In the energy sector, as everywhere else, contact requests are increasing due to regular price adjustments, questions about bills or renewable energies.
The flood of customer inquiries is increasing; customer service teams often have great difficulty keeping up with replying to emails, chat messages or messages on social media. Agents often receive inquiries via different channels at the same time. This creates a high need for categorization and, above all, prioritization. Providing excellent service under these conditions and responding quickly and satisfactorily is almost impossible. There is a need for action so that the response processes do not slow down and a large backlog can be prevented.
However, knowing about the automation options available also increases customers' expectations of customer service quality. Short waiting times as well as customized and individualized answers are not only desired, but expected. In 2021, we were able to acquire a customer from the energy sector who was faced with precisely this challenge. The aim of the implementation at that time was to automate all email-based communication as far as possible. By automating text-based customer communication, the aim was to reduce waiting times for enquirers, reduce the pressure on agents and minimize the time it takes to complete a case without compromising on quality. Our customer wanted to use, redistribute and bundle resources in the service center more effectively through targeted automation. In addition to the analysis and automation of email-based communication, the scope of the project also included the automation of telephone communication. However, this will be the subject of another article. The AI in the mailbots takes over the partial and full automation of various processes, such as the self-identification of customers based on at least three criteria, meter reading, and bank data changes. The project scope also involved implementing the Aristech email server, which handles data processing. Aristech's AI-supported tools register email addresses, names and postal addresses, order numbers, meter reading values, and bank data changes. A concrete example of a case-closing implementation in this project is the automatic meter reading recording by e-mail. This means that when an e-mail is received in which a customer submits their current meter reading, the entire process is carried out by the bot: identification, validity check of meter number, and meter reading, entry of the new data in the back end, and notification of the customer about the completed process.
Ultimately, the implementation of the mailbot was a complete success in this instance. Already in the pilot phase, the mailbot achieved a higher successful processing rate than estimated and was able to significantly reduce the company's workload. After agile optimization of the solution, the quality could be further improved even under increased load. The mailbot now takes over the classification and can process many standard requests on a case-by-case basis. As a result, resources can now be allocated more efficiently. Instead of processing standard requests, the agents can now use their time for individual requests. In addition, the client already benefits from significant cost savings in the first year. All of this contributes to a significant increase in the quality of customer service.
Our goal is to offer our clients an outstanding solution that improves service, reduces costs and relieves the pressure on employees. In the use case described, the solution has now been continuously expanded for over two years. Through innovative technological development, we ensure that the service provided by our customers remains at a top level and that they are always provided with the best technology.
