How does predictive analytics support each stage of customer service in telco companies?

In today’s fast-paced world of telecommunications, where digital interactions are an integral part of our lives, customer service is much more than just providing services. A key aspect of successful customer service in the telco industry is effective risk management at every stage of interaction. Predictive analytics has become an essential tool, enabling operators not only to identify potential threats but also to make effective decisions aimed at optimizing processes and minimizing losses.

In this article, we’ll explore how predictive analytics supports every stage of customer service in telecommunications companies, starting from identity verification, through credit assessment, all the way to selecting the appropriate follow-up or debt collection path.


Stage 1: Customer Identity Verification

Verifying a customer’s identity isn’t just a formality; it’s a crucial moment that determines both security and a successful beginning of collaboration. With the rise in frauds, particularly in the digital area, authenticating the true customer has become a priority.

According to the ZPF report from 2022, 38% of experts consider the issue of frauds to be escalating, while 50% still view it as a serious challenge. Consequently, the need to discern whether we’re dealing with a human or a bot has become crucial. Gone are the days when the accuracy of personal data was the sole criterion.

So, how do we effectively verify identity? This is where analytical tools using behavioral biometrics come into play. They analyze input data, observe on-site behavior, monitor mouse movements, and typing speed on the keyboard. Thanks to these tools, we can conclusively confirm whether we’re dealing with a genuine customer, eliminating the risk of fraud.

Stage 2: Credit Assessment

The next stage in telecommunications customer service is credit scoring. Here, analytical rules merge with modern machine learning models, business rules, and mechanisms for validating data and cross-check analysis, creating a precise assessment of a customer’s credit risk.

The advantage of utilizing machine learning and AI-based solutions is their ability to learn in real-time. Decisions aren’t solely based on existing data but also on the identification of various hidden anomalies and qualities that may impact a customer’s creditworthiness. This adaptive capability enhances the accuracy of credit assessments.

Moreover, modern analytical tools in the credit risk assessment process leverage multiple data sources — internal (payment history, behavioral records, blacklists) and external (credit bureaus, business registers, public records, debt exchanges, financial statements, etc.). If feasible, bank account analysis and transaction labeling are also conducted. All of this is aimed at verifying a customer’s credit scoring as effectively as possible.

Stage 3: Early Warning System

After a successful verification process, we move to a revolutionary stage – implementing a predictive early warning system in the credit risk assessment process.

The modern approach is shifting the dynamics for some operators who used to react only after payment issues arose. Instead of waiting for events to occur, analytical solutions enable forecasting and prevention of potential difficulties. This proactive approach marks a significant change in operations.

The predictive early warning system relies on rules supported by advanced artificial intelligence and machine learning models. These rules identify customers who might soon face difficulties in making payments or are directly at risk of default — meaning they might completely stop repaying their financial obligations. The aim is to enable earlier intervention and guide the customer toward a payment reminders path at the right moment, minimizing potential losses.

The system relies on available behavioral and external data, and the applied rules and models monitor customers’ credit risk, generating alerts as the probability of non-payment increases.

Those alerts feed into a tool where an analyst can conduct further analysis. Based on this analysis, decisions are made regarding further actions — ranging from assigning an appropriate debt collection plan and proposing repayment options to limiting or blocking the credit possibilities for that specific customer. This comprehensive approach enables effective credit risk management, minimizing losses, and increasing the likelihood of repayment.

Stage 4: Optimal Follow-Up Path

When a customer fails to meet their obligations, the key is to select an appropriate follow-up strategy. Even in this area, analytics can be leveraged to tailor the optimal path for each customer. The modern approach aims at simultaneously maximizing the chances of recovering dues while minimizing operational costs associated with the debt collection process.

Managing multiple debt collection paths and tracking customers across each of them requires the use of advanced algorithms that effectively monitor these processes, eliminating the need for manual control. This approach not only streamlines the process but also enables dynamic responses to changing conditions and the specifics of each customer’s situation.

Stage 5: Debt Collection

The final stage in the process is debt collection, involving internal actions as well as potentially selling the debt to specialized entities. Similar to the follow-up phase, the key issue here is tailoring the appropriate debt collection strategy for each customer.

The decisive criteria for selection are the business potential of a particular path and the costs associated with debt collection. The goal is to find a solution that maximizes the company’s profit. Analytics plays a crucial role in this process, enabling precise determination of optimal strategies. Additionally, it supports proper valuation of the non-performing loan portfolio that could be sold to debt collection companies.

Summing up, from identity verification to debt collection, data analysis becomes a pivotal tool, enabling operators to effectively manage risk, optimize processes, and maximize value for customers. At Algolytics, we specialize in delivering data-driven solutions that can revolutionize how you conduct your operations. If you’re interested in implementing predictive analytics into your telecommunications company, reach out to us now at this link: Together, we can create a data-driven strategy that brings tangible business benefits.

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