Chatbots in telecom industry


Chatbots in the telecom customer support

Let us take a look at the nature of inquiries handled by the customer support agents and how chatbots are better suited to handle these scenarios:


  • Customer inquiries are repetitive in nature. The inquiries addressed by agents generally follow a specific workflow. Both these characteristics indicate that most of the calls received by agents are related to routine tasks. Repetitive jobs are a common cause of employee boredom and under utilize human resources. They can be better handled by bots, so employees can focus on decision-making tasks that require human supervision.
  • The incoming volume of customer inquiries is huge. Companies have no choice but to put incoming customers in lines waiting for their turn to speak to an agent. By employing chatbots to deal with this volume, companies can rein in the cost of setting up and training support agents.
  • Many inquiries require agents to connect to a different department. From a customer’s point of view, he is speaking to the company and does not understand why he has to wait while the call gets transferred between departments. He expects the first agent he speaks to, to resolve the issue. However, the biggest technology challenge that telecom companies face today in having a single view of the customer is that their departments are quite siloed. There isn’t a smooth flow of information from one department to another. Employees are looking after difference processes and there is usually a lag during the call transfer. A chatbot, on the other hand, is directly connected to all the processes internally and can resolve issues spanning different departments with ease. For the customer, the experience is much smoother.
  • Customers expect a 24×7 availability of support agents. Chatbots are the least expensive and most effective tools to address this need in the telecom industry.
  • Customers generally navigate through a maze of IVR options before they reach an agent that can resolve their issue. Such an experience could turn out to be frustrating for the customer, especially if he is in a hurry or if the issue is fairly simple. A chatbot, armed with natural language processing (NLP), can easily process human speech and get to the issue directly. The experience is much more comfortable for the customer. Text as a medium is also preferred to voice as it is less intrusive and allows the customer to multitask.



Customer support bot

Customer support using chatbots

As we saw earlier, several aspects of incoming queries to the customer support center are more suited for bots to handle. The Customer Support Bot can be omnipresent on all the channels of the brand – website, SMS, social media channels and as an app too. The bot continuously listens on social media and responds to users who share their issues in public domain.

In addition to customer support, chatbots can be useful for several other functions as well. Here are some of them:


Recharge assistant

In most developing countries, recharge generally happens offline or through the company’s website. The user has to enter his details, browse through a list of plans and then make a selection.

Instead, a Recharge Assistant bot could help the customer zero in on the best suitable plan. It could make suggestions based on the customer’s location and usage history and past purchases. Once the customer decides which plan to buy, the bot sends him a payment link. The bot remains the single point of contact for the customer during the entire transaction.


Reminder bot

The Reminder bot reminds customers when their plan is about to expire, or when their bill payment is due. This bot can boost the topline for the brand with very low investment.

Feedback collection bot

As we discussed previously, chatbots are better at eliciting feedback from customers than agents calling or emailing them. The Feedback Collection Bot springs into action every time the customer interacts with customer support and initiates the feedback collection process. This bot can detect the sentiment of the customer based on the incoming feedback and can adapt the survey questionnaire accordingly. If it realises that a customer is thoroughly disappointed with the service, it immediately passes control to an agent or a manager to salvage the situation.

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