Case Study
This Communications Platform as a Service (CPaaS) company was founded in 2005 and offers services to mobile network operators (MNO), over-the-top (OTT) players and enterprises.
It helps its clients and partners overcome the complexity of consumer communications, to grow their business, and to enhance customer experience in a fast, secure and reliable way.
Headquartered in Central Europe, it has a global presence across the Americas, Asia Pacific, the Middle East, Africa, and the rest of Europe. Its portfolio comprises solutions for messaging, voice and email through a market leading custom erengagement platform.
The company has acquired a significant number of messaging companies over the last number of years and now faces the challenge of integrating processes, architectures, operations and technical platforms from these disparate companies into a single efficient entity.
Following numerous acquisitions, the company's system architecture became overly complex, incorporating various commercial and in-house tools, leading to disjointed processes. This complexity made it difficult to maintain and scale these systems, especially after the departure of key personnel. The company struggled with an unwieldy, code-intensive data pipeline, lacking in automation and proper data reconciliation, causing financial and operational discrepancies.
Operationally, inconsistencies arose in customer billing for messaging services due to unreliable usage report deliveries from carriers and invoice synchronization errors, leading to mismatches between services consumed and invoiced. The company often billed customers based on preliminary data, later facing challenges aligning this with carrier invoices and dealing with discrepancies in message delivery and billing.
These issues hindered accurate billing at the time of invoicing and complicated post-service revenue collection, contributing to concerns over revenue leakage amidst ongoing acquisitions and customer growth.
Challenges in short:
Acquisitions and systems integration causing complexity and inefficiencies.
Loss of key skilled individuals making maintenance difficult.
Limited automation and lack of reconciliation leading to data inconsistencies.
Revenue leakage is expected to increase with future growth if not addressed.
DigitalRoute proposed a solution that would improve clarity through reporting accuracy, on the customer-platform-carrier transactions that were being delivered. It included a strong recommendation toadopt a buy vs build approach, gradually replacing home-grown solutions with commercial tools - specifically for processes involved in revenue managementand the quote-to-cash landscape.
By creating a common, centralised location for the data coming in from multiple systems they would be able to reconcile transactions and identify errors or discrepancies.
With this level of visibility and controlof the data, the company could generate accurate reports to support revenue and settlement accuracy and assist in forecasting for revenue leakage avoidance.
With the DigitalRoute Usage Engine™ in place, the company implemented streamline and efficient data handling and increased automation, which significantly reduced data errors and subsequent revenue leakage.
The complete solution allowed the companyto introduce standardised processes and architecture with DigitalRoute as the glue, ensuring usage andrevenue-associated data was optimized for accuracy. Given the broad usage data management capabilities of the Usage Engine, a whole new level of opportunities to generate value from the tool will be opened to the company for the future.
With the DigitalRoute Usage Engine at the heart of the company’s solution, it now has a single source of truth and simplified access to master data across all business functions.
All data from multiple systems is located in a single location, and Usage Engine hasbecome the commercial solution for SMS Billing Mediation.
The solution will support the companywith the reporting on revenue leakage and help prevent it in the future.
Outcome in short:
Increased efficiency and reduced errors.
Eliminated revenue leakage.
Better control of data.
Prepared for future scale.
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