At this point, there is no doubt that investing in some sort of self-service tool has its positive ROI.
In the past few years, practically all businesses have invested in chatbots or virtual assistants, made available help centers and FAQ sections, or used other kinds of assisted tools with the aim of helping customers search and find answers to their requests on their own.
The goal? Reducing the number of low-value interactions their support team needs to handle.
At first, some were reluctant to jump on the train of automation and Conversational AI. However, they’ve by now seen that competing and thriving in overcrowded markets without a competitive edge is almost impossible.
The advances in Conversational AI technologies have made it possible to automate huge amounts of support requests, but some brands still struggle to find its real value.
Why are companies struggling with automation?
When implementing a self-service tool, most businesses expect short to mid-term results. When this simply doesn’t happen, they tend to blame it on different reasons, the main one being the technology.
We’ve heard thousands of times that chatbots aren’t smart enough, that they are still lagging behind and are unable to comprehend human language like a human would.
But is it really the case? Or is it that we’re expecting something more?
Only good AI solutions truly understand customer requests
Sure, in some cases, virtual assistants rely heavily on training data. If they haven’t seen a specific request before, in some cases they aren’t even able to identify the intent of that request.
Some companies have started to tackle this issue by choosing chatbots that rely on semantic logic. This means that even if they have never seen a request before, they are still able to identify the meaning of the words and find the closest answer.
AI can’t really produce content
At this point in time, even if a chatbot is able to answer like a human, it doesn’t really possess human intelligence. What does this imply? It means that chatbots either answer with a pre-made script or generate answers from scripts, but they can’t really produce reasoned answers on their own unless they have information to feed from.
Sure, they can match a request or a user query with existing content and formulate an answer, but they can’t create new content on their own.
So how does that really affect self-service rates? Let’s dig a little deeper.
Some technologies rely too heavily on training data
Many Conversational AI platforms out there are struggling to provide real value unless there are dedicated teams training the models with relevant data.
This means an AI needs to see some examples and learn how to react when encountering them. This is done by means of training the chatbot.
For this purpose, we need to extract and curate data in order to feed it to the AI. Therefore, training the solutions can be extremely time-consuming. However, many of the current Conversational AI solutions out there are based solely on machine learning and therefore require these trainings in order to improve results.
Choosing a technology like Neuro-Symbolic AI that doesn’t require training can make lives easier for project and customer experience managers, and deliver good results with less maintenance required from your teams.
Why do many chatbots and Conversational AI solutions fail to provide answers?
If you’re using a chatbot that truly understands intent, and are still experiencing a low chatbot performance regarding answer rates, chances are you are missing valuable content that your users are interested in.
Let’s say a user asks: ‘Is your store on 5th Avenue open on Saturdays?’.
A chatbot might be able to formulate an answer in different ways, but it will never be able to say yes or no unless that information is stored in a system it has access to.
The answer needs to be stored either in the chatbot’s own database, in the customer’s website, or in any other third-party systems connected to the chatbot. Otherwise, there won’t be a satisfying answer for the customer.
Customer Service and Experience teams need to take some time to analyze content gaps, see which user questions didn’t get any proper answers, and create content so that the chatbot can answer the most frequent ones at least.
The more your content is comprehensive and detailed, the less are the chances of your customers getting an awkward ‘I’m sorry but I couldn’t find an answer to your question’.
Try our chatbot and FAQ for FREE for 14 days and see for yourself how Inbenta offers the best technology and platform to provide self-serve support to your customers.
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