Train Your Chatbot to Handle Unhappy Customers
Turning conflicts into opportunities with strategic chatbot response and learning when human agents should step in.
Dealing with customers requires understanding and persuasion.
Dealing with impatient customers requires a bit of encouragement.
Dealing with unhappy customers requires empathy and a strategic response through sentiment analysis embedded in GPT-powered chatbots.
Ever since chatbots serve as customer service, with more accessibility and precision, the ability to convince users to perceive it as artificial intelligence while acting as human as possible has significantly improved. That’s where sentiment analysis comes in.
What is sentiment analysis?
Sentiment analysis is a natural language processing (NLP) technique used to identify data intent whether it is positive, negative, or neutral.
For example, phrases like "absolutely love," "incredible," and "so sleek" indicate a positive tone, while phrases like "terrible," "cold food," "slow service," and "never come back" indicate a negative tone. “Alright” or general question words are considered neutral phrases.
How do chatbots with sentiment analysis have the advantage?
Chatbots with natural language processing (NLP) like GPT have the ability to understand, analyze human conversations, and respond to complaints and requests appropriately.
- Segmentation : categorize and serve customers according to their needs while prioritizing certain customer segments that requires urgent attention
- Multichannel : provide consistent customer service across multiple digital channels
- Recognition : being able to ask follow-up questions based on previous information and conversations
- Personalization : provide tailored service or recommendations and assist with customer’s query accordingly
- Relationship: deliver the right messages at the right time to gain credibility and build trust
Get your prompt ready for different scenarios
If your chatbot were to encounter difficult and complicated situations, make sure it is instructed in the prompt and trained which guidelines it should follow, what kind of response would be appropriate, and what factors should be considered. Most importantly, emphasize on demonstrating and displaying sympathy regardless of improper or insulting message.
Scenario 1 :
Prompt guidelines to train your AI assistant bot to be a concierge :
At which stage should human takeover occur?
The conversation can be transferred to human agents at any point or stage. However, there are a few scenarios where human transfer should be considered instantly.
- If chatbot fails to understand customers’ complaints or messages.
- If feasible reimbursement and compensation were to be offered.
- If the request is urgent or needs to be fulfilled within limited time.
Train your bot to detect urgency
Create prompts to detect urgency through word cues that demand immediate actions, whether it’s physical or mental conditions like allergy, unsafe, critical. Once identified, the follow-up questions should include demand for location, time, or urgent contact numbers for service team to proceed and offer practical solutions.
Here is another example.
Scenario 2 :
Dealing with unhappy customers can be challenging. However, to deliver the best customer experience and maintain quality service, instead of relying on human agents to respond to every complaint and query, adopting the right tool like AI chatbot to lessen the workload and provide more accessibility can be a lifesaver.