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There was once a time where we all feared the idea of robots taking over. Well, we’re at a point in history where artificial intelligence and machine learning are here. Not in the form of killer robots taking over global defense systems the way Skynet would, but in the form of complex algorithms now being used for business applications.
It seems that AI has been all the rave in digital business applications as of late. There has been a massive spike in the number of businesses using AI in some form, with a 270% increase recorded in four years, AI in automated customer support is but one example. Still, it’s a great example that beautifully encapsulates where we are today as a civilization of developing machines that can do more for man and where we’re going with all of this.
Common Misconceptions of Machine Learning Applications and The User Experience
Before discussing use case scenarios of machine learning in a business, it’s crucial that we first debunk some misconceptions that can stain a company’s artificial intelligence experience. Here are some common misconceptions that companies need to break about machine learning applications to their customer service experience.
“Robots aren’t as personal.”
The human touch is essential to a customer journey, but that doesn’t necessarily mean that you can’t delegate some tasks to a bot. Truth is, much of the machine learning efforts are towards making the experience more personalized by adding tagging features and programming bots to sound less formal. More importantly, developers have been successful thus far in making AI applications more personalized.
“Artificial intelligence is complicated.”
Some companies think that coding up or setting up a chatbot or building any other machine learning system will be a long and arduous process. However, many machine learning processes aren’t hard to use. Chatbot and Engati's automation solutions, for instance, requires very minimal code and can easily be set up with little programming.
“My business is too small for machine learning solutions.”
Many small businesses think their business is way too small to merit the attention of AI development companies. But many Saas creators develop machine learning solutions with the small business in mind. What’s more, these services are available at a price point that any small business can afford and often have free plans to get started.
Use Scenarios of Machine Learning in Customer Service
Utilizing machine learning in customer service isn’t anything new. Large companies like Google, Disney, Toyota, Netflix, Amazon, and many other conglomerates have been using AI to improve customer experiences.
Through the years, the number of use cases also moved towards more small-scale applications. And as innovators discover new ways, the applications continue to expand. Here are 5 of the significant ways that companies can use machine learning in improving customer service.
1. Hyper Personalized Messenger Chatbots
One of the most popular use case scenarios of machine learning in customer service is the development of hyper-personalized messenger chatbots. The trend started when developers realized that they could transfer the highly personalized message-blasting features of email delivery marketing tools like ActiveCampaign and MailChimp to Messenger.
Hyper-personalizing messenger chatbot experiences are revolutionary mainly because it leaves people on the other end of the conversation wondering whether they’re talking to a machine or an actual agent. Passing this Turing Test adds more weight to the conversation because it can make people feel like they are highly valued - enough so that someone would go out of their way to respond within a matter of seconds when it’s chatbot automation that’s working through a sequence of messages of a string of keyword triggers. Whatever the case, it revolutionized the way companies interact with customers via chat.
2. Predictive Analysis
The way that machine learning has turned specific algorithms into oracles of sorts is yet another awe-striking application of artificial intelligence. Take Netflix for example, which so confidently recommends the next movie you should watch. In many cases, their predictive suggestions are right on the money.
There’s no sorcery behind this development. Through the power of data science and categorization, algorithms can collect data about a person and take actions towards complex analyses that take a person’s preferences and history behavior into account. Think also of how customer experiences have progressed on social media channels.
Today, ads are more targeted and smart because you only see paid content that resonates with you. This form of predictive analysis can help increase customer experiences and support by analyzing what you need even before you say it. While we’re not yet in the levels of “The Minority Report” in terms of accuracy, we as a civilization are quite far into the process nonetheless.
3. Message Tagging and Categorization
Machine learning for customer service significantly progressed since message tagging, and categorization became a staple in many chatbot and platform algorithms. Now that software and programs can add tags and categorize people, they can better organize customer concerns based on their nature, urgency, or product categories. This functionality allows teams to prioritize better and action concerns.
Another scenario where this can come in handy is using them for lead generation and nurturing. While not directly related to customer support, sometimes the best solution to a person’s concern is to upsell a product or service. Categorizing them based on what products we can sell or upsell can help improve the customer experience by only offering services and products they truly need.
4. Knowledge Hub Development
Many customer service software tools provide companies with knowledge hub builders. Here are some customer service software examples, many of which offer a knowledge base or hub developer.
Through the power of artificial intelligence, developing these knowledge hubs become so much easier. Various machine learning services can help determine what topics customers frequently raise, allowing agents to understand what topics they should include in a knowledge hub.
5. Quantifying Customer Satisfaction
Any company will agree that measuring customer satisfaction is essential, but 75% of brands will admit that while they measure customer happiness, they don’t know how to qualify it. Thankfully, there are ways to measure customer satisfaction, and machine learning can help. Moreover, artificial intelligence applications can now automate the process so that it doesn’t eat valuable time and energy.
More importantly, companies need to be on top of factors that affect customer satisfaction, especially when responding to customer support queries. Having a machine learning help desk can aid in the process. They usually have a survey feedback feature that allows a customer service department to rate and quantify customer satisfaction based on their feedback accurately.
Where Artificial Intelligence is Now and Where it’s Going
Using an intelligent customer service system will continue to become a norm in today’s highly digitized business environment. With this in mind, it only makes sense for companies to start thinking about applying some if not all of these machine learning use-case scenarios to their customer service activities.
Artificial intelligence isn’t just another fad that will come and go in a few years. It’s here to stay, and it will only keep progressing with time.