to disrupt and cannibalise themselves before someone else does. Regardless of how well they prepare though, the shift I’m talking about will go faster and faster, to the point where the willingness to pay for software will approach that of paying for watching a tiktok or youtube video. For those of us in tech, this shift demands relentless adaptation. Ideation and execution are collapsing into a single, AI-accelerated process. We all have to adapt, quickly. The bottom line is that the future belongs to those who can dream up the next big thing and then sweet-talk an AI into building it for them. I don't want to alarm anyone, but the entire SaaS industry is about to implode. Or explode. Or both, simultaneously. That’s because AI is making software creation so cheap and fast that current SaaS pricing models are starting to look like a particularly unfunny joke. Remember when people paid actual money for calculators? It's going to be like that, but for entire software suites. Let me take you to this new reality for a moment. The rise of the solo product visionary The rise of the solo product visionary Large Language Models have quickly become superhuman at generating correct code for entire applications. It's like giving everyone a team of world-class developers in their pocket. For instance, GitHub's Copilot has already shown that developers using AI are 2.2x faster at completing tasks. What could possibly go wrong? Imagine the following: a random person with good product sense and an AI sidekick decides, "You know what? I could make a better version of Salesforce." And then they just... do it. Over the weekend. This may sound crazy, but it’s almost already possible. The YouTube-ification of software distribution The YouTube-ification of software distribution Thanks to YouTube, your neighbour's cat can become a viral sensation overnight. Well, soon, your neighbour's hastily cobbled together AI-assisted app could be the next unicorn. There will be product discovery platforms very similar to the App Store that will make or break businesses, just like Youtube’s algorithm makes or breaks content creators. Instead of star ratings and likes, we'll have publicly available user numbers and retention statistics. But many of the most popular apps will be fully open source and remixable, so you will be able to not only tell if an app is good before you download it, but also tweak it yourself to match your preferences. That also means that the era of everyone accepting that most software is bad is over soon, too. We're entering a different kind of era, where most software, from recipe apps to sophisticated crypto indexes, might have a user experience that’s actually good. I firmly believe that those with the best ideas will win this competition - those with the right vision to use technology effectively. Product taste and domain expertise will become more crucial than ever. The other impact of this is that developers will need to pick up new skills, or stay in niches where a human touch will stay important. What about SaaS companies? What about SaaS companies? You might be thinking, "This all sounds great for consumers, but what about the poor SaaS companies?". Let me be blunt: in 2025, SaaS companies without a clear moat will start noticing churn to fresh competition and to internal tools built with AI to serve the same need. It’s already starting: Klarna recently announced it is replacing Salesforce with internally built AI . It’ll be like watching a slow-motion train wreck, except the train is made of bits and bytes, and the wreck is your business model. Klarna recently announced it is replacing Salesforce with internally built AI Klarna recently announced it is replacing Salesforce with internally built AI This shift won't hit everyone equally. Regulated industries will naturally be less impacted. Segments like designers and developers, who are always ready to jump on the best value proposition, will be the canaries in the coal mine, signalling the changes to come. My advice to current SaaS companies is therefore to adopt AI in development to speed up their iteration process, keep zooming into verticals where domain expertise is key, and not to be afraid to disrupt and cannibalise themselves before someone else does. Regardless of how well they prepare though, the shift I’m talking about will go faster and faster, to the point where the willingness to pay for software will approach that of paying for watching a tiktok or youtube video. For those of us in tech, this shift demands relentless adaptation. Ideation and execution are collapsing into a single, AI-accelerated process. We all have to adapt, quickly. The bottom line is that the future belongs to those who can dream up the next big thing and then sweet-talk an AI into building it for them.

