Predictive Analytics

Definition

Predictive analytics use statistical techniques from the fields of data mining, predictive modeling and machine learning to analyze historical and current facts to make predictions about future or unknown events. Predictive analytics is a subset of business intelligence and business analytics. 

How does predictive analytics work?

Efficient and reliable data management processes are increasingly in demand in order to optimally process, analyze and use the growing volumes of data ("Big Data"). Companies benefit from predictions that show how customers are most likely to react. The process is usually as follows:

• Determine the goals
• Collecting data and entering it into the appropriate predictive analytics software
• Review and analyze the data and clean it if necessary
• Create the predictive data and generate automatic predictive models for the future
• Integrate the data into the enterprise system for day-to-day decision making process
• Monitor and manage models

Benefits of predictive analytics

Companies can better plan thanks to predictive forecasts and are optimally prepared for customer requests. The benefits at a glance:

• Competitive advantages: Trends are identified early and qualified leads are generated. 
• Target group-oriented action: Customer satisfaction increases and customers are tied to the company in the long term.
• Learning from historical data: Decisions are based on the evaluation of historical data in order to be able to act better in the future.
• Minimize costs and risks
  
  

Definition of terms

• Descriptive Analytics
  • Learning from the past and trying to understand effects on the present
• Diagnostic Analytics
  • Gründe, Auswirkungen, Folgen: Warum ist etwas passiert?
Reasons, effects, consequences: Why did something happen?
• Predictive Analytics
  • Looking into the future and predicting: What will happen?
• Prescriptive Analytics
  • Recommendations for action to influence future events

  Predictive Analytics in the company

  For companies, such predictive models are a great asset for identifying risks and opportunities, since, among other things, pattern recognition of past data has proven to be very effective. The most important effect of this approach is that predictive analytics provides certain probabilities for each individual variable to support organizational processes and, in this case, to optimize sales planning as well as replenishment of a company. For example, predictive analytics supports trend and pattern recognition in sales planning, including effects of seasons and holidays on buying behavior. To use predictive analytics, you need appropriate software that can handle mass data.

  Advantages of predictive analytics in sales planning

  One of the major advantages of using these methods is the very accurate prediction of future sales figures and the related planning and organizational benefits. Whether in production, transportation or ordering, the forecasts can make each of these areas more efficient and enable new business models to be integrated. In general, therefore, value creation is increased and the newly gained resources can be invested in the company.

  Other uses of predictive analytics

  In addition to these areas, predictive analytics methods are also used in marketing, market research, human resources management or fraud detection, for example by credit institutions. In production, predictive analytics can identify when which machine needs maintenance before breakdowns occur.