August 21, 2023

Analytics for customer experience: a use case in billing ops

This model enables a targeted validation, making the bill review team twice as productive

Analytics for customer experience: a use case in billing ops

At a glance

Challenge

The problem of incorrect invoices is often larger than it appears, with visible costs like inbound customer support calls, but also many unhappy clients who do not complain. Despite heavy investments in making billing systems error-proof, incorrect invoices continue to be issued. In response, our convergent telco client established a “last resort” bill review team to manually validate and amend invoices. Given the team’s limited capacity, the challenge was determining how to efficiently select invoices to maximize issue resolution.

Solution

Using rich data from past billing issues, a model was developed to identify potentially incorrect invoices. A state-of-the-art machine learning algorithm was combined with tailored business-driven validations for maximum effectiveness. Key sources of billing issues, such as promotional offer expirations and product portfolio changes, were identified. The model was integrated into a simple and user-friendly app for the bill review team, which selects invoices for scanning in each billing cycle. Close support was provided to ensure full adoption of the solution.

Results

The developed model significantly improves invoice accuracy, with an error rate three times higher than the previous approach. By enabling targeted validation, the bill review team becomes twice as productive, leading to a sixfold increase in flagged invoices. Additionally, the model identifies undoubtedly incorrect invoices, allowing for direct amendment. Overall, this results in a 12-fold increase in issues resolved within the same time frame, greatly enhancing the reliability of the billing process.

Challenge

The problem of incorrect invoices is often larger than it appears, with visible costs like inbound customer support calls, but also many unhappy clients who do not complain. Despite heavy investments in making billing systems error-proof, incorrect invoices continue to be issued. In response, our convergent telco client established a “last resort” bill review team to manually validate and amend invoices. Given the team’s limited capacity, the challenge was determining how to efficiently select invoices to maximize issue resolution.

Approach

Solution

Using rich data from past billing issues, a model was developed to identify potentially incorrect invoices. A state-of-the-art machine learning algorithm was combined with tailored business-driven validations for maximum effectiveness. Key sources of billing issues, such as promotional offer expirations and product portfolio changes, were identified. The model was integrated into a simple and user-friendly app for the bill review team, which selects invoices for scanning in each billing cycle. Close support was provided to ensure full adoption of the solution.

Results

The developed model significantly improves invoice accuracy, with an error rate three times higher than the previous approach. By enabling targeted validation, the bill review team becomes twice as productive, leading to a sixfold increase in flagged invoices. Additionally, the model identifies undoubtedly incorrect invoices, allowing for direct amendment. Overall, this results in a 12-fold increase in issues resolved within the same time frame, greatly enhancing the reliability of the billing process.

Our
AI-generated
summary

Our AI-generated summary

Our AI-generated summary

The problem is typically bigger than it seems at first glance: besides visible costs such as inbound calls that flood the customer support lines, there are many unhappy clients that do not complain.

Heavy investments in the billing system are usually prescribed, to make interfaces error-proof and transactional processes reliable. Still, in most cases, incorrect invoices continue to be issued every month.

Faced with this challenge, our client, a convergent telco, set up a “last resort” bill review team, to manually validate invoices and send them for amendment. Since the team’s capacity is limited, how to select invoices to check, to clear as many issues as possible?

Capitalizing on rich data from billing issues detected in the recent past, a model was developed to find potentially incorrect invoices.

Our AI-generated summary

Our AI-generated summary

Several relevant sources of billing issues were identified along the development process, such as the expiry of promotional offers and product portfolio changes pushed by commercial agents.

The developed model was then embedded in a simple and friendly app, used by the bill review team to select the invoices to be scanned in each billing cycle. Close assistance was provided to the team in an initial phase, ensuring the full adoption of the solution.

In practice, the error rate in the invoices picked by the developed model is three times larger than the error rate in the invoices selected through the former approach. Additionally, by pinpointing the expected mistake, the model enables a targeted validation, making the bill review team twice as productive. The combination of both effects results in a sixfold increment in the invoices flagged by the team.

On top of that, the model also identifies invoices that are surely incorrect. Hence, no validation is needed, and such invoices are directly amended.

Overall, considering both manual and automatic markings, 12 times more issues are now solved in the same time span, significantly improving the perceived reliability of the billing process

A state-of-the-art machine learning algorithm was combined with tailored business-driven system validations, for maximum effectiveness.

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