Sharon Wingert, Stephen D. Rappaport, Yehoshua Deitel
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引用次数: 0
摘要
Mind Genomics研究了一个电子商务网站的反应,重点关注一个对用户偏好有“深入了解”的网站。为了了解Mind Genomics在现实环境中的应用,设置和现场的时间限制在120分钟内。这些数据是在2019年春季收集的。四年后,新开发的人工智能分析进一步解释了结果。2019年的初步分析解构了受访者对描述网站的小插图、信息组合的评分。受访者使用固定的5分制,锚分别为“买”和“不买”。OLS回归的解构揭示了每个元素对“购买”评级的贡献。使用16个系数对46名受访者进行聚类,发现了三种思维模式:ms1 -帮助客户成长,ms2 -客户咨询,ms3 -产生潜在客户。四年后,人工智能应用于人口中的每个群体,使用六个标准的人工智能查询应用于所有被认为是“购买”的强大驱动因素的积极因素。这篇论文展示了在新主题上快速而深刻地学习的可能性。学习是通过实验设计、人工验证和人工智能解释来促进的。
Developer Goals for e-Commerce Startups: Applying AI-enhanced Mind Genomics to Thinking about Everyday Issues
Mind Genomics explored responses for an e-commerce website, focusing on a website with ‘deep knowledge’ of the user’s preferences. To understand the application of Mind Genomics in a real-world setting, the timing of the setup and the fielding were limited to a total of 120 minutes. The data were collected in Spring, 2019. Four years later, newly developed AI analysis further interpreted the results. The initial analysis in 2019 deconstructed the ratings assigned by the respondents to vignettes, combinations of messages, describing the website. The respondents used an anchored 5-point scale, with the anchors ‘buy’ and ‘not buy’, respectively. The deconstruction by OLS regression revealed the contribution of each element to the ‘buy’ rating. Clustering the 46 respondents using the 16 coefficients uncovered three Mind-Sets: MS1-Help the client grow, MS2-Client Consulting, and MS3-Generate Leads. Four years later AI was applied to each group in the population, using six standard AI queries applied to all positive elements which were deemed to be strong drivers of ‘buy.’ This paper shows the possibility of rapid and insightful learning on new topics. Learning is promoted through experimental design coupled with human validation, and AI interpretation.