Assessing AI-Powered Personalization Strategies for Mid-Sized Finnish Fashion E-commerce: Enhancing Consumer Engagement and Conversion Rates
Mohamed, Ahmed (2024)
2024
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Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:amk-2024052314876
https://urn.fi/URN:NBN:fi:amk-2024052314876
Tiivistelmä
The undergraduate dissertation assesses AI-powered Personalization Strategies for middle-sized fashion e-commerce companies in Finland. The study is supported by a theoretical and empirical framework, which integrates insights from technology adoption, consumer acceptance and con-sumer perceptions of AI personalization’s. The studies ultimate objective is to provide actionable insights tailored to the unique challenges faced by mid-sized retailers in the fashion sector.
The research methodology utilizes a comprehensive use of quantitative data analysis to under-stand the impact of AI personalization strategies on various aspects of e-commerce. The key con-cepts include AI-driven personalization, customer engagement, and conversion rates. They are all explored within the context of mid-sized e-commerce retailers. The study excludes larger retailers and startups to ensure specific and relevant findings for the target group.
In the overlay matrix, the study addresses three investigative questions related to the effects of AI personalization on customer engagement and conversion rates, as well as Finnish consumer re-actions and consumer-related challenges to AI personalization. Having thoroughly analyzed the connection between the theoretical framework and practical applications, this research contributes to a deeper understanding of AI-powered personalization strategies for mid-sized Finnish fashion e-commerce businesses. As a result of the extensive research, the undergraduate thesis offers valuable action points for future implementations in the fashion industry
The research methodology utilizes a comprehensive use of quantitative data analysis to under-stand the impact of AI personalization strategies on various aspects of e-commerce. The key con-cepts include AI-driven personalization, customer engagement, and conversion rates. They are all explored within the context of mid-sized e-commerce retailers. The study excludes larger retailers and startups to ensure specific and relevant findings for the target group.
In the overlay matrix, the study addresses three investigative questions related to the effects of AI personalization on customer engagement and conversion rates, as well as Finnish consumer re-actions and consumer-related challenges to AI personalization. Having thoroughly analyzed the connection between the theoretical framework and practical applications, this research contributes to a deeper understanding of AI-powered personalization strategies for mid-sized Finnish fashion e-commerce businesses. As a result of the extensive research, the undergraduate thesis offers valuable action points for future implementations in the fashion industry