Attini

attini

Zuzalu is Dead. Long Live Zuzalu! - Metamorphosis Commenced v3

Zuzalu is dood. Lank lewe Zuzalu! - Metamorfose begin v3

Зузалу мёртвы. Жыве Зузалу! - Метамарфоза пачалася v3

Ο Ζουζαλού είναι νεκρός. Ζήτω ο Ζουζαλού! - Metamorphosis Commenced v3

Zuzalu ha muerto. ¡Viva Zuzalu! - Metamorfosis comenzada v3

Zuzalu Hil da. Bizi Zuzalu! - Metamorfosia hasita v3

زوزالو مرده است زنده باد زوزالو - Metamorphosis Commenced v3

Zuzalu è morto. Lunga vita a Zuzalu! - Metamorfosi iniziata v3

ズザルは死んだ。ズザル万歳！ - 変身開始 v3

Zuzalu ແມ່ນຕາຍ. ອາຍຸຍືນ Zuzalu! - Metamorphosis ເລີ່ມ v3

Zuzalu yra miręs. Tegyvuoja Zuzalu! - Metamorfozė prasidėjo v3

Zuzalu a murit. Trăiască Zuzalu! - Metamorfoza a început v3

Zuzalu Öldü. Yaşasın Zuzalu! - Metamorfoz Başladı v3

Metamorphosis Commenced, vol2: Crecimiento Aleph Review & Q4 in Lisbon and Thailand

Metamorphosis Commended, vol2: Crecimiento Aleph Review & Q4 v Lisabonu a Thajsku

Metamorphosis Commenced, τόμος 2: Crecimiento Aleph Review & Q4 στη Λισαβόνα και την Ταϊλάνδη

Metamorfosis iniciada, vol. 2: Revisión y cuarto trimestre de Crecimiento Aleph en Lisboa y Tailandia

Metamorphosis Commenced, vol2: Crecimiento Aleph Review & Q4 en Lisboa e Tailandia

変身開始、第2巻：クレシミエント・アレフのレビューとリスボンとタイでの第4四半期

Метаморфоз басталды, 2-том: Crecimiento Aleph шолуы және Лиссабондағы және Таиландтағы 4-тоқсан

Metamorphosis ເລີ່ມຕົ້ນ, vol2: Crecimiento Aleph Review & Q4 ໃນ Lisbon ແລະປະເທດໄທ

میټامورفوسس پیل شوی، vol2: Crecimiento Aleph Review & Q4 په لیزبون او تایلینډ کې

Metamorfosis qallarisqa, vol2: Crecimiento Aleph Revisión & Q4 Lisboapi, Tailandiapipas

Metamorphosis Yakatangwa, vol2: Crecimiento Aleph Ongororo & Q4 muLisbon neThailand

Metamorfoza filloi, vëll 2: Crecimiento Aleph Review & Q4 në Lisbonë dhe Tajlandë

Metamorphosis bắt đầu, tập 2: Đánh giá Crecimiento Aleph & Q4 tại Lisbon và Thái Lan

The U.S. Government: Google Has Monopoly Power In The U.S. General Search Services Market

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

5 Leading Crypto Exchanges for Navigating the Current Bull Market 

বর্তমান বুল মার্কেটে নেভিগেট করার জন্য 5টি নেতৃস্থানীয় ক্রিপ্টো এক্সচেঞ্জ 

5 führende Krypto-Börsen zur Navigation durch den aktuellen Bullenmarkt

5 principales intercambios de cifrado para navegar en el mercado alcista actual 

5 principaux échanges cryptographiques pour naviguer sur le marché haussier actuel 

वर्तमान बुल मार्केट को नेविगेट करने के लिए 5 अग्रणी क्रिप्टो एक्सचेंज

現在の強気相場を乗り切るための 5 つの主要な暗号通貨取引所

현재 상승장을 탐색하기 위한 5가지 주요 암호화폐 거래소 

5 exchanges de criptomoedas líderes para navegar no atual mercado em alta 

5 ведущих криптовалютных бирж для навигации на текущем бычьем рынке 

Mevcut Boğa Piyasasında Yön Bulmak İçin Önde Gelen 5 Kripto Borsası 

5 sàn giao dịch tiền điện tử hàng đầu để điều hướng thị trường tăng trưởng hiện tại 

