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A Comprehensive Guide to Revenue Cycle Analytics- All You Need to Know

Revenue cycle analytics is the process of analyzing the revenue cycle data to derive actionable insights for decision-making. Here, we’ll discuss the role of data analytics in revenue cycle management and how it can help a business generate more revenue and become successful.

Revenue is one of the core factors a business should consider. After all, how long can an establishment function if it doesn’t generate income while incurring expenditure? Many organizations have processes set for revenue cycle management to determine their finances and returns. Reports show that the revenue cycle management market is expected to touch $1.209 trillion by 2030.

Additionally, statistics show that the global big data market is expected to reach $90 billion by 2025 and $103 billion by 2027. Almost every industry is investing in big data analytics and business intelligence tools to derive real-time insights and streamline its businesses. 

But what is a revenue cycle? What role does revenue cycle analytics play in an organization? How can a business effectively use business analytics and big data to derive advanced analytical insights from revenue cycles? 

Let’s find out in this blog! 


What is Revenue Cycle Analytics? 

Revenue cycle analytics is the process of analyzing the revenue streams of your business to identify weak areas and find suitable solutions to address them. The aim is to effectively increase the business income and optimize the entire revenue generation process by relying on meaningful insights derived from analyzing large amounts of business data. You can use several data points and parameters to track, measure, and analyze the revenue cycle in your organization.


What is a Revenue Cycle Analyst? 

A revenue cycle analyst is the person who is responsible for optimizing the revenue cycle in your business. Their responsibilities include the following: 

  • Analyzing revenue streams
  • Analyzing expenses 
  • Comparing revenue and expenditure 
  • Reviewing billing and invoice practices 
  • Running revenue forecast analytics 
  • Preparing financial audits 
  • Overseeing bookkeeping practices 
  • Ensuring the business follows the regulatory practices, etc.

The analyst has to determine and track various revenue cycle metrics, share the reports with decision-makers, offer actionable recommendations, and assist the top management in making efficient and proactive decisions to boost business revenue. Typically, businesses look for data analysts with experience in the finance and accounting industries, as well as economics and statistics. 


What is the Revenue Cycle Process?

A revenue cycle contains multiple recurring steps necessary to complete an accounting process to record the revenue generated by the business. This involves tracking transactions like sales, returns, claims, offers, etc. 

Revenue cycle assessment helps establish financial stability in your business and find more ways for growth. It can boost your revenue and profits by evaluating various business processes and offering insights about how to increase overall efficiency. It also has a role in enhancing customer satisfaction with your business. 

The revenue cycle process starts with the order placed by a customer and ends when the payment is received. It repeats for every order by every customer. The following are the elements found in the revenue cycle process: 

  • Get orders from customers
  • Process the order
  • Generate bills and invoice 
  • Deliver the goods/ services 
  • Record the transaction 
  • Get the payment 
  • Repeat for every order 

Why Use Data Analytics in Revenue Analysis?

Revenue analysis is done by the finance and accounting department to help your business make correct investment decisions. However, traditional methods are time-consuming and have certain limitations. By using data analytics, you can take advantage of the latest technology to streamline and automate the analysis of revenue. This saves time and provides real-time insights to decision-makers in different departments and all verticals. 

Reduce Costs 

One of the main reasons for using data analytics for revenue management is the cost. Every business wants to cut costs and find ways to generate more returns from the investment. Analytics derives insights by analyzing large datasets in a quick time. It can provide reports about which areas to work on and how to plug the leaks to prevent revenue drain. 

Enhance Revenue 

While reducing costs is vital, increasing your revenue is equally important. Wouldn’t it be beneficial if you could find more revenue sources for your business? This can lead to greater profits. Revenue cycle analytics can identify ways to generate more revenue. Moreover, the analytics run continuously, allowing you to regularly improve your financial position. 

Improve Cash Flow 

When you analyze historical data using advanced tools, you can uncover trends/patterns and correlations that cannot be detected through traditional methods. This provides a clearer picture of the cash inflow and outflow channels. You can forecast the requirements in advance and plan accordingly to prevent last-minute roadblocks. 

Determine the Price 

How do you set the pricing for your products and services? Many factors come into the picture, like competitors’ rates, industry standards, your investments, time taken, resources consumed, quality, etc., by using data analytics to manage the revenue cycle, you can accurately price your offerings and bring more flexibility and transparency to the entire process. 

Streamline Internal Processes

Revenue cycle analytics is not limited to making financial decisions. It can also help streamline other internal processes to increase overall efficiency and performance. For example, automation is a great investment choice and gives high returns. It can be implemented in all departments to save time and allow employees to focus on the core aspects of the job. 

Prevent Revenue Leaks 

Revenue leaks are a concern for many businesses. Missed payments, extra charges, fines, dues, double orders, cancelations, etc., can drain your revenue over time. While the payments may be small, they add up and can impact your revenue. Revenue cycle management analytics helps identify such leaks and can plug them into by automating workflows. 

