Chatbot & Virtual Assistant Testing
Test your chatbot, language assistant, or conversational AI with real people and optimize its capabilities for the real world.
Advantages of Chatbot & Virtual Assistant Testing
Ensure that your chatbots and virtual assistants deliver information correctly and quickly, while behaving as naturally and human-like as possible. Due to the diversity and variety present in natural languages, both language training and verification, as well as the correct reproduction of information, are major obstacles to overcome.
Human interaction is diverse – and so is testing a chatbot conversation
The development of natural language processing (NLP), machine learning, and personal chatbot capability is constantly improving. However, the intricacies of local dialects and language itself are still a real challenge – like the surprisingly different respective German dialects spoken in Berlin and Bavaria.
Customers expect to be understood by a language assistant – even if they speak a dialect, have a cold, or have background noise like traffic in their vicinity. Additionally, a virtual assistant designed for everyday use should be able to handle input with spelling or grammar errors, just as well as it handles error-free input.
Optimize your chatbot’s conversations and behavior with our large and diverse Crowd
For designing and testing the language capabilities of your chatbot, we have 1,000,000 experienced testers in our global Crowd.
They’re available for both functional tests and usability feedback, in everyday conditions and from your exact target group – including native speakers of specific languages and dialects.
Our intelligent bot and AI tests don’t only reveal weaknesses or faults in the language ability and information processing of artificial intelligence and natural language processing. We’re also able to deliver hundreds of training data sets that can be used to address weaknesses and close language gaps.
“Testing a chatbot implicates special requirements that could be met thanks to the use of crowdtesting. Of course, we appreciate the positive feedback from the testers, and their recommendations that are at least as important to us.”
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What is Chatbot & Virtual Assistant Testing?
Chatbots, conversational AI, virtual assistant services – there’s a lot of different names for one of the most buzzworthy trends in human-machine interaction.
These AI services are expanding in the digital realm, taking over tasks that have traditionally been done by humans. Handing these tasks over to artificial intelligence means that more people can be served without having to increase the number of employees serving them.
With the continuous growth of AI-powered language processing and interfaces like Siri, Alexa, and Google Assistant, a vast variety of tools have popped up that bring new approaches for how we interact with digital products and services. For example, you can order a pizza or get flight details from a chatbot, but it still needs to be trained and tested.
Humans interact with the AI through auditory or textual methods and ideally, this should feel like you’re actually interacting with a human being. This provides new opportunities for customer support and service, customer engagement, as well as the overall customer experience.
Imagine you have a project for the customer service of an airline. The chatbot is supposed to offer customers an additional channel in which they can inform themselves about flight times, destinations, luggage regulations, etc.
In order to provide customers with the best possible customer experience, chatbots must first of all adapt to the customer’s needs and communication style. Different language patterns, ways of questioning, spelling and grammar, but also colloquial terms are the biggest challenges. A practical example could be the question of hand luggage on board. Even in this simple case, there are different ways to formulate the query and many synonyms for the term “luggage”, as well as the constant risk of spelling and grammar errors.
To be able to cover this large variety, an extensive use of testers and test specifications is necessary. Independent testers, which can be selected for specific target groups, increase the efficiency of the training and the development speed of the bots enormously. In chatbot development, an iterative approach has proven to be particularly useful in ensuring continuous improvement with regard to the learning ability of the bots. Thus, with every additional loop a “smarter” bot is available, which in turn can answer more queries correctly.
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