One of the most compelling use cases for enterprise bots is customer support. Traditionally, as companies grow in revenue, customer service costs grow in tandem. Companies constantly seek ways to minimize these servicing costs through nearshoring, offshoring, and now customer service bots. Companies ranging from RBS to KLM and Disney Stores to Overstock are eagerly innovating intelligent bot technologies, hailed as the solution to scaling customer care delivery.

There are two basic types of customer service bots, “front end bots” and “bot assisted agents.” A “front end bot” is a conversational computer program that interacts directly with a customer without human intervention. They’re also known as “virtual assistants” or “automated assistants”. A “bot assisted agent” is human agent is supported by bot technology. Other terms for the model include “cyborg” or “human in the loop.”

Over the last decade, countless websites have implemented virtual agents or “front end bots.” [24]7 is a leading customer service company that has created more than 180 chatbots for companies such as Duke Energy, CIBC, RBC, and the Disney Store. Daniel Hong, Senior Director, Product Marketing Strategy at [24]7 explains, “These automated website chats agents handle the first level of queries such as FAQs. When the Virtual Agent doesn’t know the answer, it transfers the user to a real human agent.” Using chatbots to automate answers to basic customer questions decreases the average agent handle time (AHT) by 10% or more.

Despite the advances in NLP and AI, many of these front end bots cannot fully understand complicated user inquiries. A customer contacts support because they have an issue, and an unintelligent bot that fails to answer the question leads to more frustration. Therefore, some companies implement “bot assisted agents” instead.

Robert LoCascio, CEO of LivePerson notes that “Customer satisfactions on the traditional front-end bots is below 70%, which is really low for customer care and sales. We think the best way is a hybrid model called cyborg, which is having the bot and the agent working in tandem next to each other.”

In this cyborg model, the bot interprets the conversation and suggests answer choices to the human agent. The bot can also change the reply format based on the inquiry platform, like elaborate longer in an email and keep Twitter responses to 140 characters. The human agents determine whether the answer fits the question and add further elaboration if needed. Rather than search their knowledge base for an answer and generate a custom response each time, agents simply quality control the bot’s answer. The artificial intelligence learns from the agent’s customizations to incrementally improve the automated answers.

Initial results of this “bot assisted model” have been extremely positive. LivePerson sees 30% to 35% gains in efficiency through the cyborg model. DigitalGenius, an AI company for customer service recently reported that 30.1% of KLM cases are resolved with the power of “bot assistance.”

Which model of customer service bots is better for your company? The answer depends on the complexity of your customer service cases, the cost of low customer satisfaction, and your technical and financial resources. Small and medium businesses who deliver straightforward products and services often see the same handful of customer service inquiries over and over. Delivering answers to customers is often easier and cheaper through a standalone front-end chatbot rather than a lengthy FAQ, an expensive call center, or a more complex cyborg model.

Large enterprises typically have too much variance in service requests to be handled by standalone front end bots. Some multinationals, like airlines, service such a high volume of customers with nuanced preferences, plans, and exception conditions that many service cases still require human judgment and overrides. Other corporations, like large financial or enterprise software firms, deal with inquiries too complex for a bot to handle alone.

Another factor to consider is the risk of customer dissatisfaction. In highly competitive industries where a consumer can easily switch providers, customers frustrated by your customer service will simply churn. In high stakes scenarios with money or reputation on the line, an incorrect answer given by a standalone bot can be disastrous. However, there are scenarios where speed of response overrides quality of response in driving customer decisions. E-commerce is one example where customers ready to make purchasing decisions will often prioritize the most responsive service providers.

Whether you choose to use standalone bots or cyborgs, customer service bots can reduce the Average Handle Time (AHT) per contact, increase First Contact Resolution (FCR) rates, reduce escalations to higher-cost channels, and decrease agent training times. Choosing the right model for your company is key to capitalizing on these advantages.

For more guidance on choosing the right customer service bot strategy for your enterprise, read my article on 9 Critical Decisions Behind Successful Customer Service Bots.

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