What are other chatbot testing approaches?

Testing the chatbot's performance in speed and security No one likes to wait and one of the major advantages of chatbots is to be able to respond immediately. Testing is required to ensure speedy responses. Security is critical in any application and the infinite possibilities enabled by text can complicate testing a bot in terms of security features however these are crucial tests for operational bots. Ad-hoc testing Many of the key metrics and key test mentioned in this article are broad test categories. It is possible to test further and generate ad-hoc categories and methods, but it is important to note that chatbots are bots. No matter how hard the people try, at the current stage, chatbots have limits. So, expecting a human-like performance is expecting a god-like performance from a human. It happens every once in a while but doesn’t happen overnight or all the time. Agile development is still the key to success. Even after the chatbot is released, the process continues. Feedback is the most essential element to shape the chatbot. Real life performance should be monitored closely to keep the chatbot versatile and robust.

A/B testing is defined as comparing two versions of a product to see which one performs better. You compare two products by showing the two variants to similar visitors at the same time. Even though it has been used for ages in other fields of marketing. Currently, there are not many A/B testing alternatives available for chatbots. But basically, it is a way to experiment distinct characteristics of the chatbots, through randomized trials, companies can collect data and decide on which alternative to use. Testing a chatbot is conducted through an automated testing process. This makes it possible to hasten development processes of the chatbot and ensures further quality assurance. We can dissect the process into two separate test steps of the chatbot design. One is deciding on the visual factors of the chatbot such as the design, color or the location of the chatbot on the web page. The other factor to decide is the conversational factors, such as the quality and the performance of the algorithm. Those two factors need to be tested for a better user experience.

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Chatbot Testing: A/B, Auto, & Manual Testing

Cem Dilmegani

updated on Sep 4, 2025

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Achieving chatbot success can be challenging. Claims such as “10 times better ROI compared to email marketing” are only realistic if the chatbot is designed, tested, and implemented effectively. A structured testing process plays a key role in ensuring that a chatbot delivers reliable results.

Discover the main types of chatbot tests, explain pre-launch considerations, and explore post-launch testing approaches, including A/B testing, ad-hoc testing, and performance evaluation.

Pre-Launch Testing: What to Complete Before Launching a Chatbot

Before making a chatbot available to users, its performance needs to be validated through automated and manual testing. Automated tests help ensure that updates do not introduce new errors, while manual testing complements automation with real-world user interactions.

The three main categories of pre-launch tests are:

1. General Testing

Covers essential functions such as greetings and simple responses. If the chatbot fails here, it risks losing users immediately, leading to high bounce rates and low engagement.

2. Domain-Specific Testing

Evaluates the chatbot’s ability to understand product- or service-specific queries. For example, an e-commerce chatbot should correctly interpret variations of product names like “strappy sandals” and “gladiator sandals”.

Because it is impossible to test every query variation, automated domain-specific tests should focus on covering the most critical categories.

3. Limit Testing

Examines how the chatbot responds to irrelevant or malformed inputs. This ensures the system can handle unexpected cases gracefully rather than breaking the conversation flow.

Manual Testing

Manual testing provides additional assurance. Services like Amazon Mechanical Turk allow companies to integrate human intelligence into their testing pipeline. This can increase the variety of test inputs and provide higher confidence in chatbot performance.

Source: The Register

Key Considerations in Pre-Launch Testing

Intent Understanding: Chatbots need accurate intent classification. Machine learning models can predict intent for known and unknown cases, but errors will remain. Developers should focus on minimizing misunderstandings in the most common queries.
Conversation Flow: The chatbot should allow flexible navigation (e.g., updating delivery address mid-conversation) and avoid unnecessary steps. UX elements like buttons and menus can simplify user interactions.
Error Handling: When a chatbot cannot understand a query, it should respond clearly, provide guidance, or escalate to a human agent.

Post-Launch Chatbot Testing Techniques

After launch, continuous monitoring and testing are required to maintain performance and adapt to user behavior. Post-launch methods include:

Conversational Factors

Engagement Tests: Use A/B testing to compare different opening messages (e.g., a formal greeting vs. an emoji-friendly tone).
Language Formality: Test whether formal or informal language better suits the target audience.
Personalization: Analyze how user data (e.g., location, history) affects engagement and retention.

Analytics platforms such as Botanalytics can provide insights on session length, drop-offs, common keywords, and engagement patterns.
Source: spadeworx.com

Visual Factors

Visual design also impacts user experience. A/B tests can be run on button colors, frame designs, and chatbot placement. Although design is less technical than conversation flow, it directly affects user perception and engagement.

A/B Testing in Chatbots

A/B testing compares two versions of a chatbot experience to determine which performs better. Although common in marketing, it is still an emerging area for chatbots.

Typical A/B testing steps include:

Choose a platform for testing
Define the chatbot funnel and identify factors to test
Test both conversational and visual elements
Collect sufficient data and analyze results
Adjust designs or responses based on findings
Repeat continuously for ongoing improvements

Source: spadeworx.com

Other Testing Approaches

Speed Testing: Measures how quickly the chatbot responds. Delays in response time can negatively affect user experience.
Security Testing: Evaluates data handling, user privacy, and potential vulnerabilities. Given the variety of text-based inputs, these tests are essential for safe operations.
Ad-Hoc Testing: Involves unstructured testing for unexpected scenarios not covered in automated scripts.

For more on chatbot testing

If you are interested in learning more about chatbots, read:

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Principal Analyst

Cem Dilmegani

Principal Analyst

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Cem has been the principal analyst at AIMultiple since 2017. AIMultiple informs hundreds of thousands of businesses (as per similarWeb) including 55% of Fortune 500 every month.

Cem's work has been cited by leading global publications including Business Insider, Forbes, Washington Post, global firms like Deloitte, HPE and NGOs like World Economic Forum and supranational organizations like European Commission. You can see more reputable companies and resources that referenced AIMultiple.

Throughout his career, Cem served as a tech consultant, tech buyer and tech entrepreneur. He advised enterprises on their technology decisions at McKinsey & Company and Altman Solon for more than a decade. He also published a McKinsey report on digitalization.

He led technology strategy and procurement of a telco while reporting to the CEO. He has also led commercial growth of deep tech company Hypatos that reached a 7 digit annual recurring revenue and a 9 digit valuation from 0 within 2 years. Cem's work in Hypatos was covered by leading technology publications like TechCrunch and Business Insider.

Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.

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