Predictive Analytics: Everything You Need to Know

Predictive analytics is becoming more and more popular and when one understands how it works and the results that it is able to yield, it is easy to see why. In this article, we will explain what predictive analytics models are, and how they can be utilised in a variety of industries.

What Are Predictive Analytics Models?

There are many types of analytics; however; ultimately, a predictive analytics model makes use of historical data to predict future trends. It does this by assessing historical data, identifying patterns, observing certain trends, and then predicting what future trends might be, based on that information. It makes use of three types of predictive models to achieve this. Each model has its own uses depending on what predictions one is trying to make. The three models consist of:

  • Classification: Falling under supervised machine learning, this model categorises data based on historical data. The historical data it utilises describes relationships within a specific data set. Therefore, this model is typically used to classify current and potential customers into groups. It is also often used to give yes or no answers.
  • Clustering: Classified under unsupervised learning, this model groups data according to similar attributes. One can therefore group customers into similar groups such as frequent buyers, products they are interested in, and more.
  • Time Series: This model makes use of multiple inputs that are scheduled at specific time frequencies. These can be daily, weekly, monthly, yearly, etc. this will then plot trends over time.

Where is Predictive Analytics Used?

Predictive analytics can be utilised in a variety of industries to improve the performance of a business. Some of the industries that already benefit from predictive analytics include:

  • Finance: Financial institutions such as banks make use of predictive analytics to identify potential credit risks. The models are used to analyse a customer’s history such as their previous payment behaviour and credit history. This then informs the bank whether they would be trustworthy when applying for a loan or other services.
  • Marketing and Sales: Customer data can be analysed to identify past behaviour and predict potential future buying trends. This is particularly useful within marketing and sales, as one can then maximise promotional strategies and when certain products should be marketed. For example, there might have been an increase in customers purchasing blankets a month before winter officially commences. Therefore, a marketing team can then increase their marketing efforts in the months leading up to this previous trend.
  • Logistics: Predictive analytics is able to analyse historical data pertaining to previous transportation routes and shipping times. Logistics companies can then use this data to make decisions on which routes would be best suited for faster deliveries.
  • Healthcare: The possibilities that predictive analytics can offer to the healthcare industry are life-changing. By analysing a patient’s medical history, one can identify whether or not they are susceptible to developing certain diseases. This could include diabetes, heart disease, and more.

Business Intelligence Predictive Analytics Tools

Both Qlik and Microsoft Power BI have predictive analytics capabilities. Which one you use depends on your business model and what would work best for your business. At B2IT we specialise in finding and implementing solutions within businesses that help them manage, analyse, and utilise their data more efficiently. Predictive analytics is one of the ways in which one can do this effectively.

Contact B2IT to find out whether Qlik or Microsoft Power BI is the better solution for your business.