从大数据到好猜想：如何用大模型做市场研究？

Core Viewpoint - The article discusses how large models are reshaping consumer demand research, emphasizing a return to the fundamental understanding of user needs through first principles rather than traditional data collection methods [2][3][5]. Group 1: User Demand Research - Large models can act as personal agents that simulate real user thoughts and behaviors, providing deeper insights into consumer needs [4][6]. - The article questions the effectiveness of traditional methods that rely on scraping vast amounts of social media data, highlighting the challenges of legality, cost, and data cleaning [8][9]. - A case study illustrates that a new consumer brand successfully predicted market trends by conducting in-depth interviews with just 30 users, rather than relying on extensive data scraping [9][18]. Group 2: The Orange Juice Theory - The article presents a thought experiment comparing two laboratories studying orange juice: one focuses on precise chemical analysis, while the other aims to create a drink that evokes the experience of fresh orange juice [10][11]. - The distinction between "real" (objective data) and "true" (subjective experience) is emphasized, suggesting that businesses often find the former without grasping the latter [12][13]. Group 3: Limitations of Big Data - A beauty brand's data analysis revealed significant trends, but failed to understand the deeper motivations behind consumer desires, leading to a misalignment in product development [15][16]. - The successful new brand's approach involved understanding the emotional and psychological context behind consumer statements, rather than just the surface-level data [18][19]. Group 4: The Dilemma of Induction - The article discusses the limitations of inductive reasoning in data analysis, using the example of turkeys that expect food at a certain time based on past experiences, only to face an unexpected outcome [20][21][22]. - It highlights the fallacy of assuming that past patterns will always predict future events, stressing the need for deeper understanding beyond mere data collection [24][25][26]. Group 5: The Role of Good Hypotheses - The article argues that scientific progress relies on bold hypotheses rather than mere data observation, citing examples from physics and biology [27][28]. - Good hypotheses are characterized by their resistance to modification, testability, and explanatory depth, which are crucial for effective business insights [29][31][32]. Group 6: Challenges of Implementing Good Hypotheses - Despite the importance of good hypotheses, many companies still rely on big data due to its perceived safety and ease of use, which often leads to superficial insights [33][34][36]. - The article suggests that the lack of tools to enhance hypothesis generation contributes to the reliance on data-driven approaches [36]. Group 7: Enlightenment through Large Models - The emergence of large language models offers a shift from data dependency to a rational understanding of consumer behavior, enabling the generation of scalable hypotheses [37][39]. - Atypica.AI exemplifies this approach by simulating consumer behavior through intelligent agents, allowing for a deeper exploration of psychological mechanisms behind consumer decisions [39][44]. Group 8: Case Studies - A case study on a food company launching a Christmas gift box reveals that understanding consumer motivations goes beyond surface-level data, leading to more effective product offerings [41]. - Another case study on a skincare brand highlights that consumers are not just buying products but seeking a sense of control, demonstrating the importance of understanding underlying motivations [43][44].