Top 10 artificial intelligence (AI) terms that customer teams should know
1. Generative AI
AI systems that create new original text, images, or other media, based on existing data.
Why customer teams need to know
Generative AI can automate personalized communications and content creation, significantly enhancing customer engagement and saving time for CS professionals.
2. Conversational AI
AI-powered systems for automated conversation that use NLP and ML to understand and respond to user queries more naturally.
Why customer teams need to know
Conversational AI can handle routine inquiries, freeing up CS teams to focus on more complex issues and providing 24/7 support to customers.
3. Explainable AI (XAI)
Artificial intelligence systems designed to provide clear, understandable explanations for their decision-making processes and outputs, making AI models more transparent and interpretable to humans.
Why customer teams need to know
Customer success teams should understand XAI because it provides transparency around data collection and processing that can build trust.
4. Natural language processing (NLP)
AI technology for understanding and generating human language.
Why customer teams need to know
NLP powers chatbots, sentiment analysis, and automated responses, improving support efficiency and customer understanding.
5. Machine learning (ML)
A subset of AI that allows systems to automatically learn and improve from experience without being explicitly programmed. Additional terms related to NLP include:
- Supervised learning: The algorithm learns from labeled training data, often curated by humans.
- Unsupervised learning: The algorithm learns from hidden structures in unlabeled data.
- Neural networks: Processors operating in parallel and arranged in tiers to process information.
- Deep learning: A specialized form of machine learning that uses neural networks with many layers to analyze data.
- ML anomaly-based detection: Employing ML algorithms to identify unusual patterns that don’t conform to expected behavior.
Why customer teams need to know
ML drives insights from customer data, enabling better decision-making and predictive analytics for proactive customer management.
6. Predictive analytics
The use of data, statistical algorithms, and machine learning techniques to forecast future outcomes. Additional terms related to predictive analytics related to customer success include:
- Churn prediction: Identifying customers likely to leave based on patterns in data.
- Customer lifetime value (CLV) prediction: Estimating the total value a customer will bring over their entire relationship with a company.
Why customer teams need to know
Predictive analytics helps identify churn risks and upsell opportunities, enabling proactive customer management and targeted retention efforts.
7. Sentiment analysis
The use of natural language processing to determine the emotional tone behind words.
- Aspect-based sentiment analysis: Analyzing sentiment towards specific aspects or features of a product or service.
- Real-time sentiment tracking: Monitoring sentiment changes over time or in response to specific events.
Why customer teams need to know
Sentiment analysis helps gauge customer satisfaction and identify areas for improvement in real-time, allowing for quick responses to customer needs.
8. Customer health score
A customer health score is a value that measures a customer’s engagement and satisfaction with your company and its product or service.
Terms related to customer health scores include:
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- Product usage metrics: Measures of how frequently and deeply customers use the product.
- Engagement metrics: Indicators of customer interaction with the company, such as support tickets or feature requests.
- Engagement sentiment: Overall tone customers’ express in engagements with your company.
Why customer teams need to know
Customer health scores indicate the customer’s likelihood to renew or expand.
9. AI data policies
Guidelines governing the collection, use, and management of data for AI systems, ensuring ethical and responsible AI practices. Here are some important sub-terms related to AI data policy, along with brief definitions:
- Data privacy: Protecting personal information from unauthorized access or use.
- Data security: Safeguarding data from breaches, leaks, or unauthorized access.
- Ethical AI: Developing and using AI systems in a manner that respects human values and rights.
- Regulatory compliance: Adhering to laws and regulations governing AI and data use.
- Bias mitigation: Reducing or eliminating unfair prejudice in AI systems and their outputs.
- AI governance: Frameworks and processes for managing AI development and deployment.
- AI assurance: Measures to ensure AI systems are safe, reliable, and trustworthy.
- AI audit: Review and assessment of AI systems for compliance and proper functioning.
- Algorithmic fairness: Ensuring AI systems treat individuals and groups equitably.
- Data leakage: Accidental exposure of sensitive or confidential data.
Why customer teams need to know
This knowledge enables teams to leverage AI responsibly, enhancing customer experiences while protecting privacy and maintaining ethical standards.
10. Hallucinations
Incorrect or nonsensical outputs generated by AI.
Why customer teams need to know
Helps teams understand limitations of AI tools and manage customer expectations effectively.