Challenges of Billing Optimization in a Usage-Based Model

April 26, 2024

Enterprises with usage or consumption-based business models face significant challenges when it comes to billing optimization. As these companies scale, the volume of usage data coming from various source systems increases exponentially. This creates a massive data management problem that drives up IT costs and requires constant firefighting to keep critical billing systems running. 

Thomas Igou
Reading time: 7 minutes


The process of transferring usage data to the billing system is predominantly manual and error-prone. As data volumes grow, manually moving this data becomes tedious and can lead to inaccuracies that cause downstream issues. Month-end closing cycles drag on too long as teams struggle to reconcile large data sets across systems. Ultimately this leads to incorrect or delayed customer invoices, resulting in bill disputes and lost revenue. Ernst & Young estimates that every company leaks between 1% and 5% of realized EBITA.

Beyond revenue impacts, poor billing practices create larger operational inefficiencies, regulatory non-compliance risks, and customer dissatisfaction. Usage-based enterprises require optimized billing processes to scale efficiently, comply with regulations, and satisfy customers. 

Large Data Volumes Drive Costs and IT Firefighting

Enterprises with usage-based or consumption-based business models often have large volumes of usage data coming from various source systems like billing systems, ERPs, CRMs, IoT devices, and more. This high volume of usage data can be difficult and expensive to store, process, and analyze.   

Some key challenges with large usage data volumes include: 

    • Storing and processing large amounts of data requires scaling up expensive database and analytics infrastructure. This drives up IT costs significantly. 
    • With usage data in multiple siloed systems, IT teams spend a lot of time on ETL and data integration. This takes away from more value-add analytics initiatives. 
    • It becomes operationally challenging to get a consolidated view of usage data for reporting, forecasting and decision making.  
    • Deriving insights from large volumes of granular usage data requires complex big data analytics capabilities. Most enterprises struggle to build this competency. 
    • Usage anomalies get lost in the volume of data and go undetected until they hit the billing stage. This causes revenue leakages. 
    • Storage and compute costs keep rising as usage data volumes increase over time. This makes costs unpredictable and hard to control. 

Handling large volumes of usage data across systems is riddled with cost and productivity challenges for most enterprises with usage-based business models. It often leads to increased IT firefighting instead of innovation. 

Manual Data Transfer is Error-Prone

Manually capturing, preparing and transferring usage data from various source systems to the billing system is inefficient and error-prone. Employees must extract data from different sources, reformat it, and upload it to the billing system. This tedious process leaves room for human error at multiple touchpoints.   

Data gaps or inaccuracies get introduced when employees extract incomplete data, apply incorrect transformations, or upload the data incorrectly. Without automation, there are no validations to catch errors. 

Even small mistakes compound into big problems. Incorrect or incomplete usage data leads to inaccurate customer invoices. When customers receive faulty bills, they quickly lose trust and satisfaction.  

Manual processes simply can’t scale as usage volumes grow. Enterprises need large teams to handle the data transfers. But more people also increase the risk of errors slipping through. Relying on manual methods wastes time and hurts data quality. 

Automating the collection and transfer of usage data is essential. When machines handle data extraction, transformation, and loading, the process becomes scalable, efficient, and accurate. Automation also provides data validation to prevent errors. 

Long Invoice Cycles

Invoice generation and monthly closing often take an excessively long time for enterprises with large volumes of usage data and manual processes. This lengthy invoice cycle results in several issues: 

    • Revenue recognition is delayed when invoices aren’t sent promptly after usage. This impacts financial reporting and metrics. 
    • Customers get frustrated waiting for invoices, especially when they need them for their own accounting and reporting. Long invoice cycles can damage customer relationships. 
    • Operational inefficiencies like manual invoice generation tie up staff resources. This takes time away from more strategic initiatives.  
    • Regulatory compliance can be put at risk if invoices aren’t sent within required timeframes. Usage-based businesses may fail to meet industry standards. 
    • Decision making is hindered when timely usage data isn’t available. There’s less visibility into recent performance and trends. 

Manual processes like transferring usage data and generating invoices often take weeks when dealing with large volumes of data. This leads to lost revenue, poor customer experience, operational inefficiencies, and regulatory non-compliance. Automating the billing process can optimize and accelerate invoice cycles by 80%

Bill Disputes Cause Many Credit Notes

One of the biggest challenges with billing optimization is the number of bill disputes that lead to credit notes. When usage data is transferred manually, errors often occur which lead to incorrect charges on customer bills. Even small errors can cause customers to dispute entire bills.   

Resolving these disputes is a manual and tedious process that involves crediting the disputed bill and issuing a new corrected invoice. This results in lost revenue from the credited amounts as well as operational inefficiencies from the manual resolution process. Additionally, it leads to customer dissatisfaction and loss of trust in the billing process. 

The number of credit notes issued can be staggering for enterprises with large customer bases. Without automated checks in place, a small error rate compounds across thousands of bills leading to excessive credits and revenue loss. Optimizing the billing process is critical to reducing errors and minimizing invalid disputes and credit notes. 