5 家领先的加密货币交易所助您应对当前的牛市

Source After the first year in business, it is necessary to look at certain performance metrics so as to grade how well your business has done and also plan future growth. These metrics highlight what is working, not working and where improvements can be made. Business owners who concentrate on important metrics like revenue, customer-acquisition costs and operational efficiency will stay ahead of the competition - not just surviving but thriving with the benchmarking guidance. Revenue and Sales Metrics Evaluating revenue and sales metrics is crucial for understanding the financial health of your business. Constantly monitoring these metrics can provide you a heads-up as an entrepreneur to build decision-making and growth opportunities. Below are the Three important limits - Total revenue, sales growth rate and Gross Annual income Total Revenue Total Revenue is the amount of income in which your business makes over a given period (a year usually). It encompasses all sales from products or services before any expenses are deducted. Checking out your overall turnover can reveal trends, seasonality and areas for potential growth. Sales Growth Rate This helps you understand how fast your sales numbers are growing, which in turn paints a neat picture of the performance of all those impactful sales strategies. However, to compare this the company looks at data from the start of the financial year until today. So for example, if at the beginning of a period your sales were $100k then by the end they reach $120K, means you have grown 20% This KPI helps you know if your business is growing or where to improve. Gross Annual Income Gross Annual Income - this income is your total revenue less the cost of goods sold (COGS). That is the total amount of money left over from your business before all other expenses are paid out such as salaries and rent. Once you know how to calculate gross annual income, you can spot which expenditures need to be shaved away in order to raise margins. Customer Metrics Customer metrics matter for any business that wants to shine in a competitive market. The other three relevant metrics those of Customer Acquisition Cost (CAC), the Lifetime Value of a customer (LTV) and the Retention Rate tell you more about how well are handling your customers-related issues. Customer Acquisition Cost CAC is the cost of attaining a new customer. This includes costs like the money spent on marketing, advertising, salary for a sales team and essentially all expenses it takes to acquire new customers. CAC is key because it can tell businesses how much cost-efficiency they have in acquiring a customer. To calculate CAC, use the following formula: A high CAC could tell you that your marketing strategy is inefficient, or perhaps, you are spending too much on customer acquisition. ProfitWell reported that the average CAC for SaaS companies rose more than 60% over five years, emphasizing just how crucial it has become to optimize acquisition costs. Customer Lifetime Value (CLV) Customer Lifetime Value or CLV is a prediction of all the value that your business will derive from its entire relationship with you. The importance of CLV is it allows business owners to measure the lifetime value of customers and plan their strategies to retain these as long-term, loyal customers. To calculate CLV, multiply the average purchase value, purchase frequency, and average customer lifespan. For example, if a customer spends an average of $100 per purchase, buys three times a year, and remains a customer for five years, the CLV would be $1,500. A higher CLV is a direct representation of their ability to extract as much value from customers. Customer Retention Rate The Customer Retention Rate, basically is the percentage of previous customers who returned to a specific business over time. High retention indicates a great disparity in favorability of service and customer loyalty, which is essential for long-term expansion. To calculate the customer retention rate, use the following formula: For example, if a company starts with 500 customers, gains 100 new customers, and ends with 550 customers, the retention rate would be 90%. Businesses should focus on enhancing customer experiences to improve retention rates. According to a study by Bain & Company, increasing customer retention rates by just 5% can boost profits by 25% to 95%, underscoring the significant impact of customer loyalty. Financial Health Metrics Evaluating financial health is crucial for any business. Three key metrics offer valuable insights into your company's financial stability: Net Profit Margin, Operating Expenses, and Cash Flow. Financial management tools for startups can help streamline the tracking and analysis of these metrics. Net Profit Margin measures how much of your revenue turns into profit. It’s calculated by dividing net profit by total revenue and multiplying by 100. For instance, if your business earns $100,000 in revenue and nets $10,000 in profit, your Net Profit Margin is 10%. This percentage reveals how efficiently you’re converting sales into actual profit. Operating Expenses are the costs required to run your business, excluding production expenses. Keeping these costs in check is vital for maintaining profitability. Regularly review your operating expenses—like rent, salaries, and utilities—to identify areas where you can cut costs or improve efficiency. Cash Flow tracks the movement of cash in and out of your business. Positive cash flow means more money is coming in than going out, which is essential for paying bills, investing in growth, and avoiding debt. Regularly monitor your cash flow to ensure you have enough liquidity to cover short-term obligations and sustain operations. Marketing Metrics ROI measures the profitability of your marketing efforts. It’s calculated by dividing the net profit from a campaign by the cost of the campaign. For instance, if you spent $1,000 on a campaign and earned $4,000 in return, your ROI is 300%. This metric helps you understand which campaigns are delivering value and which need adjustment. The conversion rate tells you the percentage of visitors who take a desired action, like making a purchase or signing up for a