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.

Promising results were obtained with the gradient boosting classifier. Predicting the success of a business venture has always been a struggle for both practitioners and researchers. However, thanks to companies that aggregate data about other firms, it has become possible to create and validate predictive models based on an unprecedented amount of real-world examples. In this study, we use data obtained from one of the largest platforms integrating business information – Crunchbase. Our final training set consisted of 213 171 companies.

This project (code, data, and results) is publicly available on Domino. Crunchbase recently converted its backend database to a Neo4j graph database. This will give it great flexibility in the future, but for now, the data is exposed similarly to how it always has been: individual entities are retrieved and attribute data must be used to form edges between them prior to any graph analysis. Aside from traversing links manually on the web pages, there are no provisions for graph analysis. To enable more powerful manipulations of this data, during my time at Zipfian Academy, I created my "Visibly Connected" project.

If you work in tech, you've heard about artificial intelligence: how it's going to replace us, whether it's over-hyped or not and which nations will leverage it to prevent, or instigate, war. Our editorial bent is more clear-cut: How much money is going into startups? Who is putting that money in? So let's talk about the state of AI startups and how much capital is being raised. Here's what I can tell you: funding totals for AI startups are growing year-over-year; I just don't know precisely how quickly. Regardless, startups are certainly raising massive sums of money off the buzzword.

If you haven't heard, artificial intelligence (AI) startups are sort of a big deal. It's a tech category that has left the halls of academia in favor of entrepreneurs' innovative embrace. In turn, those entrepreneurs are holding their hands out to investors who have shown an increasing willingness to invest. Whether the current cohort of AI startups find market traction, and, eventually, the exits their backers anticipate, is an open question. Of course, for AI startups being funded, there are two paths to exit for founders and their investors: going public or being acquired.

Salesforce Ventures recently announced its fourth investment fund, the Salesforce platform fund. Given Salesforce's critical market position in both SaaS and enterprise-facing software, what does the new fund mean for entrepreneurs seeking investment, and what could it tell us about Salesforce's broader ambitions? Under the umbrella of the venture sector is corporate venture capital (CVC). Oftentimes, CVC investments are deployed by large firms, such as Intel, in order to fund startups who are particularly innovative or will help propel the the parent company's strategic goals. The concept has also expanded to cash-rich incumbents like Slack, which uses its CVC investments to encourage developers to build on Slack's bot platform.