Will the Chatbot understand the questions of my users? Will it be able to correctly identify the topics? Will you have the right answers?

It is particularly complex to develop a software tool that interacts in natural language with clients, each of which uses constructive styles, lexical forms and synonyms of personal experience and culture. It is also difficult to anticipate the topics to which it will be held to respond, because each user has specific needs and needs timely answers. Often the learning phase takes place after the release, breaking down the quality perceived by users who become, in spite of themselves, testers, instead of users.

AppQuality can ask to the crowd to identify the typical questions of the sector, in order to identify most of the topics that the chatbot needs to know. For each topic, the crowd could be asked to find sample questions to evaluate the different lexical, verbal and grammatical choices. Assuming to activate 50 testers, out of 15 topics, each of which explored with 10 different questions, you get 7,500 iterations useful for enhancing the learning of the chatbot: all in a few days!

By carrying out this activity downstream of the development, production times would be lengthened; AppQuality allows to parallel the training phase to the development of the chatbot as it does not require the presence of a product MVP in order to identify possible interactions.

Chatbot training takes place through real people outside the team, freeing up design and development resources.

Another important advantage is the possibility of identifying, within the crowd, testers superimposable to the end customer, the user of the chatbot. In this way the software is prepared for the questions to which it will actually be submitted by the market. Usually this phase is managed in collaboration with the marketing team: what are our customers? What age groups do they represent? Which cultural level? According to this information the profile to be searched in the community is outlined and only the profiles in question are asked to simulate an interaction with the chatbot.

With crowdtesting it is therefore possible to check the status of maturity of a chatbot and its competence for the role it is going to undertake. But above all, it is possible to enhance its learning process before it is released: deepen our support with a real case!

let's start testing