As a product leader, I have experienced artificial intelligence (AI)’s tremendous impact on SaaS and other industries, especially in the last few years. In this article, I’d like to delve into what AI in customer service means and describe our team’s approach to incorporating it into the Touchpoint product.
In recent years, improvements in customer support software, cloud technologies, and mobile platforms have transformed how we meet customer expectations. As we continue to explore ways to enhance interactions, the standard for customer support keeps getting higher.
AI is a groundbreaking technology that enables computer systems to perform tasks that typically require human intelligence. Despite the buzz surrounding AI or machine learning, what truly matters is that it has become increasingly recognized as one of the most helpful tools for customer service.
To educate an AI, we provide abundant information for it to learn from the different features in the data. This process resembles how we teach children by exposing them to various experiences and explaining things to them. From the examples given, the AI learns to identify patterns and comprehend the functioning of various phenomena.
AI systems have extensively permeated our daily lives, powering things like voice assistants, autonomous vehicles, personalizing recommendations for website content, and self-service support.
It certainly will. The question is not really whether it will, but by how much and what.
Machine learning and AI are not new. Many AI systems have been implemented over the years, but over the last year, developments have accelerated significantly. We have seen a big jump in pre-trained generative language models.
The most significant breakthrough in text processing has been ChatGPT, created by OpenAI. ChatGPT has made generative machine learning accessible to the average user as a simple chat-like user interface.
ChatGPT is a particular type of AI model OpenAI has created to engage in conversations with people. It understands speech and subsequently can offer valuable and informative replies to inquiries. ChatGPT is not alone, as Google has implemented Bard, Microsoft has a new generation of its Bing, and many other companies are working on analogs.
Significant quality gains in generic AI-based generative models have opened up new opportunities for customer service. Before we look at the specific features, let me tell you a little about the impact of AI from both a customer and a business perspective.
What we are seeing is that self-service environments are becoming much more efficient. Even if answering customers isn’t fully automated, AI technology can enable customer service agents to answer more customers. That way you can quickly resolve customers’ issues.
AI technology can pre-generate an answer for a service agent, who then needs only to review the content of the answer and add important details if necessary.
When someone needs help with an app, AI can display relevant information from within the application based on what the user was doing and the context in which they got stuck.
An AI training video generator can also create customized video tutorials, offering step-by-step guidance that addresses specific user needs. This can make the process even more efficient.
AI can automatically tailor its answers based on the customer’s profile and previous communications with them. Moreover, when a service agent creates an answer, the AI can assess its quality before it is sent and warn the agent if it thinks any of the content needs improvement.
AI can translate between languages. Say a multinational company builds a knowledge base (KB) in English. Customers can submit queries in their native language and then receive answers back in the same language.
AI-powered customer support can provide financial benefits for businesses, as they can automate specific tasks. This allows customers to find answers to their inquiries without human intervention.
Moreover, staff productivity can also be significantly increased. Thus, in addition to lower direct labor and indirect costs, less time is spent on recruiting and training staff.
Experts believe that with the latest AI-powered technologies, 50–80% of customer support requests can be automated with no reduction in response quality. Note that this depends on how standardized the customer support requests are in a specific industry.
AI can make an important contribution to improving the findability and systematization of information. This applies both to customer segmentation based on customer communication and to improving the findability of information in other ways.
One example is automating call transcripts, allowing you to store the information in phone calls. It makes it easily findable and automatically analyzable.
AI can assess service quality. An important factor in customer service quality management is the feedback provided by the customers themselves. In addition, it is also common to carry out random quality checks on customer service interactions.
AI can greatly improve the efficiency of such a quality assessment system, because instead of agents evaluating individual tickets, the AI can automatically assess all the customer responses to identify potential problems in service delivery.
If the number of support queries is high, triaging them can take a considerable amount of time. If it is not done and not done properly, some of the most urgent tickets will not get answered.
