Will your refrigerator become a psychologist? The countdown is running: “In 2022, your intelligent device will know more about your emotional state than your own family,” says Annette Zimmermann, Research Vice President at Gartner.
Change, and especially progress, through artificial intelligence? AI in service, too? Yes! This is the area where study results and experts see the greatest potential in the next few years. In the future, companies will use AI to customize their offers precisely to customer needs. Customer contact will be more precise, not least because customer emotions will be detected. And the service desk will enjoy optimal relief. Intelligent self-service and virtual assistants will ensure more customer satisfaction and, together with the flesh-and-blood service team, a first-class customer experience.
Find out what AI trends might have an especially great impact on your services in the next 24 months.
“Emotional AI” – a contradiction in terms?
Empathy and emotions are the definitive difference between humans and machines, aren’t they? This contrast will at least be less pronounced in the future, Gartner analysts believe.
So-called affective computer technologies can use sensors, microphones, and cameras to detect users’ emotional states and react in a pseudo-empathetic manner. This technology can be used to create more personal user experiences such as an intelligent refrigerator that interprets feelings and recommends foods correspondingly. Or plays soothing music if someone kicks or hits it.
There are many potential fields of application. Examples are virtual assistants, learning platforms, medical examinations, fitness trackers, or retail advertisement displays. Customer service in particular will benefit from “emotional AI” – these technologies will provide both service centers and retail outlets with their assessment of customer emotions. This will allow service offerings to be adapted more precisely and individually, and deliver better measurements of customer service effectiveness.
Interpreting customer moods correctly and reacting appropriately is especially important at the service desk. For example, end-user moods at the time of ticket generation can be determined based on word usage and the previous “customer satisfaction score” (CSAT) – results of tools based on machine learning (ML) can assist the service team as it identifies appropriate action. Words such as “again” can indicate that the user is dissatisfied.
While we are addressing upset or stressed customers, remember that first-level support employees must always remain friendly, and that is easy for Alexa and similar services. Speech-controlled virtual assistant technology has matured to the degree that Alexa can be used as an upstream support instance to detect the customer’s mood from his voice and put an angry customer right through to second-level support.
But be careful: Pattern recognition does not guarantee success, even though various parameters can be combined and development continuously refined. To interpret a facial expression, for example, contextual knowledge is needed. Apps always interpret a smile as happiness, but it might also be malicious glee or embarrassment, or might just be fake.
AI and the service desk – knowledge optimization for optimum services
Let us continue to develop this example: What specifically can AI do for the service desk besides the sentimental analysis that has already been described? The service desk has for years faced the dilemma of appreciation vs. value added – customer service must be personalized while remaining efficient and economical. AI-supported systems are one way out of this dilemma, since they support both goals.
The service desk is always on the front lines. And it is more than just the hotline. Important components include context-sensitive FAQs on the website, virtual assistants, and intelligent self-service tools (such as chatbots). And the latter are increasingly offering AI support for their services.
The proper use of artificial intelligence and integrated systems can deliver significant improvements, especially in the areas of self-service and knowledge management.
AI detects errors
An example: If the user receives an error message, AI-supported imaging recognition can facilitate quick incident resolution. If the user enters the message into the self-service portal, the system requests that he send a screenshot of the error. The service-management software uses AI to analyze the screenshot, recognizes the error, fills out the ticket, and forwards it. The software monitors incidents, and after identical error correction has been carried out several times, it automatically sends the next caller a link to the self-help knowledge base solution on his smartphone.
AI learns from context
Or another scenario: The user reports a malfunction. “Alexa, my presentation keeps crashing.” The AI-supported service-management application can handle semantic vagueness and has learned the relationship between “presentation” and “PowerPoint”, the related application. And it is intimately familiar with the user and his computer’s configuration. It finds both solution variants in the knowledge base. Alexa answers, “You can start an automatic re-installation of PowerPoint. That will take 20 minutes. Or you can request a replacement device. That will take three hours.” The user decides: “Re-install!” The service desk software initiates the process directly via an integrated workspace management solution. Half an hour later, Alexa lets the user know that installation is complete and asks whether he is satisfied with his experience. The customer assigns five stars for perfect service, and the ticket is automatically closed.
AI provides optimum order
A final example addresses AI automation of backend processes with the goal of efficient ticket routing. Service employees spend lots of time classifying and assigning tickets. AI technologies are capable of automatically assigning incoming tickets in incident management to the responsible service areas. Intelligent text analysis is used to check the ticket content automatically, and the ticket is classified appropriately and then assigned to the correct service group. The system uses algorithms to teach itself classification rules and thus learns continuously. Investment in AI-based ticket analysis and assignment is profitable at an average manual ticket processing time of just ten seconds.
But above all, the positive effects of AI for the service desk must go in both directions. The goal is more than efficient processes and unstressed, satisfied service agents. The primary goal is customer satisfaction. The entire customer experience (CX) must remain the focus of all AI initiatives.
AI and customer experience – win-win with machine and human
Customer first! Market researchers such as Gartner and PWC largely agree that in the next few years, customer experience will become the central distinguishing feature during a purchase decision. AI is driving this development.
Gartner forecasts that by 2022, about 70% of customer interactions will involve new technologies such as machine learning (ML) applications and mobile messaging, up from 15% in 2018.
Such technologies reduce customer effort and are increasingly capable of creating personalized, context-related customer loyalty. The transition to a human service agent over the course of the customer journey has become fluid.
Why is AI so important, especially for CX? A few reasons:
- AI is available to the customer 24-7-365
- AI knows what the customer thinks, feels, and wants
- AI learns and uses data to improve CX
- AI can optimize personalization
- AI allows the customer to ask questions in his own way
- AI ensures differentiated human interaction at important touchpoints
The last point deserves special emphasis: AI will allow complex tasks to be automated, but will not replace people to a great degree. And that’s the way it should be.
Dr. Benjamin Strehl
Dr. Benjamin Strehl promovierte 2012 erfolgreich zum Thema "Innovationsmanagement im Service Center" und ist Autor zahlreicher Fachartikel. Seit 2015 verantwortet er als Vorstand der USU Software AG vor allem das akquisitorische Wachstum sowie die Bereiche Forschung & Entwicklung und Consulting.