Latching onto the concept that Charlie should maintain the status quo or improve customer satisfaction, we pivoted and approached her introduction differently: She’s a very respectable lady and only introduces herself when she’s at least 85% sure she has the right answer.
How is this possible? We developed a unique introduction and fallback system so that if she knows the answer, she shares it. If she doesn’t, the conversation gets routed to a live agent, and the customer has the same experience they would normally have pre-Charlie.
The only change for the customer is that if their question can be automated, they receive an instant answer instead of queueing for a live agent. It’s a win-win for the customer and the support team because both save time! The customer doesn’t waste time queueing for a live agent and the live agent doesn’t waste time answering a question that Charlie could easily automate.
By not introducing herself unless she’s confident in the answer, Charlie doesn’t need to know everything. Together with the customer service team, we defined a clear scope of easy-to-answer, frequently asked questions. This translated into 30-40 intents covering approximately 15% of all incoming questions.
Beginning with a small set of intents enabled us to test, train and monitor Charlie’s natural language processing (NLP) model to near perfection. When we first introduced her to real customers, we reviewed every interaction to ensure that she said the right thing and created a net positive customer experience. After a few weeks of intense monitoring and some minor tweaks, it became clear that Charlie knew what she was talking about and was ready to work independently.
The vocabulary associated with banking is complex and nuanced. Since many banking intents overlap with one another, we usually recommend working with entities, such as debit card, credit card, limit, etc. Using contextual entities, we built a unique entity fallback system. If Charlie’s between 70-85% sure of the intent, then she rephrases the intent as a question to confirm her suspicions. If she recognizes an entity, she responds with a custom menu that proposes the most asked intents related to that entity.
Basic data capture is a time-sink for a live agent. It can take up to 10 min. for a user to respond with their name, address, email and phone number. That’s valuable time that an agent could use on more complex conversations.
One of customers’ most frequently requested items is a new card reader. The batteries expire over time and customers ask for a replacement. This question is easy for a bot to recognize, so instead of bothering a live agent with this simple request, Charlie detects when a user is asking for a new card reader and responds with a data capture flow that captures the user’s name, address, email, etc. Charlie then forwards that information and the new card reader request to a specific team within Argenta, where they check the request and prep the card reader for dispatch.
Another frequently asked question is how customers can increase their daily transactional limit. For certain amounts, it’s an easy fix that Charlie can automate. But for amounts that exceed Є25,000, the customer needs to answer some questions. Charlie can detect when a customer wants to increase their daily limit and can also detect if the amount exceeds Є25,000 so she can already get answers to the questions before handing the conversation off to a live agent to make the final limit adjustment.