AI 投資模型商用情境選擇優化

Abstract

在評估金融商品價格走勢時，投資者常面臨不確定性和壓力，無法確定最佳交易時機。Beta+ AI 投資模型旨在解決這些問題，利用量化分析和人工智慧技術，為客戶提供可控風險且高利潤的交易方式。然而，區塊鏈市場推展速度不如預期，即使年化績效高於市場，仍有許多低於此模型的投資組合獲得青睞。這促使我們反思商業模式中的問題，如目標群體選擇、投資模型利潤週期、定價方式及市場方向等。我們需要進一步研究，觀察是否存在其他消費者心理或專業經理人的不同訴求，導致市場反應不如預期。 本研究旨在重新審視產品解決方案，通過消費者洞察訪談獲得新見解，並應用 EiMBA 課程所學知識，探索並調整策略，以實現產品與市場的契合，達到 Product-Market Fit。研究目標包括確認消費者需求、評估目標客群準確性、檢驗市場接受度、分析推銷渠道有效性、評估產品使用便捷性、檢查定價合理性、估算現金流，並統整研究結論，為未來策略調整提供依據。最後，依據 Alex Osterwalder 的價值主張圖(2005)Alex Osterwalder等人在《商業模式新生代》（Business Model Generation）一書中提到的商業模式九宮格。整理各客戶素描，實現價值主張與顧客的適配，進而投入新市場進行後續追蹤。

When evaluating the price trends of financial products, investors often face uncertainty and pressure, making it difficult to determine the optimal trading times. The Beta+ AI investment model aims to address these issues by leveraging quantitative analysis and artificial intelligence to provide clients with a trading approach that balances risk control and high returns. However, the expansion of the blockchain market has been slower than expected. Even though the annual performance of our model exceeds the market average, many lower-performing investment portfolios still attract significant attention. This has prompted us to reconsider elements of our business model, such as target audience selection, profit cycle of the investment model, pricing strategies, and market direction. We need further research to observe whether there are other consumer psychology factors or demands from professional managers that lead to the market response being less than expected. This study aims to re-evaluate the product solutions by gaining new insights through consumer insight interviews and applying the knowledge learned from the EiMBA courses. We will explore and adjust strategies to achieve product-market fit. The research objectives include identifying consumer needs, assessing the accuracy of target audiences, testing market acceptance, analyzing the effectiveness of marketing channels, evaluating the ease of product use, checking pricing rationality, estimating cash flow, and consolidating research conclusions to inform future strategy adjustments. Finally, using Alex Osterwalder's Value Proposition Canvas, we will map out customer profiles and match them with the value propositions to achieve customer fit, ultimately entering new markets for subsequent tracking.