newsletter. To calculate it, divide the number of conversions by the total number of visitors and multiply by 100. If 50 out of 1,000 visitors make a purchase, your conversion rate is 5%. A higher conversion rate indicates that your marketing strategies are effectively persuading potential customers. Tracking website traffic and engagement metrics—such as the number of visitors and their interactions (e.g., time spent on site, pages viewed)—provides insights into the effectiveness of your online presence. Tools like Google Analytics can show how many people visit your site and how they interact with your content. Higher traffic and engagement levels suggest that your marketing efforts are successfully attracting and retaining audience interest. Operational Metrics Operational metrics are essential for evaluating and improving the internal processes of a business. By focusing on inventory turnover, employee productivity, and supply chain efficiency, businesses can ensure smooth operations and sustained growth. Inventory turnover is a key metric that measures how often a company's inventory is sold and replaced over a specific period, usually a year. It is calculated by dividing the cost of goods sold (COGS) by the average inventory during that period. A higher inventory turnover ratio indicates that a company is efficiently managing its inventory by selling products quickly, which can lead to lower holding costs and reduced risk of obsolescence. Employee productivity assesses the efficiency and performance of employees. Tracking productivity helps identify areas for improvement and ensures that employees are contributing effectively to the business's goals. Research from the Harvard Business Review highlights that businesses prioritizing employee productivity see up to a 22% increase in overall performance. Supply chain efficiency evaluates the effectiveness of the supply chain processes. Efficient supply chains minimize delays, reduce costs, and improve customer satisfaction. According to a report, companies with streamlined supply chains have 15% lower supply chain costs and 50% less inventory holding costs. Conclusion Evaluating essential metrics after your first year in business is crucial for understanding your company's performance and setting the stage for future growth. You get lots of context from revenue, customer metrics, financial health, marketing effectiveness and operational efficiency in terms of what is doing very well or not so well. Checking in with these numbers helps keep you grounded and competitive so that your business can flourish over a long time horizon. Using these measurements will also provide a foundation that can be used for ongoing improvement, which means the groundwork you are placing in now has long-term value and promotes future achievement. Source Source Source Source After the first year in business, it is necessary to look at certain performance metrics so as to grade how well your business has done and also plan future growth. These metrics highlight what is working, not working and where improvements can be made. Business owners who concentrate on important metrics like revenue, customer-acquisition costs and operational efficiency will stay ahead of the competition - not just surviving but thriving with the benchmarking guidance. performance metrics performance metrics Revenue and Sales Metrics Evaluating revenue and sales metrics is crucial for understanding the financial health of your business. Constantly monitoring these metrics can provide you a heads-up as an entrepreneur to build decision-making and growth opportunities. Below are the Three important limits - Total revenue, sales growth rate and Gross Annual income Evaluating revenue and sales metrics Evaluating revenue and sales metrics Total Revenue Total Revenue is the amount of income in which your business makes over a given period (a year usually). It encompasses all sales from products or services before any expenses are deducted. Checking out your overall turnover can reveal trends, seasonality and areas for potential growth. Sales Growth Rate This helps you understand how fast your sales numbers are growing, which in turn paints a neat picture of the performance of all those impactful sales strategies. However, to compare this the company looks at data from the start of the financial year until today. So for example, if at the beginning of a period your sales were $100k then by the end they reach $120K, means you have grown 20% This KPI helps you know if your business is growing or where to improve. Gross Annual Income Gross Annual Income - this income is your total revenue less the cost of goods sold (COGS). That is the total amount of money left over from your business before all other expenses are paid out such as salaries and rent. Once you know how to calculate gross annual income , you can spot which expenditures need to be shaved away in order to raise margins. how to calculate gross annual income how to calculate gross annual income Customer Metrics Customer metrics matter for any business that wants to shine in a competitive market. The other three relevant metrics those of Customer Acquisition Cost (CAC), the Lifetime Value of a customer (LTV) and the Retention Rate tell you more about how well are handling your customers-related issues. Customer Acquisition Cost CAC is the cost of attaining a new customer. This includes costs like the money spent on marketing, advertising, salary for a sales team and essentially all expenses it takes to acquire new customers. CAC is key because it can tell businesses how much cost-efficiency they have in acquiring a customer. To calculate CAC, use the following formula: A high CAC could tell you that your marketing strategy is inefficient, or perhaps, you are spending too much on customer acquisition. ProfitWell reported that the average CAC for SaaS companies rose more than 60% over five years, emphasizing just how crucial it has become to optimize acquisition costs. ProfitWell reported that the average CAC for SaaS ProfitWell reported that the average CAC for SaaS Customer Lifetime Value (CLV) Customer Lifetime Value or CLV is a prediction of all the value that your business will derive from its entire