Lost Revenue

Revenue leakage is a major issue for enterprises with usage-based billing models. There are several ways revenue can be lost due to poor billing processes: 

    • Usage data not collected or lost before billing – If usage data is not fully collected from all systems, or data gets corrupted or lost before billing, revenue will be missed. 
    • Incorrect usage calculation – Errors in usage calculation, such as incorrect units, wrong rates applied, or calculation errors will lead to lost revenue through under-billing. 
    • Unbilled usage – Any usage events that fail to get billed will directly result in revenue loss. Common causes are usage not getting loaded into the billing system, or errors preventing usage from being invoiced. 
    • Untimely billing – Delays in billing of usage events push revenue realization further out. 
    • Failed charges – Declined credit cards or unpaid invoices lead to written-off revenue.   
    • Billing errors – Incorrect product, customer, or pricing data results in faulty invoices and lost revenue through rework, credits, and write-offs. 

Poor billing processes open holes that let hard-earned revenue slip away. Enterprises leave millions of dollars on the table due to deficiencies in their usage data collection, rating, billing, and collections. Plugging these holes is imperative. 

Operational Inefficiencies

Manual and error-prone processes for transferring usage data to billing systems often lead to operational inefficiencies for enterprises with consumption-based models. IT teams end up spending significant time troubleshooting data issues and fixing errors instead of working on more strategic initiatives. 

The manual processes typically involve extracting data from various source systems, normalizing and mapping it, and then uploading it to the billing system. With large volumes of usage data, this is very time consuming and prone to human error. Even small mistakes can lead to major downstream issues like incorrect customer invoices.  

IT staff then have to spend time researching discrepancies, identifying root causes in the upstream data or processes, and correcting the problems through one-off fixes. This reactive work distracts from more impactful technology projects that could drive business value. There is also opportunity cost, as IT resources are being allocated to repetitive manual work rather than automation or innovation. 

Overall, the lack of automation and integration in usage data processing hinders operational efficiency for enterprises. IT teams operate in a constant firefighting mode, leading to frustration, turnover, and technical debt across systems. Implementing seamless, automated usage data pipelines is critical to unlocking IT productivity and enabling digital transformation. 

Regulatory Non-Compliance

Enterprises with complex usage models often struggle to comply with usage reporting regulations. For example, telecom companies must comply with strict reporting rules on subscriber usage and traffic data. Healthcare providers face stringent requirements around reporting and auditing of patient usage and billing records. Financial services firms must track and disclose extensive usage metrics across trading systems and end users. The FTC is also cracking down on click-to-cancel regulations in 2024.

Failing to comply with usage reporting regulations can lead to steep fines and damage to the brand reputation. However, compiling the required usage data across all systems and formats is extremely difficult without automation. As well, the data required for billing purposes is not always sufficient for regulatory reporting. This results in separate teams extracting and manipulating large datasets in an attempt to comply. A lack of end-to-end usage visibility also makes audits very difficult and time consuming. 

Usage reporting regulations continue to expand into more industries and geographies. At the same time, authorities are increasing scrutiny and issuing larger fines for non-compliance. Usage data systems that cannot adapt will leave enterprises vulnerable to regulatory risk. Automating usage data collection, normalization and reporting is critical for regulatory compliance. 

Customer Dissatisfaction

Poor billing hurts customer experience. When enterprises have issues with their billing process, it directly impacts their customers. Lengthy billing cycles mean customers don’t receive invoices promptly. Bill disputes lead to inaccurate charges that frustrate customers. The enterprise loses trust and loyalty when customers feel overcharged or receive confusing bills.  

This damages the customer relationship in several ways: 

    • Customers lose confidence in the accuracy and fairness of billing. 
    • Unexpected charges erode goodwill and satisfaction. 
    • Time spent disputing bills creates a poor experience. 
    • Delays receiving invoices disrupt customer finances and planning. 

To retain and grow their customer base, enterprises must optimize billing to deliver timely, accurate, understandable invoices. When the billing process works smoothly, it strengthens trust and loyalty. Customers feel treated fairly and empowered to easily manage their accounts. This improves the overall customer experience and positions the enterprise as a reliable partner. 


Enterprise billing optimization is critical for companies with usage-based revenue models. As outlined in this article, there are several key challenges that make billing complex: 

    • The massive volumes of raw usage data from various systems are difficult and expensive to manage. Consolidating and normalizing this data requires automation. 
    • Manually transferring usage records into billing systems is inefficient, prone to errors, and makes it hard to close the books each month. 
    • Long invoice cycles reduce efficiency and cash flow. Automated usage-to-billing processes enable faster invoicing. 
    • Bill disputes from customers are common when usage records are inaccurate. Automating usage data collection improves accuracy.  
    • Lost revenue, operational inefficiencies, regulatory non-compliance, and customer dissatisfaction result from poor billing practices. 

Optimizing billing is essential for usage-based enterprises. Automating the collection, normalization and transfer of usage data is key. This improves accuracy, shortens invoicing cycles, reduces disputes and unlocks significant benefits. Check out how DigitalRoute’s Usage Engine delivers these outcomes

The Pains of Billing and Monetization

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Thomas Igou

Head of Content Marketing @ DigitalRoute

Thomas heads the content marketing team at DigitalRoute. He’s been working with content his whole career, in various formats (hosting podcasts, organizing conferences, writing blogs and reports) and on various topics (usage data, digital sales room, servitization, IT security, i-gaming).

He has two passions in life: football and music. When he’s not playing, watching, or coaching a football game, you’ll likely find him strumming his guitar to a classic 70’s rock song (very likely a Led Zeppelin one).

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