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Title |
Data Science and Machine Learning Approach to Improve E-Commerce Sales Performance on Social Web |
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Author |
Hussain Saleem, Khalid Bin Muhammad, Altaf H. Nizamani, Samina Saleem, M. Khawaja Shaiq Uddin, Syed Habib-ur-Rehman, Amin Lalani, Ali Muhammad Aslam |
| Citation |
Vol. 23 No. 8 pp. 137-145 |
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Abstract |
E-Commerce is a buzzword well known for electronic commerce activities including but not limited to the online shopping, digital payment transactions, and B2B online trading. In today’s digital age, e-commerce has been playing a very important and vital role in areas such as retail shopping, sales automation, supply chain management, marketing and advertisement, and payment services. With a huge amount of data been collected from various e-commerce services available, there are multiple opportunities to use that data to analyze graphs and trends. Strategize profitable activities, and forecast future trade. This paper explains a contemporary approach for collecting key data metrics and implementing cost-effective automation that will support in improving conversion rates and sales performance of the e-commerce websites resulting in increased profitability. |
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Keywords |
data science, digital payments, machine learning, sales automation, web science. |
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