Artificial Intelligence and Machine Learning

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Table of Contents

Introduction
Discussion
Conclusion
References

Introduction

The appropriate handling of client complaints is unquestionably the most crucial aspect of offering exceptional customer service. Customers post unprompted reviews of one’s goods and services on online forums, social media, and pretty much anywhere else on the internet. And since customers feel most obliged to express their ideas when they are unhappy, spontaneous feedback is typically negative. These internet complaints are increasing daily and might be challenging to control. Artificial Intelligence (AI) and Machine Learning (ML) are the recent tools that can be used by government agencies to enhance the process of managing complaints.

Discussion

Giving machines access to data sources and allowing them to learn the knowledge without being explicitly taught is referred to as machine learning. Large amounts of adequately structured data are produced by customer service, for example, when consumers ask queries and support teams respond (Gacanin & Wagner, 2019; Marinchak et al., 2018). The solution is to classify customer complaints using machine learning. Wherever they may appear, client comments can be tracked by AI-powered text analysis tools to identify those that are complaints, route them automatically to the right team, and analyze them for immediate valuable insights (Roldos, 2020).

The industry tested tens of thousands of messages and offers in the fall of 2020 while collaborating with the AI start-up OfferFit, changing the creative content, channel, and delivery times. It redesigned its organization to focus on client acquisition, service, and renewal and started utilizing AI to schedule service calls more effectively, support call center agents’ cross-sell recommendations, and reach out to customers about upgrading their wireless systems (Edelman & Abraham, 2022). Brinks increased A/B testing from two or three tests per day to almost 50,000 tests in less than two years (Edelman & Abraham, 2022).

However, not all cases of the use of ML and AI technologies in managing customer complaints were successful. Giving machines access to data sources and allowing them to learn the knowledge without being explicitly taught is referred to as machine learning. Large amounts of adequately structured data are produced by customer service, for example, when consumers ask queries and support teams respond. According to F33 (2021), the harsh truth is that, despite the fact that artificial intelligence (AI) and machine learning (ML) are currently very trendy terms and that almost every tech company’s product and solution is AI-enabled, the majority of the customer’s entities have largely failed to implement ML within their own organizations. It is common for an enterprise to stagnate when there are too many ideas or prospects for one to evaluate (“How can AI,” n.d.). This could be due to a lack of interest in committing to one idea because it is likely that a better machine-learning project will emerge.

There are both benefits and challenges to the use of AI and ML in the customer complaint resolution process. A company’s capacity to deliver a customer experience that competes with the competition depends on its ability to provide quicker solutions, 24/7 support, and predictive learning (Vaught, n.d.). Today’s high expectations for customer service make it impossible for a company to ignore AI-powered support systems. AI integration isn’t always simple, though. An enterprise’s support staff will need to adjust in a variety of ways.

Institutions may maximize the potential of their extensive multilingual databases by using AI. They can also reach international markets more quickly. For example, language technologies like NMT make translation faster and less expensive. When it comes to translating vast volumes of text and detecting languages, no human translator can compete with a machine. AI allows greater scalability and scope, whether for gisting purposes or content intended for post-editing by human translators. A deep learning technique called NMT enables MT engines to train on their own. It employs a synthetic neural network, which is akin to how one’s brain functions.

Conclusion

In summary, the most critical component of providing excellent customer service is the proper management of customer complaints. Customer complaints can be categorized with the aid of machine learning. The ability of a company to provide a customer experience depends on that business’s power to offer quicker answers, round-the-clock service, and predictive knowledge. An entity cannot disregard AI-powered support systems, given the high standards for customer service that exist today.

References

Edelman, D. C. & Abraham, M. (2022). Customer experience in the age of AI. Web.

Gacanin, H. & Wagner, M. (2019). Artificial intelligence paradigm for customer experience management in next-generation networks: Challenges and perspectives. IEEE Network, 33(2), 188-194.

How can AI (and machine learning) add value to your brand? (n.d.). Web.

F33. (2021). Why most companies fail with machine learning. LinkedIn. Web.

Marinchak, C., Forrest, E., & Hoanca, B. (2018). Artificial intelligence. International Journal Of E-Entrepreneurship And Innovation, 8(2), 14-24.

Roldos, I. (2020). How to do customer complaint classification with AI. Web.

Vaught, L. (n.d.). What is needed to overcome ai challenges in customer support? Web.

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Artificial Intelligence and Machine Learning

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