We've all been there: you click the little chat icon for help and are greeted with a maze; menus of irrelevant options, requests for your name and email before you've even described the problem, and a “helpful” article that solves nothing. Within seconds you're hunting for a human.
Let's break out just a few of the issues with flows like this, which are seemingly omnipresent:
- You're greeted outright with a menu of options that may or may not be relevant to what you need
- Data is collected outright (name, email, etc.), making it slower to reach support
- The AI trying to help you doesn't have access to the tools needed to solve your issue, and only redirects you to other support content
- These systems aren't focused on speed to resolution, they're focused on filtering down your inquiry and reducing support contact rate
Why the menu of options?
When you think about chatbots that existed over five years ago, this list of “instant answers” and predefined flows makes sense; the technology available at the time made it so that you had to predefine common flows and hope that they could help with user issues. This is no longer the case. LLMs (large language models) make it possible to figure out what a user needs help with based on their natural language input; there is no need to present a menu of options to users.
Collecting the minimum data required
Often the impulse is to collect user data up front, before you even know if you might need it. Typically, this just frustrates users: why do I need to give you my name and email before you've connected me to an agent, or looked up my order, or before I even know if you can help me? This pattern degrades user trust.
Form inputs like this are common in legacy products, but a modern experience can collect data only when it's needed. User is just trying to track their order? Maybe you don't even need to collect their name. Is more data required from the user? The chatbot should be able to prompt them for it, and intelligently extract it from their response.
By limiting up-front data collection and only prompting users for information that's needed to help with their query, you reduce frustration, build trust, and improve resolution time.
I need to talk to a human!
The reason we're all so quick to try to get through the phone tree, or chat tree, and to a human is that we assume that the robot can't solve our issue. This was very much the case in the past decade, the robots weren't really smart enough to handle most issues, and unless you were working with a large corporation that had built out extensive self-service functionality, it was hard to get anything done without talking to a real live person.
The best way to reduce support cost, contact rate, and improve customer experience is to empower AI-enabled support tooling to resolve customer issues. Having an AI that can help a customer with a return, pull order information that might only be visible on the backend, or triage any other type of issue is key to making it useful.
The most effective AI chatbots are those armed with the tools to resolve customer queries, not just spew FAQs.
Make the AI better than talking to a person
Empowering your AI chatbot with the tools it needs to resolve issues can make it the preferred option for your customers (even if they don't know it). The computer can more quickly diagnose their issue, it's always available, and it's on their side.
AI doesn't have to be a bandage on your existing support setup; it can augment it in a way where your support team can focus on the actual issues that require their attention, and everyone else walks away with a faster, more efficient resolution.
Ready to build a chatbot customers actually love? Contact us to see how Valiopt can help.