relationship with you. The importance of CLV is it allows business owners to measure the lifetime value of customers and plan their strategies to retain these as long-term, loyal customers. To calculate CLV, multiply the average purchase value, purchase frequency, and average customer lifespan. For example, if a customer spends an average of $100 per purchase, buys three times a year, and remains a customer for five years, the CLV would be $1,500. A higher CLV is a direct representation of their ability to extract as much value from customers. Customer Retention Rate The Customer Retention Rate, basically is the percentage of previous customers who returned to a specific business over time. High retention indicates a great disparity in favorability of service and customer loyalty, which is essential for long-term expansion. To calculate the customer retention rate, use the following formula: For example, if a company starts with 500 customers, gains 100 new customers, and ends with 550 customers, the retention rate would be 90%. Businesses should focus on enhancing customer experiences to improve retention rates. According to a study by Bain & Company , increasing customer retention rates by just 5% can boost profits by 25% to 95%, underscoring the significant impact of customer loyalty. According to a study by Bain & Company According to a study by Bain & Company Financial Health Metrics Evaluating financial health is crucial for any business. Three key metrics offer valuable insights into your company's financial stability: Net Profit Margin, Operating Expenses, and Cash Flow. Financial management tools for startups can help streamline the tracking and analysis of these metrics. Financial management tools for startups Financial management tools for startups Net Profit Margin measures how much of your revenue turns into profit. It’s calculated by dividing net profit by total revenue and multiplying by 100. For instance, if your business earns $100,000 in revenue and nets $10,000 in profit, your Net Profit Margin is 10%. This percentage reveals how efficiently you’re converting sales into actual profit. Operating Expenses are the costs required to run your business, excluding production expenses. Keeping these costs in check is vital for maintaining profitability. Regularly review your operating expenses—like rent, salaries, and utilities—to identify areas where you can cut costs or improve efficiency. Cash Flow tracks the movement of cash in and out of your business. Positive cash flow means more money is coming in than going out, which is essential for paying bills, investing in growth, and avoiding debt. Regularly monitor your cash flow to ensure you have enough liquidity to cover short-term obligations and sustain operations. Marketing Metrics ROI measures the profitability of your marketing efforts. It’s calculated by dividing the net profit from a campaign by the cost of the campaign. For instance, if you spent $1,000 on a campaign and earned $4,000 in return, your ROI is 300%. This metric helps you understand which campaigns are delivering value and which need adjustment. The conversion rate tells you the percentage of visitors who take a desired action, like making a purchase or signing up for a newsletter. To calculate it, divide the number of conversions by the total number of visitors and multiply by 100. If 50 out of 1,000 visitors make a purchase, your conversion rate is 5%. A higher conversion rate indicates that your marketing strategies are effectively persuading potential customers. Tracking website traffic and engagement metrics—such as the number of visitors and their interactions (e.g., time spent on site, pages viewed)—provides insights into the effectiveness of your online presence. Tools like Google Analytics can show how many people visit your site and how they interact with your content. Higher traffic and engagement levels suggest that your marketing efforts are successfully attracting and retaining audience interest. Operational Metrics Operational metrics are essential for evaluating and improving the internal processes of a business. By focusing on inventory turnover, employee productivity, and supply chain efficiency, businesses can ensure smooth operations and sustained growth. Inventory turnover is a key metric that measures how often a company's inventory is sold and replaced over a specific period, usually a year. It is calculated by dividing the cost of goods sold (COGS) by the average inventory during that period. A higher inventory turnover ratio indicates that a company is efficiently managing its inventory by selling products quickly, which can lead to lower holding costs and reduced risk of obsolescence. Employee productivity assesses the efficiency and performance of employees. Tracking productivity helps identify areas for improvement and ensures that employees are contributing effectively to the business's goals. Research from the Harvard Business Review highlights that businesses prioritizing employee productivity see up to a 22% increase in overall performance. Research from the Harvard Business Review Research from the Harvard Business Review Supply chain efficiency evaluates the effectiveness of the supply chain processes. Efficient supply chains minimize delays, reduce costs, and improve customer satisfaction. According to a report, companies with streamlined supply chains have 15% lower supply chain costs and 50% less inventory holding costs. supply chains have 15% lower supply chain costs supply chains have 15% lower supply chain costs Conclusion Evaluating essential metrics after your first year in business is crucial for understanding your company's performance and setting the stage for future growth. You get lots of context from revenue, customer metrics, financial health, marketing effectiveness and operational efficiency in terms of what is doing very well or not so well. Checking in with these numbers helps keep you grounded and competitive so that your business can flourish over a long time horizon. Using these measurements will also provide a foundation that can be used for ongoing improvement, which means the groundwork you are placing in now has long-term value and promotes future achievement.

