Predictive analytics are a series of intelligent algorithms which consider historical data, predictive modelling, and computer intelligence/learning to predict future trends. Nowadays, with multiple contradictory sources of data, businesses need a toolset that eliminates both the complexity of online media and the confusion caused by competition between the different options.

As a result, more businesses are making the shift towards using predictive analytics to increase their bottom line and gain valuable competitive advantage. We explain why predictive analytics are important and how to use them.

Why predictive analytics is important

Companies can utilise predictive analytics to solve challenging issues and highlight new opportunities. Below are a few uses of predictive analytics:

  • Developing operations: Predictive analytics can be used to estimate stock levels, or the levels of service that will be required. For example, a restaurant owner could use predictive analytics to predict how many tables will be used on an evening. This allows companies to run their operations more smoothly.
  • Minimise risk: Companies can minimise risk by using predictive analytics. For example, for banks that offer loans they can use predictive analytics to measure an individual’s credit score, and ultimately how likely they are to pay back the loan.
  • Fraud detection: Similarly, to minimising risk, predictive analytics can aid in detecting fraud. By using the analytical software to detect criminal behaviour, it can enable organisations to prevent it in the future.
  • Optimising marketing campaigns: Predictive analytics have the capability to estimate consumer response rate and highlight any potential opportunities. By using the software to predict consumer response rate, organisations can attract and retain valuable customers.

How to use predictive analytics

The three main techniques for predictive analytics:

There are several techniques that data scientists use to map predictive analytics:

  1. Decision trees
    • Decision trees allow data scientists to visualise a path of decisions. As the decision tree progresses the branches represent a possible choice between two or more options, which lead to the leaves as classifications (yes or no). Decision trees are easy to understand and can overcome missing values, making them one of the more valuable techniques.
  2. Regression
    • Regression is used with continuous data, not binary data. Regression can be applied in different ways depending on the different data questions. For example, if there are multiple independent variables which could affect the outcome, multiple regression would be the most suitable. However, if only one independent variable could affect the outcome, then linear regression would the most appropriate. Another type of regression is logistic regression. Where linear and multiple regression use continuous data, logistic regression is used when the dependant variable is binary.
  3. Neural Network
    • Neural Networks is arguably the most complex technique out of the three. It is becoming more widely used by companies as perfectly linear patterns are scarce. Neural Networks use artificial intelligence to recognise patterns and learn from previous data to make predictions.

What are the inputs for building predictive analytics models?

Input data is the data that we use within the models for the predictive analysis. The two types are internal data (first party) and external data (third party).

  • First-Party Data: This is the data that can be found on the company website, the social media channels, and past analytical data from the companies’ processes. First party data can be structured or unstructured, and is all owned by the company, yet is incredibly valuable for data analysis.
  • Third-party Data: This data is external and can be purchased from data providers. However, it may not all be available at the customer level, which is where segmentation is applicable.

Examples of predictive analytics

  • Retail
    • For retail, predictive analytics will improve their sales position by forecasting consumer needs. For example, Amazon recommendations. When a consumer makes a purchase, the software gives examples of what other consumers purchased which are similar
  • Weather
    • Due to predictive analytics models, weather forecasting has exponentially improved over the years. Weather forecasting can stretch as far as 10 days thanks to modern predictive analytics, and bad storms and hurricanes can now be predicted 72hrs in advance, allowing people to prepare. Data is fed into models that represent our atmospheric and physical pressures.
  • Health
    • By tracking the general public’s health records, predictive analytics can forecast flu and virus outbreaks. Predictive analytics have been used throughout the coronavirus pandemic to predict which areas would suffer the most and gauge how many people would get infected. This helped many countries prepare for and deal with the Covid-19 pandemic.
  • Sports
    • Predictive analytics can predict the likely outcome of sports games. It considers the history of matches and how teams perform combined with social media sentiments and assesses the probability of a particular team winning.
  • Insurance/risk assessment
    • Insurance companies and loan providers can use predictive analytics to predict and catch fraudulent activity, forecast future losses, and plan out marketing campaigns. Predictive analytics give better insight into the risks these organisations face.

The future of predictive analytics

All the techniques listed above are becoming more frequently used by companies as they adopt the use of the cloud and predictive analytics.

Before the cloud was introduced, predictive analytics was near enough impossible, as computer software simply didn’t have the storage space to cope with the amount of digital data required to run predictive analytics. The cloud can offer companies the space to combine enormous data sets and ultimately scale their processes. Many new cloud-based analytics programs are emerging, that can find patterns in data for a companies set of goals.

Benefits that come from using cloud-based analytics include the reduction in manual labour and time as the software is run on AI and will operate behind the scenes without requiring human interference, but also there is more room for interpretation and extrapolation. Using the cloud will also allow for more granular assessment into consumer behaviour, due to the volume or data and accuracy of the software.

BOSCO™ is a predictive analytics software which enables companies to see where their marketing budget is best spent. By using a combination of first and third-party data, BOSCO™ can predict trends and help you get the most return from your marketing budget.

If you need help setting up predictive analytics for your company or would like to know more about BOSCO™, book a demo with our team.