The workplace energy of a small and midsize technology business is unlike anything seen in a large enterprise. In the midst of a fast-paced, caffeine-fueled day, the freedom to make a difference at work is always present. This spirit is certainly not limited to growing companies, but it’s certainly easier to spot these opportunities and see the fruits of your labor in one.
However, not every function may feel the love—especially customer service representatives. They are on the front line, fielding calls from customers who are often angry or disappointed. Whether the instructions were misread, passcodes were forgotten, or a new part is needed, reps can feel burned out after working on the same requests day after day.
It doesn’t have to be this way for customer service reps. By combining machine learning with the recent slew of chatbots, service organizations have a distinct opportunity to focus on the experience of each interaction from the perspective of the customer and the rep.
What Are Chatbots? And How Does Machine Learning Make Them Even Better?
Chatbots are computer programs that mimic human-to-human written and voice-enabled communication by using artificial intelligence. From self-initiating a series of tasks to holding a quasi-natural, two-way conversation, this technology is beginning to change how consumers and the brands they love engage with each other online, on the phone, and even through e-mail.
Suppose you wanted to know if today’s ballgame will be rained out. If a chatbot is not available, you would direct your browser to weather.com, for example, and then type in your zip code for the forecast. However, the use of a chatbot can turn this experience into a fast, more-meaningful interaction. For instance, the Weather Channel’s chatbot allows you to send a chat text asking for current conditions of a three-day forecast. And immediately, the chatbot replies.
Yes, this is a very simplistic example of a chatbot. But with artificial intelligence evolving into more sophisticated forms, such as machine learning, chatbots no longer need to be governed by just a series of preprogrammed rules, scripts, and prompts. Now, they can pull from the entire company’s collective expertise and experience and sift through it all to find the best-possible resolutions to a customer’s query.
Directing Interest in Machine Learning towards a More-Rewarding Service Experience
For years, technology firms have been primarily focused on setting a digital foundation with tools such as the cloud, Big Data, and analytics. However, some of that attention is now being pulled towards machine learning to turbocharge their business processes, decision-making, and customer interactions.
In fact, the Oxford Economics study, “The Transformation Imperative for Small and Midsize Technology Companies,” suggests a higher rate of investment in machine learning among technology firms than their peers in other industries. Although adoption numbers were still low at 6% for small and midsize technology companies in 2017, that same figure is projected to become more substantial as it nearly quadruples in 2019. Technology firms are leading the way, but companies in other industries should also consider how these tools can support their customer service function.
That said, chatbots present a clear opportunity for embracing machine learning in a way that is profoundly human, efficient, and meaningful without breaking the budget. They can help automate simple tasks, provide immediate service, and trigger specific, rules-based actions—whether a customer contacts the business through a messaging app, social media, phone, or e-mail—by learning how reps resolve frequently occurring queries. By mimicking simple, real-life conversations, chatbots can quickly become a low-cost way to offer around-the-clock customer assistance.
Chatbots can also transition the customer service organization from a point of customer interaction to a source of business intelligence and marketing opportunities. As the technology addresses customer issues and triggers processes, it captures every request, piece of feedback, and action and pushes it into a cloud-based ERP system that every business area can assess. Marketing and sales teams, for example, can use this information to find new opportunities for cross- or up-selling, new promotions, bundled offers, and even new services.
Investing in Chatbots Drives Untapped Value for Customer Service
With all the above said, it may seem that chatbots are a natural next step for small and midsize companies in all industries to expand their customer service capabilities. However, it can be intimidating to go through the process of producing them.
Here’s the good news: there’s more than one way to design a chatbot.
Businesses can choose to develop their own bot with a low-cost app, subscription-based cloud service, usage-based collaborative bot platform, or technology partner. But no matter the chosen path, the development process must be defined by specific capability needs, data to be accessed and captured, system integration requirements, and intended goals. It is very important to find a platform—such as recast.ai—that doesn’t limit API calls and allows the creation of unlimited bots within a few minutes or hours, rather than weeks and months.
When matched closely to the needs of customer service reps and customers, chatbots can deliver a potential benefit that is more valuable than the price tag itself. No one likes to be bogged down by repetitive, mundane tasks that provide no real value to the company’s growth. But if chatbots take on those activities, the technology may be the godsend that customer service reps need to handle more-challenging exceptions that allow them to learn and grow their skills and contribute directly to the bottom line.
- In the meantime, check out the Oxford Economics report, “The Transformation Imperative for Small and Midsize Technology Companies,” for more insights into this emerging technology.
- Visit us at SAPPHIRE NOW to see how small and midsize companies are benefiting from chatbots/machine learning. Register today!
This article originally appeared on Growth Matters Network and has been republished with permission.