Essential Metrics to Evaluate After Your First Year in Business

Authors:
(1) Enis Chenchene, Department of Mathematics and Scientific Computing, University of Graz;
(2) Hui Huang, Department of Mathematics and Scientific Computing, University of Graz;
(3) Jinniao Qiu, Department of Mathematics and Statistics, University of Calgary. Table of Links Abstract and 1 Introduction 2 Global convergence 2.1 Quantitative Laplace principle 2.2 Global convergence in mean-field law 3 Numerical experiments and 3.1 One-dimensional illustrative example 3.2 Nonlinear oligopoly games with several goods 4 Conclusion, Acknowledgments, Appendix, and References Abstract In this paper, we present a novel consensus-based zeroth-order algorithm tailored for nonconvex multiplayer games. The proposed method leverages a metaheuristic approach using concepts from swarm intelligence to reliably identify global Nash equilibria. We utilize a group of interacting particles, each agreeing on a specific consensus point, asymptotically converging to the corresponding optimal strategy. This paradigm permits a passage to the mean-field limit, allowing us to establish convergence guarantees under appropriate assumptions regarding initialization and objective functions. Finally, we conduct a series of numerical experiments to unveil the dependency of the proposed method on its parameters and apply it to solve a nonlinear Cournot oligopoly game involving multiple goods. Keywords: Non-convex, multiplayer games, Nash equilibrium, swarm optimization, Laplace’s principle. 1 Introduction Multiplayer games [48], ranging from strategic board games to complex economic systems, have always fascinated researchers due to their intricate dynamics and strategic interactions among multiple players. Understanding and analyzing the outcomes of these games is crucial in various fields such as economics [41], social sciences [30], and computer science [22]. In recent years, significant progress has also been made in developing advanced learning approaches within the field of artificial intelligence towards multiplayer scenarios. One notable example is the extension of adversarial learning, which was originally applied to settings with a single generator and discriminator, to accommodate multiple agents [43, 56, 62]. This process can also be observed in reinforcement learning, where multiplayer game theory has been integrated to enhance learning algorithms [8, 19, 42]. The search for global NE in non-convex settings remains an open problem [45, 46]. This challenge arises not only due to the absence of powerful tools compared to those available in convex scenarios but also due to the diversity of non-convex structures, which may require specific methodologies for resolution. While efficient techniques have been developed within convex conditions that have led to significant advancements in multiplayer game models [15, 61], these approaches may prove insufficient when confronted with non-convexity. In such cases, they may become trapped in local NE or approximate solutions while following pseudo-gradients, failing to converge to a global NE. Moreover, while inspiring breakthroughs have been made in solving non-convex two-player min-max games in different contexts, such as Polyak– Lojasiewicz cases [23, 50] or concave cases [44, 52], these advancements may not readily apply to multiplayer settings. This is because the global stationary conditions in multiplayer games are intricately coupled and cannot be independently addressed by each player. Consequently, novel effective methods are required for attaining them, which is exactly the main contribution of this article In this paper we introduce a novel zero-order consensus-based approach for finding global NE points in multiplayer games. This method draws inspiration from the consensus-based optimization (CBO) framework initially introduced in [10,51]. CBO belongs to the family of global optimization methodologies, which leverages systems of interacting particles to achieve consensus around global minimizers of the cost functions. As part of the broader class of metaheuristics [2, 29], CBO orchestrates interactions between local improvement procedures and global strategies, utilizing both deterministic and stochastic processes. This interplay ultimately results in an efficient and robust procedure for exploring the solution space of the cost functions. More importantly, the CBO approach possesses an inherent advantage of being gradient-free. This characteristic makes it particularly desirable when dealing with cost functions that lack smoothness or when computing their derivatives is computationally expensive. This paper is available on arxiv under CC BY 4.0 DEED license. Authors: (1) Enis Chenchene, Department of Mathematics and Scientific Computing, University of Graz; (2) Hui Huang, Department of Mathematics and Scientific Computing, University of Graz; (3) Jinniao Qiu, Department of Mathematics and Statistics, University of Calgary. Authors: Authors: (1) Enis