How is Machine Learning Useful for Predictive Analytics?

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In today’s modern world, many organizations are dependent on big data. It has become an untapped resource of intelligence that supports the decision-making process and enhances operations.

However, as data continues to diversify, it is also noticeable that more and more organizations are moving towards predictive analytics to tap the data and benefit from it at scale.

How Machine Learning can Boost your Predictive Analytics?

Nearly 75% of business leaders say that analytics is the main source of growth for their business. However, only 65% of them had accepted that they have predictive analytics capabilities.

Well, let us look into the things that are preventing many organizations from achieving predictive analytics capabilities.

To be precise, the primary thing in this is applying the right set of tools at the right time, which will help the business pull out powerful insights from data present in the database.

But, to apply all this and generate value from it, one thing we should consider that big data system requires the right amount of space for storing information that data processes.

Moreover, by using AI and Machine learning algorithms, businesses can discover new statistical patterns, which also serve as a backbone for predictive analytics.

Working of Predictive Analytics

Predictive analytics, which depends on predictive modeling, is an approach that goes hand-in-hand with machine learning. This is because of the presence of a machine-learning algorithm in predictive modeling.

Predictive modeling overlaps with machine learning, and its models can be trained over time to respond to new data or values, delivering the results the business needs.

Those organizations that have an abundance of data but are still struggling to turn their data into meaningful insights can fix this problem by using both mechanisms together.

No matter what amount of data an organization has, if they are not utilizing it and are unsuccessful in converting it into meaningful insights, then this data is of no use.

Use of Predictive Analysis and Machine Learning in Different Sectors

Banking Sector

Here, predictive analytics and machine learning algorithms are highly beneficial to reduce fraud. Also, companies can use them together to map the market risks and identify growth opportunities.

Security Sector

Both mechanisms play a key role in managing an organization’s cybersecurity.

They are used together to improve the data security of an organization, reduce fraud activities, and detect such things that are happening.

Moreover, they also help in improving the services and tracking consumer behavior. This further helps in the decision-making process that directly impacts the performance of an organization.

Retail Sector

Predictive analytics and machine learning are used together by retailers to understand consumer behavior, like which consumer buys what, when, and from where.

All this helps the retailers to plan for placing the appropriate stock and plan for the events and seasonal sales.

Implementing predictive analytics together with machine learning solutions can be a boon for any organization. This will help an organization to get insights into the data present in its database.

To get the most out of their combo, an organization needs to ensure they have an appropriate architecture to support such solutions, as well as an abundance of data.

Well, to implement this, an organization needs to develop a data governance program ensuring that they record or store high quality data.

Moreover, the organization’s existing processes will need to be modified to ensure that predictive analytics and machine learning can be implemented, which will, in turn, help the organization to drive efficiency at every step.

Also, to ensure the best model is taken into the process, the organizations need to assess the problems they are facing.

Use Cases of Machine Learning for Predictive Analytics:


Netflix uses the consumers’ watching information to provide them with recommendations of similar tastes and preferences.

This happens because the company wants to keep you engaged on the website/application, thereby making sure that you continue with your subscription plan.

They post such thumbnails on the main screen, which have the highest rate of user opening. This is based on the taste of similar people like you who have used this service earlier.

They use your past viewing data to predict the bandwidth usage to use a server, which improves the load time.


Amazon uses machine learning for predictive analytics to provide you with better search results. Have you ever noticed the “Best Seller” or “Amazon’s Choice” written over some products?

This is because the use of both mechanisms inevitably helped the organization. The company shows the products with the most sales and useful reviews to you so that you keep scrolling and surfing the website with the influence.

This way of making sales worked big time for Amazon. Also, many products received ranking just because of their sales potential.


Machine learning for predictive analytics is beneficial as it helps in many ways. Predicting and curating consumer behavior, understanding the market, getting data insights, etc. are a few of its advantages

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