Feb 01

Predictive Analytics

At Basal Smart Solutions we have developed a new form of predictive analytic that performs in ways and with accuracy unlike anything else in the industry. To fully understand this tool, we must first understand exactly what is meant by predictive analytics.

First, what is Analytics?

Simply put, analytics is a field of software engineering, or in a more general sense, computer science that uses mathematical techniques such as, but not limited to, statistics, and machine learning to find patterns in data.

How does that get to be a Predictive Model or Analytic?

Predictive analytics is a type of advanced mathematics that takes an analyzed dataset and makes predictions about the future outcome of that data set. To do this, it uses statistical models, some advanced data mining techniques, in addition to machine learning (a type of artificial intelligence discussed later in this post).

The current and growing interest in predictive analytics is from the capability it provides companies and investors, as well as individuals, to identify risks as well as opportunities.

Data scientists everywhere are working with predictive models for their clients, and there is no shortage of applications. There have been a few new methodologies that basically involve just throwing more computer processing power at a given problem. Very few developments have involved extending the capabilities by adding new, more elegant techniques that require less computer processing capabilities (thereby reducing cloud computing costs) while allowing predictive models to increase in accuracy as well as alter the timeframes in which they are accurate.

The very recent explosion of predictive analytics use has been because of the explosion of available datasets. The world is storing data at a rate faster than ever before. This is from log files, social media, blogs, news websites, company repositories, images, video, and several other sources. All this data is available from repositories all over the world, and typically data is bought and sold between companies – both legally and illegally. We do not use and encourage no one else to use illegally acquired data.

In order to gain useful results from this data, computer scientists (in this case usually referred to as data scientists) use deep learning and machine learning (branches of artificial intelligence) algorithms to find patterns in that data and then extend those patterns into the future thereby making some form of prediction of future events. The more accurate the predictive analytic the more useful that prediction. That accuracy is based on how accurate the data set is and the type of analytic used.

Typically, the techniques used to do this are linear regression, neural networks, and decision trees. In our case we use chaos theory and nonlinear dynamics (discussed later).

Is there more than one kind of predictive model already?

Of course, and they can generally and typically be divided into three broad categories:

  • Classification models,
  • Clustering models, and
  • Time Series models.

Arguably our technique bridges between all of these to produce the best result possible in the least amount of processor consumption based on the input dataset.

In these three there are also two more broad terms that are useful to understand. Some are supervised and others are unsupervised models. These are exactly what they sound like. One basically requires human supervision the other does not. Any of them can be made to be supervised and depending on the type of analytic used that may be desirable.

Classification models are useful when your desired output is divided into categories. In other words, this type of model uses an algorithm that separates data into categories. In general, this is a type of supervised model. It builds these categories and classifies data based on historical data and the descriptions of relationships within a given dataset.

As a prime example – these models are used by business-to-consumer companies to classify customers or potential customers into groups. These can also be used to answer simple questions as the backbone for a type of chatbot. Classification models are also found in fraud detection and credit risk evaluation in the banking industry.

Clustering models can be either supervised or unsupervised. These group data based on similar characteristics. This can be something as simple as an e-commerce website that separates customers into similar groups based on common purchasing patterns. For instance, at the bottom of some Amazon pages links can be seen that are titles “customers also purchase” or “commonly purchased together.” These are also used by companies like Netflix to show a selection of movies and shows to stream that clients with your viewing background have watched and enjoyed.

Time Series models can be across any period of time (days, weeks, months, seconds, nanoseconds, etc.). In these datasets cyclical behavior is identified that can predict behavior over different periods of time into the future. Industry wide these tend to be moving average, and regression models. In our case this is far more advanced and will be discussed later, however what is important at this stage is that the time period over which the prediction can be made is proportional in ways to the time resolution of the data available.


Industries that use predictive analytics include, but are certainly not limited to:

  • Banking/Investment
    • Financial Fraud Detection.
    • Credit Risk Evaluation.
    • Stock Trends Prediction.
  • Marketing and Sales
    • Predict Churn.
    • Identify dissatisfied clients faster.
    • Help promote customer retention.
  • Supply Chain
    • Help meet demand without acquiring and having to store overstock.
  • Social Media
    • Increase viewership.
    • Increase engagement rate.

As can be seen, there are many benefits of using this type of technique, therefore the utility of an improvement can be incredibly valuable. Benefits of predictive modeling are many, however ours finds use in the following industries:

  • Improved security
    • Keep data more secure by predicting what a normal network load would look like and be able to trigger an alert when something falls out of pattern.
  • Risk reduction
    • Understand when a customer is not a worthy credit risk.
    • Determine if insurance coverage is reasonable.
  • Increasing operational efficiency
    • Bad workflow design can be a place where large amounts of productivity are lost.
  • Decision Making
    • Expansion of a product line might be in order if a market can be predicted.
    • Where to put a new facility can be a make-or-break decision for a company and improved predictive analytics can help with this process.
  • Social Media
    • Laser focusing on a particular demographic is a challenge on social media, predictive analytics can help predict how that group will be looking for information.

Our One For All Social online tool brings together the ability to post to multiple social platforms and captures the available analytics providing our users with the ability to make informed decisions.



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