Data-Driven VC

Do you want to leverage data & AI with limited resources? Here is your playbook by Alex Patow from Inflection.xyz👇 6 lessons learned from incorporating a data-driven strategy at an emerging micro VC firm with $50M AUM: 1️⃣ Start Small, as Small Projects Snowball 2️⃣ Take Calculated Risks 3️⃣ Embrace Nimbleness 4️⃣ Secure Buy-in from the Team 5️⃣ Find a Product-Engineering Hybrid 6️⃣ Develop Guiding Principles, Not a Backlog Read the full case study and Alex' perspective on why and how even small firms with limited resources can become data-driven and leverage AI across the value chain in today's episode of Data-Driven VC🔥

📈 Investment Criteria and Fundraising Success 🫶 One thing I love reading on my way to the office in the morning: Dr. Andre Retterath‘s Data-Driven VC Newsletter. Always a short, insightful knowledge snack! And here‘s what I really liked this morning: Techstar’s Marting Schilling analyzed the success of 58 companies in the Tech Start DeepTech Berlin portfolio (2022-2024) against 17 investment criteria to identidy the strongest signal for early-stage fundraising success. 1️⃣ Each investor is different: Even if VC assessments can differ widely, there were trends in their portfolio that they could analyse for follow-on fundraising success. 2️⃣ Most correlated with success: Monetisation potential (0.61), Domain Expertise (0.59), Speed of Execution (0.57), Market & Timing (0.55). 3️⃣ Least correlated with success: Role clarity (0.12), Team Co-Location (0.08), Number of Cofounders (0.02). Q: Andre Retterath (2024). Data-Driven AI Newsletter from 23/07/2024.

These founders are 6x more likely to raise ++ How to find the right pricing strategy ++ Powerful CTAs & More Get 6 unique startup "Insights" in your inbox every week via Data-Driven VC. Today: #1 Investing in Iconoclasts #2 SaaS Growth Stagnates in H1 2024 #3 Investment Criteria and Fundraising Success #4 Try It Yourself: The New CTA King #5 Beware of High Liquidation Multipliers in VC Deals #6 The Impact of Pricing Experiments

Excited to discuss AI in Finance & Investing with Nicolas Boucher next Tuesday! What: Free webinar on AI in Finance & Investing When: Tuesday 23 July (3PM ET / 12PM PT / 9PM CEST) This webinar will be focused on the following topics for Finance & Investing: • Current state of AI • How you can use AI • Future of AI • AI tools Join us here: https://lnkd.in/dgq6i7GX

How much employee churn is too much?🏃🏼♀️➡️🚪 Wanted vs unwanted churn, benchmarks, reasons & what to do about it👇 In today's episode of Data-Driven VC we dive into one of founders' most pressing issue: employee churn. It’s important to note that employee churn includes wanted churn (=fired) and unwanted churn (the ones you wanted to keep). The difference is difficult, oftentimes impossible to distinguish from the outside so when looking at aggregate numbers, it carries different implications, i.e. high wanted churn means talent acquisition needs to improve whereas high unwanted churn means talent retention needs to improve. STATUS QUO Recent studies find that 1 in 3 employees across Europe plan to leave their job within the next 6 months. Even more alarming, US churn rates average at 57%, meaning you turn around your company every other year. REASONS Main reasons for churn include dissatisfaction with career progression (79%), poor company culture (77%), and dissatisfaction with salary and rewards (72%). ACTION Equity is a potent retention tool, significantly lowering turnover rates. Researchers exhibit a 20% reduction in talent churn for companies with equity options. Equity holders are even 25% more likely to stay beyond three years. Additional factors that are proven to retain top talent: 📌Career Development: The primary factor impacting retention. 📌Work-Life Balance: Flexible working options are crucial, including remote and hybrid models as stated above. 📌Company Culture: A positive environment significantly reduces turnover intentions. Balancing strategic hiring and firing with strong retention practices, equity incentives, and addressing the identified key factors helps build a committed workforce and prevent unnecessary churn. Catch the full story here: https://lnkd.in/dSUw7p8f

Make vs Buy: How can investors become more data-driven? How to differentiate if everyone has access to the same data & tools? I was lucky to have Sarah from Atomico, Ben from InReach Ventures, Olivier from Red River West, and Max from Harmonic join the Data-Driven VC Summit 2024 to discuss their perspectives on make vs buy, and how VCs can differentiate and gain a competitive edge in the future. ✍️ Key Take Aways 1. Focus Beyond Sourcing: Move beyond just sourcing to areas like evaluation and portfolio management. 2. Codify Investment Thesis: Translate the firm’s unique investment thesis into data-driven models. 3. Leverage Private and Public Data: Use both types of data to identify promising opportunities and generate insights. 4. Maintaining Trajectory: Push for continuous innovation and alignment with the firm’s goals. Stay agile and keep exploring new ways to leverage data for a competitive edge. 5. Community Knowledge Sharing: Foster collaboration and knowledge sharing within the community. Check out the full panel summary and access all summit recordings via today's episode of https://lnkd.in/dt3HKMEb

