Proceedings of the International scientific and practical conference ―Oxford International Science Forum‖ (April 17-19, 2026) / Publisher website: www.naukainfo.com. - Oxford, United Kingdom, 2026. - 367 p.

100 integrating numerical, textual and behavioral indicators within a single analytical solution. The features of common crowdfunding platforms are analyzed and their limitations from the point of view of further analytical use are identified. Based on the review, key insights that can be obtained from crowdfunding data are systematized, including campaign success factors, funding time patterns, the impact of text content, audience behavioral characteristics, typical campaign profiles, and anomalous scenarios. For each class of insights, appropriate analytics methods are summarized, including statistical analysis, machine learning, natural language processing, clustering, time series analysis, and anomaly detection tools. The problem statement of the intellectual analysis of crowdfunding data is formulated as a complex task of identifying patterns, assessing success, and forming an analytical basis for decision-making. Keywords: crowdfunding analytics, crowdfunding data, consolidated data, project success prediction, text analytics, explainable AI, crowdfunding decision support, information system, intellectual data analysis. 1. Introduction Crowdfunding has become an important mechanism for financing entrepreneurial, creative, social and community-oriented initiatives in the digital environment. Its development considers the accumulation of large amounts of data related to campaigns, including financial indicators, textual descriptions, interaction records and dynamic traces of support over time [1-3]. At the same time, many practical solutions in crowdfunding ecosystems remain intuitive, while the analytical potential of available data is still underutilized [1, 4]. The research field has demonstrated that campaign outcomes are determined not only by the target amount or project category, but also by a combination of social, temporal, semantic, and behavioral signals [1-2, 4-6]. Recent studies have shown that crowdfunding analytics benefit from the combined use of structured metrics, text features, machine learning models, and temporal analytics, especially when the goal is not only to describe success, but also to explain and predict it [3-6].