Chenchene, Department of Mathematics and Scientific Computing, University of Graz; (2) Hui Huang, Department of Mathematics and Scientific Computing, University of Graz; (3) Jinniao Qiu, Department of Mathematics and Statistics, University of Calgary. Table of Links Abstract and 1 Introduction Abstract and 1 Introduction 2 Global convergence 2 Global convergence 2.1 Quantitative Laplace principle 2.1 Quantitative Laplace principle 2.2 Global convergence in mean-field law 2.2 Global convergence in mean-field law 3 Numerical experiments and 3.1 One-dimensional illustrative example 3 Numerical experiments and 3.1 One-dimensional illustrative example 3.2 Nonlinear oligopoly games with several goods 3.2 Nonlinear oligopoly games with several goods 4 Conclusion, Acknowledgments, Appendix, and References 4 Conclusion, Acknowledgments, Appendix, and References Abstract In this paper, we present a novel consensus-based zeroth-order algorithm tailored for nonconvex multiplayer games. The proposed method leverages a metaheuristic approach using concepts from swarm intelligence to reliably identify global Nash equilibria. We utilize a group of interacting particles, each agreeing on a specific consensus point, asymptotically converging to the corresponding optimal strategy. This paradigm permits a passage to the mean-field limit, allowing us to establish convergence guarantees under appropriate assumptions regarding initialization and objective functions. Finally, we conduct a series of numerical experiments to unveil the dependency of the proposed method on its parameters and apply it to solve a nonlinear Cournot oligopoly game involving multiple goods. Keywords: Non-convex, multiplayer games, Nash equilibrium, swarm optimization, Laplace’s principle. Keywords: 1 Introduction Multiplayer games [48], ranging from strategic board games to complex economic systems, have always fascinated researchers due to their intricate dynamics and strategic interactions among multiple players. Understanding and analyzing the outcomes of these games is crucial in various fields such as economics [41], social sciences [30], and computer science [22]. In recent years, significant progress has also been made in developing advanced learning approaches within the field of artificial intelligence towards multiplayer scenarios. One notable example is the extension of adversarial learning, which was originally applied to settings with a single generator and discriminator, to accommodate multiple agents [43, 56, 62]. This process can also be observed in reinforcement learning, where multiplayer game theory has been integrated to enhance learning algorithms [8, 19, 42]. The search for global NE in non-convex settings remains an open problem [45, 46]. This challenge arises not only due to the absence of powerful tools compared to those available in convex scenarios but also due to the diversity of non-convex structures, which may require specific methodologies for resolution. While efficient techniques have been developed within convex conditions that have led to significant advancements in multiplayer game models [15, 61], these approaches may prove insufficient when confronted with non-convexity. In such cases, they may become trapped in local NE or approximate solutions while following pseudo-gradients, failing to converge to a global NE. Moreover, while inspiring breakthroughs have been made in solving non-convex two-player min-max games in different contexts, such as Polyak– Lojasiewicz cases [23, 50] or concave cases [44, 52], these advancements may not readily apply to multiplayer settings. This is because the global stationary conditions in multiplayer games are intricately coupled and cannot be independently addressed by each player. Consequently, novel effective methods are required for attaining them, which is exactly the main contribution of this article In this paper we introduce a novel zero-order consensus-based approach for finding global NE points in multiplayer games. This method draws inspiration from the consensus-based optimization (CBO) framework initially introduced in [10,51]. CBO belongs to the family of global optimization methodologies, which leverages systems of interacting particles to achieve consensus around global minimizers of the cost functions. As part of the broader class of metaheuristics [2, 29], CBO orchestrates interactions between local improvement procedures and global strategies, utilizing both deterministic and stochastic processes. This interplay ultimately results in an efficient and robust procedure for exploring the solution space of the cost functions. More importantly, the CBO approach possesses an inherent advantage of being gradient-free. This characteristic makes it particularly desirable when dealing with cost functions that lack smoothness or when computing their derivatives is computationally expensive. This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv

A Consensus-Based Algorithm for Non-Convex Multiplayer Games: Abstract and Introduction

