4 reasons why your financial institution’s Chatbot project failed
When conversational AI banking is successfully designed and implemented, it can be a game-changer for your digital customer experience. A great banking chatbot can save people time, allow them to troubleshoot issues on their own, and help free up your contact center staff for more complex customer inquiries.
However, when a chatbot isn’t quite up to the task, it can set you back instead of advancing in your progress towards improved online infrastructure.
Chatbots that don’t deliver on the promise of better customer satisfaction can not only drive people away from functionality, but maybe even drive people away from your financial institution altogether. Four common failures can negatively impact a chatbot’s ability to serve customers:
- Poor coverage / poor precision
- Lack of precision
- Bad bot memory for context
- Be model-centric, not data-centric
These problems can all be avoided, as long as your chatbot provider is aware of the possible shortcomings and has methods in place to avoid these common pitfalls.
1. Poor coverage / poor precision
While subtly different, and sometimes difficult to tell apart, these two issues can lead to the same result: the chatbot providing poor, incorrect, or incomplete responses.
Bad coverage It’s when a chatbot doesn’t receive the information it needs to answer a user’s question. If a chatbot is not told about interest rates on a certain credit card, or is not informed of the opening hours of a certain branch, it simply cannot answer questions about one. either of these.
Poor precision occurs when the chatbot is unable to understand the question itself. It can know the interest rate for that particular card, but if it can’t analyze that the user is asking for that information, the bot still won’t be able to respond to it anyway. He can try to answer a different question instead, as shown below:
No matter which of these issues affects your chatbot, the result will be a chatbot unable to accomplish its only task: answering user questions. This leaves a bad impression on your user and will cause them to view this new feature as unnecessary.
Chatbots need good coverage and precision if your financial institution wants the technology to work well. Otherwise, it will fail in its primary mission: to deal with people’s questions and concerns.
2. Lack of precision
Similar to low precision, a chatbot with lack of precision is unable to provide concise and granular answers to a user’s questions. It will provide the same answer to many different queries, often trying to contain as much information as possible to cover each variation of the question under a larger umbrella. This scattered method of answering questions will confuse your user more than it helps them, and make them do a lot more work than necessary to locate the answers to even the simplest questions. See how poor a lack of precision in your answers can seem in the example below:
If a chatbot has not been trained to recognize subtle differences in various user requests or to ask additional clarifying questions, it will need to give all possible answers to a certain area of questioning at a time to compensate. This is rather unsatisfactory in a digital messaging environment, where short answers are necessary for a natural conversation.
3. Bad bot memory for context
The ability to remember what has been said earlier in a conversation and to shape our responses around it is a vital aspect of human conversation. This is something that comes naturally to us, but a chatbot needs to be specially trained to mimic this behavior.
If a chatbot cannot remember what the user has told them previously, it will lead to a frustrating user experience. No one likes to repeat themselves, so having to enter the same information over and over again will force your user to search for a live agent before long. This issue is even more common in cases where the chatbot transfers the user to a different space, such as a new webpage or a live chat with an agent.
4. Be model-centric, not data-centric
All AI-driven chatbots rely on good domain-specific data in order to perform optimally.
You can find a chatbot that uses one of the newer, best performing, state of the art models to work, but how well will it perform if it doesn’t have the data to back it up? ? Some mistakenly prioritize quantity of data over quality, or focus on model accuracy above all else, and also forget to invest time and energy in their data models. Getting good data that is both specific to the banking industry and your specific organization is crucial for the success of your chatbot implementation.
Has your financial institution found a chatbot with all the functionalities? If so, make sure your tech team checks that the behind-the-scenes data models can keep up with the chatbot.
A good banking chatbot is an invaluable asset to your financial institution, but it’s essential that you avoid these common missteps while running. The best way to avoid these all-too-common mistakes is to find a full-service banking AI chatbot provider who has all the financial data and training to serve your customers intelligently and efficiently. .
Be sure to seek the expertise of industry professionals who know how a banking chatbot can fail, and your new conversational AI asset will immediately improve customer satisfaction.
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