We use historical data, statistical methods, and machine learning to predict future results. Our predictive analytics & forecasting takes into account different values, trends, cycles, and/or fluctuations in the data to make predictions.
Your predictive analytics and forecasting quality largely depend on both the quantity and quality of your data. To enhance your data sets, we can also support you with data mining for a higher quantity of data and analyze your data sources to improve the quality of your data. VITech also utilizes big data predictive analytics solutions to minimize the impacts of insufficient data. Our top-tier experts analyze and interpret your historical data to create a comprehensive prediction and forecasting solution.
Predictive Analytics combines a variety of analytics tools including statistical algorithms, predictive modeling, and machine learning to reduce overall business risk, improve operations, optimize marketing campaigns and pricing strategies by creating predictions based on historical and new data. The most common use cases among others are: possibilities to detect fraud, or predict churn and demand.
The most basic use of predictive models in the healthcare industry involves detecting health insurance claims fraud. But, it can be used for much more. Industry leaders are using data analytics combined with predictive modeling in the identification of patients at risk of developing chronic diseases. Once identified, data analysis can take it a step further and even help determine which treatment methods have the highest rate of success.
The Fintech industry has almost always relied on some form of data analysis. Nowadays, the industry can leverage all types of data to pull accurate insights for reducing fraud, identifying credit risks, and even improving sales. These data analytics tools rely on both old and new data with some implementation of data mining tactics to expand their data sets.
Retailers throughout the world now use predictive modeling alongside a variety of analytics tools to inform pricing, merchandise layout, inventory tracking and customer behavior. These models help maximize profit margins on a holistic standpoint instead of on a per-item basis making entire businesses more profitable. It also helps improve consumer convenience by informing where to place items within a store based on which items are frequently purchased together.
At this point, just about every marketing and sales team utilizes some form of data analytics to inform their strategies and marketing campaigns. Unfortunately, it is often the case when the data becomes too cumbersome to pull meaningful insights from. This is where predictive analytics comes in by pulling together all types of data and processing it to deliver actionable insights for improving your marketing campaigns.
