How AI, Machine Learning And Other Disruptive Trends Are Defining The Future Of Customer Service
By 2020, 85% of all customer service interactions will be handled without a human agent.
As rapid advancements in technology continue transforming how businesses are developed in the modern world, entrepreneurs are actively exploring smarter ways to operate more efficiently and eliminate issues that have traditionally plagued progress and performance.
From enterprise apps that make it easier to track real-time insights and manage customer relationships, to emerging tools that allow clearer communication between teams and a streamlined exchange of data — one of the most important decisions startup founders face today is selecting the right software. As a result, the average small business is accustomed to integrating a wide range of cloud-based apps to address a complex array of business needs.
In 2016, small businesses spent over $55 billion on these cloud-based services, with the average professional using between 10-16 apps daily. By 2020, nearly 80% of small businesses are projected to use cloud-based services, increasing the current total by 37%.
With spending and adoption of SaaS apps swelling exponentially, there’s also a greater demand for automated customer support systems. By 2020, an estimated 85% of all customer service interactions will be handled without a human agent.
Founded in 2013, DigitalGenius is an AI-powered customer service tool that uses machine learning and natural language processing to completely automate the consumer support process. Using a proprietary technology referred to as Conversational Process Automation, the AI platform is capable of understanding conversations, automating repetitive tasks and personalizing user interactions for the purpose of assuring quality service.
This technology uses deep learning to identify customer needs, resolving inquiries through APIs that connect directly with third-party backend systems. This approach eliminates the need for human customer service reps to deal with common customer service issues such as refunds, finding lost items and changing passwords. Once these tasks are handled through artificial intelligence, customer service agents can focus their time to more specialized and challenging tasks.
Co-Founder and President Mikhail Naumov explains the vision behind his company and how both artificial intelligence and machine learning are defining the future of customer service in a digital world.
What was the void or opportunity you discovered that inspired the idea behind DigitalGenius?
Mikhail Naumov: Customers have very high expectations when interacting with a brand, and this is especially true for customer service. Customers will not wait for a company’s contact center to catch up with their expectations. At this point, it might be easier for a customer to switch providers after one poor customer service experience. In a world where customers expect immediate attention and quick resolutions to their support issues, how can brands compete? They can try to double the number of people they employ in their contact center to cope with the increase in message volume. But, what good is hiring more people when you’re not equipping them with the right technology to make them successful at their job? That’s why DigitalGenius was born — to help companies rapidly scale their customer support operation and keep up with the increasing customer expectations. It turns out that a lot of inefficiencies in contact centers can be automated through the use of machine learning and AI. DigitalGenius leverages the latest advancements in AI to help companies remove unnecessary costs from their contact centers, while improving customer satisfaction.
Describe how your proprietary technology works and the specific advantages it offers?
Mikhail Naumov: Contact centers are one of the first business functions that will be completely transformed with AI. The abundance of historical customer service logs provides the necessary training data, and the repetitive nature of customer support requests makes a perfect use case for predictive capabilities provided by machine learning. DigitalGenius uses deep learning algorithms to train on historical customer service logs; things like chat logs and email transcripts. As a result, the machine learning model is able to predict answers to new incoming questions, even if they are phrased in new and unexpected ways.
Our deep learning models can understand the objective of the customer when they reach out to a customer service team for help. From there, our product seamlessly connects the conversation with a series of backend processes which are required to actually resolve the customer’s issue. For example, a customer might reach out to a movie subscription service, asking for a refund. Our AI models will understand the conversation and automatically execute a sequence of actions and verifications in third-party systems to perform the refund. As a result, the customer is validated, their refund-eligibility confirmed, the actual refund is processed, and the customer gets a reply telling them that their order has been refunded. All of this happens within minutes, which is exactly how long customers today are willing to wait.
How do you see AI shifting the space and what disruptive trends do you see emerging?