It is possible to let an AI triage queries. However, for small and medium-sized businesses, a system based on defined rules for prioritization and ticket routing will work better.
As we are in the initial product development phase, we are in a great position to harness modern technologies right from the start. Thus, we have built an awesome AI-powered customer service platform.
Here’s an overview of how we have implemented AI in our Touchpoint product.
A quick overview of a previous conversation with a customer is often essential when replying to a ticket. It’s also useful when a colleague needs a summary of a similar ticket successfully resolved, which they can then apply to the current support request.
AI can automatically analyze calls and generate a transcript. The quality depends on the specificity of the conversation and how much the model has been trained in the particular language. Still, our experiments have shown that the output is of very high quality for the most commonly used languages.
The results are so good that using the transcript in parallel with listening to the words can help an agent understand what the customer said. We have designed the system so that when an agent listens to a call, they can simultaneously read the relevant part of the transcript.
If you need to serve customers in a language other than the native language of your staff, automated translation comes to the rescue.
An operator no longer needs to manually copy text into a translation program, as the Touchpoint AI automatically translates content directly in the user interface. Google Translate and DeepL, for example, are also based on machine learning models.
Changing the tone of messages written by an agent is a fairly common application of AI and one that most market leaders have implemented in their customer support platforms. Implementation is quite straightforward with the GPT model.
Once an agent has drafted a raw answer to a customer, they choose the Touchpoint “Expand” function.
The AI will then rephrase the answer into a good customer service response. This is, once again, a great way to save agents time and increase customer satisfaction.
Once a client has built their high-quality knowledge base (KB), they can use a machine learning generative model to generate answers to customer questions. It is crucial, however, that they design their KB well to cover most situations that customers encounter. Read more about how to create an outstanding KB.
Since AI answers may not always be 100% correct, we recommend initially using these models to pre-generate answers, leaving the final responsibility of sending a correct answer to a customer service representative.
This approach can reduce the time needed to write a response while maintaining the human interactions, as the service agent can tailor the response to the specific situation and ensure the quality of the response.
Reviews of pre-generated answers are valuable input for training the AI model. Ultimately, it should be possible to fully automate creating answers for a subset of customer support requests.
These are just some AI-powered product features we are currently working on, as this is just the beginning. Our initial aim is to implement those AI features already in common use and have proven their usefulness.
We also have ideas about implementing more sophisticated machine learning algorithms. Still, first, we will focus on improving the AI that will have the most significant impact on how you process support interactions.
Note that the utility of a machine learning model depends very much on the volume and variability of the queries received by a particular customer support team. Since machine learning is based on training an algorithm with data, the quality of the results depends directly on both the quality and quantity of the data used for learning.
We are committed to being at the forefront of AI and to creating as much value as possible for Touchpoint users through the rapid application of AI.
⚡ Teams with a high volume of inquiries and a deep KB will benefit the most from applying AI. However, this does not mean that smaller customer service teams cannot benefit from implementing AI for customer service.
It depends on which customer support platform you are using and what AI-powered features you have implemented, but generally, the answer is yes.
You should leverage OpenAI’s conventional generative ChatGPT to improve customer support. We have published an article that will give you some ideas about how you should implement ChatGPT for customer support.
Most of the features I’ve described in this article already exist in one form or another. However, if your customer service process is answering customer queries via email or Facebook messenger, you’ll need to do a bit more manual work when using AI.
Even if you are not able to implement AI, there are a number of ways to make customer service more efficient with traditional tools.
You can apply AI to provide better customer service and more efficiency than ever before. We’ll see more and more examples in the coming years.
I recommend exploring which AI features your customer support platform can already support today. You should then discuss with your team how to implement them for maximum benefit.
Although there are many ways to implement AI, doing so is no substitute for human beings. You shouldn’t expect to put customer support on autopilot.
However, AI can be a great way to improve the efficiency and speed of customer responses and, in turn, enhance customer satisfaction.
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