QuixellAI:E-Commerce Sales Growth Optimization Service
Many e-commerce retail businesses have yet to fully harness the advantages of artificial intelligence (AI), missing crucial opportunities to boost sales and streamline operations. This research introduces the “E-commerce Sales Growth Optimization Service,” an integrated AI-driven solution designed specifically to enhance the online retail experience, particularly for small and medium-sized enterprises (SMEs). This service seamlessly integrates with existing e-commerce platforms and offers a suite of tools including personalized product recommendations, dynamic pricing, automated promotions, and detailed sales trend analysis and forecasting, all while ensuring robust customer data security and supporting efficient real-time data management. Engineered for effortless adoption across various digital platforms, the service addresses critical challenges in e-commerce such as the cold-start problem in product recommendations, the complexities of dynamic pricing affected by real-time market dynamics, and the optimization of data-driven promotional strategies. Furthermore, it tackles platform heterogeneity, ensuring that the service operates effectively across diverse e-commerce systems, thus eliminating the need for businesses to develop their own AI expertise. The findings highlight the service's significant impact on enabling smarter business decisions and boosting market competitiveness, marking a substantial advancement towards an AI -empowered future in e-commerce. This initiative not only drives sales growth but also establishes a new benchmark for integrating AI to enhance the e-commerce experience, signifying a crucial evolution in online retail operations.
Authors
- Dassanayake D.M.G. T
- Perera L.A.M. D.
- Ariyarathna B.M.N.D. S.
- D. M.
- Karunathilake D.N. M.
Institutions
- Sri Lanka Institute of Information Technology (LK)
Publication Details
- Published
- 2024-08-16
- DOI
- https://doi.org/10.1109/icbim63313.2024.10823406
- Citations
- 1
- Primary Topic
- Big Data and Business Intelligence
- Type
- article
- Field-Weighted Citation Impact
- 0.53