Business owners usually work with information that pertains to events that have already happened, such as sales accounts after they close, expenses after spending, and employee records.

But that is not enough to make the best decisions. It will help if businesses can draw out a plan for the future. And, for that, businesses should know the future. For example, it will be of great help to businesses if they can figure out things, such as how many sales will be in the next year. Or how many products should be in stock to meet that demand?

Businesses should look for patterns in their existing records about past events to get the answers to such questions concerning the future, which can help them project things forward.

The process is called predictive analytics, which has many different applications, such as:

• Manufacturers can analyze past failures to formulate impactful strategies
•  Manufacturers can know in advance when to service equipment to prevent breakdowns
• Businesses can analyze their best target customer group to create impactful marketing campaigns to get new customers.

Despite predictive analytics is a new domain, the associated statistical techniques — Bayesian analysis and regression – are more than 200 years old.

However, modern predictive analytics started after the development of digital computing in the 1950s.

From the late 1950s, modern algorithms, including neural networks, started to be developed. And in recent years, there have been notable developments in predictive analytics and artificial intelligence.

Data storage has become cost-effective due to the availability of the cloud. In addition, data has become more complex, constituting not only structured records but images, sound files and documents.

Moreover, the computational power has become more, meaning handling complex tasks has now become easier.

Also, the notable developments in the software can leverage all these developments to render building, testing, deploying and using predictive analytics more reliable than ever before, apart from the simplicity element.

Industries Using Predictive Analytics

Due to the immense capabilities of predictive analytics, it is found in ever-growing use cases and industries.

Here are some examples of industrial usage of predictive analytics.

Financial Services

In the financial services industry, the prediction of stock prices and other financial indicators is essential. And they can attain that objective through predictive analytics.

At the same time, banks, mortgage lenders, insurance and credit card companies should identify fraudulent transactions.

Data analytics can help such businesses to identify fraudulent transactions. In addition, businesses can offer the best rates to their best customers and sell their financial products to new customers due to the knowledge of the future market from predictive analytics.

Retail

Predictive analytics has proved to be immensely valuable to retail and other consumer-facing industries, such as telecom.

Such businesses use predictive analytics to strengthen their customer relationships or handle customer relationships.

With predictive analytics, businesses can determine in advance whether their services are adequate to keep customers happy.

Predictive analytics in retailhas already made many retailers grow their businesses significantly.

Likewise, businesses can figure out whether their customers will be unhappy or likely to move to another business. And this is called churn analysis.

Airlines

With predictive analytics, airline companies can predict in advance how many seats they can fill over a period. However, such companies confront inaccuracies in the predictive results.

Transport and Logistics Companies

With predictive analytics, transport and logistics companies can effectively optimize their supply chains. Again, the analytics poses some inefficiencies, too, in some cases.

Conclusion

It has become important for companies belonging to different segments to know about their future business scenarios. It can help them to formulate effective strategies for the future. As a result, businesses can better respond to customer needs to strengthen their brand and reap more profits.