What mechanisms do you have in place to continuously collect and incorporate customer feedback into your AI models?

Here are the mechanisms we have in place:

• RLHF: Reinforcement learning from human feedback (RLHF) is a machine learning (ML) technique that uses human feedback to optimize ML models to self-learn more efficiently. Reinforcement learning (RL) techniques train software to make decisions that maximize rewards, making their outcomes more accurate.

• Employee Insights: Encourage customer success professionals and other employees who interact with customers to provide feedback on AI tool performance and customer reactions.

• BETA Releases: Early release of AI features to select but diverse group of customers to discuss their experiences and gather qualitative feedback that can inform AI model improvements.

• Sentiment Analysis Tools: Use AI-powered sentiment analysis tools to analyze customer feedback from various sources, such as emails, support tickets, and social media mentions, to gauge customer sentiment and identify common themes.

• Surveys and Questionnaires: Regularly send out surveys and questionnaires to customers after interactions and at key points in their journey to gather feedback on their experiences and satisfaction.

• In-App Feedback: Integrate feedback forms and rating systems within our software applications, allowing customers to provide input directly within the platform.

• Customer Advisory Boards: Engage with customer advisory boards to gather in-depth feedback and insights from key customers, which can be used to refine AI models and customer success strategies.