Leveraging Data in Impact Investing: A Comparison with Data-Driven VC Processes

Recently, Dr. Andre Retterath has shed light on the data-driven venture capital (VC) landscape (DDVC Landscape 2023 (datadrivenvc.io)), recognizing me as one of the global thought leaders in this field. My organization, Invest-NL , despite being a young organisation (3.5 years old), was also highlighted, which underscores the significance of our data-oriented endeavours.

The recognition has undoubtedly been invaluable. Not only has it connected me with like-minded individuals, but it has also exposed me to a wealth of ideas from the articles of these individuals.

Nevertheless, it's crucial, in my view, to underscore the distinctions between VC investing and impact investing, particularly in terms of their data/AI strategies, despite the fact that these two fields are increasingly intersecting.

In the past 4-5 years, VCs have embraced data, advanced analytics, and most recently, AI. The latter is perceived as the future of the industry, revolutionizing the entire VC value chain. Simultaneously, there's a growing trend of impact investing, driven by increasing global ambitions for change (We are tech founders and investors who pivoted to (profitable) impact investing. Now we’re sharing the secret to our success–and encouraging others to copy it | Fortune). Hence, AI-powered impact investing will likely be a focal point of discussions in the near future.

Thus, it's critical to understand how impact investors differ from VCs when it comes to utilizing advanced analytics and AI.

But what is impact investing? Is it merely pouring capital into ventures centered around impact? Or is it about investing in solutions that generate impact, irrespective of financial returns? The European Venture Philanthropy Association ( Impact Europe ) suggests two strategies: investing for impact and investing with impact (Investing for Impact – EVPA Impact Strategy Paper | EVPA). The former encompasses investors who prioritize impact over substantial financial returns, while the latter includes VCs who seek financial returns from impactful companies.

It's worth mentioning that some studies, such as the annual survey by The Global Impact Investing Network (GIIN), have indicated that a significant portion of impact investors achieve market-rate returns or better (2020 Annual Impact Investor Survey | The GIIN). This suggests that while impact may be the priority, it does not necessitate accepting lower returns. But again, it would be wise to keep GIIN's impact investing definition in mind:

Impact investments are investments made with the intention to generate positive, measurable social and environmental impact alongside a financial return (https://thegiin.org/impact-investing/need-to-know/#what-is-impact-investing)

Risk also plays a part in EVPA's explanation of impact investing. Those investing for impact are often willing to take on more risks that other investors shy away from. However, it's important to note that risk tolerance in impact investing, like in traditional investing, can vary greatly depending on the investor's strategy, goals, and capacity.

Several approaches to impact investing, such as the one proposed by Prof. Ludovic Phalippou , emphasize the concept of additionality (Business Ethics, ESG and Impact Investing from a different perspective). In essence, an investment can only be impactful if it goes beyond the market norm. This means if there are no other investors willing to invest in a specific venture, then it can be considered genuinely impactful. Additionally, the impact investor should not push other investors out of the deal. Despite this, simply having an impact business model is not sufficient. This approach suggests that impact investing should accept low, if not zero, or even negative returns, as Prof. Ludovic posits.

Therefore, impact investing requires a balance of additionality and risk, and is not limited to sourcing companies with impactful business models. An impact investor should support companies that other investors avoid due to risk levels. This philosophy guides our work at Invest-NL, where we strive to finance what might seem unfinanceable, creating a balance between risk, additionality, and financial returns. Although I believe that the myth of low returns will disappear even faster in coming years with the growing interest of VCs in tackling with environmental solutions, my view on impact investing for this article is more aligned with Prof. Ludovic's approach.

In order to comprehend the distinct approaches to data utilization by impact investors, it's crucial to understand their differences. Unlike VCs, who prioritize speed, impact investors are not necessarily in a hurry to clinch deals. Rather, they aim to attract other investors to the table. So if we try to summarize the differences, we can create the following table.

Let's consider these differences in the context of the investment processes of both VCs and impact investors.

Ecosystem Intelligence

Ecosystem intelligence revolves around comprehending the environment in which we operate. Here, the approach of impact investors stands in contrast to that of venture capitalists (VCs), focusing on identifying opportunities where their contributions can be most effective. Unlike VCs, who prioritize uncovering the next breakthrough technology or the most lucrative sectors, impact investors aim to pinpoint gaps in funding and areas of market failure, necessitating a more thorough analysis. This task is inherently more challenging for impact investors, as predicting technological successes or industry trends has received more attention than the complex study of market failures, which requires consideration of numerous factors, from investment in innovation to infrastructure and government policies.

Deal Sourcing and Screening

This phase involves assembling a diverse array of potential investments, experts, co-investors, events, funds, limited partners (LPs), and other resources from a variety of sources, both public and private, and selecting the most suitable options. The sourcing aspect demands similar strategies from both impact investors and VCs. A key challenge lies in automating this process through a data-driven approach, which includes aggregating information, refining data quality, enhancing precision, and classifying companies based on the specific criteria of investors. However, the screening process diverges significantly between impact investors and VCs based on what constitutes the “right” investment. For a VC, the ideal investment is one with the highest financial return, while impact investors seek opportunities where their investment can make a significant difference. This means VCs look for less risky investments, whereas impact investors may pursue riskier ventures. Additionally, VCs use machine learning (ML) models to forecast which companies are likely to succeed, creating a shortlist of attractive investment opportunities. Despite the wealth of data from sources like Dealroom.co, PitchBook Data, LinkedIn, and Product Hunt available for this analysis, even for companies at the seed stage, forecasting a company's potential impact is markedly more difficult due to the limited availability of relevant data. Nonetheless, impact investors can leverage patent information, academic research, and the frequency of citations in scholarly work to develop ML models capable of predicting the success of specific technologies designed to have a positive impact.

Due Diligence:

Both VCs and impact investors can employ AI/ML techniques to automate the Due Diligence (DD) process. This can involve extracting valuable information from a company's legal and financial documents. For impact investors, understanding market additionality and predicting the future of impactful technologies is also crucial. The significant difference in using data for DD for an impact investor lies in the focus on risk and additionality, which can be better assessed by combining textual and financial data.

Evaluation:

Evaluation involves various activities from assessing investment thesis, evaluation portfolio performance to evaluating venture building programs. VCs can leverage data and AI/ML models to monitor portfolio companies better, predict anomalies, determine optimal fund-raising times, and even assist in finding the best talent. The same holds for impact investors, but they also focus on monitoring impact data from portfolio companies, a task that can be simplified using AI-supported processes. Furthermore, they can use sentiment analysis based on social media posts to find suitable employees with strong impact and environmental ambitions for portfolio companies. Identifying potential funds for the next round of investment is also crucial for an impact investor's portfolio.

In conclusion, while impact investors can utilize data in ways similar to VCs, their focus differs. They are not primarily concerned with speed or solely financial wins. Instead, they use data to understand market dynamics, identify impactful start-ups, and predict the future of impact technologies. Impact investors aim to attract, not oust, other investors. They seek out "impact champions" rather than just financial "winners", often financing more risky deals to be additional. Consequently, for impact investors, identifying risky cases and ensuring additionality is more important than finding the next unicorn. This is why the added value of utilising data shifts from sourcing to more ecosystem intelligence and more to latter stages of investment process for impact investors while VCs win with data mostly during sourcing/screening phases.

At the end, I am looking forward to exploring more topics and discussions related to impact investing within the realm of data-driven investment.