Chatbot Development: Your Guide to Getting It Right
Chabots are hot. They’ve become the marketing tool du jour, and the use of chatbots is expected to grow as machine learning enables them to handle an increasingly wider array of capabilities and as companies consider new ways to deploy them. According to a Research and Markets report, the chatbot market is expected to enjoy a 24.3% compound annual growth rate (CAGR) between 2018 and 2022. But like any tool, chatbots require a lot of thought put into them prior to launch.
Remember that a chatbot is not just a way to deflect calls and enhance customer service; it will also reflect your brand, experts say. Your immediate goal, for example, may be to empower customers to self-serve and to reduce call volume. Your chatbot will be standing in as the face of your brand, and that means it needs to faithfully reflect brand messaging, product marketing strategies, and the quality of experience your customers have come to expect. However, according to a Pitney Bowes white paper, chatbot failure rates can be as high as 70%. A failure is defined as any time a chatbot cannot complete the query, sending it to a human.
With all of this in mind, we talked to several experts to get their thoughts on the key elements of chatbot development at every stage of the process.
Define Your Chatbot Strategy
“In order to ensure a successful chatbot deployment, businesses need to first define their ideal business outcomes,” says Jen Snell, vice president of product marketing at Verint Intelligent Self-Service. “That might sound obvious, but in the rush to deploy conversational AI solutions, it’s an essential first step that is too often overlooked. Any successful technology implementation should support your bottom line. Chatbots are no different. Your KPIs need to directly reflect your overall goals and broader business strategy.”
For most, that broader business strategy will mean some combination of chatbots and live agents for call center interactions, said Verint president Elan Moriah, Verint CEO Dan Bodner, and several other speakers at the Verint Engage19 conference in May. They agreed that the best return from chatbots is to use them to handle the mundane, routine interactions (e.g., “What is my balance?”) so that human agents can handle complaints and other, more complex issues. Due to the increasing interactions from social media and other channels—some estimate a 350% increase in customer interactions—companies can’t rely on humans alone but instead need chatbots to handle the increasing number of communications in an always-on era.
It’s critical to understand how your customers want to interact with you, and how you can most effectively deploy chatbots to meet their needs. “Don’t assume you know what your customers want, or how they might be trying to get it,” Snell advises. “Look at the data, then design and deploy your chatbot in ways that address the reality, not your assumptions, about what customers want.”
Get Specific to Deploy Quickly
Another consideration, according to Brent Schneeman, senior director of engineering at Alegion, is whether the company will use several chatbots, each developed for specific services—such as payment—or fewer, more generic chatbots that will handle a wider range of subjects.
Snell recommends determining which areas automation will provide the quickest return for and developing chatbots for those first. “Define the primary users and the different types of experiences that you want to offer to each user. Then organize the content. Context is the key. The better that you can develop the context, the more valuable that you make the chatbot—people will be able to use it better.”
To determine which deployments will yield the best returns, Snell recommends that companies consult individuals from marketing, communications, IT, business development, and customer service when designing chatbot strategies and implementation, to ensure that the solution meets the needs of the entire enterprise and matches your vision and goals.
Snell says it’s vital to clearly identify both the quantitative and qualitative outcomes a company wants. Quantitative data gathered through common search terms or web page traffic prior to designing and deploying the chatbot can be used to generate systematic analyses regarding customer behavior and expectations. “By keeping [the above] tenets at the forefront of their deployment strategy, organizations can ensure that the solutions they deploy will generate desirable and profitable outcomes,” Snell says.
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