Engaging with companies has become an increasingly digital experience. Customers will often interact with a chatbot on a website or app before visiting a business in person or even calling its customer service center,though the outcome of that interaction isn't always satisfactory.
Most organizations only deploy rule-based chatbots, which operate based on if/then logic and therefore can only assist in predefined Q&A situations. In an era when customers expect immediate, personalized solutions to their problems, businesses need more dynamic tools for capturing and addressing inquiries without having to speak with a customer service rep. Otherwise, they risk customers abandoning the interaction and, ultimately, the business.
According to a recent Forrester study, many firms'chatbots today lack the most important capabilities, such as the ability to understand customer history and provide personalized responses beyond simplistic FAQ replies. The right tools with the most critical functionalities are necessary to automate actions based on customer responses.
Delivering the level of service that today's digital-era customers expect will require organizations to reinforce their chatbots with advanced automation. With these innovations working in tandem, customers receive the benefits they expect of chatbots: immediacy, ease of access, and increased intelligence to ensure they feel taken care of once they conclude the interaction.
A More Thoughtful Level of Service
Whereas rules-based chatbots aren't much more helpful than company FAQ pages, intelligent automation can be the difference between chatbots that frustrate customers and ones that live up to Gartner's Hype Cycle for Natural Language Technologies. Pairing chatbots with robotic process automation (RPA), in which software robots perform business processes like humans would, enables chatbots to deliver more personalized responses to customers. When customers engage chatbots with requests, the chatbots communicate with software robots to mine relevant data stored in other systems, from CRM tools to sales platforms, to support the interactions. This way, what could have been an anonymous and static transaction feels like customized support.
Some interactions will ultimately require agents' attention; in these cases, RPA can continue to support by giving agents an omnichannel view of customers and their situations. Given how many touchpoints customers can have with companies today (e.g., email, web, mobile), it's unlikely agents would be able to capture the entire experience quickly enough for customers' satisfaction. Just as the robot sourced data for customers via the chatbot, it can do the same for agents so they can focus their attention on resolving inquiries and even upselling products or services instead of searching for the information to do so. Agents only need to enter a command into the chatbot for the robot to get to work.
With the combination of automation and chatbots, employees can use chatbot interfaces to get information or post data into applications without needing to switch from screen to screen, leading to faster and more accurate responses for customers. Likewise, the combination of chatbot and RPA saves agents from having to navigate endless systems—a knowledge overload that can quickly become overwhelming. As a result, customers will feel like a priority and agents can do their work more efficiently.
Businesses can further impress customers by programming their chatbots with artificial intelligence (AI) and machine learning, thereby enabling chatbots to understand customer intent and make highly contextualized decisions. Machine learning can assess a customer interaction with a chatbot and infer how the customer is feeling (calm and curious or frustrated) and make recommendations accordingly. If the automation software detects that the customer is growing impatient, it might elevate the conversation to an agent who can diffuse the tension.>
More than sourcing existing data like RPA does, an AI-armed chatbot facilitates net new data for the organization. The technology's cognitive capabilities can summarize customer interactions for agents to reference later on and add that information to the customers' history. Even if the interaction moves to the phone, natural language processing (NLP) software can capture the discussions and note key points.>
AI-enabled chatbots can monitor customer engagements for trends that might be valuable for contact center managers to know. Are customers most commonly using chatbots to initiate password resets, for clarification on their subscription terms, or for more advanced scenarios? Is there one business offering that customers are notably more interested in than others? This feedback signals where other departments might need to reinforce the company offerings or even simply provide updated information on their websites.
Offering seamless yet sophisticated customer service experiences can be a major brand differentiator. Chatbots are on the front line for customer inquiries, so organizations can't jeopardize valuable relationships by only deploying rules-based bots that are incapable of meeting modern expectations. Winning customers' favor and long-term business will require organizations to reinforce their chatbots with automation so they can deliver the personalized support customers expect.
Brad Beumer is customer experience and contact center automation lead at UiPath.