Cross-Channel Sales Data Fusion and Optimization in Omnichannel Retail Systems
DOI:
https://doi.org/10.63282/3117-5481/AIJCST-V7I3P102Keywords:
Omnichannel Retail, Data Fusion, Cross-Channel Optimization, Sales Forecasting, Machine Learning, Retail Analytics, Inventory Management, Personalization, Linear Programming, Big Data in RetailAbstract
The customer purchasing patterns are quickly evolving and it is the high digitization of trade that has compelled retailers to resort to omnichannel retailing mechanisms. The tactics combine different intermediaries of sales such as physical outlets, online stores, mobile apps and social networks in such a manner that the customers experience uninterrupted shopping. It is a hassle to handle and optimize the cross-channel sales information because these systems are convenient but in data isolation and other formats that are inconsistent across platforms. The provided paper proposes an integrated approach to the cross-channel sales information integration and profitability in the omnichannel retail architecture. The model provided encompasses the solution to heterogeneous data sets by utilizing the top-notch data preprocessing, normalization and feature engineering. The general purchasing system with a hybrid engine that works with machine learning (ML), linear programming (LP), and time-series forecasting is suggested to optimize all the activities of the purchase process such as pricing, inventory processes, and personalized marketing. The methodology is verified by using real retail data in businesses as well as simulated multi-channel data. The results indicate increased level of predictability, stock turnover and income. The fusion engine is one of such innovations, where the incoming sales streams are adjusted dynamically and provide the opportunity to conduct real-time analytics and optimization. Besides customer interactions information may be utilized to enact feedback loops via the system to personalize and engage the customer. The research delivers a scalable and interchangeable design of omnichannel data optimization and viable information on the matter of implementation as well as possibilities. This paper shows the importance of data synergy and computational intelligence in application in the modern retail environment
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