Mikhail Naumov: AI is a game-changer in the customer service industry. It will enable companies, large and small, to remove unnecessary costs from their contact center, while significantly improving customer satisfaction. Today, companies are looking at customer service as the competitive advantage which lets them stand apart from others in their industry. By using machine learning, these companies will be able to flourish. As an example, DigitalGenius has customers that have used AI to reduce the handling time of customer support tickets, which led to customer service agents having up to 30% more time each day. One customer trained their support agents to use this spare time to offer up-sells and upgrades to customers, and as a result, their contact center became profitable. That’s something you don’t see every day, a contact center becoming profitable and driving value for the company’s top line growth.
What are the mistakes you see both companies and digital platforms alike making when it comes to delivering quality customer service?
Mikhail Naumov: The biggest blindspot I see today, given the hype around AI, is that most companies are looking at AI to deflect or automate conversations in their contact center. The reality most people miss is that conversations are just 30% of the average customer service experience. The other 70% is a series of actions and processes that customer support agents have to perform manually each time a customer asks them for a refund or the status of an order. In short, customer service is not just about conversations. No matter how good or conversational your AI models are, it doesn’t actually guarantee results. The important thing is to manage these conversations in the context of a customer service process. Our AI-driven functionality, Conversational Process Automation (CPA), is the seamless integration of conversations with backend systems and processes in the contact center. CPA allows for end-to-end resolution for customer support queries, not just deflecting FAQ articles. Instead, we’re actually connecting conversations with back-end processes to drive meaningful automation in the contact center, while improving customer and employee satisfaction.
As deep learning and AI make customer service more automated, how does your technology account for errors or replace the comfort real human reps provide?
Mikhail Naumov: At the end of the day, customers don’t reach out to customer support because they have no one else to talk to. They reach out because they have specific issues such as refund requests, subscription upgrades, or a problem with the product. In this problem-focused domain, the two most important factors are speed and quality. Being able to provide a fast and accurate resolution, goes much further than providing a real human rep level of comfort that makes the customer wait for three days.
What have been some of the biggest challenges you’ve faced in the process of building the company and what obstacles do you foresee in the future?
Mikhail Naumov: As a technology, AI is still in its early stages. There are thousands of people, researchers and engineers, working on advancing the science and theory behind AI. What’s changing now is that we finally see some practical applications of machine learning making their way into various business functions, with customer service being the first. As with any emerging technology, the biggest challenges are avoiding over-hype in the market. We’ve done this by always connecting our proposition with concrete and quantifiable evidence of ROI that companies using DigitalGenius have achieved. Everything else is just a matter of your marketing budget. But, when you have real case studies of customers using your AI product, you should share them proudly, it’s still the early days for AI applications and not a lot of companies have achieved tangible results.
What industries or areas are you primarily focused on servicing and who do you anticipate serving going forward?
Mikhail Naumov: DigitalGenius works primarily with B2C companies that have a high volume of text-based customer service traffic. We have seen a lot of traction in verticals including travel, hospitality, online services, education and on-demand services. We have customers from various industries, and the AI models we use are not limited by industry or language, making our product actionable for many companies around the world. In addition, we have partnered with customer service software leaders like Salesforce Service Cloud and Zendesk. Together, we are able to onboard customers much faster and accelerate the rate of adoption of AI inside customers support departments.
How do you see the customer service space evolving in the next 3-5 years and where does DigitalGenius fit in the scope of this shift?
Mikhail Naumov: Every single company in the world will have some form of AI in their contact center in the next few years. Based on what we see today, it doesn’t make sense not to leverage AI for customer support. Your company saves money and your customers are happier as a result. DigitalGenius has a vision to rapidly become the most prominent AI Platform for Customer Service. Our goal is to connect conversations with processes using Conversational Process Automation to help thousands of companies around the world provide cost-effective and delightful support to their customers. We are well on our way to do this — with a lot of exciting work still ahead of us.