To be competitive, companies need to be able to take advantage of current data to predict what might happen in the future. Predictive analytics plays a key role in being able to capture useful information and use it to model customer behaviors, sales patterns and other trends for the future.
Although, predictive analytics is usually related to data mining to describe how information or data is processed, there are significant differences between these techniques. Predictive analytics and data mining use algorithms to discover knowledge and find the best solutions. Data mining is a process based on algorithms to analyze and extract useful information and automatically discover hidden patterns and relationships from data. Instead, predictive analytics is closely tied to machine learning, as it uses data patterns to make predictions, where machines take historical and current information and apply them to a model to predict future trends. In essence, the difference between predictive analytics and data mining is that the former explores the data and the latter answers “What is the next step?”
What is predictive analytics?
Predictive analytics is the use of data, mathematical algorithms and machine learning to identify the likelihood of future events based on historical data. The main goal of predictive analytics is to use the knowledge of what has happened to provide the best valuation of what will happen. In other words, predictive analytics can offer a complete view of what is going on and the information we need to succeed .
Thanks to the diffusion of text analytics that have made the analysis of unstructured data less time consuming, predictive analysis is increasing. Today, we are increasingly looking to machines that can take past and current information to forecast future trends, such as sales trends for the coming months or years, or anticipating customer behavior such as in the case of fraudulent credit card use (learn more about how you can manage the operational risk here).
How predictive analytics works
Predictive analysis uses various models to assign a score to data. The most common is the predictive model that is focused on the behavior of an individual customer. Using sample data with known attributes, the model is trained and is able to analyze the new data and determine its behavior. This information can be used to predict how the customer might behave next.
This is different from the descriptive model, which is used to classify data into groups. With descriptive models, customer data is classified by characteristics such as age or previous buying behavior. This information is often in marketing campaigns to hit a target group.
Today, more and more organizations are using predictive analytics to increase their business and:
- Produce valuable insight.
- Increase competitive advantage.
- Forecast trends and identify new business opportunities in time.