Co-Author | MS in Business Analytics, Golden Gate University
International Journal of Computer Science and Information Security (IJCSIS), Vol. 18, No. 10, October 2020
This research focused on transforming traditional e-commerce platforms into intelligent, adaptive systems by leveraging Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL). The study proposed a new framework to replace static site structures with dynamic, data-driven platforms capable of delivering personalized and revenue-optimized experiences in real time.
The project involved building predictive models to analyze customer behavior and recommend products based on prior interactions and engagement signals. These models supported personalized cross-sell strategies and improved targeting across the digital shopping journey.
We developed a dynamic pricing strategy known as Price Promotion Optimization (PPO), which adjusted pricing in real time using customer demand trends, historical purchase behavior, and competitor benchmarks. This approach helped e-commerce platforms maximize sales or profitability while staying competitive in high-volume markets.
Another key contribution was the design of a Business Resilience System (BRS), which incorporated real-time data processing, fuzzy logic, and a “Risk Atom” model to proactively identify threats to site performance. This allowed for automated mitigation strategies that reduced downtime and improved operational continuity.
The project also explored how neural networks—specifically MLP, CNN, and RNN architectures could support front-end optimization by adapting user interface elements based on behavioral data. Demonstrated that user interface design, colors, animations, responsiveness, and personalized visuals can significantly influence engagement and brand perception when guided by AI-generated insights.
Finally, the research assessed the broader business impact of AI integration in e-commerce, including reduced customer service friction, increased ROI, and long-term customer retention through automation and personalized digital experiences. The project demonstrated how AI is not only reshaping back-end intelligence but also driving front-end creativity and strategic digital transformation.
The machine learning models increased the platform’s potential for ROI while simultaneously improving customer retention forecasting accuracy. The prototype demonstrated how intelligent data pipelines powered by AI and DL can transform legacy e-commerce systems into predictive engines that optimize both marketing and operational decisions.
Specialization:
Google Ads, Bing Ads, Google Analytics, Google Tag Manager, SEM, SEO, A/B Testing, B2B Marketing, Automation, HubSpot, social Media, Salesforce CMS,