Artificial intelligence improves customer service in banking

Banks must move away from a one-sided approach to efficiency and move towards a sustainable approach to customer concerns. Technology can help increase customer focus. Connectivity reasons and AI-powered tools play a major role.

The discussion about customer service performance finally reached management. Banks in particular realize that they will make no progress in improving their customer experience (CX) as long as customers complain about long wait times, unresolved concerns and short staff on the phone. At the same time, they realize that the continued focus on efficiency and savings in customer service has reached its limits.

At an average of 10.2 days per year, employee-level sick leave is twice that of team leaders or staff positions. Customer productive time is now only 68 percent. This is demonstrated by the premium service cockpit.

Digital tools are rarely used (except for electronic banking).

These numbers show that there is an urgent need to change the management of customer service units. Therefore, many companies try to respond to customer communications using digital self-service tools or bots. Robots have also become very successful today. Chatbots answer more than two-thirds of the questions they are asked, a study by Forward Benchmark and HSLU shows.

However, if only 2-4% of all inquiries to banks in Germany, Switzerland and Austria are routed via chat, these tools are only marginally helpful. There is clearly a widespread lack of understanding of how to use technology to truly focus on customers.

Analyzing the reasons for contacting customers is a basic requirement

The key to developing such a strategy is a deeper understanding of the reasons for customer contact and the associated volume of contact across the entire company. Over the past four years, we have repeatedly seen lists of customer contact reasons in more than 50 organizations that were, for various reasons, not suitable for making an informed decision about automation. We especially noticed the basic contact lists

• More than 100 entries were included, with more than 50 percent of contacts categorized as “other” or “general” in the assessment.
• Product oriented rather than customer oriented for statistical purposes and not very meaningful.
• It was created more than 10 years ago, and for example, all the communication reasons related to the company’s digital touchpoints are missing. However, Gartner already pointed out in 2019 that poor implementation of digital customer touchpoints leads to significant additional work on the phone. We attribute this to the evolution of digital tools in banking silos.

Specialists are often busy with calls

In addition, in our discussions with banks, we have noticed that product or portfolio specialists, for example, increasingly spend a significant portion of their time communicating with clients. In general, these are indicators that the scope and content of customer service has not been reviewed for a long time.

It is also worth noting that call centers do not communicate fully with the marketing or corporate communications departments of large banks in particular. For example, how many calls or MKILs could be avoided if customer inquiries were answered on the website?

Every MKIl is a failure of the site. doctor. Uwe Stohldrier, HUK24

He’s right. It just needs to be clear across all products how many customers are calling because they couldn’t find something or misunderstood something. This examination should be performed regularly. The same applies to mass sending of marketing campaigns, invoices or extracts from contracts or operations. A government information agency recently found that it employs two-thirds of its customer service staff to answer customer inquiries about campaigns or invoices. This issue is reported as “General” in the customer contact tool.

AI-powered tools help with personalization

Once the reasons for customer contact and the associated volumes were accurately recorded based on a specific touchpoint, there are now great technology options to automate, simplify, or even avoid customer contact altogether. For example, once you understand which target groups have trouble understanding invoices, generative AI tools like ChatGPT can help you quickly write the right texts for a specific target group (such as Best Ager or Generation Z) in order to perfectly “capture” their first contact. Service automation and marketing grow together perfectly using this example.

AI can also make in-person conversations better and contact customers on the phone faster. Almost all manufacturers of large contact management systems today focus on examining the customer history of the caller using caller ID and drawing conclusions about the possible reasons for the call. The systems can initially select the appropriate agent based on the employees’ stored skills and then provide them with the most important information (invoices, instructions on how to use mobile banking, for example, or information about a suitable investment product).

Text analysis is minimized

However, in our view, the most important application of AI is speech or text analysis. A systematic examination of customer contacts from a certain period can be very useful, especially in order to rewrite the list of neglected reasons for contacting. But before and after that, it’s all about leadership. Up front, because managers have to decide how useful it is to review customer contacts. A large backlog of work, sick leave, or employee disruption at the call center could be an indicator of this. Next, because the reasons for contacting customers must also be recorded further so that we can work strategically and in the long term to either systematically reduce contacts or enable a great customer experience between employee and customer. There are a lot of good examples of this.