Do you know what predictive analytics is? Thanks to it, you can discover insights about the future. It's not only learning what happened and why. This is much more! Read and learn in what way predictive analytics shapes the world we live in!

What is meant by predictive analytics?

The term predictive analytics is connected with the use of modeling techniques and statistics in order to predict the future outcomes and performance. Predictive analytics focuses on current and historical data patterns in order to specify if there's any possibility for those patterns to emerge again. There are a few statistical techniques such as predictive modeling, artificial intelligence, data mining and machine learning.

It is very useful for investors and businesses because it allows them adjusting where to use their resources. It means that thanks to predictive analytics, people can exploit possible future events. Also, people make use of predictive analytics so that improving operational efficiency and reducing risk is possible. Simply saying, predictive analytics helps us identify risks and get rid of them! Silicon Cities offers services designed for your business needs. Thanks to them, you will make smarter business decisions!

How to understand predictive analytics properly?

It is an area of statistics whose goal is to get the information from data and use it to know trends and behavior patterns in advance. This can be understood as a form of technology which makes it possible to predict certain unknowns in the future.

As you already know, there are several techniques to make these determinations. For example, data mining is all about large sets of data analysis. The aim is to detect patterns from it. Analysis of the text does almost the same. The only exception are large blocks of texts.

What are the types of predictive analytical models?

The most common techniques which are used in predictive analytics are regression, neural networks and decision trees. Depending on the situation, we might need to use a certain technique. Let's check when to use which!

Regression

The first one - regression - is the model people use mainly in statistics, when they need to determine patterns in big sets of data. It is also significant when there is a linear relationship between the inputs. The point of this method is to figure out a formula that represents the relationship between each input found in the dataset. Use regression to learn how price etc. can affect the performance of a security.

Neural networks

They were created in a way that they imitate a human brain! This model uses pattern recognition and artificial intelligence. Use it whenever you have too much data, when you don't own the needed formula, or when you want some predictions, not explanations or big data.

Decision trees

Sometimes we want to understand the reasons why people make some decisions. In this case, decision trees may be useful. According to this technique, data is placed into different sections which are based on price, market capitalization and so on. Decision trees are the easiest to understand. You can easily use it when you're running out of time. 

What are key advantages of predictive analytics?

First of all, predictive analytics is sometimes crucial in marketing and management departments. Taking appropriate decisions is worth a lot, especially in these types of work. Using statistics and modeling techniques really helps businesses to determine future performances and to make important decisions.

Secondly, predictive models are "involved" in making weather forecasts. That's not all! They also help to develop video games and to translate voice-to-text messages. Understanding service customers' decisions and developing investment portfolios gets easier.

What is the purpose of each predictive model?

Their purpose is to be used in many kinds of applications, which in turn use descriptive statistical models of existing data. It's all about predicting. It is beneficial for businesses because it helps them to develop marketing strategies, manage inventory and to forecast sales. Not only people from marketing and management can benefit from it, but also the ones from health care and retail! Predictive models might be useful for inventors and financial professionals, who want to reduce the potential risk.

Predictive models determine patterns, relationships and structures in data which can be used in order to give people a definite analysis. The subject of this analysis is how changes in the underlying processes generating the data will change all the results. Such models shall be based on descriptive models. They analyze data collected in the past in order to determine the probability of certain future outcomes. Current conditions or expected future conditions shall be taken into account.

How predictive analytics can influence your business?

Predictive analytics CAN influence your business, no doubt about it. The question is how to do it? You can benefit from it in many different kinds of jobs, especially if you work in forecasting, marketing, underwriting or credit service. 

Forecasting

Forecasting is necessary in manufacturing. It provides the optimal resource utilization in a supply chain. It doesn't matter if we mean the shop floor or inventory management - everything needs accurate forecasts in order to function properly.

People use predictive modeling techniques pretty often, especially in the case of cleaning and optimizing the quality of data which is used for such forecasts. Thanks to modeling, the system can ingest more data such as from customer-facing operations. This is a way of ensuring an accurate forecast!

Marketing

Marketing specialists keep a close eye on consumer behavior. People who work in marketing look at how their consumers react in particular situations and how they view the overall economy. This is particularly important when a new campaign is being planned. Individuals working in this field want to determine whether the current mix of products will enable consumers to buy something or not. That's why they can use shifts in demographics. Predictive analysis is significant here!

When it comes to traders - the active ones look at metrics from the past events before they buy or sell a security. Moving averages, breakpoints and bands are based on historical (and transactional) data. They are used so that forecasting future price movements is possible.

Underwriting

Another department where predictive analytics and data are important is underwriting. All the insurance companies examine policy applicants. People who want to use the service have to be able to pay out for a future claim which is based on a current risk pool of policyholders. What also gains importance are past events which have resulted in payouts. Underwriters use predictive models and credit risk models which pay attention to characteristics in comparison to data about past policyholders.

Financial services

There is one more department which makes use of predictive analytics pretty often - credit scoring! In a situation when a business or a consumer applies for credit, financial services' specialists make use of all the data from the applicant's credit history. The credit record of borrowers who have similar characteristics is being used too. Bankers want to predict the risk that some of the applicants may not be able to fulfill their obligations.

Summary

Predictive analytics can definitely make our lives easier. There are many reasons why to use predictive analytics. Above all, they can effectively influence businesses (forecasting, marketing, underwriting, financial services and many more) especially by keeping a close eye on consumer behavior!