What is Predictive Analytics: Definition, Concepts, and Application

Data analysis is necessary for companies, starting from large and small-scale companies. The study is used to find out the problems that have occurred, predict the future, and determine the right solution to apply. From this explanation, three analytical methods carry out three functions, respectively.

The three methods are predictive analysis, descriptive analysis, and prescriptive analysis. However, this article will focus on discussing predictive analytics. To find out more about predictive analytics, see the following explanation.

Predictive Analytics Overview

Predictive analytics or predictive analysis is an analytical method for processing data sets to predict future conditions. Business intelligence commonly uses this method to see business conditions based on historical data, which are processed with various models and sophisticated processing machines.

Predictive analytics is performed to see conditions in the future and various possibilities that may occur. Companies can prepare for bad things that might happen as well as design solutions. Thus, the company will be better prepared to face business challenges in the future.

Predictive Analytics Application

Predictive analytics can be applied in various industries according to their individual needs. The following are two examples of using predictive analytics for companies.

1. Hospitality

Lodging companies desperately need predictive analytics to see how many visitors will stay at their establishments. A customer service that handles hotel calls and reservations via an online site can see how many visitors will arrive in the next week.

This analysis can maximize hotel performance by predicting the number of servers, food, and so on needed for the next few days. The more ready a company is to face the challenges that will come, the better its evaluation will be in front of consumers.

2. Educational Institution

Not only needed in business companies, but predictive analytics is also required for educational institutions, such as universities. With a wider volume and scale, universities will accept new students from all over the country. From there, it is necessary to have a system to select and manage various administrative needs.

Without using predictive analysis, it is possible that a university is not ready to accept an overwhelming number of students in a certain period.

Not only that, but the analysis method is also used to see how much fees are needed by the campus and how much will come from students.

Predictive Analytics Techniques

To perform predictive analysis, several techniques can be by business intelligence, such as decision trees, text analytics, neural networks, regression models, and others. A decision tree is one of the most well-known and fairly easy predictive analytics techniques.

Decision trees are visual data describing decisions and the possibilities of carrying out these decisions. Every decision will have various options, and each case will have its risks.

Why is this technique the most commonly used? This is because the decision tree technique combines visual data so that it is easily understood by ordinary people who need help understanding data analysis.

However, in the end, the data processing results must be conveyed and understood by all company employees, so they know how to proceed.

Predictive Analytics Process

Several processes go through to be able to make a perfect predictive analysis. The perfect Analysis is needed to find and exploit the patterns in the data to detect risks and opportunities. So what are the analytical predictive processes:

1. Defining the Project

Determine project outcomes to be predicted, deliverables, business scope, business goals and prospects, and identify data sets to be used. Defining your project will make it easier for you to do analytics later.

2. Perform Data Collection

Of course, data is needed to do analytics. This data can be obtained from various sources according to the project to be predicted by collecting and collecting data. It will simplify the analysis process and provide a complete view of customer interactions.

3. Data Analysis

The next process is conducting data analysis. Data analysis is the process of inspecting, cleaning, transforming and modelling data to find useful information to determine predictive Analysis. After completing data analysis, the process of concluding data is carried out from all the data that has been analyzed.

4. Statistical Analysis

After analyzing the data and making conclusions about all the data, proceed with statistical Analysis. This Analysis is carried out to validate assumptions, hypotheses, and tests using standard statistical models.

5. Modeling

Predictive modelling is carried out to create an accurate predictive model about the future. Modelling also provides the ability to determine the best solution by conducting multi-model evaluations.

6. Predictive Model Deployment

In this process, the deployment model provides options for deploying analytical results. The analytical results are propagated into the decision-making process every day, which will be used to get results, reports and output by automating decisions based on modelling.

7. Monitoring

Monitoring is carried out to monitor the model’s performance to ensure that the predictive analytical process can provide the expected results.

Predictive analytics models

Models are the foundation when performing predictive analytics – Models allow users to transform old data and turn it into actionable insights, which in turn will create positive long-term results. Some types of predictive models include:

1. Customer Lifetime Value Model: This predictive model determines which customers are most likely to invest more in your products and services.

2. Customer Segmentation Model: Segmenting group customers based on the behaviour and characteristics of customers who make the same purchase

3. Predictive Maintenance Model: Forecasting the possibility of critical equipment damage occurring.

4. Quality Assurance Model: Find and prevent defects to avoid disappointment and additional costs when providing products or services to customers.

Descriptive Analytics Funtion

Several organizations and companies have used predictive analytics to help increase their sales and analyze competitors’ capabilities. The following is an example of using predictive analytics.

1. Detect errors/cheating

Detection of the potential for an error in a system must be done to prevent the error from happening. A company or organization can predict when an error/damage will occur through a predictive analytical method.

The company can also study behaviours as an indicator that the error/damage has the potential to occur. For example, a production company predicts how many productions are most likely to produce defective/damaged products.

Another example of application is to prevent the occurrence of a fraud or criminal act by predicting a behaviour (variables) that indicates a threat.

2. Optimizing marketing campaigns

The predictive model helps businesses attract and retain their customers, especially the most profitable ones. A company can use predictive methods to predict potential customers who match the products they offer and what customers currently need. So, companies can optimize the marketing campaigns they will run.

3. Operations research

Many companies use predictive analytics to plan raw material inventory when stock will run out, when to order etc.

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