What is Predictive Analytics? Top 10 Use Cases of Predictive Analytics


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In simple terms, predictive analytics helps to predict future trends and patterns using historical data. It uses different data patterns and identifies the correlations between the variables. It helps to reduce business risks and costs by predicting the future values of certain variables.

For instance, the organization can determine the profits for the coming months by analyzing the output and revenue of the company. The model focuses on two variables; one is dependent while the other is independent.

There are numerous predictive analytics models such as classification models, clustering forecasts, time series, etc. These models help predict future variables based on the insights and data arranged in multiple ways.

10 Top Use Cases of Predictive Analytics

Today, businesses regularly use predictive analytics to analyze the target customer to gain operational results. The list of predictive analytics applications in various industries is never-ending. Therefore, below are some of the everyday use cases for predictive analysis in multiple domains:

1. Churn Prevention

When a business loses a customer, it has to replace the loss of revenue by bringing a new customer. It proves to be expensive as the cost of acquiring a new customer is much higher than retaining the existing customer.

Predictive analytics models help prevent churn in your customer base by analyzing the dissatisfaction among your current customers and identifying customer segments at most risk for leaving. Businesses can make the necessary modifications using predictive data to keep customers happy and satisfied, eventually protecting their revenue.

Key Industries: Banking, Telecommunications, Retail, Automotive, Insurance

2. Customer Lifetime Value

It is pretty challenging to identify the customer in the market who is most likely to spend large amounts of money consistently over a long period.

This kind of data through predictive analytics use case allows the business to optimize their marketing strategies to gain customers with the most significant lifetime value towards your company and product.

Key Industries: Insurance, Telecommunications, Banking, Retail

3. Customer Segmentation

Customer segmentation enables you to group the customer by shared traits. Different businesses determine their market differently depending on the aspects that offer the most value to their company, products, and services.

Profound use of predictive analytics techniques helps target the markets based on accurate insights and indicators and analyze the segments of those most interested in what your company offers. Using these predictive analytics applications, you can make data-driven decisions for each part of your business. The same data also enables you to potentially identify the entire markets that you didn’t even know existed.

Key Industries: Banking, Pharmaceutical, Automotive, Retail, Insurance, Telecommunications, Utilities

4. Next Best Action

Determining your primary marketing goals and customers is a critical use case for predictive analytics. It only provides an incomplete picture of what your marketing approach should be.

Predictive data analytics is the best way to approach such individual customers within given segments and analyze everything, from buying patterns to customer behavior and interactions, which offers you insights into the best times and modes to connect those customers.

Key Industries: Banking, Telecommunications, Insurance, Education

5. Predictive Maintenance

In businesses, maintaining cost plays an essential role in increasing revenue. It is difficult for an organization with a significant investment in equipment and infrastructure to manage capital outlay. It’s where predictive maintenance machine learning techniques come in.

By analyzing the insights and metrics of the maintenance cycle of technical equipment, companies can set timelines for maintenance events and upcoming expenditure requirements by streamlining the maintenance cost and downtime. You can simplify your maintenance costs by performing actions that can increase the lifespan of your equipment.

Commonly, most systems become inoperable during maintenance. Predictive analytics use cases will help you with the best time to perform maintenance to avoid lost revenue and dissatisfied customers.

Key Industries: Automotive, Logistic & Transportation, Oil & Gas, Manufacture, Utilities

6. Product Propensity

Product propensity combines purchasing activity and behavior data with online behavior metrics from social media and e-commerce. It enables you to identify the customer’s interest in buying your product and services and the medium to reach those customers.

It helps to correlate the data to provide insights from different campaigns and social media channels for your business services and products. Predictive analytics applications never fail to maximize those channels that have the best chance of producing significant revenue.

Key Industries: Banking, Insurance, Retail

7. Quality Assurance

Quality assurance is a key to your customer experience and the bottom line to all your operational expenses.

Ineffective quality control will affect your customer satisfaction scale and ultimately impact the revenue and market share. Also, it leads to more customer support costs, warranty issues, and repairs for inefficient manufacturing. Industries using predictive analytics use cases can provide insights into potential quality issues and trends before they become critical issues.

Predictive analytics use cases can help identify high-risk modules in your application, prioritize critical areas, and reduce time to market through shift-left testing. With predictive analytics, your approach to QA shifts from reactive to proactive.

Key Industries: Pharmaceutical, Manufacturing, Automotive, Logistics and Transportation, Utilities

8. Risk Modeling

Prevention and prediction are two sides of the same coin. Risk comes in various forms and initiates from a variety of sources. Predictive analytics can draw potential risk areas from significant data insights collected from most organizations.

It sorts them to analyze the potential risks and suggests the development of situations that can affect the business. By combining the results of the predictive analytics applications with the risk management approach, companies can evaluate the risk issues and decide how to mitigate those risk factors.

For instance, health organizations generate risk scores to identify the patients who might benefit from enhanced services, preventative care, and wellness consultations.

Key Industries: Banking, Manufacturing, Automotive, Logistics and Transportation, Utilities, Oil and Gas Utilities, Pharmaceuticals

9. Sentiment Analysis

In this era of the online world, it is difficult to be everywhere at all times. Reviewing and capturing everything said about your business or organization is virtually impossible.

However, by crawling tools with customer posts and feedback, you can create analytics that can give you a clear picture of your business reputation within the market. Predictive analytics models provide you with proactive recommendations as the best way to enhance that reputation.

Key Industries: Pharmaceutical, Education, Retail, Telecommunications, Insurance, Entertainment

10. Up-Selling and Cross-Selling

The customer base is the source of your business’s existing revenue and revenue growth. Eventually, maximizing the possible revenue opportunities within your product set and target market segment becomes critical.

Purchasing history data can be utilized to determine which goods and services might benefit from being offered together. Predictive analytics use case provides suggestions on market segments to increase your customer value and revenue derived from your customer. The business sales are raised, and your customer walks away with items that work together.

Key Industries: Banking, Retail, Telecommunications, Insurance, Ecommerce

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Mitul Makadia
Mitul Makadia is Founder of Maruti Techlabs and a true technophile. With his industry experience, he has rapidly developed Maruti Techlabs in specialized services like Chatbot Development, Artificial Intelligence, Natural Language Processing and Machine Learning. Makadia has considerable expertise in Chatbot Development and NLP.


  1. As Garter rightly predicted, more businesses are using predictive analytics and leveraging these analytics on a daily basis by democratizing data and handing augmented analytics solutions to business users. When the business transitions business users to Citizen Data Scientists, it is easier to achieve results, accurately forecast and predict. With auto-suggestions and recommendations, business users can select the right type of algorithm and analytical technique to get the results they need for the type of data they are analyzing. When the business implements predictive analytics at this level, it should review and change the culture to support the new role. Courses like https://www.udemy.com/share/103I2cCUsTc1laQXQ=/ allow the business to build the foundation, understand the new roles and how they will work with existing data science and business analysis models, and how to use the solutions and the techniques.


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