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Real-time sentiment analysis for churn prevention

Introduction

The telecommunications industry can be defined as the process of using technology in sending and receiving information especially in the society that is connected digitally. It has played an important role in the construction of the information society as it has enabled the exchange of information between people and organisations. However, the industry is also known to have relatively high levels of customer turnover. This is where Natural Language Processing (NLP) AI comes in especially using real-time sentiment analysis to minimise customer churn. NLP AI has become indispensable in real-time customer sentiment analysis, which helps the telecom firms to identify and resolve customer complaints effectively thus reducing the churn rate.

Challenges

There are several challenges that the telecommunications industry faces in the process of implementing solutions to minimize churn rates. The first challenge is the large number of contacts that have to be evaluated. It is impossible to examine all the interactions on a manual level due to the millions of customers. In addition, customers’ sentiment can be complex and fluctuate over time, which makes assessment of their sentiment using conventional approaches a challenging task. Another challenge is the real-time nature of the analysis. When the customers are insatiable and can report their complaints instantly, it becomes a challenge to address negative sentiments within the desired time frame without losing customers. Also, there is an issue of consolidating several data sources and handling unstructured data such as texts from customers through email and calls.

AI Solutions

These challenges can be met with the help of AI, especially NLP. This is because NLP uses machine learning algorithms to process text data that is collected from different channels such as emails, social media, and call transcripts among others. Real-time sentiment analysis tools can therefore identify negative sentiments in real time which gives companies a chance to act before the customer leaves. Also, NLP is capable of dealing with vast amounts of unstructured data and transforming it into meaningful information. For example, IBM’s Watson leverages NLP to contextualize human language, therefore offering a better sentiment analysis.

Benefits

There are numerous advantages of applying NLP AI for real time sentiment analysis especially in the telecom sector. First, it enhances the ability to capture and analyze customer emotions in real time. This can in turn result in better mitigations thus saving a customer from leaving. Secondly, it enables the telecom companies to deal with big data easily. This can result in reduction of costs that could have been incurred in terms of manpower and time. Thirdly, it can assist the telecom companies to address their customers’ needs more effectively, thus enhancing the customers’ satisfaction. This is especially so in an industry where products and services are fairly homogeneous.

Return on Investment

In the telecom industry, the ROI of NLP AI can be very high. A research done by MIT Sloan Management Review and Boston Consulting Group revealed that companies that adopt AI have the potential of increasing their profitability by an average of 38% by 2035. When it comes to churn prevention, the ROI can be estimated as the costs of the AI solution versus the revenues gained from lower churn rates. For instance, a telecom company may have to spend $1 million in installing an NLP AI system and if it can keep 1000 customers from leaving who would have generated $2 million in revenue then the ROI is 100%.

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