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Top 5 predictive analytics, Customer experience, Predictive analytics in business, Predictive analytics applications marketing

Top Five Predictive Analytics Application to Improve Customer Experience

Predictive analytics technology is composed of statistics, data mining, algorithms, and machine learning.



With organizations harnessing technology to better their market performance, emerging trends like predictive analytics are continually evolving. Data is a valuable asset to companies. However, the stored data becomes more valuable when it is utilized in the right way and is implemented in the working system. Most of the companies use only 1% of their data in the right way. Ultimately, with the help of predictive analytics, companies can get a whole framework of their customers’ choices.

Predictive analytics is a form of business intelligence technology that focuses on combining the existing data for patterns and using that data to make predictions on future outcomes or identity risks. The technology is composed of statistics, data mining, predictive algorithms, and machine learning. It gives companies a powerful new edge, compensating for the ever-expanding array of choices available on the internet anytime and anywhere. Predictive analytics is revolutionizing the customer-marketer relationship by boosting sales while simultaneously increasing shopper satisfaction. Predictive analytics tools are helping companies improve their bottom line and reach customers effectively. Henceforth, GlobalTech Outlook brings you a list of predictive analytics use cases that are improving customer demands.


Hyper-personalized marketing

Hyper-personalisation leverages artificial intelligence (AI) and real-time data to deliver personalized marketing to supply more relevant content, product, and service information to consumers. Already, business organizations are quickly adopting the technique to serve their customers with the right choice of products they want. However, the personalization of marketing is still on the growth path. Researches show that only 9% of marketing professionals surveyed say that they have completed the development of a hyper-personalization strategy. Fortunately, it is expected to bring a breakthrough in the B2C market in the future.


Data to solve problems

Big data is often the answer to many marketing questions. Real-time data analytics help companies track complaints, so it can understand the big issues and predict what questions or complaints customers may have. Accessing data quickly also ensures that customer issues are recognized and addressed properly. Predictive analytics helps organizations put all the data in a single dashboard making it easy to understand customers and predict what they want.


Faster shipping options


A lot of customers prefer same-day or next-day delivery of products. Generally, customers want to ensure that the products they order are safe and quick to get. Predictive analytics unveils brands with a complete customer experience that shows their shipping preferences. Predictive analytics can not only be used to identify a customer’s behavioral pattern or interests but also to determine the optimal solutions for complex problems or situations that might arise in the future.


Forecasting customer need

Technology knows more about an individual’s preferences even before he/she make up their mind. Predictive analytics also flags changes in customer behavior, making it easy for marketers to approach them with the right choice of product. Machine learning is a frontrunner technology that clearly detects which customer segments should be added and removed from a campaign.


Real-time product feedback

Predictive analytics is a rapid technique that can help figure a customer’s experience as it happens. This is the major matrix behind services like Netflix and Spotify. A customer’s choice of songs or shows is closely watched by predictive analytics. Hence giving them suggestions of what they might want to watch next.