Algorithmic Identification of Relevant Investors Using Machine Learning

Аннотация

In this article, the problem of the low OPEN ACCESS efficiency of traditional cold communications with SUBMITTED 22 July 2025 ACCEPTED 27 July 2025 venture capital funds is examined. The relevance of the PUBLISHED 12 August 2025 study is determined by the need to develop automated VOLUME Vol.07 Issue 08 2025 tools for targeted search of relevant investors capable of overcoming the limitations of warm recommendations CITATION and expanding access to capital for startup teams COPYRIGHT without an extensive network. The aim of the paper is to © 2025 Original content from this work may be used under the terms demonstrate an algorithmic approach based on machine of the creative commons attributes 4.0 License. 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 recommendation. The integration of the ML ranking model with a structured cadence strategy increases the controllability of the process, turning fundraising from a lottery into a repeatable business process with continuous model learning on feedback data. Practical implementation includes not only the development of an investor ranking model but also the creation of infrastructure for large-scale mailings: configuration of mail domains, optimization of message templates, A/B-testing, and integration with meeting-scheduling tools. This allows startups to systematically increase open, click, and reply rates as well as conversion into negotiations. The article will be useful to startup founders, venture analysts, and The American Journal of Management and Economics Innovations 46 https://www.theamericanjournals.com/index.php/tajmei fundraising specialists seeking to improve the efficiency analysis of deliverability and conversion—comparing of cold communications with investors. average open and reply rates with industry benchmarks to results of algorithmic selection; extraction of Keywords: algorithmic investor identification, machine multilayer features (Piloterr), thematic embeddings learning, cold outreach, fundraising, investor ranking, from startup descriptions, clustering of investor tweets multistep outreach, email marketing, link prediction and accounting for dynamics of rounds and headcount