Earlier this year, Meta released its new AI chatbot BlenderBot 3, inviting people to stress-test the new tool.
And, of course, users attempted to cause mischief and turn the bot against CEO Mark Zuckerberg.
In doing so, they managed to get the AI persona to call the CEO “too creepy and manipulative.” It also said: “I don’t like Facebook.”
High-profile publications like The Verge, Insider, and even the BBC quickly lapped up the bot’s gaffes.
This may be a humorous example, but it’s one that highlights the possible lapses within even the most sophisticated of emerging AI bots.
Yet, when a customer service bot makes such blunders, it’s far from a laughing matter.
Chatbot Testing Safeguards Customer Experiences
After a chatbot interaction, customers will likely either feel happiness or frustration. There’s little in between.
Indeed, the overriding thought is often: “Wow, that was easy!” Or, alternatively, “I want to throw my laptop out the window.”
Those polarizing emotions create memories that are crucial to whether a customer considers a brand through a positive or negative lens.
And, as Nobel Prize Winner Daniel Kahneman once said:
We actually don’t choose between experiences; we choose between memories of experiences.
Apply that to CX, and the logic is: customer loyalty is a function of positive memories.
Whether a chatbot fuels those positive or negative memories often comes down to testing.
Such testing ensures the bot provides accurate answers, understands context, seamlessly transitions users to an agent when necessary, and functions across multiple channels.
Moreover, testing confirms that the bot is secure, personalized, continually learns, and is accessible to the customer at all points relevant to their journey.
Yet, unfortunately, there is no “one and done” test for contact centers to carry out. Instead, there are various functional and non-functional tests that safeguard bot-driven service experiences.
Here are five excellent examples of testing that service teams may employ to maintain high-quality standards.
1. Natural Language Processing (NLP) Testing
There is a saying in the world of conversational AI: “Garbage in = garbage out.” NLP can test and analyze chatbot training data, ensuring it’s up to scratch. It also allows you to figure out which intents may be causing confusion in your bot
Moreover, it may provide guidance for developers, helping them continuously enhance a chatbot’s ability to understand a customer – which often proves tricky.
2. Regression Testing
Regression testing ensures that when developers adjust the bot’s architecture, they don’t introduce any breaks or changes to existing features or capabilities.
Automated regression testing programs will guarantee conversational flows work as expected and that the chatbot delivers accurate answers to customers in a timely manner.
3. End-to-End Testing
End-to-end testing confirms that customers can access the chatbot across various browsers, channels, and devices as intended.
Moreover, such tests guarantee consistency in the bot’s performance across each of these mediums, as well as seamless transfers and handovers.
4. Performance Testing
Contact center platforms sometimes fail when demand surges to unprecedented levels. Chatbots are similar in this regard.
Performance testing ensures the chatbot can carry heavy loads while continuing to respond to engagements at a fast pace – safeguarding the service operation, even during peak traffic.
5. Security & Privacy Testing
Chatbot API vulnerabilities, unencrypted chats, and data theft attempts pose security threats to contact centers, with the recent rise of generative AI-embedded bots bringing the latter to the fore.
Security testing against the latest and highest security and data privacy requirements is critical to mitigate these potential problems.
Automate These Tests with Cyara
Tests like those detailed above ensure chatbots provide seamless and personalized customer experiences. It’s critical that testing is ongoing at all times to ensure that whenever issues do occur, organizations are immediately alerted and can promptly remedy the problem. Yet, this takes a lot of time and may even be impossible to do manually.
That’s where chatbot test automation comes in, saving significant resources for businesses.
IT teams spend less time on repetitive tasks, get to release stage quicker, avoid stranded investments, and more quickly identify and remedy problems. All in all, Cyara reports:
Customers who use Botium (its automated and AI-enabled bot testing and monitoring solution) can automate up to 85 percent of their testing and cut testing time altogether by up to 95 percent.
Meanwhile, these organizations will likely reduce turnover of their top developer talent by removing painstaking, manual and monotonous work such as bot testing.
Indeed, Cyara Botium can significantly reduce your testing times, resource requirements and costs, as well as provide reliable results in real-time.
To learn more about how the solution safeguards chatbot investments, visit Cyara.