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