Predictive Analytics uses historical data sources and uses them to create a mathematical model that can be used to predict future events. Such a model recognizes trends or patterns in historical data and can predict them for the future. On this basis, companies have a tool to make better decisions or to identify possible risks at an early stage.

Synonyms that are often used for predictive analytics are data mining, data science, machine learning, and artificial intelligence. 

The requirements for predictive analytics

 

Predictive analytics brings some conditions with it. The data and data quality are a very important point. But there are other requirements for predictive analytics. Here are the requirements that the data entails and what also needs to be considered: 

 

The data: 

The data represent the limiting basis of any predictive analytics method. The better the data quality, the better the forecast. But the data also has to be relevant. So, you should first of all define the basic problem in particular, in order to then decide which data, contain crucial information and add value to your analysis. A sensible data management system is therefore indispensable in order to access all data quickly and easily. The preparation of the data is also a focus of the predictive analytics workflow.  

 

Corporate culture and infrastructure: 

The corporate culture and the internal infrastructure are often decisive when it comes to the software used. How is the data volume managed? Is there a huge data warehouse or just a small database? Are the algorithms of predictive analytics programmed independently or is ready-made software used in which the preprocessed data only has to be integrated? 

 

Employees: 

Your employees don’t just need to understand the business problem. You need a team of specialists who contribute their expertise in different areas. You need employees who are familiar with data and who can also manage and store data. 

However, the preprocessing of the data should also be carried out by specialists. Likewise, employees who understand, apply and interpret the algorithms behind predictive analytics are required. The spectrum thus ranges from data engineers to data analysts and data scientists. Depending on the size of the company, several teams may even be necessary. 

 

Variants of predictive analytics

Predictive analytics is divided into different variants. The predictive model is certainly the best known when it comes to actually predicting probabilities, classes, and values. Descriptive models, on the other hand, describe the relationships between the data. 

• Predictive models: This variant is the classic model of predictive analytics. Using input data, a model is trained which recognizes patterns and trends in the data and makes predictions for the future. The forecast is usually expressed in terms of probabilities, such as whether or not a product will be bought. Machine learning methods or regression approaches are often used as algorithms.  
• Descriptive models: Descriptive models describe the relationships between data, for example between your customers and the products. In this way, customers can be categorized in terms of their product preferences, unlike predictive models, which are more likely to be based on probabilities. 

These variants have different goals and the underlying statistical methods differ significantly from one another.  

 

Procedure and process of predictive analytics

A predictive analytics process or approach consists of a series of steps that build on each other. The entire process is iterative, in other words, the desired result is approached in several runs; so, the steps can be repeated.

1. The objective: Here, the objectives of the analysis for the company and the industry are defined. The data sources and their availability are also checked here.

2.The data acquisition: The unifying data collection from different data sources takes place second.

3.Checking and processing the data: The most important thing about data is its quality and the reliability of the delivery. For this reason, after recording, the data is checked and cleaned and the data is made available for pending analyzes.

4.Building Predictive Models: Precise predictive models are built. The future forecasts can either refer to points in time and values (such as sales predictions for a defined point in time) or to classifications (such as a risk analysis).

5.Model test and adaptation: The models are “fed” with the data and tested, and any corrections are made.

• Provision for integration in company processes: Now you can use the findings for making decisions in the company. 

 

Benefits of predictive analytics

The advantages of predictive analytics are manifold and bring you more than just a glimpse into the future. Rather, use your years of experience and the accumulated data and develop a system that simplifies your work, recognizes risks for your company and improves decision-making. Here is an overview of the advantages of predictive analytics:

• Reduce the consumption of resources also in terms of time capacity
• Save costs
• Minimize Risks
• Optimize marketing campaigns
• Improve your operational business
• Identify fraud and protect yourself from it 

Applications for predictive analytics

Predictive analytics is widespread and used in a wide variety of disciplines. In particular, predictive analytics takes place in different areas such as:

• Marketing and CRM,
• Finance and insurance,
• Business,
• Meteorology and many more. 
• Use of predictive analytics in CRM and marketing

Predictive analytics methods are particularly used in customer relationship management (CRM). With this use, the collected customer data serve as the basis for predictions and forecasts. 

In addition to the CRM area, predictive analytics is also used in online marketing to predict click behavior or to place the right advertising at the right time.

In customer relationship management, the optimization of marketing campaigns, sales processes and customer service are key tasks. Marketing campaigns are tailored more specifically and individually to customer needs, which leads to a significant increase in conversion. 

With Predictive Analytics, you can correctly predict the buying behavior and habits of your customers and thereby advertise relevant products in a targeted manner. This way, you can retain your existing customers better and reduce the risk of losing customers. Identify the problems and act accordingly.