Name:Shengxuan ZHANG
姓名 Shengxuan ZHANG
Programme: MSc Digital Business and Data Analytics
课程： 数字商业与数据分析硕士
Provisional supervisor’s name:David Lane
临时监管人姓名：David Lane
Provisional title
临时标题
“The Impact of AI-Driven Personalization on Consumer Behavior in E-commerce: A Data Analytics Approach in the Fashion Retail Sector”
“ 人工智能驱动的个性化对电子商务中消费者行为的影响：时尚零售行业的数据分析方法 ”
Introduction
介绍
This study examines how Artificial Intelligence (AI)-driven personalisation affects consumer behavioural metrics such as purchasing patterns, engagement, speed of decision-making and customer loyalty in the fashion retail e-commerce sector. With the rise of data-centric marketing and increasing consumer expectations for personalised experiences，an article by the Forbes Technology Council highlights that 71% of consumers expect personalised interactions, while 76% get frustrated when they don't get them. Companies that excel at personalisation generate 40% more revenue than those that don't. Online retailers are deploying AI tools such as recommendation engines, dynamic pricing and personalised content. This study explores how such AI applications impact customer engagement, purchase decisions and overall satisfaction.
本研究探讨了人工智能 （AI） 驱动的个性化如何影响时尚零售电子商务领域的消费者行为指标，例如购买模式、参与度、决策速度和客户忠诚度 。随着以数据为中心的营销的兴起和消费者对个性化体验的期望不断提高 ， 福布斯技术委员会的一篇文章强调，71% 的消费者期望获得个性化的互动，而 76% 的消费者在得不到个性化互动时会感到沮丧。擅长个性化的公司比不擅长个性化的公司产生的收入高出 40%。Online 零售商正在部署 AI 工具，例如推荐引擎、动态定价和个性化内容。本研究探讨了此类 AI 应用程序如何影响客户参与度、购买决策和整体满意度。
The major research questions include：
主要研究问题包括：
1. How does AI-driven personalization influence purchasing decisions and cart abandonment rates in fashion retail e-commerce?
1.AI 驱动的个性化如何影响时尚零售电子商务中的购买决策和购物车放弃率？
2. Which AI personalisation strategies (e.g. clothing recommendations, style-based email campaigns, loyalty rewards) are most effective at driving repeat purchases and improving customer retention in online fashion shops?
2. 哪些 AI 个性化策略（例如服装推荐、基于风格的电子邮件活动、忠诚度奖励）在推动在线时装店的重复购买和提高客户保留率方面最有效？
3. What are the ethical implications of AI-based personalisation in fashion e-commerce, particularly in terms of data privacy, algorithmic bias and consumer trust?
3. 时尚电子商务中基于 AI 的个性化有哪些道德影响，尤其是在数据隐私、算法偏见和消费者信任方面？
Project Background
项目背景
Artificial intelligence (AI)-powered personalisation has transformed the e-commerce landscape, enabling retailers to offer tailored shopping experiences based on consumer data. This is particularly evident in fashion retail, where recommendation engines, dynamic pricing and personalised marketing are widely used to improve customer engagement and drive sales
人工智能 （AI） 驱动的个性化改变了电子商务格局，使零售商能够根据消费者数据提供量身定制的购物体验。这在时尚零售业中尤为明显，其中推荐引擎、动态定价和个性化营销被广泛用于提高客户参与度和推动销售.
E-commerce giants such as ASOS, Zara and H&M use AI technology to predict consumer preferences, recommend clothing combinations and optimise pricing strategies in real time. According to Kapoor et al (2020), fashion retailers are increasingly adopting AI-powered systems to improve customer satisfaction and increase sales. These systems use machine learning algorithms to analyse user data and provide customised content and recommendations to enhance the shopping experience.
ASOS、Zara 和 H&M 等电子商务巨头使用 AI 技术来预测消费者偏好、推荐服装组合并实时优化定价策略。根据 Kapoor 等人（2020 年）的说法，时装零售商越来越多地采用人工智能驱动的系统来提高客户满意度并增加销售额。这些系统使用机器学习算法来分析用户数据并提供定制内容和推荐，以增强购物体验。
Academic research has shown that personalisation techniques can significantly influence consumer purchasing behaviour, increasing conversion rates, average order value and customer retention (Gao et al., 2021). However, research by Smith and Johnson (2023) highlights the challenges of balancing effective personalisation with data privacy, particularly in terms of algorithmic bias and consumer trust. Despite these advantages, organisations must consider ethical considerations in order to maintain transparency and customer loyalty.
学术研究表明，个性化技术可以显着影响消费者的购买行为，提高转化率、平均订单价值和客户保留率（Gao et al.， 2021）。然而，Smith 和 Johnson （2023） 的研究强调了平衡有效个性化与数据隐私的挑战，尤其是在算法偏见和消费者信任方面。尽管有这些优势，但组织必须考虑道德考虑，以保持透明度和客户忠诚度。
Project Aims and Objectives
项目宗旨和目标
This study will specifically explore:
本研究将具体探讨：
1. Influence on Buying Behavior：How Artificial Intelligence-driven recommendations and personalised content can impact consumer decision-making in fashion e-commerce.
1. 对购买行为的影响 ： 人工智能驱动的推荐和个性化内容如何影响时尚电子商务中的消费者决策。
2. Effectiveness of personalisation strategies: analysis of specific techniques (e.g. ‘full picture’, sized recommendations, personalised email campaigns) and their success in increasing conversion and loyalty.
