In this blog we will discuss three ways of doing your chatbot evaluation by using:
- real world evaluation data
- synthetic data
- "in scope" or "out of scope" queries
Chatbots can improve customer experience in contact centers by:
- Reducing customer wait time
- Achieving a higher customer satisfaction
- Cutting down contact center expenses and increasing productivity
- Getting to know your customer better
- Using human agents only when it is necessary
Most customer service and contact center executives are honing in on bots because they can handle large volumes of queries. Thus, their service center staff can focus on more complex tasks. As the technology behind bots has improved in terms of natural language processing (NLP), machine learning (ML), and intent-matching capabilities, companies are increasingly willing to trust them to handle direct customer interaction.
The following practices will help you design a great conversation between your chatbot and your client:
- Transparency
- Avoid using an excess of predetermined links and buttons
- Use an NLP middleware approach
- Include politeness and small talk
- Tolerate typos and slightly ambiguous formulations
- Always include domain specific terminology
- Define your bot's tone
- Keep the number of possibilities limited
- Let the user know when the bot is "thinking" or processing the query
Reducing complicated, confusing processes down to a natural conversation is potentially a huge business opportunity for anyone willing to jump headfirst and create a great user experience. Chatbots are only as smart as the words you feed them. If a bot is too rudimentary, people will lose trust in the company and will feel ignored and unappreciated. UX problems appear when the user deviates from the designed linear flow.
One of the flaws of usual training data generation is that, when you ask somebody to manually create training data for you, they will make an effort to write these sentences correctly, following the spelling and punctuation norms of your language. Even if some errors appear, they will be minimal, because they are trying to do things right —this is, to provide “orthographically right” sentences.
All Machine Learning (ML) engines that work with text can benefit from a solid linguistic background. If they are working in a multilingual environment, the need of a good lexicon (with forms, lemmas and attributes) is overwhelming. Even so, basic features such as Word Embeddings hugely improve when enriched with linguistic knowledge, and if this is not usually applied, is because of a lack of linguists working for ML companies.