When Chatbots Fail: Lessons from My Frustrating Experience with Automated Customer Service

In this post, I share an actual dialog between myself—the user—and a chatbot, and then provide an in-depth analysis of the customer experience and the bot’s performance. I detail the specific problem I encountered, examine key issues and the emotional impact of the interaction, and offer recommendations for improvement.

I attempted to book two flights for my wife and myself to visit our daughter in Lisbon. During the online booking process, I was given the option to “freeze” the flights—that is, to hold the flights for a certain period while I verified the details. This allowed me to later confirm the flights at the same conditions and price. Although I was charged one euro for each frozen flight (as shown on my credit card statement), I did not receive any confirmation for the frozen flights—neither via email nor within the app.

When I later tried to book the flights and discovered that I could not access them, I decided to contact customer support. Now, you can see the entire dialog.

My User Experience as a Customer

During the conversation, the chatbot repeatedly provided the same generic reply that focused only on the confirmation email process, even though I raised additional concerns about my missing flight details and the fact that my Amex card was charged. This repetitive response demonstrated that the bot completely ignored the specific issues I mentioned and was locked into a single, unhelpful script.

The core problem I was facing remained unresolved despite multiple attempts to explain it. I clearly stated that I had booked a flight freeze, my Amex card was charged, yet I never received a confirmation email, and the flight did not appear in the app. However, the conversation devolved into a loop with the chatbot offering the same response over and over. This cyclical interaction left me increasingly frustrated and feeling ignored.

Furthermore, when I explicitly requested to speak with a human agent, the chatbot did not escalate the conversation appropriately. Instead, it continued with its automated responses. The repeated apologies and emojis were intended to show empathy but ultimately came across as superficial since they did not result in any meaningful resolution of my problem.

Overall, my emotional experience during this interaction was marked by growing frustration and a sense of desperation, as the chatbot’s repetitive responses failed to address the unique issues I was facing. While the use of emojis was meant to soften the tone, their overuse in this context only emphasized the disconnect between the bot’s scripted responses and my real problem.

Analysis of the Bot’s Performance

The chatbot’s poor performance stemmed from an over-reliance on a fixed script. It repeatedly delivered the same standard response without adapting to the specific details of my situation. This rigid approach prevented the bot from recognizing the complexity of the issue I was experiencing.

Another critical shortfall was the lack of contextual understanding. The chatbot did not pick up on important details such as the fact that I was missing my flight information and that my card had been charged. As a result, it was unable to process the multiple intents within my message, leading to a failure to address my actual problem.

Moreover, the escalation mechanism was inadequate. Even after I repeatedly asked for a human agent, the conversation remained trapped in an automated loop. The absence of effective triggers for escalation—such as sentiment analysis to detect frustration—meant that my concerns were never passed on to live support.

Lastly, the use of empathy in the chatbot’s responses was mechanical. Although the bot included apologies and emojis, these empathetic gestures were not paired with any concrete action to resolve my problem. This superficial engagement only further diminished my trust in the automated system.

Recommendations for Improvement

To improve the overall customer experience, it is essential to implement contextual understanding by integrating advanced natural language processing (NLP) techniques. This would enable the chatbot to detect multiple intents within my input and tailor responses to address specific issues such as missing flight details and an unexpectedly charged card.

The conversation flow should be dynamic and adaptive instead of remaining locked in a rigid script. Flexible conversation trees that adjust based on the context and conversation history can help the bot respond to my unique situation, ensuring that key details from earlier in the conversation are remembered and addressed.

An effective escalation protocol is also crucial. Clear triggers must be established that automatically transfer the conversation to a human agent when signs of frustration or repeated unhelpful responses are detected. Incorporating sentiment analysis to gauge my emotional state in real time would help the system recognize when to adjust the conversation flow accordingly.

Finally, the chatbot should personalize its responses and combine genuine empathy with actionable solutions. Acknowledging my specific problem with clear, detailed responses—and not just generic apologies—would greatly improve the overall customer experience.

Conclusion: Bridging the Gap Between Automation and Human Support

The analysis demonstrates that the chatbot’s repetitive, generic responses and its inability to recognize the complexity of my issue have significantly harmed my customer experience. The lack of contextual understanding and ineffective escalation procedures left my concerns unresolved and led to mounting frustration.

I am writing this because I was truly frustrated. I appreciate technology, but I become very upset when it is employed poorly. In sharing my experience, I hope that someone responsible might read this and take steps to improve the bot. While AI has enormous potential and can be incredibly helpful (disclaimer: I used AI to format the dialog and even to help write this text), when it is poorly implemented it leaves people feeling powerless. We must address these issues because AI, despite its potential, also carries the risk of frustrating people.

To improve the overall customer experience, it is essential to implement contextual understanding by integrating advanced natural language processing (NLP) techniques. This would enable the chatbot to detect multiple intents within my input and tailor responses to address specific issues such as missing flight details and an unexpectedly charged card.

The conversation flow should be dynamic and adaptive instead of remaining locked in a rigid script. Flexible conversation trees that adjust based on the context and conversation history can help the bot respond to my unique situation, ensuring that key details from earlier in the conversation are remembered and addressed.

An effective escalation protocol is also crucial. Clear triggers must be established that automatically transfer the conversation to a human agent when signs of frustration or repeated unhelpful responses are detected. Incorporating sentiment analysis to gauge my emotional state in real time would help the system recognize when to adjust the conversation flow accordingly.

Finally, the chatbot should personalize its responses and combine genuine empathy with actionable solutions. Acknowledging my specific problem with clear, detailed responses—and not just generic apologies—would greatly improve the overall customer experience.

Ultimately, bridging the gap between automation and human support is essential for creating a seamless, effective, and emotionally intelligent customer experience. By adopting these recommendations, businesses can significantly enhance their chatbot interactions and build greater trust and satisfaction among their customers.