2. 个性化策略的有效性：分析特定技术（例如“全貌”、大小建议、个性化电子邮件活动）及其在提高转化率和忠诚度方面的成功。
3. Ethical implications: Assessing issues related to data privacy, algorithmic bias and consumer trust when deploying AI personalisation in fashion retail.
3. 道德影响：在时尚零售中部署 AI 个性化时，评估与数据隐私、算法偏见和消费者信任相关的问题。
This study aims to provide data insights into the role of AI personalisation in shaping consumer behaviour and to explore the ethical challenges of deploying AI personalisation in digital fashion retail platforms.
本研究旨在提供有关 AI 个性化在塑造消费者行为中的作用的数据见解，并探讨在数字时尚零售平台中部署 AI 个性化的道德挑战。
Expected Research Contributions
预期研究贡献
This study will provide an analytical assessment of how AI personalisation tools influence consumer behaviour. It can offering evidence-based insights into personalisation effectiveness,identify potential risks and ethical issues in personalised practice and helping companies balance personalisation with consumer trust and privacy.
本研究将对 AI 个性化工具如何影响消费者行为进行分析评估。 它可以对个性化的有效性提供基于证据的见解 ，识别个性化实践中的潜在风险和道德问题 ， 并帮助公司平衡个性化与消费者信任和隐私。
Research Approach/Programme
研究方法/计划
I will use several different method,the first one is Quantitative Analysis,which including transactional and interaction data from fashion e-commerce platforms (e.g., ASOS, Zara, H&M) or publicly available datasets (e.g., Kaggle, UCI repositories)And Data points include click-through rate (CTR), conversion rate, average order value, cart abandonment rate and repeat purchase rate.The key mersurement include Engagement Metrics, Purchase Behavior and Loyalty Indicators. I would like to know if AI-driven personalisation has improved conversion rates on fashion e-commerce platforms?
我将使用几种不同的方法，第一种是定量分析， 其中包括来自时尚电子商务平台（例如 ASOS、Zara、H&M）或公开可用的数据集（例如 Kaggle、UCI 存储库） 的数据点包括点击率 （CTR）、转化率、平均订单价值、购物车放弃率和重复购买率 ，关键改进包括参与度指标购买行为和忠诚度指标 我想知道人工智能驱动的个性化是否提高了时尚电子商务平台的转化率 ？
The another method is Qualitative Insights, surveys or focus groups with consumers who have experienced personalised shopping on platforms such as ASOS or Zara. Questions will explore the perceived value of personalisation, trust in the use of data and privacy issues. Finally, I wondered if personalised fashion advice could increase user satisfaction and brand loyalty
另一种方法是 Qualitative Insightssurveys 或焦点小组，与在 ASOS 或 Zara 等平台上体验过个性化购物的消费者进行讨论。问题将探讨个性化的感知价值、对数据使用的信任以及隐私问题。最后，我想知道个性化的时尚建议是否可以提高用户满意度和品牌忠诚度.
I will summarise the data in Excel and then analyse it in r-studio.
我将在 Excel 中总结数据，然后在 r-studio 中进行分析。
Deliverables
交付
I will submit literature review on artificial intelligence personalisation in e-commerce, data analysis results and visualisation，discussion of consumer perceptions and ethical implications and final document with actionable insights for practitioners, and the process of data collection and data analysis
我将提交有关电子商务中人工智能个性化、数据分析结果和可视化的文献综述，讨论消费者感知和道德影响，以及为从业者提供可作见解的最终文件以及数据收集和数据分析的过程.
Resources required
所需资源
Access to anonymised data sets of interactions with consumers or publicly available data , I need using some software such as r-studio and Excel and survey tools (e.g., Google Forms, Qualtrics). Interviews with consumers who regularly shop on platforms such as ASOS, Zara and H&M through social media promotions, online communities and email campaigns
访问与消费者互动的匿名数据集或公开可用的数据，我需要使用一些软件，例如 r-studio 和 Excel 以及调查工具（例如，Google Forms、Qualtrics）通过社交媒体促销、在线社区和电子邮件活动采访经常在 ASOS、Zara 和 H&M 等平台上购物的消费者.
References
引用
Gao, H., Zhang, L., & Chen, Y. (2021). AI in E-commerce Personalization: Opportunities and Challenges. Journal of Digital Commerce, 12(3), 155–170.
Gao， H.， Zhang， L.， & Chen， Y. （2021）.电子商务个性化的 AI：机遇和挑战。 数字商务杂志 ，12（3），155-170。
Kapoor, K., Dwivedi, Y.K., & Piercy, N. (2020). Consumer responses to AI-enabled personalization in e-commerce. Technological Forecasting & Social Change, 162, 120379
Kapoor， K.， Dwivedi， Y.K.， & Piercy， N. （2020）.消费者对电子商务中支持 AI 的个性化的反应。技术预测与社会变革，162,120379.
Smith, T., & Johnson, M. (2023). Ethics in AI-Powered Retail Environments. International Journal of Information Management, 58, 102443
史密斯，T.和约翰逊，M.（2023）。AI 驱动的零售环境中的道德规范。国际信息管理杂志， 58， 102443.
Forbes Technology Council. (2023). Personalization in Fashion E-commerce. Retrieved from [URL]
福布斯技术委员会。（2023）. 时尚电子商务中的个性化。 取自 [URL]