November 23, 2021
Machine Learning's Role In Raising Service Standards
Nowadays, customer service is no longer just the domain of humankind. Computer systems and software simulated with human intelligence are increasingly utilized to help customer support representatives keep their customers content with their service.
Just consider the following stat about how organizations are trying to keep their customer service standards high with the help of technology advancements. According to an article focused on artificial intelligence statistics by ZoomInfo, 80% of all sales and marketing leaders state that they are already utilizing artificial intelligence solutions in their customer experience efforts or plan to do so by 2021, a statistic that’s speaking volumes on what the brightest minds in the customer service industry think about incorporating artificial intelligence and its subcategories into their line of work.
There’s no denying that artificial intelligence will continue to play a significant role in customer service and customer experience for years to come. And to compete effectively in today’s ultra-competitive business landscape, you need to provide outstanding customer service experiences for your clients by facilitating efforts through technology to keep your organization’s customer service standards in line with what the others are offering.
In this blog post, we’ll take a close look at artificial intelligence’s application, machine learning, and its role in creating first-rate customer experiences.
Defining Machine Learning
In order to understand how companies can make the most of utilizing machine learning for raising their customer service standards, first, we need to define the application of artificial intelligence. In simplest terms, machine learning is a subfield of artificial intelligence that allows systems to automatically learn and improve from experience without being explicitly programmed. Or, in other words, the primary aim of machine learning is to enable machines to learn automatically so that they can respond accordingly depending on the data provided to them.
Let’s take AI-equipped virtual assistants as an example. Assistants can offer different responses depending on the data given to them. So, for example, if you ask the virtual assistant of a bank to help you open a new bank account, it will interpret your question and provide an answer in the form of asking you for the documents (ID, social security number, valid driver’s license) you’ll need to begin the procedure without the help of a human employee. Put differently, machines and systems powered by artificial intelligence technologies like machine learning can interpret the input data correctly and learn from it over time.
Essentially, can help enable machines and systems to learn when they should use individual responses in client communication and gather data from users, and single handedly determine when they should hand over the conversation to a human customer service agent.
Each machine learning or ML algorithm is a set of procedures that help a model respond to its data. The algorithm is the one that specifies how the data should be transformed from input to output and how the machine should learn the mapping throughout the whole process. Needless to point out, there are different types of ML algorithms:
- Supervised ML algorithms: This machine-learning algorithm can apply what’s learned to new data using labeled examples to predict events. Supervised ML algorithms analyze a known training data set and then successfully make predictions about the output values. They can also make comparisons between the intended and correct output and then make modifications.
- Unsupervised ML algorithms: If the information used to train the model is neither labeled nor classified, the unsupervised machine learning algorithm comes in really handy. These algorithms don’t determine the correct output, but they explore and describe hidden structures from unlabeled data.
- Semi-Supervised ML algorithms: As the term suggests, semi-supervised ML algorithms use both unlabeled and labeled data for training.
- Reinforcement ML algorithms: This type of ML algorithm allows machines and systems to determine the ideal behavior given a particular context for optimal performance. It’s called the reinforcement ML algorithm because a simple reinforcement signal or reward feedback is needed to learn which action is best.
The Role Of ML For Customer Service Standards And Customer Experience
ML applications for customer service are, in fact, more ubiquitous than what most people think. As a matter of fact, utilizing ML in customer service isn’t anything new. Large corporations like Amazon, Google, Netflix, Toyota, Disney, and other conglomerates have been using AI and machine learning for years to improve their customers’ experiences.
Over the years, many machine learning technology’s use cases have also moved towards smaller-scale customer service applications. And as innovators discover new ways, the technology’s applications continue to expand. Below, we’ll showcase five significant ways companies can use machine learning to up their customer service game.
Categorization And Message Tagging
Since categorization and message tagging became a staple in ticketing systems’ algorithms, machine learning for customer service has significantly progressed. Nowadays, when programs can add tags and accurately categorize people, machines and computer systems can better organize customer service concerns based on their urgency, nature, or product categories. In addition, this machine learning functionality allows customer service teams to up their customer service standards by prioritizing customers through categorization and tagging.
Other scenarios where this can come in particularly useful is when using them for lead generation and nurturing. While not directly tied to customer support, sometimes the top solution to a client’s concern is to upsell a product or service. Categorizing your customers based on what products or services you can sell or upsell to them can help improve the customer experience by only offering products and services that the individual customer truly needs or desires.
The incredible way ML has turned particular algorithms into oracles of sorts is another striking application of AI and its subfields. For instance, take Netflix, which successfully recommends the next movie, TV show, or documentary that you should watch. In many cases, their predictive suggestions are spot on. How is that possible?
There’s no sorcery behind this ground-breaking development. Through utilizing the power of categorization and data science, algorithms can collect data about a user and take actions towards complex analysis that will take the user’s previous behavior and preferences into account. This form of predictive analysis can help increase your customers’ experiences and support by analyzing what they might need even before they say it.
Hyper Personalized Customer Support
One of the most popular uses of machine learning in modern-day customer service is the development of hyper-personalized customer support. Hyper-personalized customer support experiences are revolutionary because they leave people on the other end of the conversation wondering if they’re talking to a brilliant machine or an actual customer support agent.
This way, you can make your clients feel like they’re highly valued, enough so that they will go out of their way to respond within a matter of seconds, even when it’s software automation that’s working through a sequence of messages a string of keywords triggers. It can also be used to assess all previous interactions with a customer and use this valuable information to provide highly-personalized experiences, empowering better customer engagement and making them feel valued and listened to. Machine learning definitely revolutionized the way organizations interact with their customers and raised the customer service standards even higher.
Quantifying Customer Satisfaction
Any organization will probably agree that measuring customer satisfaction is essential to their success, but 75% of companies admit that while they measure customer satisfaction, they certainly don’t know how to qualify it. Luckily, there are ways to measure customer satisfaction, and machine learning plays a huge role in it. Even more, artificial intelligence technologies can now automate the measurement of the customer satisfaction process so that it doesn’t eat energy and valuable time.
For that reason, serious organizations need to be on top of factors that affect customer satisfaction, particularly when it comes to responding to customer support queries and requests. Having a machine learning-powered help desk can aid in the process as they usually have a survey feedback feature that allows the customer service department to quantify customer satisfaction based on their real-time feedback.
Providing Faster And More Efficient Service
If there’s one thing that all customers commonly dislike, it’s being made to wait—particularly when they need help regarding a customer service issue. Outdated customer service systems would route customers to the wrong places and make them wait in long call lines, increasing frustration and making things stressful for them and the agent on the other side.
Thankfully, with the help of artificial intelligence and especially machine learning and natural language processing, customers can now use their natural language and words to describe what they need assistance with and have their issues resolved much quicker. In addition, AI-powered machines and computer systems can now understand, interpret, and manipulate human language, which helps solve customer issues promptly and efficiently.
From providing faster and more efficient customer service to delivering highly personalized service levels, machine learning is an indispensable technology for providing better customer service and raising your organization’s customer service standards across all levels.
Whether your business is online or based in a physical store, ML for customer service standards can help your business market your products and services more effectively, provide faster solutions to customer service requests, and identify valuable prospects.
Since it consistently learns and improves itself, machine learning can scale alongside your business, all while easing the load on your customer service employees. Repetitive and mundane tasks can be handled by ML, freeing up your agents to focus on more complex functions, which is, as we can all agree, a win-win situation for all parties concerned.
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