Dmitrii Khasanov is an angel investor, digital marketing expert and founder of Arrow Stars.
The integration of data analytics into investment strategies and all the related processes has become more than just a trend. It’s a necessity. The reliance on intuition and personal networks is being updated to include data-driven methods that improve accuracy and effectiveness in assessing startups’ potential. This is especially important for identifying businesses that are not only groundbreaking but capable of consistently evolving to achieve long-term success.
The venture capital industry is increasingly using data tools to revolutionize the process of finding and evaluating potential investments. These tools help investors analyze large amounts of data and identify patterns and insights that specialists might miss. The implementation of data analytics allows investors to make more informed decisions, reduce dependence on the human factor and increase the objectivity of their assessments.
Evaluating Investment Potential
The essential element of evaluation, total addressable market (TAM), reflects the scalability of the startup’s product or service. A substantial market suggests greater opportunities for expansion and revenue generation. For instance, a startup developing a revolutionary medical device would be more attractive if TAM covered a large segment of the healthcare industry, which would imply extensive expansion opportunities.
Evaluating R&D
One of the important criteria for investor’s evaluation of a startup is how effectively the company turns its investments in research and development (R&D) into in-demand products. This ability indicates a higher chance of returning the investment. In this context, an important indicator is the research effectiveness coefficient (RQ), which measures the effectiveness of investments in research and development by production volume. A higher RQ score means that the company is effectively converting its research and development costs into revenue growth. Such efficiency is vital because it demonstrates a firm’s ability to turn research initiatives into market-relevant products or services. This strategy will increase its competitiveness.
Evaluating Startup Teams
You can predict the probability of success of a startup based on team dynamic, past business successes and industry knowledge. A face-to-face meeting and live communication with the team might be a good idea, not only in the office but also in an informal setting, for example, a cafe or pub. During a conversation, you should be able to tell if startup participants can actually realize a large-scale project.
Evaluating Customer Satisfaction
These evaluations may include high growth in the user base, a high level of retention, positive feedback (real, not written by company employees or their relatives) and the results of in-depth customer interviews. At the same time, it is important not only how a startup attracts new customers but also how it interacts with dissatisfied customers. If the company’s employees work with negative reviews, try to correct or compensate for their own mistakes, then the percentage of customer retention will be high.
A Unified System For Evaluation
A unified system for evaluating the innovative potential of a startup by an investor includes total addressable market (TAM), R&D efficiency, team dynamics, financial health and customer engagement. It looks like a large amount of analytical work for a very long time. The good news is that it’s no longer necessary to use the old approaches and do it all yourself (or even through your employees). Here, a data-oriented approach together with AI technologies can help.
How AI Can Help
The introduction of artificial intelligence technologies and, in particular, machine learning can already be called a revolution in the field of investment valuation of startups. AI algorithms can analyze materials hundreds of times faster than humans. For me, the most important technologies that are mandatory for any investor are the following: optical character recognition (OCR), intelligent document processing (IDP), large language models (LLM) and retrieval-augmented generation (RAG). Unsurprisingly, the popularity of such technologies grows every day. According to the results of a survey conducted by Allvue Systems among participants in the private equity and venture capital sectors, only 47% of respondents used AI in the fourth quarter of 2023, while in the fourth quarter of 2024 this number grew up to 87%.
Where AI Falls Short
However, there are still areas where AI can’t help you. For example, as mentioned in the paragraph about evaluating startup teams, you should meet in person with employees. This is not something AI can do. It is not able to have a heart-to-heart conversation. It has no experience and no intuition. In this regard, AI cannot replace humans. Only you can evaluate corporate culture, the enthusiasm of the team, their cohesion and willingness to set aside their own interests for the success of the project.
Remember that the work of an investor is more an art than a sum of many numbers and documents. Leave the routine task of studying a million parameters to artificial intelligence. Do what only you (and no AI) can do.
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