Conversational AI can offer relief to physicians during the rising talent shortage, increasing their capacity to care for patients.
Implementing conversational AI chatbots - How to maximize ROI
Have a look at these best practices in implementation or supplier analysis to see how to maximize the ROI of conversational AI chatbots and automation.
It’s never easy for an enterprise to adopt new technology, and conversational AI is no different. But the technology offers excellent potential ROI. For example:
- AI Chatbots can interact with thousands of users simultaneously, 24 hours a day. This translates into many hours saved for customer service employees.
- AI Chatbots can offer “just-in-time” communication, stepping in at opportune moments to provide the information and prompts that customers need to make a purchasing decision.
- Through integration with UiPath RPA, AI Chatbots can also handle repetitive internal tasks, such as back-office business functions, freeing up employees’ time in multiple departments, from Accounting to HR.
By following a few best practices in implementation, you can maximize the ROI of conversational AI chatbots.
Ensure Access to High-Quality Data
What’s the “secret sauce” of conversational AI? A machine can process data much more quickly than a human. This means that a conversational AI chatbot can act more proactively than a human could, even potentially anticipating why a customer has initiated interaction. But that capability is only as valuable as the data it relies on.
Thus, the first step to successfully implementing conversational AI is to ensure that your chatbot will have access to updated, high-quality data. That data might come from a variety of existing sources, including the website, CRMs, internal knowledge bases, or ERP systems. Generally speaking, modeling your data in the form of knowledge graphs is a great way to ensure that it’s complete and well structured.
Before you get started with any implementation, take time to evaluate the state of your data. Then make sure that your AI chatbot has been configured to access the data through APIs, ODBC, or even RPA bots. This step will help your chatbot to learn more efficiently, improving ROI.
Choose the Right Candidates for Automation
It can be tempting to implement chatbots as a “first line of defense” for any interaction, but this approach isn’t usually the right one; some interactions lend themselves to automation better than others do. As you consider which processes to automate, consider two key factors first:
- Complexity: Does an interaction require creative thinking or complicated communication? The simpler the interaction, the easier it is to automate.
- Volume: How often does this kind of interaction occur? A primary driver of ROI is a reduction in workload for human employees, so it’s usually best to focus first on repetitive work that consumes the most time for your teams.
Given these two criteria, the best candidates for conversational AI are interactions with high volume and relatively low complexity. These might include common customer questions about simple topics like account status and payment schedules, or repetitive processes like booking services or onboarding customers. Starting with these interactions gives your chatbot ample chance to learn, and it can also instantly reduce your employees’ workload--providing immediate ROI.
Next, move forward with automating interactions that have moderate complexity or lower volume. These might impact ROI less dramatically, but they do serve to help your conversation AI chatbot continue to learn.
Thanks to technology like NLU (Natural Language Understanding) and NER (Named Entity Recognition), conversational AI is now more sophisticated than ever. This means that it can handle more complex interactions that require an understanding of tone and context or the ability to navigate multi-step processes. If these interactions also occur at a high volume, it makes much more sense to automate them.
What shouldn’t be automated? Highly complex interactions that occur infrequently. These can require significant resources in terms of chatbot configuration, offsetting any potential time savings. Usually, this category of interactions is best left to humans.
Decide How Handoffs Will Work
The goal of conversational AI is to give users an experience that equals or surpasses what they would experience with a human user. In some cases, a chatbot might not be able to deliver that experience. This could be because a request is too complex, or because the AI hasn’t learned yet how to navigate that interaction.
In either case, it’s important to think through this handoff from bot to human before implementation begins. One best practice is to always offer users the option to interact with a human operator instead when the chatbot cannot provide the answer. This ensures that users get the experience they are most comfortable with. Other triggers for a handoff might include the following:
- Complexity: If the complexity of an interaction exceeds the scope of the conversational AI chatbot, the chatbot should be able to recognize this and invite the user to talk with a human.
- Urgency: A chatbot should be able to recognize words and phrases that indicate an urgent issue. For example, if a water company’s customer says “broken water main,” that could be an emergency that requires immediate attention from a human.
- Negative sentiment: Conversational AI can use sentiment analysis to guess when a user might be feeling frustrated or angry. This would be a cue to automatically suggest the transfer to a human agent.
Additionally, there may be instances where some human oversight is still desirable, but the chatbot can still do most of the work. For instance, if a chatbot is helping a user with some complicated troubleshooting, a human might have to approve the chatbot’s suggestions before they are sent to the user. This configuration might be preferable for highly technical interactions.
Choose the Right Metrics to Measure Success
How do you know if your chatbot is a success? First and foremost, it’s whether people get the right answers from the chatbot, making accuracy rate one of the critical metrics. That is why the chatbot solution needs to provide ways to analyze both chatbot performance and NLP model performance through analytics dashboards and language model evaluation tools. Here's an example of one of the tools available in the DRUID Platform, that allows conversational AI developers enhance the chatbot's performance.
Other accuracy-related criteria to consider might be how many intents (what the user wants to achieve, like changing a password or finding out the status of their order) the chatbot can respond to and how many training phrases are configured per intent. Generally speaking, AI chatbots with more conversation flows and training phrases per intent will provide a superior user experience.
It’s also essential to think about core chatbot metrics. Regardless of industry, it’s worthwhile to measure engagement rate, total and active users, total conversations, fallback rate (rate of intents not recognized), and the top triggered flows and the intents the bot failed to understand. Additional core metrics might vary depending on the purpose of the chatbot. For example, lead capture or conversion rate might be important for a chatbot whose primary function is marketing. However, a resolution rate might be more useful for customer service bots.
Select the Right Platform
Not all conversational AI technologies are created equal. Look for solutions that offer the right features for your business:
- Scalability: As a business grows, its technology must keep up. Search for a conversational AI solution that uses an enterprise multi-tenant platform, which allows for thousands of chatbots to be deployed at the same time.
- Customization: Every industry is a little different; A retail business might seek a more interactive customer experience and automated product suggestions, while a healthcare organization will need to maintain HIPAA compliance. Choose a provider that allows you to tailor the solution to your specific needs, from use cases to regulatory requirements.
- Data security and deployment models: Financial institutions might need on-premise data to meet government regulations. Other businesses might opt for a cloud or hybrid model. Your conversational AI provider should provide different data security options that work for your business.
- Integration capabilities: Look for a system that seamlessly integrates with any internal or third-party apps, so that data can be collected and updated from anywhere. This will help you consistently surpass customer expectations by providing personalized, relevant interactions.