Check examples of use cases in different industries with CLICK AI
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It is very important to prevent churn in the financial industry. There are a number of factors that can trigger this churn. Banks are able to predict customer churn based on existing customers' account and credit information to prevent churn.
Health insurance is a type of insurance service that covers one's risk for medical expenses. The cost may vary depending on an individual's age, medical records, and other personal conditions. With access to data containing various personal information, it becomes possible to predict the cost of one's health insurance and find out which aspect has the biggest impact on it.
The importance of discovering high-margin potential customers has become more important than ever, and as a result, finding customers is raising the need to leverage a data-driven science. Therefore, we will examine the effectiveness of the bank's marketing strategy through ClickAI by predicting if the client will subscribe to a term deposit.
With previous accident data sets, industrial accident risk predictions become possible and these predictions may be able to prevent accidents. Industries would also be able to predict the probability and level of industrial accidents from these data sets.
Hourly energy consumption data could be utilized to predict future energy consumption and to find out what affects energy consumption.
Price changes are made depending on various internal and external factors. In this example, we will predict the daily wheat closing price using the time series analysis method based on the data.
No-show cases often occur across various industries like restaurants, hospitals, and beauty salons. It causes economic and employment loss for many individuals working in the industries. Previous hospital no-show data analysis and future no-show predictions could become a potential solution for no-show problems.
With the recent rise in the delivery industry, it has become increasingly important to be able to estimate a delivery time and predict late deliveries. This project aims to provide late delivery predictions and find out which factors have a significant impact on late deliveries.
Sentiment analysis is a type of natural language processing (NLP) that analyzes emotions contained in text data. Through this sentiment analysis, you are able to quantify review data in sentence form and derive new insights along with other quantitative data.