When we talk to the companies that use Klaus, we often first ask them what customer service quality means to them. A typical response is that they want to support their customers in the best way possible, in the fastest way possible. It’s a precarious endeavor, balancing speed with precision. If you weigh it wrong, your support tower will topple.
To help get an A+ from customers, there are two technological ‘A’s grabbing the limelight at the moment: automation and AI.
Customer service automation refers to the use of technology and software to automate certain aspects of support, such as answering frequently asked questions, routing customer inquiries to the appropriate department, and providing self-service options for customers to resolve issues on their own.
The goal is to improve efficiency and customer satisfaction.
Customer service AI refers to the use of artificial intelligence technologies, such as machine learning and natural language processing, to enhance various aspects of support. This can include chatbots to handle customer inquiries, sentiment analysis to gauge customer emotions, and predictive analytics to anticipate customer needs and proactively offer solutions.
The goal is to enhance personalization, customization, and compliance.
Why you can’t jump into the deep end
Eventually, your aim is to employ both AI and automation to bring the best of what you have to the fore for customers.
But instead of thinking of them in tandem, it’s better to understand automation as the precursor to artificial intelligence.
Deloitte’s 2022 study into the state of AI reported that 94% of business leaders agree that AI will be critical to success over the next 5 years. They give this era the moniker ‘The Age of With’ – an age defined by humans working alongside machines.
But companies aren’t making the requisite investments. The report also found that 79% of companies are not educating their employees to apply and use AI effectively.
We may have entered a new age of work, but the eagerness to prosper with AI technologies far outmatches willingness to adapt.
If you automate aspects of customer service first, your team will be in a much better position to use AI to its full potential. This is for two main reasons:
- Automation frees up the time necessary to work successfully with artificial intelligence technologies.
- Automation also helps you maintain data hygiene. Your data needs to be in good working order for AI to flourish.
Let’s talk time-saving
Automating the more monotonous tasks keeps time-consuming work off your team’s desks, so their energy is fresher for more productive endeavors.
Many definitions of automation say that it helps reduce workload. However, it’s better to think of it more like it increases work potential. Instead of wasting your team’s time on repetitive tasks, you can automate the work and save their brains for work which requires more of a human touch.
Example #1: Canned responses
Setting up macros for canned responses to automate responding to repetitive requests or sending updates. While you may have a fully accessible, fleshed-out Knowledge Base that answers all of the most typical questions, this will still pass some of your customers by.
Instead of taking up your agent’s time typing out the same answers, having a bank of canned messages saves on time (and repetition-exhaustion). Many helpdesks offer templates and easy ways for you to create team macros – or even let your agents create their own templates.
Example #2: Assigning reviews
Your quality assurance process is only effective if reviews are regularly conducted. But having to find and assign certain conversations to certain reviewers – to uphold a regular and consistent feedback loop – can be a headache.
Solutions like Klaus help you automate this workflow. So that you can eliminate the organizational back and forth for your team.
How to invest this extra time for AI
AI is there to make us better at our jobs. But this is only useful if people are aware of and know how to use it. Proper onboarding of AI tools and analytics takes time and consideration.
For example, the Chat GPT craze hit the world by storm. But the astute leader wouldn’t tell their agents to rush to ask it to help them converse with customers. The technology has its limitations as well as its capabilities. If you’re equipped with the right prompts, Chat GPT for customer service is a viable tool that can help you train soft skills, create answer templates, create knowledge base articles, and more.
But if you use Chat GPT to fill all knowledge gaps, then your customers will come away as annoyed as they are confused.
With every new AI functionality, your team needs to be brought up to speed, and your ways of working carefully considered, to ensure its impacts are positively felt.
Now, let’s look at why automating is your data savior
Data hygiene is about making sure that your data is error-free. If your data is not stored correctly, you may have duplicates, incomplete information, conflicts across platforms or tools, etc.
Effective automation handles the mass of information that customer service departments have at hand. Everything from customers’ past CSAT reviews to their purchase history to their basic contact details.
Keeping this data structured and accessible is paramount to knowing how best to make your customer happy. However, collecting, categorizing, and analyzing this information is a mammoth of a challenge without the right processes.
The fine webs of data can all weave together to depict a high-level overview of how your customer ecosystem functions. Or they can tangle, with information strewn disconnected across platforms and teams.
Unfortunately, it’s a lot harder to pull value from disorganized data.
Automation helps you collect, store, and manage your data properly. In other words, it helps you keep your data clean.
Example #3: Surveys
Automating surveys streamlines the feedback collection process and improves the response rate by certifying that the right question is sent to the customer at the right time. This allows teams to gather real-time insights from customers more efficiently and effectively.
Ensuring consistency in the questions asked and allowing for data analysis makes it easier to identify patterns and trends.
Customers expect personalization: they expect support teams to have a holistic view of what they, as a customer, need. Collecting and analyzing their feedback through surveys is the first step towards this.
Example #4: Conversation Insights
Klaus’ Conversation Insights are data tools which help you make sense of and analyze the information stored in your customer interactions, prior to reviews. Only clean, closed conversations are displayed.
There are many customer interactions which offer no real reviewing value, such as auto-generated messages, or messages that contain no proper dialogue with your support rep.
Tools like Conversation Insights are an easy way to show clean, applicable data.
Why data hygiene is important for AI
Data is the foundation upon which artificial intelligence can prosper: AI algorithms require clean data for accurate and effective decision-making. This is because AI models are trained on historical data and use this data to make predictions and decisions. The quality and quantity of the data used for training directly affects the accuracy and effectiveness of the AI system.
Additionally, ongoing data collection and analysis is necessary for AI systems to continuously improve and adapt to changing circumstances.
The roadmap to next generation customer support
The pace at which AI technologies are reshaping how we work and interact gathers momentum with each new iteration. And for customer service leaders it’s exciting to know that personalization and efficiency is ever easier with these advancements.
Providing next-generation experiences with AI, however, requires a proactive redesign.
Going back to Deloitte’s study, the concern was that many companies are primarily interested in the cost-cutting potential of AI technology. But this is a case of expectations falling too short. In focusing solely on the lower hanging fruit (reduced costs), many businesses are overlooking the broader range of transformative possibilities AI presents.
Taking the time to think things through and plan carefully can lead to better outcomes in the long run. By first automating how your team works to save time and instil good data practices, you can set your support up for more eventual success with AI.