The SaaS Apocalypse and How aI Will Give Birth to One-person Tech Giants

Authors:
(1) Mark Potanin, a Corresponding (authorpotanin.m.st@gmail.com);
(2) Andrey Chertok, (a.v.chertok@gmail.com);
(3) Konstantin Zorin, (berzqwer@gmail.com);
(4) Cyril Shtabtsovsky, (cyril@aloniq.com). Table of Links Abstract and 1. Introduction 2 Related works 3 Dataset Overview, Preprocessing, and Features 3.1 Successful Companies Dataset and 3.2 Unsuccessful Companies Dataset 3.3 Features 4 Model Training, Evaluation, and Portfolio Simulation and 4.1 Backtest 4.2 Backtest settings 4.3 Results 4.4 Capital Growth 5 Other approaches 5.1 Investors ranking model 5.2 Founders ranking model and 5.3 Unicorn recommendation model 6 Conclusion 7 Further Research, References and Appendix ABSTRACT Predicting startup success presents a formidable challenge due to the inherently volatile landscape of the entrepreneurial ecosystem. The advent of extensive databases like Crunchbase jointly with available open data enables the application of machine learning and artificial intelligence for more accurate predictive analytics. This paper focuses on startups at their Series B and Series C investment stages, aiming to predict key success milestones such as achieving an Initial Public Offering (IPO), attaining unicorn status, or executing a successful Merger and Acquisition (M&A). We introduce novel deep learning model for predicting startup success, integrating a variety of factors such as funding metrics, founder features, industry category. A distinctive feature of our research is the use of a comprehensive backtesting algorithm designed to simulate the venture capital investment process. This simulation allows for a robust evaluation of our model’s performance against historical data, providing actionable insights into its practical utility in real-world investment contexts. Evaluating our model on Crunchbase’s, we achieved a 14 times capital growth and successfully identified on B round high-potential startups including Revolut, DigitalOcean, Klarna, Github and others. Our empirical findings illuminate the importance of incorporating diverse feature sets in enhancing the model’s predictive accuracy. In summary, our work demonstrates the considerable promise of deep learning models and alternative unstructured data in predicting startup success and sets the stage for future advancements in this research area. 1 Introduction The prediction of startup success is a crucial task for various stakeholders, including investors, entrepreneurs, and policymakers, as it has significant implications for resource allocation and decision-making. It is estimated that approximately 90% of startups fail within their first five years, a failure rate that has remained relatively constant over the past few decades, despite considerable advancements in technology and business practices. Consequently, the accurate prediction of startup success can assist investors in more effectively allocating their resources and enable entrepreneurs to make better-informed decisions. Recently, the proliferation of data from sources such as Crunchbase has intensified interest in the application of machine learning techniques for the prediction of startup success. Machine learning models can harness various types of data, encompassing funding history, market trends, team composition, and social media activity, to identify patterns and generate predictions. This study presents two distinct methodologies for predicting startup success: a supervised deep learning approach leveraging multiple data sources, and a ranking-based approach focusing on the identification of characteristics common to successful startups and investors. The supervised approach entails collecting and labeling data, constructing a prediction model, and evaluating its performance. In contrast, the ranking-based approach centers on identifying startups and investors that exhibit shared characteristics with successful ones. Our train dataset consists of 34,470 companies The primary novelty of this research lies in the application of deep learning techniques and the integration of heterogeneous input data types. A crucial feature of our research is the simulation of fund operations based on historical data, resulting in a projected 14x capital growth of the fund’s portfolio. As per machine learning metrics, our model exhibits a robust 86% ROC_AUC. The remainder of this paper is organized as follows: Section 2 reviews the related works in the area of startup success prediction and machine learning. Section 3 describes dataset collection, preprocessing, and feature selection. Section 4 presents the experimental results of the supervised approach. Section 5 describes some other ideas about company and investor scoring. Finally, Sections 6 and 7 provide the conclusion of the study and discuss prospective research avenues in this domain. This paper is available on arxiv under CC 4.0 license. Authors: (1) Mark Potanin, a Corresponding (authorpotanin.m.st@gmail.com); (2) Andrey Chertok, (a.v.chertok@gmail.com); (3) Konstantin Zorin, (berzqwer@gmail.com); (4) Cyril Shtabtsovsky, (cyril@aloniq.com). Authors: Authors: (1) Mark Potanin, a Corresponding (authorpotanin.m.st@gmail.com); (2) Andrey Chertok, (a.v.chertok@gmail.com); (3) Konstantin Zorin, (berzqwer@gmail.com); (4) Cyril Shtabtsovsky, (cyril@aloniq.com). Table of Links Abstract and 1. Introduction Abstract and 1. Introduction 2 Related works 2 Related works 3 Dataset Overview, Preprocessing, and Features 3 Dataset Overview, Preprocessing, and Features 3.1 Successful Companies Dataset and 3.2 Unsuccessful Companies Dataset 3.1 Successful Companies Dataset and 3.2 Unsuccessful Companies Dataset 3.3 Features 3.3 Features 4 