Buried under false positives and time-consuming, costly research processes, our client needed a modern, automated method of Revenue Assurance. RevGen brought our data expertise and married it to business processes to recover millions in revenue.
Our client, a national telecommunications provider, like many in its industry, faces persistent challenges in accurately capturing, validating, and reconciling revenue across complex service portfolios and fragmented systems. The client brought in RevGen to provide a modernized solution that could streamline Revenue Assurance (RA) processes, enhance yield accuracy, and surface actionable insights to close revenue gaps effectively. Leveraging the power of dbt (Data Build Tool) to consolidate and transform many of the fragmented data points enabled a solution to better isolate leakage points, operationalize reconciliation, and ensure revenue is fully and correctly realized across customer touchpoints.
The client’s existing RA solution lacked the efficiency and precision needed to support scalable operations. High volumes of false positives created unnecessary research overhead, while manual investigation steps slowed down resolution cycles. Additionally, limited visibility into potential revenue gaps and their root causes hindered the organization’s ability to address leakage and proactively optimize financial performance.
Our client, like many organizations, stores data across a fragmented landscape, ranging from enterprise data warehouses to isolated databases supporting departmental point solutions. This lack of centralized integration leads to misalignment with business processes, ineffective resolution workflows, obscuring critical business insights, and impairing effective decision-making.
Because of the complexity of both the data sourcing and multi-step processes to go from customer order to service fulfillment, traditional RA methods flagged a high percentage of inconsistencies that weren’t errors at all, wasting time and resources.
Researching these issues, whether false positives or correctly identified errors, was incredibly time intensive, tying up valuable resources for hours—or days—often for little return. Not only was this negative for the client’s profitability, it also negatively impacted the affected customers by prolonging the resolution.
As we do with most systemic problems, RevGen began by taking a holistic look at the people, processes, technologies, and data supporting the client’s RA program. Our aim was to achieve three primary goals: enhance issue detection by reducing false positives and increasing accuracy, pinpoint process failures by isolating where breakdowns most frequently occur and enabling preventative controls, and laying the groundwork for automation and AI to minimize manual efforts.
To do this, we focused on establishing cleaner, aligned data that mapped directly to the revenue-generating value chain and its underlying processes.
Given our approach to standardizing and linking data sources to reduce false positives and stewarding the client into more sustainable, automation-based processes, we crafted our solution from the data up.
First, we unraveled the tangle of source data by cleaning and normalizing inputs and integrating only trusted, engineered source data. We also eliminated redundancy through de-duplication, preventing duplicate entries from triggering false positives.
One of the main issues with the client’s RA program was that it didn’t have a good picture of the operational workflows behind their value chain. In our new process, we embedded business logic to mirror the full value chain, enriching data as necessary for contextual accuracy. We also segmented this data by establishing categories based on process stage and associated risk levels, to better tailor detection strategies.
Once the data was in its enriched state, we used machine learning and AI to identify true anomalies, prioritize errors by risk, and direct manual resources to the most high-impact issues. We also built in continuous oversight through regular monitoring, reviews, and a feedback loop that improved its performance over time.
By integrating our analytics with their associated business processes, we identified underlying drivers of many common discrepancies. The contextual analytics empowered us to harness metadata—such as system inputs, user identifiers, automation workflow steps, and RPA triggers—to more precisely isolate intent and contextual nuances within the process. This allowed us to surface them to our client for proactive process updates.
Through our efforts, the client recouped over $15 million in resolved underbilling issues and now has a process that can be run with fewer errors and fewer resources, freeing up their experts to work on more complex issues.
Over the last two years, our team has recovered significant revenue in just this one line of business.
The new process, because of the clean, contextual data, has reduced the rate of false positives in flagged errors from 80% to 40%, saving hundreds of work hours annually. And, as our model is further refined through use, this rate will continue to decrease.
While our work focused on just one of our client’s many customer segments, the process we built, including the holistic view of data sourcing, contextualizing that data, and automating issue identification, can be applied elsewhere within their organization.
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