Although chatbots have become quite popular in recent years, there is still room for improvement. A well-trained chatbot must correctly react to any query sent by a user creating a successful human-like conversation. Is that happening? We don’t think so.
Round-the-clock service, cost reduction and delivering a better customer experience are, among others, the main benefits of chatbots for customer service automation. If you are thinking of setting up a conversational agent to take care of your customers, it’s all-important for you to know not only the bright side, but also the dark one. You just can't spin up a basic chatbot and expect it to work well.
When Chatbots Miss the Point
When talking about customer service chatbots, we think of a tripartite structure: Interface, Intelligence and Integration, where Intelligence is the key point for a successful interaction. What is happening with enterprise chatbots in call centers right now?
It’s time for industries using chatbots to start evaluating the quality of these intelligent conversational agents. What could be the main reasons for such a misunderstanding?
- Manual Training and data scarcity problem: The most-cited issue in poor chatbot implementations is manual training. Having conversational logs at disposal could be helpful, nevertheless, they must be organized, classified and tagged for chatbots to understand natural language correctly. Even if you are using prebuilt agents, they will probably not cover all the needs of your company because either the amount of training data available or the lack of variants.
- Customization: Each sector (banking, hospitality, retail…) has its own distinctive mark and its own terminology (money, food, shipping costs…). For instance, if you are a retail company selling clothes, the language used will not be the same as if you were selling cosmetics.
- Small talk: Believe it or not, one of the main problems which confuses chatbots is the so-called ‘small talk’. For example, Dialogflow agents support this point, but there are still some understanding issues that must be resolved.
- Lack of in-house solutions: Each platform (Dialogflow, LUIS…) has its own peculiarities and works differently from the others. According to Gartner in their 2019 Governance and Best Practices for Chatbot Development, for a chatbot to run properly, there are a handful of developers and linguists behind it. If these prerequisites do not exist in-house, third-party providers that specialize in data preparation should be considered.
- Insufficient maintenance: There is a clear need for IT support in order to add new intents or correct mistakes on an ongoing basis. That way, the chatbot always stays up to date.
At Bitext, we can feed up your virtual agent with your own customized training data in a short time. Personalized natural language interactions via chatbots can now be enabled using a technology relying on linguistic NLP tools. Would you like to test our demos? Try our virtual assistants for retail, home and news.
As Henry Ford said: "Failure is simply the opportunity to begin again, this time more intelligently". Now that you know why chatbots fail, learn how to avoid it and increase customer satisfaction:
- Try our API: Query Simplification and Artificial Data Generation in 9 languages.
- Download our Chatbot Beginners Guide.
- Download our whitepaper: How to solve 3 common chatbot issues.
- Read our Blog Post: How to reduce the training time of your chatbot.
- Visit our resources section at our website.
- Request a Demo to reach 90% accuracy.
Conversation image composed with Botframe