Predictive Analytics – Examples, Algorithms, Advantages and Techniques

What is Predictive Analytics?

Predictive analytics analyzes contemporary and historical facts to make future predictions using different statistical methods like data mining, machine learning, AI, and predictive modeling. It is a process in which collected data and statistics are analyzed to predict future outcomes.

Modern technology allows you to generate future insights that are accurate. The science of predictive analyses is about looking at past patterns to assess when certain key events are likely to happen again.

Taking a close look at the old collected data also helps in the identification of future market trends.

How does Predictive Analytics work?

Many techniques can be used to perform predictive analyses.

These techniques are used to create predictive data models, using which a statistician or a data scientist can predict future trends. But first, the organization that wants to perform predictive analysis needs to create a database.

Different techniques are employed by data scientists to organize this data in a way that makes sense.

This organized data is then further analyzed to create predictive models.

Predictive models virtually try to foresee the future by figuring out the patterns in this organized data.

Examples of Different Industries where Predictive Analytics is Used

Various industries use predictive analytics for their benefit. Some of the examples are:

1. Insurance Companies

Insurance companies use predictive analytics to determine the likelihood of a successful claim in the future. This information helps them to keep their finances in check for any future contingencies.

2. Law enforcement

Law enforcement agencies use this tool to look at crime trends. This allows law enforcement agencies to predict any future upsurges in crime in particular locations. They can use this information to increase law enforcement officers in such areas beforehand, thus reducing criminal activity chances.

3. Marketing

Marketers can use this tool to study the behavioral patterns of their customers. Using this information, they can assess the period in which a consumer is most likely to spend their money. This, in turn, allows marketers to organize successful campaigns.

4. Financial Institutions

Banks and other financial institutions can study the pattern of behavior of their customers. This allows them to reduce fraudulent activity by analyzing suspicious behavior.

5. Automobile

This tool can be used by automobile manufacturers to study the behavior of their consumers. This information allows automobile manufacturers to develop technology that provides their customers with ease of use.

6. Trading

Traders can look at the market’s past activity to decide whether they need to sell or buy shares of a company. They can also predict the rise and fall in a company’s share by using predictive analyses.

7. Aerospace

Airlines can use predictive analysis to figure out the reliability of an airplane. They can also use this data for maintenance purposes including, refueling and availability of the plane.

8. Retail

Predictive analytics tools for retail can improve its sales position and establish better relations with the target audiences.

Amazon’s recommendations are one of the key examples of predictive analytics use in retail. For instance, when you buy an item, you must have seen the list of similar items that other buyers have purchased.

9. Health

Predictive analytics tools for healthcare are also quite common, and Google Flu Trends (GFT) is one example of all this. It helped in predicting flu patterns by tracking and comparing health behaviors and historical data.

It is quite useful in predicting public health issues and epidemics.

10. Sports

Bing Predicts one of the common examples of predictive analytics, which is a prediction system of Bing Search Engine by Microsoft. Different statistics and social media sentiment are used by it for making assessments.

Another example suggested by ‘what is Predictive Analytics Guides’ is “Moneyball,” which is based upon a book regarding how the Oakland Athletics baseball team utilized different evidence-based data and analytics for assembling a competitive team.

11. Weather

Understanding’ what is predictive analytics’ has been quite useful in forecasting the weather has been quite revolutionary.

Because of today’s predictive analytics tools, 96-hours predictions of hurricanes are more accurate than the 24-hr. forecasts 40 years ago. The data from the satellite monitoring of land and atmosphere is fed in data models to make better future events predictions.

In addition to these 11 examples of predictive analytics, many other sectors like energy, social media, advertising, etc. use it for foreseeing future outcomes. Many other industries use predictive analyses to assess their future options and gain the first mover’s advantage.

Let us now have a look at the advantages of Predictive Analytics-

Advantages

Predictive analytics is the latest tool in the market right now.

Its novel nature is not the only plus point of this dynamic tool. It is also the most accurate and reliable tool in comparison to its peers.

The most beneficial aspect of this tool is reducing the risk by enabling an organization to understand the market in a detailed manner. This thorough understanding allows the organization to create contingency plans for the future.

Predictive analytics allows its users access to detailed real-time insights into any business. Predictive analysis can help the user to ascertain different forms of patterns, allowing them to relocate their time and effort towards more pressing matters.

Companies use the help of external developers to design tailor-made predictive analytics tools for their use.

Predictive Analytics Models

Vendors such as Acxiom, IBM, SAS Institute, and many more offer predictive analytics tools. They create custom models under the needs of the organization. Some of the predictive models are listed below:

1. Customer Lifetime Value Model

This allows the organization to identify customers that will be more interested in procuring more services from the organization.

2. Customer Segmentation Model

This model allows the organization to group customers based on their preferences and choices.

3. Predictive Maintenance Model

This allows an organization to keep tabs on the upcoming maintenance of expensive equipment and assets. This lowers the chances of equipment breaking down.

4. Quality Assurance Model

This allows the organization to detect any defective product. This enables the organization to provide its customers with a high-quality product.

Common Predictive Algorithms

The classification of predictive analytics algorithms suggested by ‘what is predictive analytics guides’ can be done in two groups-

1. Machine Learning

It is associated with the structural data that we generally notice in a table. It comprises linear as well as nonlinear varieties.

2. Deep Learning 

This group is understood as a subset of machine learning, which effectively deals with video, audio, images, and texts.

Some of the common algorithms of predictive analytics are-

• Random Forest
• Generalized Linear Model (GLM)
• Gradient Boosted Model (GBM)
• K-Means
• Prophet

Predictive Modeling Techniques

The most standard predictive models include decision trees, regressions techniques, and neural networks. Let’s take a closer look at them to understand how they function:

1. Decision trees

A decision tree is a diagram or chart that people use to determine a course of action or show a statistical probability. It forms the outline of the namesake woody plant, usually upright but sometimes lying on its side.

Each branch of the decision tree represents a possible decision, outcome, or reaction. The farthest branches on the tree represent the results.

2. Regression analysis

In statistical modeling, regression analysis is a set of statistical processes for estimating the relationships between a dependent variable and one or more independent variables.

3. Neural network

A neural network is a series of algorithms that endeavors to recognize underlying relationships in a set of data through a process that mimics how the human brain operates. In this sense, neural networks refer to systems of neurons, either organic or artificial.

Neural networks can adapt to changing input. So, the network generates the best possible result without needing to redesign the output criteria.

Misinformation

People often tend to conflate predictive analytics with machine learning, but they are not the same. Machine learning is a tiny cog in the vast machinery called predictive analytics.

Machine learning allows the user to sift through and understand new data. Predictive analytics uses this structured data to predict future outcomes.

In a nutshell, machine learning is a part of predictive analyses, rather than a similar process.

Finally, let us go through some of the common predictive analytics tools here and now-

Predictive Analytics Tools

• EverString
• Infer
• Radius
• Halo
• Board
• Statistica Decisioning Platform
• SAS Advanced Analytics
• RapidMiner Studio
• IBM SPSS
• SAP HANA
• TIBCO Statistica
• Oracle DataScience, etc.

Conclusion

Predictive analytics is helpful to multiple organizations. It works by predicting future trends by studying patterns in old data.

Any company that wants to ensure its future should incorporate predictive analytics.

Now, on the concluding note, we would like to see your predictive analytics definition in the comment section below.