How to be a top country to launch and scale a startup? The suggested Norwegian "exit tax" triggered a long-requested debate on the data informing the startup ecosystem, the effect of politics, and what it takes for Norway to take the lead. The viral mobilization of founders, and political responses, proved at least two things: It is a long way to the top, and it's critical to get there. We need endless numbers of new companies solving some of the biggest problems globally, and for Norway - and the world - it's critical to substitute the coming decline in traditional and fossil-based industries. For the Norwegian Association for Startups and Tech (NAST), the focus has been on the most upvoted topics by Norwegian Scaleup founders: Tax, Talent, and Venture Capital. This week Data-Driven VC newsletter (Jérôme Jaggi and Dr. Andre Retterath) wrote up a meta-analysis on the topic, filled with links to some of the leading data and research. Summarised with this top-five list: - Talent: The level of accessible human capital. - Culture: The inclusion and appreciation of entrepreneurship (reflected in politics). - Community: The level of connectivity, engagement, and support mechanisms - Funding: The maturity of competent Venture Capital and its regulations - Maturity: The number of startups, investment level, connectedness to incumbents and industry It is not that complicated to get right, and as they summarise the analysis; "Talent is distributed equally, but opportunity is not". Consequently, for small countries, it is even more crucial to create the politics of opportunity. For Norway, it's a long way to the top, a bit like for the European Football Championship, and it is up to us to change that. With exponentially more data available to analyze this - and with higher data quality - i.e. like the datasets Yoram Wijngaarde has built with Dealroom.co, these factors will be more and more prevalent, and we can go from the vague "startup ecosystem" terminology to more precise data-driven political insights. AI will be here to stay, also for political analysis... Anders Mjåset Håkon Haugli Merete Nygaard Ingar S. Bentsen Marit Rødevand Adam Scheuring Johan Brand, FRSA Kristian Jul Røsjø Anne Solhaug Tutar Vegard Aakre Vik Jon Kåre Stene Karl-Christian Agerup Karl Munthe-Kaas Karen Dolva Odd Skarheim Per Christian Goller Cecilie T. Myrseth Linda Hofstad Helleland Alfred Bjørlo Ingrid Liland Leo A Grunfeld Jarand Rystad

Be like England goalkeeper Jordan Pickford. Trust the data and win🤓 If you want to leverage data & AI for investing, check out Data-Driven VC https://lnkd.in/dV62qJ5y

Excited to launch "Picks of the Week" to spotlight the hottest Stealth, Early & Growth Startups via Data-Driven VC🔥 A snapshot of what to expect: Hamutal Meridor 🇮🇱 Experience: Vintage Investment Partners, Palantir, Microsoft Education: University of Edinburgh (MSc), The Hebrew University of Jerusalem (BSc) Yanick Slikboer 🇳🇱 Experience: SAS, IBM, Vodafone, DHL Education: Maastricht University (MSc & BSc) Andy T. 🇬🇧 Experience: Cohere, Shopify, RBC, Vector Institute, Uber, Meta Education: University of Toronto (MSc), University of Waterloo (BSc) Elizabeth Hu 🇺🇸 Experience: Snorkel AI, Airwallex, Harvard (Researcher), Google, VMware Education: Harvard University (BSc) More Stealth Founders, Early & Growth Startups with strong data-driven signals in today's episode of Data-Driven VC. Don't miss out and join 24k+ free subscribers from VCs like a16z, Accel, Index, Sequoia, and more.

Researchers from Princeton & Oxford just published how you can use LLMs in Finance & Investing👇 Here are the top 5 use cases: 1️⃣ Linguistic Tasks LLMs can be utilized to summarize and extract financial document information, addressing challenges by dividing long documents into shorter segments for efficient analysis. Other use cases include converting PDFs into machine-readable text, data extraction and classification of specific financial entities. 2️⃣ Sentiment Analysis LLMs excel in deciphering financial language complexities, adeptly navigating informal expressions, emojis, memes, and specialized terminology, crucial for accurate sentiment analysis across various formats. Recent advancements in LLMs have significantly improved sentiment analysis from diverse data sources such as social media, news, corporate disclosures, and policy and economic indicators. 3️⃣ Time-Series Analysis LLMs can perform tasks such as forecasting, anomaly detection, classification, data augmentation, and imputation, showcasing their versatility and potential for advanced financial analysis. 4️⃣ Financial Reasoning LLMs enhance data analysis by identifying patterns and trends, aid in predictive modeling to forecast market conditions, and offer personalized advisory services by analyzing individual financial situations. They provide real-time monitoring and alerts, improving accessibility and engagement through user-friendly interfaces like chatbots. 5️⃣ Agent-Based Modeling (ABM) ABM represents a significant advancement in simulating complex systems. It involves creating autonomous agents that interact within a defined environment, allowing complex phenomena to emerge from the bottom up, which is particularly useful for capturing the diverse behaviours in financial markets. Integrating LLMs with ABM enhances the cognitive functions of agents, enabling them to interpret vast amounts of unstructured data such as financial news and reports, leading to more realistic and adaptive simulations. This synergy results in robust investment strategies, improved market predictions, and better policy analysis. --- Find the full summary and link to the original paper in today's episode of Data-Driven VC