| By: |
Ologunebi, John;
Taiwo, Ebenezer |
| Abstract: |
E-commerce personalization has emerged as a critical capability for online
retailers to drive engagement and conversions by delivering relevant content
and experiences tailored to each customer's preferences. This study presents a
comprehensive analysis of how a leading UK e-commerce platform implemented
advanced personalization tactics across its digital channels and quantifies
the resulting business impact. Through in-depth examination of a multi-year
personalization initiative, the research evaluates the real-world performance
of various machine learning powered techniques including collaborative
filtering, predictive segmentation, dynamic ad optimization, and multichannel
targeting strategies. A mixed methodology combines analyzing performance data
from A/B tests and control groups with insights from user surveys and
qualitative feedback. Key findings reveal significant uplifts from
personalization across metrics like click-through rates, conversion rates,
revenue per visitor and customer lifetime value compared to
pre-personalization benchmarks. Automated recommendation engines and targeted
ad content resonated strongly with UK consumer preferences. However, the study
also highlights nuances like mitigating choice overload, maintaining
transparency, and avoiding excessive personalization that could negatively
impact outcomes. The "personalization paradox" emerged as a recurring
challenge in needing to balance relevance with privacy and diversity of
content discovery. Overall insights synthesize drivers of personalization
success, quantify substantial ROI, and outline best practices tailored to UK
audience contexts. The research provides a comprehensive playbook for how
e-commerce brands can leverage first-party data, predictive analytics, and
multi-pronged personalization tactics to create more engaging, profitable
customer experiences. |
| Keywords: |
E-commerce platform, Personalized advertising, Ad content, User preferences, Conversion rates, User engagement, User behavior, User satisfaction, Privacy considerations, Data security, Ethical implications, GDPR compliance, User perceptions, Customer loyalty, Product recommendations, Decision-making, Marketing strategies |
| JEL: |
M21 M30 M31 M37 M38 |
| Date: |
2024–04–01 |
| URL: |
http://d.repec.org/n?u=RePEc:pra:mprapa:120595&r=mkt |