Authors:
(1) Joseph Latessa, Department of Computer Science Wayne State University, Detroit MI USA (jlatessa@wayne.edu);
(2) Aadi Huria, Senior, Salem High School Canton, MI USA (huria.aadi@gmail.com);
(3) Deepak Raju, Senior, Salem High School, Canton MI USA (Deepak.Raju294@outlook.com). Table of Links Abstract and Introduction Related Work Project Prerequisites Project Implementation Insight and Reflections Conclusions, Acknowledgement and References ABSTRACT Software Engineering concepts such as version control, continuous integration, and unit testing are often not presented in college computer science curriculums until the third year of study, after completing several semesters of programming courses. Throughout the summer of 2023, two high school students volunteered in our lab at Wayne State University where I’m a graduate research assistant and Ph.D. student in computer science. The students had taken AP Computer Science but had no prior experience with software engineering or software testing. This paper documents our experience devising a group project to teach the requisite software engineering skills to implement automated tests that meaningfully contribute to open-source scientific computing projects developed in connection with our lab. We describe the concepts covered, tools used, and software tests written in this early introduction to software engineering while maintaining shared emphases on education and the deployment of our work. KEYWORDS Automated Testing, Continuous Integration, GitHub Actions, High School Summer Research, Software Engineering, Version Control, Website Debugging 1 INTRODUCTION During the summer of 2023, two high school students volunteered to assist in one of Wayne State University’s computer science research labs, which focuses on high-performance computing and software engineering for scientific applications. The students aspire to study computer science as their major when they begin their undergraduate studies in 2024. Prior to volunteering in our lab, they had successfully completed AP Computer Science courses where they learned Java and Python but had no prior experience with software engineering concepts such as version control, continuous integration, and unit testing. Our goals were to develop a summer project that would be appropriate for the students’ current experience and education, provide a learning opportunity to gain new skills relevant to assisting in a laboratory environment, and to provide an opportunity to produce work that meaningfully contributes to the scientific computing applications developed in connection with our lab. Two scientific computing applications developed in connection with our lab include JETSCAPE [1] and GOMC [2]. JETSCAPE is a multi-institutional collaboration that develops event generators to simulate ultra-relativistic heavy-ion collisions. GOMC is an application to simulate molecular systems using the Metropolis Monte Carlo Algorithm [3]. Both projects are opensource and maintain their respective repositories on GitHub. They also use GitHub Pages to maintain and deploy their project websites. To meet our project goals, we decided to emphasize automated software testing. JETSCAPE [1] and GOMC [2] have previously developed and deployed some tests, but there was an interest among the collaborators to further expand upon the tests in place. Since the student volunteers had not yet studied software engineering and were not yet familiar with concepts such as continuous integration and unit testing, attaining these skills while working on this project would provide a unique educational value. Since the students had already completed AP Computer Science and were already comfortable with the Java and Python languages, the students could apply the programming skills they already learned to develop these automated tests. Although having studied Java and Python, the students had no prior experience with Linux or with working at the command line. They also had no previous experience working with Git or any version control platform. This project would therefore have to begin with preliminary introductions to the Linux shell environment and version control. Since continuous integration would also be a new concept, a demonstration of GitHub Actions and writing YAML files would conclude our preliminary introduction. The main substantive tasks of the project would be the implementation of three automated tests. The first two tests, an HTML validator, and a link checker, would be deployed on the JETSCAPE [1] and GOMC [2] websites. The implementation of the third test would require discussing the concept of unit testing and then deploying a series of unit tests to ensure the correctness of JETSCAPE’s XML formatted input. The tools used throughout the project include Visual Studio Code, the Ubuntu Distribution installed on WSL and accessed through Windows Terminal, version control with Git and GitHub, automation with GitHub Actions, static hosting with GitHub Pages, Python scripting to customize and enhance the functionality of GitHub Marketplace Actions, and Python’s UNITTEST framework. In the following sections, we provide a detailed description of the project and the concepts covered. We then reflect upon the successes and challenges we faced and report this experience so that other students at a similar level in their computer science education might find comparable opportunities to study these software engineering concepts earlier than they otherwise might while also attaining practical lab experience to supplement their traditional high school computer science curriculum. This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license. Authors: (1) Joseph Latessa, Department of Computer Science Wayne State University, Detroit MI USA (jlatessa@wayne.edu); (2) Aadi Huria, Senior, Salem High School Canton, MI USA (huria.aadi@gmail.com); (3) Deepak Raju, Senior, Salem High School, Canton MI USA (Deepak.Raju294@outlook.com). Authors: