Algorithmic Identification of Relevant Investors Using Machine Learning

In this article, the problem of the low efficiency of traditional cold communications with venture capital funds is examined. The relevance of the study is determined by the need to develop automated tools for targeted search of relevant investors capable of overcoming the limitations of warm recommendations and expanding access to capital for startup teams without an extensive network. The aim of the paper is to demonstrate an algorithmic approach based on machine learning methods for identifying relevant investors and to investigate the integration of ML ranking with a disciplined multistep-outreach strategy. The novelty lies in the use of a multilayer feature architecture combining an investment graph, thematic embeddings, soft signals from public channels, and dynamic indicators of fund activity, as well as in the construction of a controlled cycle of cold communications with two follow-ups in each three-day window. The obtained results confirm an increase in the efficiency of the cold channel: algorithmic selection enabled maintaining an open rate at the level of 74–80%, a reply rate in the range of 10–17%, and provided 96 scheduled calls per quarter without a single warm reco