How Predictive Analytics can improve your business

Harnessing data has become one of the most pivotal tasks today. Companies are consistently searching for techniques that help generate huge amounts of data which can be studied and electronically processed to draw predictions.

Predictive Analytics is a dedicated term wherein businesses use historical data with the intent of guessing future outcomes. It is an advanced tool which helps companies in anticipating changes in market behavior. It also empowers companies to prepare, modify, and adjust, their operational, production, and marketing methods.

For organizations, Predictive Analytics is a sought-after tool as it’s extremely potent in creating a personalized experience for consumers. Let us take a look at why this advanced tool is being increasingly used by companies across the globe.

Outperform Competitors

An effective predictive model helps in anticipating the behavioral patterns of users amongst your target audience.
This is unequivocally a competitive advantage as it helps you track down the aspirations of a user.
By creating personalized content, your company stands a better chance of winning the loyalty of users.

Create Opportunities

As Predictive Analytics render crucial insights into buying patterns of potential and existing customers, you can push customers by offering discounts and privileges to generate additional revenue streams. By collecting valuable data of users, your company can diversify its product range as well.

Also Read: Why Health Care Organizations are focussing on Predictive Analytics

Understand Target Audience

Using historical or past data, predictive analytics can reveal the preferences of the target audience.
For instance, while recording a specific user’s data, you can note down the current preferences whereas once this data is processed using this advanced tool, you can make a well—informed guess regarding a user’s future preference.

This is a major benefit enabling businesses to understand its users or customers.


One of the most crucial aspects of running a business is to understand how to retain its stakeholders. Regardless of the type of company, it is not easy to acquire and then retain a customer or an employee. Both of these stakeholders need to be understood by the company.

Predictive Analytics as a method assists in understanding the reason behind the exiting of such stakeholders.
For instance, a retail venture can use its predictive analytical skills to take note of reasons that push customers to choose their competition.

Operational Improvement

A whole lot of companies use predictive analytics to improve the functioning of their inbound and outbound processes.
For instance, Airline companies use it to determine ticket prices in the coming months. Some companies use predictive models to anticipate the inventory levels necessary for sustaining through a financial year.

With such varied application, predictive analytics is undoubtedly a significant tool which can either make or break a business.
It does take years to accumulate valuable data and create a full-fledged roadmap but once that stage is through, you can be assured of positive results and steady growth.

Why Health Care Organizations are focussing on Predictive Analytics

As an integral part of every major healthcare facility, the process of Revenue Cycle Management generates a huge amount of data, constantly used by stakeholders within the health care industry.

This data is carefully managed, stored, and interpreted by every health care facility to improve the quality of patient care whilst simultaneously reducing claim denial cases to increase revenue collection.

The ever-growing need for establishing a better grip on data analytics also directed the health care industry to look deeper into Predictive Analytics. As a concept adopted and appreciated by health care industry stalwarts, Predictive Analytics, is touted to use data in ways never imagined before.

Black Book Research conducted a survey where financial juggernauts and revenue cycle leaders of approximately 1500 hospitals were questioned regarding the relevance of Predictive Analytics. 76 percent of these individuals shared concrete plans to invest 10 percent (or more) of their respective facilities’ IT budget towards Predictive Analytics. Here’s why-

Reckoned as a game-changer in health care, facilities use Predictive Analytics to forecast revenue collection and rectify errors that can affect the flow of revenue. Besides, more than 50 percent of health care leaders believe that predictive analytics could help reduce overall costs by 15 percent or more.

Predicting contingencies in the revenue cycle process

For health care facilities, it is pertinent to conduct eligibility checks during patient registration.
These checks need to be rechecked at the time of claim submission. If both these steps are not completed with coherence, providers are at risk of experiencing a claim denial as the patient might outrun coverage from a specific insurance payer.
This contingency hampers cash flow and increases administrative expense. But predictive analytics can recognize such breakdowns and bring them to your attention at the earliest.

Payers’ remittance pattern

Predictive Analytics enables providers to determine when a specific payer will complete claim submission. The approach is so accurate that facilities can even predict the date and time of a specific remittance. Innovative machine learning techniques are put to use to achieve a foresight of such sophistication.
This information empowers health care providers to manage their revenue cycle operations with ease and efficiency.

Predicting Claim Denials before they take place

On average, a hospital carries a risk of losing $5 million annually towards claim denials. Even though 63 percent of such denials can be rectified, the entire process causes a surge in administrative overheads which eventually eats into the revenue of the facility.
Predictive Analytics helps the facility in identifying claims (before submission) which have a higher probability of denial. This creates a window for employees to correct these claims driving an increase in the number of clean claims.

Foresee changes in payer rules

Payer-specific rules for claims adjudication are constantly changing and if the facility’s revenue cycle doesn’t improvise such changes, there is a good chance of a claim being denied or delayed. In both scenarios, the facility stands to lose a considerable chunk of revenue whilst incurring additional costs.
With the help of predictive analytics and machine learning, operators can make necessary changes in advance to adjust accordingly and improve the function of the revenue cycle.