Model Training, Evaluation, and Portfolio Simulation and 4.1 Backtest 4 Model Training, Evaluation, and Portfolio Simulation and 4.1 Backtest 4.2 Backtest settings 4.2 Backtest settings 4.3 Results 4.3 Results 4.4 Capital Growth 4.4 Capital Growth 5 Other approaches 5.1 Investors ranking model 5.1 Investors ranking model 5.2 Founders ranking model and 5.3 Unicorn recommendation model 5.2 Founders ranking model and 5.3 Unicorn recommendation model 6 Conclusion 6 Conclusion 7 Further Research, References and Appendix 7 Further Research, References and Appendix ABSTRACT Predicting startup success presents a formidable challenge due to the inherently volatile landscape of the entrepreneurial ecosystem. The advent of extensive databases like Crunchbase jointly with available open data enables the application of machine learning and artificial intelligence for more accurate predictive analytics. This paper focuses on startups at their Series B and Series C investment stages, aiming to predict key success milestones such as achieving an Initial Public Offering (IPO), attaining unicorn status, or executing a successful Merger and Acquisition (M&A). We introduce novel deep learning model for predicting startup success, integrating a variety of factors such as funding metrics, founder features, industry category. A distinctive feature of our research is the use of a comprehensive backtesting algorithm designed to simulate the venture capital investment process. This simulation allows for a robust evaluation of our model’s performance against historical data, providing actionable insights into its practical utility in real-world investment contexts. Evaluating our model on Crunchbase’s, we achieved a 14 times capital growth and successfully identified on B round high-potential startups including Revolut, DigitalOcean, Klarna, Github and others. Our empirical findings illuminate the importance of incorporating diverse feature sets in enhancing the model’s predictive accuracy. In summary, our work demonstrates the considerable promise of deep learning models and alternative unstructured data in predicting startup success and sets the stage for future advancements in this research area. 1 Introduction The prediction of startup success is a crucial task for various stakeholders, including investors, entrepreneurs, and policymakers, as it has significant implications for resource allocation and decision-making. It is estimated that approximately 90% of startups fail within their first five years, a failure rate that has remained relatively constant over the past few decades, despite considerable advancements in technology and business practices. Consequently, the accurate prediction of startup success can assist investors in more effectively allocating their resources and enable entrepreneurs to make better-informed decisions. Recently, the proliferation of data from sources such as Crunchbase has intensified interest in the application of machine learning techniques for the prediction of startup success. Machine learning models can harness various types of data, encompassing funding history, market trends, team composition, and social media activity, to identify patterns and generate predictions. This study presents two distinct methodologies for predicting startup success: a supervised deep learning approach leveraging multiple data sources, and a ranking-based approach focusing on the identification of characteristics common to successful startups and investors. The supervised approach entails collecting and labeling data, constructing a prediction model, and evaluating its performance. In contrast, the ranking-based approach centers on identifying startups and investors that exhibit shared characteristics with successful ones. Our train dataset consists of 34,470 companies The primary novelty of this research lies in the application of deep learning techniques and the integration of heterogeneous input data types. A crucial feature of our research is the simulation of fund operations based on historical data, resulting in a projected 14x capital growth of the fund’s portfolio. As per machine learning metrics, our model exhibits a robust 86% ROC_AUC. The remainder of this paper is organized as follows: Section 2 reviews the related works in the area of startup success prediction and machine learning. Section 3 describes dataset collection, preprocessing, and feature selection. Section 4 presents the experimental results of the supervised approach. Section 5 describes some other ideas about company and investor scoring. Finally, Sections 6 and 7 provide the conclusion of the study and discuss prospective research avenues in this domain. This paper is available on arxiv under CC 4.0 license. This paper is available on arxiv under CC 4.0 license. available on arxiv

Startup Success Prediction and VC Portfolio Simulation Using CrunchBase Data

POS Financing vs. BNPL: Understanding the Differences and Evolution of Solutions

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„Multimodal ist der am wenigsten gewürdigte KI-Durchbruch“, sagt DoNotPayCEO Joshua Browder

"El multimodal es el avance de la IA menos apreciado", dice el director ejecutivo de DoNotPay, Joshua Browder

« Le multimodal est la percée la plus méconnue de l'IA », déclare Joshua Browder, PDG de DoNotPay

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DoNotPayCEO Joshua Browder는 '멀티모달은 가장 인정받지 못한 AI 혁신'이라고 말했습니다.

‘Multimodal é o avanço de IA menos apreciado’, diz Joshua Browder, CEO da DoNotPay

«Мультимодальные перевозки — это самый недооцененный прорыв в области искусственного интеллекта», — говорит генеральный директор DoNotPay Джошуа Браудер.

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