Businesses today suffer from data overload that is costing them money. Insufficient and incorrect data enters business systems, hindering accurate billing and growth. In order to avoid lost revenue due to incorrect billing and to enable business expansion, companies must delete their data before the billing process begins.
According to IBM (via Harvard Business Review), negative data cost US companies $3.1 trillion in 2016 alone. Gartner, Inc. Poor data quality is estimated to cost individual organizations $12.9 million, and those costs don’t just directly impact the bottom line. Poor data quality leads to poor decision making.
The higher the billing volume, the more data has to be processed, which leads to more errors. As your subscription and consumption-based business scales, so does the complexity of billing.
• Account data must be collected from multiple sources, increasing the margin for error.
• Data switching/normalization is required to convert usage data into chargeable events; the higher the account volume, the more difficult the brokerage.
• Complex business rules such as volume discounts, free trials, prepaid discounts, local taxes, and affiliate fees can make the process more complicated and error-prone.
To create error-free customer invoices at scale, you need to start with clean and accessible data.
The global subscription e-commerce market is expected to grow from US$650 billion in 2020 to US$1.5 billion in 2025 at a compound annual growth rate (CAGR) of 18%. Enterprises are generally unwilling to manage the data volume, accuracy, and speed required for scalable subscriptions and usage-based billing. Any lack of data leads to scaling inaccuracies in billing. Losses also increase.
Usage data creates incentives for subscription companies. Whether it’s energy, telecom, media, entertainment or retail, watts, minutes and media rentals need to be converted from the amount consumed to the amount to be billed. Inaccurate billing information is a major source of lost profits. The billing engine needs accurate customer consumption data to create accurate bills. If the data is inaccurate, incomplete or out of date, the generated invoice will be incorrect and classified as Garbage In/Out (GIGO).
Most companies manually manage payment details and inaccuracies. Employees must manually review spreadsheets or other data sources and ensure data matches accounts. Using manual steps to correct data inaccuracies is immeasurable and fraught with errors of its own. The best way to avoid the inaccurate billing problem is to start with reliable normal data.
Practice data hygiene
Keeping data clean from the start is the best strategy to encourage flexible and scalable billing. There are several best practices to keep in mind.
Data is typically collected from multiple sources to create a single payment record. Improper data normalization is a major source of data errors. Make sure you have a data quality plan that constantly calls for best practices and uncovers sources of dirty data. Review key data entry points and standardize information where possible. Real-time data volumes tend to be disruptive, so your billing system needs to be fast and accurate.
Use a programmable billing platform that automates workflows and processes. Automation of data transformation, synchronization, cleansing and classification. Also, you solve problems like duplicating data and adding records. Billing systems must be programmable to accommodate changes in data processing and increase billing flexibility. Its purpose is to ensure a clean flow of data in the system to ensure error-free iterative processes.
As part of billing planning, business rules must be changeable. Businesses will grow and business rules will need to change, including maintaining accurate and complete data for accurate billing and regulatory compliance.
The key to scalable billing is elasticity – adapting quickly to changing requirements, including provisioning resources according to usage patterns. Cloud-native billing can provide unlimited capacity and scalability with reduced latency.
Using solid accounting data can also provide solid business intelligence. Account data analytics can inform decisions across the organization. Using billing and credit data can reveal trends such as product performance, customer retention and churn. Real-time billing data can be used for predictive analytics to forecast and make decisions on cash flows, inventory issues and unexpected market changes such as a global pandemic.
Implementing the right data management and billing technologies can provide the scalability and flexibility required for subscription billing. Correcting errors and inconsistencies between data sources is very important. It would also be better to eliminate or at least minimize the need for manual verification of invoices. This requires a robust billing engine that can process and settle transactions quickly and at scale. Finally, don’t force yourself to do what you always do. Use this as an opportunity to adapt your business processes for maximum business agility.