Data is the most valuable resource for retail industry and it enahnces decision making process in business operations. A new wave of technological advancements includes machine learning and predictive analytics which are beneficial in generating better retail solutions.
How does Predictive Analytics differ from Machine Learning?
Machine Learning, a subset of Artificial Intelligence, involves data and pattern recognition and helps systems to self-learn without being explicitly programmed whereas predictive analytics is the advanced form of analytics which uses many techniques like artificial intelligence and machine learning to make analysis on current data in deriving predictions about future.
Role of predictive analytics and machine learning in generating better retail solutions
Data-driven decisions with the help of predictive analytics and machine learning have been business drivers for retailers. Most of the retailers monitor customer shopping trends through availabale data resources such as CRM database, social media activity, purchase history, and marketing tendencies. However, the ability to apply compound mathematical calculations on the data automatically is now attainable with machine learning.
Machine learning is an evolution in pattern recognition which will generate patterns regarding consumer behaviors, market fluctuations and more. Predictive analytics is an approach of retailers to utilize data obtained from the past events and forecast expected sales growth.
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Through machine learning, data is sufficient for retailers but it's about putting it to work in the right areas will be done through predictive capabilities. Based on the predictions, retailers can optimize operations to anticipate customer behavior and can improve productivity.
An organization can transform its trade promotion process by automating operational tasks, such as setting shelf pricing, determining product assortments and much more with the help of these advanced systems.
The oversight and optimization of machine learning and predictive analytics are becoming more prevalent and beneficial in retail businesses, and it will continue to progress exponentially in its ability to operate without any requirements of human inputs.