Nowadays, almost every site uses Chatbots, whether it’s a social network, a website, or an e-commerce platform. Chatbots help expand your business and manage your CRM (Customer Retention Management) interaction like a professional.

The futuristic benefits and timely investment into chatbots have leveraged many companies to realize their full potential. A chatbot proves to be an excellent addition to enhance your marketing plans and benefit your organization if applied correctly.

However, successfully deploying a chatbot doesn’t warrant goal achievement. You need thorough testing before applying it to your marketing strategy. If you’re a beginner to this topic, you might wonder about the techniques available for testing a chatbot?

This guide will help in solving your concerns regarding tips and techniques related to chatbot testing. Let’s take a closer look below:

Regulation of testing frameworks

Generally, almost every testing procedure is devoid of standardization. It becomes challenging to measure the amount of communication covered by test cases, particularly before launching a bot. The objective of the testing procedure should be to include the most anticipated use cases.

The testing framework developed broadly follows three categories listed below.

• Expected scenarios
• Possible scenarios
• Almost impossible scenarios.

These testing use cases can be charted to sigma distances. When testing for the third category (nearly impossible use cases) is completed (known as 3-sigma distance), the chatbot’s performance can be said to be evaluated at a 99% confidence interval. Any testing beyond this level would incur high costs because there are endless possibilities in which humans use language.

Domain of testing:

Chatbot testing offers seven kinds of domain for testing:-

• Conversational flow
• Natural language processing model
• Intelligence onboarding
• Personality
• Understanding
• Answering
• Security
• Speed
• Navigation
• Error management
• Intelligence
• Response time

Agile and regular Testing

Chatbots are good instances of software technology developed using the Agile approach. It offers the best possible viable products that can be obtained after every loop. It captures new phrases with the help of error handling functions.

To prevent bugs from creeping into the bot, testing needs to be done at each iteration. The initial phase includes manual testing, which ensures the execution of the business workflow. The end phases include automatic testing that reduces wastage of time and helps programmers to launch better versions to market.

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Developer Testing

This kind of testing is simple and direct, which the developers are familiar with. It includes executing a validation and verification test and defining the chatbot’s answers and user questions beforehand. These tests will check whether the bot gives precise answers to an imaginary question or cannot do so.

Chatbot Testing Frameworks

Developers and testers have to follow analytical thinking to overcome elements of uncertainty in their test objects to understand how a chatbot will operate. The following list shows some of the techniques testers can utilize when dealing with a chatbot:

• Advanced automation framework: it is essential to test for the point-to-point flow of conversation, understanding natural language, and scope for self-improvement.
• Domain-specific testing: evaluating selected products and services mainly for the business- and consumer end benefits limits the range of testing. Therefore domain testing helps test all possible use cases.
• Real-time monitoring and KPI (Key Performance Indicators): KPI for measuring chatbot performance are different and include parameters like rate of goal accomplishment, AI and machine learning rate, self-service rate, and fallback rate.
• Advanced security mechanism: The security mechanism must incorporate user authentication, end-to-end encryption, two-factor authentication, compliance validations, authentication timeout, channel authentication, intent authorization, and self-destructing messages.

Testing the latest technologies and practically implementing them can be exciting and challenging at the same time. Especially viewing the approaches and tools that have worked in your favor failing in chatbot testing can cause frustration among the best developers. Therefore, considering these up-to-date strategies will assist the developers in testing a chatbot in a better way.

Botanalytics

Botanalytics is a dynamic AI-enabled conversational analytics tool that assists you in capturing engagement across your user lifecycle. With the cutting-edge AI-based solution, you can enhance the capability to interact through sentiment analysis and A/B testing, etc.

Chatbottest

Chatbottest is a free guide with 120 questions that help you assess your chatbots’ user experience. It evaluates the bot based on the seven domains of testing mentioned above.

Dimon

Dimon is a tool for testing tests your chatbot’s flow of conversation and user experience. It even has added functionality of integrating with social media platforms such as Facebook, Slack, Messenger, Telegram, and WeChat.

Techniques for Testing Chatbots

There are different techniques available to test chatbots which depends upon the type of tool being used. A straightforward way is to try the entire training data in your model and correctly predict your model. Moreover, the testing techniques are broadly divided into two main types:

Industry Standard Cross-Validation Techniques

Models based on MI (Machine Learning) are tested using a statistical approach called cross-validation. In this testing, the model’s capability to predict new data dissimilar from the one used for training is evaluated. This kind of testing in interactive AI systems implies testing the bot using queries from the scope examples used for training it.

Standard practices include Leave-one-out cross-validation (LOOCV) and K-fold. The K-fold method divides the data into k groups, in which one part is used for testing the model and the remaining part (k-1) for training. It is iterated k times with every split taking turns at being used for testing.

The LOOCV approach is an extensive method in which the model is tested over possible combinations that the original sample could be divided into testing and training sets. It is bears less computational cost and is appropriate for training small data sets. Experts recommend using cross-validation techniques before switching to blind testing.

