Chatbot Development: Your Guide to Getting It Right
The qualitative information will come from chat logs after deployment and can provide invaluable insights about how customers are asking for what they want or need, according to Snell. “This will help you better understand your customer’s intentions and adapt your systems to better serve them over time.” That might mean finding new ways to answer questions.
For instance, your chatbot should connect to available multimedia within the organization, Sadasiva adds. You’ll be able to go beyond just delivering voice or text responses and start including videos, relevant articles, blogs, and other information from the company’s own knowledge base.
Chatbots can also be deployed to deliver answers, knowledge base articles, and other information either for the agent’s own education or for the agent to forward to the customer. For example, the chatbot can be programmed to send agents spec sheets or a how-to video on a product fix when a conversation starts moving that direction. The agent can go over the details with the customer, and forward the information if warranted.
Another element that can make a chatbot more valuable to the organization is inbound-outbound message manipulation, according to PubNub’s Pollock. “Companies usually run inbound messages through their own custom code.” If, instead, an open code is used, it can be better manipulated depending on how many times a particular question is asked. For chat and chatbot applications, proprietary back-end code is often used for content moderation, profanity filtering, and machine learning-based language translation, Pollock explains. This adds undesired latency to the chat experience, in that typically many more network hops are involved in the chat data flow, but just as important, this approach generally results in less adaptive logic. With a serverless compute module—available at the edge of the network, where messages flow into the chat and chatbot applications—it becomes possible to separate out the low latency logic into easy-to-modify functions.
For example, a chatbot’s translation feature can be easily modified if the code is running on the network. A company can modify it and incorporate routing logic so that different inbound messages can follow different paths—e.g., one may find that Amazon’s translation service is better for Spanish and Microsoft’s better for German, and so, based on message properties, messages of different languages are routed to different translation services.
Potential Pitfalls
While chatbots have proven successful for many implementations, there are still pitfalls that can lead to failure—or at least, much lower-than-expected returns.
When first deploying a chatbot, organizations often make the mistake of siloing the project rather than considering the technology as a broadly capable business solution, according to Snell. As a result, these companies confine their understanding of chatbots as purely technological assets when chatbots have the ability to actively support teams and their goals across an organization. That’s why it’s important to include stakeholders from across the organization in the initial development stage.
But experts agree that any chatbot deployment should start small so that any holes in the logic layer, integration between applications, and so on can be quickly recognized and corrected.
“You need to have a flexible integration framework; you need to determine what services you will need to interact with for customer business reporting and for accessing the customer data,” Pollock says. “You also need to have the right analytics tracking technology. You can leverage cloud providers that have tracking in their platforms.”
Continue to Refine After Deployment
Organizations need to continuously review user interactions with the chatbot to determine not only if it is functioning as expected, but also if there are ways to enhance or expand the performance, Snell says. “Evaluating performance for your chatbot can be difficult, as many operate on the scale of 200,000 unique customer interactions per day. Clearly, assessing success when the dataset is that large requires more work than any human could reasonably manage. This is why a blend of human and machine-driven analysis is crucial in any chatbot deployment.”
Combining the machine and human analysis enables businesses to create accurate (for them), actionable benchmarks, even with huge data goals, Snell explains. “Consider an example when a chatbot is assisting a customer with purchasing a plane ticket. Machine analysis can determine whether the user successfully purchased a plane ticket by the end of their chatbot conversation. Human analysis can look for opportunities for future conversation enhancement, including upselling of services.”
Though the success metrics for every company will be different because every company will have different goals for its chatbot deployments, there are a few basic elements central to most of the implementations. Does this chatbot drive revenue? Does it foster cross-team collaboration? And finally, does this deployment streamline existing processes? If you can answer these questions, you’re on your way to chatbot success. x
Phillip Britt is a freelance writer based in the Chicago area. He can be reached at spenterprises@wowway.com.
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