Risk Mitigation 

Financial decisions are always considered a risk. Additionally, fraudulent transactions, false claims, and other issues can lead to losses. By analyzing revenue cycles using data analytics and business intelligence tools, you can find effective means to minimize such risks. Moreover, by partnering with reputed managed analytics service providers, you can ensure you comply with the industry and regional standards/ regulations.


How Can Big Data Analytics be Used in the Revenue Cycle?

Big data analytics in the revenue cycle can help your business in many ways. 

Streamline Claims

The insurance and healthcare industries often deal with claims which are complicated, confusing, and stressful. The procedure can be simplified and streamlined through data analytics to speed up reimbursements and reduce manual paperwork.  

Accurate and Quick Billing 

Accurate billing is a key factor for a successful business. It ensures that the orders are perfectly completed and customers are satisfied with the organization. The billing process can be automated to eliminate mix-ups and errors. Big data analytics helps in identifying the loopholes and correcting them. 

Denial Management in Healthcare 

The healthcare industry needs a separate and well-thought-out process to handle claim denials. Escalations in such issues can cause many problems for the healthcare center and patients. With big data analytics in revenue cycle management, you can understand why claims are rejected and find solutions to reduce the denial rate. 

Greater Financial Control 

With revenue cycle analytics, you will have more control over your finances. You can analyze various factors like customer/ patient information, popularity of the products/ services you offer, repeat orders, cancelations, etc. The insights derived will provide the bigger picture as well as the minute details necessary to make some tough decisions and re-distribute the cash flows. 

Better Resource Allocation 

Analyzing revenue cycle data using predictive and advanced analytical tools will help identify areas that need more investment or underutilized ones. This optimizes different processes, simplifies production/ billing cycles, improves recruitment and training, and leads to greater employee and customer satisfaction. You can also manage your customers more effectively. 


What are the Insights of the Revenue Cycle?

Revenue cycle data analytics dashboards can provide various insights based on your business requirements and objectives. A few of them are listed below: 

  • Customer Payment Behavior: It includes information about how each customer pays the bills, the type of payment option used, discounts availed, etc. 
  • Missing Payments: It shares insights about how many customers missed the payments (subscriptions) or which payments the businesses missed (to vendors and third parties).
  • Claims: It gives information about insurance claims, their acceptance and denials, etc., in the healthcare industry. 
  • Operational Information: It shows which operational tasks are delivering results and which ones are draining your money. 
  • Profitability Data: It shares details about which areas are profitable for the business and how much revenue is generated from those.  

What Does KPI Mean in the Revenue Cycle?

KPIs are key performance indicators or revenue cycle metrics used to track, measure, and analyze if the business processes are aligned with the objectives to deliver the expected results. The KPIs are diverse and change from one business to another depending on the industry, niche, size, market, goals, etc. However, the following are some common KPIs that should be included in the revenue cycle analytics dashboard: 

  • Abandonment rate 
  • Bad debt rate 
  • Cost to collect 
  • Gross cost to collect rate
  • Net collection rate 
  • Aged accounts receivable rate (A/R) 
  • Average days in A/R 
  • Point-of-sale collection rate 
  • Right-party contact rate
  • Resolve rate 
  • The average payment per call

Revenue Cycle Dashboard Examples

Let’s consider healthcare revenue cycle analytics as an example to understand the types of dashboards to be created for effective revenue cycle management. Business analytics service providers will set up customized dashboards using tools like Power BI and Tableau to help you manage various parameters and make real-time decisions based on data-driven insights. A few examples are as follows:

  • Claim denial rates 
  • Days in accounts receivable 
  • Net collection rate 
  • Cost to collect 
  • Patient financial responsibility 

How can Data Analytics Increase Revenue?

Modern-day data analytics uses artificial intelligence, machine learning, and other latest technologies. The whole process can be automated and implemented in a business of any size and volume. In simple terms, data analytics can increase revenue in the following ways: 

  • Providing accurate data to sales teams 
  • Increasing overall data quality 
  • Sharing insights to drive targeted campaigns 
  • Integrating data sources across all channels 
  • Managing customer loyalty programs 
  • Identifying new areas for revenue generation, etc.

Conclusion 

Revenue cycle analytics helps businesses make decisions based on data-driven insights. This data comes from multiple sources and offers a greater perspective of the company’s financial situation. 

Moreover, the goal of revenue cycle analytics is to improve business efficiency, reduce unwanted costs, optimize resources, increase customer and employee satisfaction, and generate more revenue. Make sure to partner with a reliable managed data analytics company to get tailored solutions for revenue cycle analytics. 

Schedule a 1-on-1 with our analytics expert for more information.

Fact checked by –
Akansha Rani ~ Content Creator & Copy Writer

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