Blind Tests — Testing Data Sets with Statements

Blind testing includes testing data with utterances or questions that users might enquire about along with the equivalent exact answer. These queries are executed through the model via a batch test. During this process, every query is marked as to whether the prediction made by the model was correct or not.

Irrespective of the methods used, it is critical to detect the action steps relying on the result. Data visualization techniques help better understand how similarly or dissimilarly the model understands the data by displaying them as close or far away.

A confusion matrix is also quite beneficial in representing objectives predicted by the model so that the NLP trainer can detect patterns and retrain the objectives as per requirements.

Every project doesn’t need to evaluate through both kinds of tests. The selection relies on the developer’s knowledge and potential to conduct the tests.

Create a Good Test Set In Case of Non-Availability of Current Data

Both the testing of the interactive AI and its successful implementation mainly depend upon the selected data set. Before preparing a blind test set for AI, keep the following rules in mind.

• Scenario-based — reflect on as many scenarios possible that users could encounter while communicating with a conversational AI. It will help in grouping intent-based questions that map to unique answers.
• Well explained descriptions — having a detailed description of a problem is always considered better since the bot needs to offer a solution to the user. You need to incorporate the following things -user type, the difficulty faced, and how the user will express the query.
• Align interpretations- it is better to arrange the questions asked to the bot by the users in a systematic order.
• Well-defined answers — ensure that the queries used in the training set carry their corresponding solutions well-phrased and carry value.
• Questions based on ground reality — always opt for the best data source regarding testing that includes genuine questions asked by real users.

Few Common Errors to avoid

Often, the training test data set does not meet expectations when training the bot is concerned. It occurs due to common errors like –

• Irrelevant test questions for solving the scenario — improper preparation could lead to participants of the test ending up enquiring peculiar and arbitrary questions to test conversational AI or for fun.
• Similar expressions carry different intents — which could create conflict and confusion.
• The explanations of scenarios being very general
• The questions are lengthy and lacking clarity — often, while preparing the data set for training, questions could become verbose and include unrequired content.

Coverage Ratio — Critical Analytics Parameter

Continuous monitoring of the analytics tool is essential in a software deployment project. It becomes even more critical when iterative reviewing and testing of the chatbots’ performance is concerned.

Tune your analytics to track the Coverage Ratio. It will help you know what questions users ask and how many of those questions are featured in the AI-based assistant trained for answering correctly.

• For coverage greater and equal to at least 70 to 80%, the questions selected by you for training the chatbot are good and closely represent how actual users might ask.
• For coverage values lower than the above limit- implies that few queries made by you come under the training set for the chatbot but are not precisely what the actual user is asking.
• For such a scenario, the best option is to delete a few of these inappropriate queries and include many relevant questions that the users need assistance with.
• Having fewer examples per intent and similar expressions grouped into different purposes is the most common cause behind wrong predictions.
• It is essential to gather good examples to train the bot according to the predefined test sets. As per the rule, you should target 10–20 examples per intent.

Integrating Email and SMS With Chatbots

Email

With advanced technical frameworks available these days, your chatbots can be aligned with email marketing easily. These two technologies function together well, as can be seen through the following use-cases, highlighting three situations where chatbots can sync with email marketing.

• Chatbot for Email subscription list
• Conversational chatbots for email marketing
• Chatbots for online purchases

These are some of the most straightforward use cases that can be directed to sign up for a mailing list. When the lead gets successfully converted, the bot can ask for some basic details along with the subscriber’s email address.

So basically, the entire workflow comprises of the following-

• Email — data and essential documents that customers require for making a decision
• Chatbot — Answers to queries and delivers information instantly when leads are about to be converted
• Chatbots are even used for managing online purchases.

SMS Chatbot-Marketing Through Text

Just like email marketing chatbots, SMS chatbots are also being utilized for marketing and promoting brands. SMS marketing services offer a consistent channel for marketing since you re-engage with the user repeatedly.

It uses permission-based text messaging to spread marketing messages, such as new product launches, analyses, or feedback.

Conclusion

It eventually comes down to testing -which forms the foundation for including desired features of the conversational AI. However, these features can be enhanced with constant effort and deploying of apt technologies.

Chatbot testing forms the most critical characteristic of the whole chatbot lifecycle. The techniques mentioned above and tools will guide you in extensively checking your bot before launching it on any platform.

It would be better to ensure that your bot is interactive enough, execute a domain-specific test, and carefully examine the results. It should tell you how good your bots are at handling unexpected queries.

You can either go for manual checking through the developer’s help or use the described tools to evaluate them. Last but not least, to make the chatbot more interactive, always encourage small talk, look for matching intent, and define a fallback along with excellent navigation.

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Source: https://chatbotslife.com/a-guide-to-chatbot-testing-framework-techniques-2021-7b894313fd87?source=rss—-a49517e4c30b—4