Authors: (1) Joseph Latessa, Department of Computer Science Wayne State University, Detroit MI USA (jlatessa@wayne.edu); (2) Aadi Huria, Senior, Salem High School Canton, MI USA (huria.aadi@gmail.com); (3) Deepak Raju, Senior, Salem High School, Canton MI USA (Deepak.Raju294@outlook.com). Table of Links Abstract and Introduction Abstract and Introduction Related Work Related Work Project Prerequisites Project Prerequisites Project Implementation Project Implementation Insight and Reflections Insight and Reflections Conclusions, Acknowledgement and References Conclusions, Acknowledgement and References ABSTRACT Software Engineering concepts such as version control, continuous integration, and unit testing are often not presented in college computer science curriculums until the third year of study, after completing several semesters of programming courses. Throughout the summer of 2023, two high school students volunteered in our lab at Wayne State University where I’m a graduate research assistant and Ph.D. student in computer science. The students had taken AP Computer Science but had no prior experience with software engineering or software testing. This paper documents our experience devising a group project to teach the requisite software engineering skills to implement automated tests that meaningfully contribute to open-source scientific computing projects developed in connection with our lab. We describe the concepts covered, tools used, and software tests written in this early introduction to software engineering while maintaining shared emphases on education and the deployment of our work. KEYWORDS Automated Testing, Continuous Integration, GitHub Actions, High School Summer Research, Software Engineering, Version Control, Website Debugging 1 INTRODUCTION During the summer of 2023, two high school students volunteered to assist in one of Wayne State University’s computer science research labs, which focuses on high-performance computing and software engineering for scientific applications. The students aspire to study computer science as their major when they begin their undergraduate studies in 2024. Prior to volunteering in our lab, they had successfully completed AP Computer Science courses where they learned Java and Python but had no prior experience with software engineering concepts such as version control, continuous integration, and unit testing. Our goals were to develop a summer project that would be appropriate for the students’ current experience and education, provide a learning opportunity to gain new skills relevant to assisting in a laboratory environment, and to provide an opportunity to produce work that meaningfully contributes to the scientific computing applications developed in connection with our lab. Two scientific computing applications developed in connection with our lab include JETSCAPE [1] and GOMC [2]. JETSCAPE is a multi-institutional collaboration that develops event generators to simulate ultra-relativistic heavy-ion collisions. GOMC is an application to simulate molecular systems using the Metropolis Monte Carlo Algorithm [3]. Both projects are opensource and maintain their respective repositories on GitHub. They also use GitHub Pages to maintain and deploy their project websites. To meet our project goals, we decided to emphasize automated software testing. JETSCAPE [1] and GOMC [2] have previously developed and deployed some tests, but there was an interest among the collaborators to further expand upon the tests in place. Since the student volunteers had not yet studied software engineering and were not yet familiar with concepts such as continuous integration and unit testing, attaining these skills while working on this project would provide a unique educational value. Since the students had already completed AP Computer Science and were already comfortable with the Java and Python languages, the students could apply the programming skills they already learned to develop these automated tests. Although having studied Java and Python, the students had no prior experience with Linux or with working at the command line. They also had no previous experience working with Git or any version control platform. This project would therefore have to begin with preliminary introductions to the Linux shell environment and version control. Since continuous integration would also be a new concept, a demonstration of GitHub Actions and writing YAML files would conclude our preliminary introduction. The main substantive tasks of the project would be the implementation of three automated tests. The first two tests, an HTML validator, and a link checker, would be deployed on the JETSCAPE [1] and GOMC [2] websites. The implementation of the third test would require discussing the concept of unit testing and then deploying a series of unit tests to ensure the correctness of JETSCAPE’s XML formatted input. The tools used throughout the project include Visual Studio Code, the Ubuntu Distribution installed on WSL and accessed through Windows Terminal, version control with Git and GitHub, automation with GitHub Actions, static hosting with GitHub Pages, Python scripting to customize and enhance the functionality of GitHub Marketplace Actions, and Python’s UNITTEST framework. In the following sections, we provide a detailed description of the project and the concepts covered. We then reflect upon the successes and challenges we faced and report this experience so that other students at a similar level in their computer science education might find comparable opportunities to study these software engineering concepts earlier than they otherwise might while also attaining practical lab experience to supplement their traditional high school computer science curriculum. This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license. This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license. available on arxiv

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