How ML Helps to Develop Robust Retail Applications
Check out how machine learning is transforming the retail industry. Below I've curated a few links that explain the importance of machine learning in the Retail business.
What is machine learning in retail with examples? Explore machine learning in retail use cases and find its benefits for the industry.
How machine learning in the Retail Industry helps businesses to increase their profits. Check out this blog to know the growing significance of ML for retailers.
How companies can use Machine Learning and AI in retail to strengthen their competitiveness today. Learn more.
This guide explains how machine learning tackles retail’s demand forecasting challenges and how to make it work for your business.
1.0 Preliminaries Artificial Intelligence (AI) and Machine Learning (ML) have become increasingly prevalent in the retail industry in recent years.
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Andrew Bithell, Sales Team Lead, CTS, examines five ways in which technology is helping retailers to harness the power of their data resources.
Machine learning (ML) has the potential to take on a variety of routine and non-routine tasks in brick-and-mortar retail and e-commerce. Many tasks previously executed manually are amenable to computerization using ML. Although procedure models for the introduction of ML across industries exist, the tasks for which ML can be implemented in retail need to be determined. To identify these application areas, we followed a dual approach. First, we conducted a structured literature review of 225 research papers to identify possible ML application areas in retail, as well as develop the structure of a well-established information systems architecture. Second, we triangulated these preliminary application areas with the analysis of eight expert interviews. In total, we identified 21 application areas for ML in online and offline retail; these application areas mainly address decision-oriented and economic-operative tasks. We organized the application areas in a framework for practitioners and researchers to determine appropriate ML use in retail. As our interviewees provided information at the process level, we also explored the application of ML in two exemplary retail processes. Our analysis further reveals that, while ML applications in offline retail focus on the retail articles, in e-commerce the customer is central to the application areas of ML.