Key terminology for AI in customer and employee experience
The ABCs of AI
AI offers numerous opportunities to customize, simplify, and enhance Customer Experience (CX) and Employee Experience (EX). However, it's crucial for companies to grasp essential terminology to identify the most suitable use cases for their strategies and optimization objectives.
Discover 8 commonly used AI technologies and tools so you can move forward with AI confident it will deliver specific, measurable results.
Key Takeaways:
1. Large Language Models
2. Machine learning
3. Natural language processing
4. Natural language understanding
5. Predictive analytics
6. Prescriptive analytics
7. Conversational AI
8. Generative AI
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There are many ways to use artificial intelligence (AI) to personalize, streamline and optimize both the customer and employee experience. Successfully using these technologies starts with understanding the AI terminology most relevant in CX. With that understanding, you can determine which use cases are best suited to your organization and your current CX and EX strategies and optimization goals. Then you can move forward with AI in ways that are sure to deliver specific, measurable results like increasing conversions and reducing response times.
Here are explanations of 8 commonly used AI technologies and associated tools as they relate to customer and employee experience, along with some common use cases for each:
1. Large Language Models
Large Language Models (LLMs) are large deep-learning models trained on extensive sets of data (typically from 2 billion to upwards of 1 trillion parameters) that support a variety of Natural Language Processing (NLP) tasks, including many of those performed by generative AI. By analyzing massive data sets, LLMs learn how language is structured. This allows technologies that use LLMs to create content, such as code and text that mimics what a person might create. They can process many complex multi-step inquiries simultaneously to support a high-volume of self-service interactions, for example. Use cases for large language models include conversational AI, generative AI, information retrieval and sentiment analysis.
2. Machine learning
Machine learning (ML) helps to improve the performance of other software applications over time by predicting outcomes and adapting as it learns based on data sets. ML models use algorithms trained on large data sets to power tools like predictive analytics and prescriptive analytics.
ML provides more personalized experiences based on customers’ behaviors and preferences, improving customer satisfaction. Machine learning can also analyze massive data sets that might be impossible using other means.
Use cases for machine learning include predictive engagement (e.g., proactively engaging specific customers with an action or resource to drive conversion) and predictive routing (e.g., routing interactions to employees best able to resolve them). It can also power predictive outbound campaign management and segmentation.
3. Natural language processing
Natural language processing (NLP) is a technology that can understand natural language without having to write in code or in a specific context. For customer service, NLP enables conversational AI, including speech and text interactions between customers and tools like chatbots, voicebots and IVR systems. It also helps to capture insights through techniques such as classification to understand sentiment, tone and intent. NLP improves efficiency and can reduce the number of mundane interactions agents handle.
Use cases for natural language processing include automated assistants, outbound calling, real-time coaching for agents, sentiment analysis and speech recognition.
4. Natural language understanding
Natural language understanding (NLU) enables customers to communicate through speech and text and receive a conversational reply. NLU generally supports multiple languages and underlies conversational chatbots and voice assistants.
Use cases for natural language understanding include online chat, resolving basic issues and collecting information to forward to an agent.
5. Predictive analytics
Predictive analytics can look at data to make predictions or recommendations. In customer service, it’s often used to analyze customer behavior and purchase history to determine preferences and predict future actions. Predictive analytics can also identify potential issues so companies can address them preemptively. Machine learning often powers predictive analytics.
Use cases for AI-powered predictive analytics in customer service include powering predictive engagement and predictive routing; identifying potential customer churn or fraud; determining the optimal time to engage customers; and identifying opportunities for cross-sell, up-sell and retention campaigns.
6. Prescriptive analytics
Prescriptive analytics can make specific recommendations based on findings from predictive analytics. Often powered by AI, prescriptive analytics provides insights that enable organizations to optimize customer interactions in real time.
Use cases for AI-powered prescriptive analytics in customer service include next-best-action recommendations and identifying customers most likely to convert via personalized offers.
7. Conversational AI
Conversational AI uses a combination of machine learning and NLP to enable interactions with customers via chatbots and virtual assistants. It can also power agent-assist tools and streamline access to knowledge articles. Conversational AI tools are ideally trained on large volumes of speech and text data from an organization’s previous customer interactions. It can reduce response time, increase response accuracy, enable 24/7 customer support and free CX employees to handle complex interactions.
Use cases for conversational AI include answering frequently asked questions, helping with appointment scheduling, providing personalized recommendations based on customer behavior, and triaging interactions to direct customers to the next-best channel or employee. Conversational AI works well in numerous channels; for example, mobile or web-based chatbots and IVR.
8. Generative AI
Generative AI can create various types of content, including audio, computer code text and visuals. Often trained on large language models, generative AI uses NLP to create content requested via prompts. It can streamline tasks like post-interaction summaries and wrap-up codes, improving employee productivity and satisfaction. It often underlies agent-assist solutions.
Use cases for generative AI in CX and EX include generating content for knowledge articles, drafting post-interaction summaries, crafting personalized emails, generating conversational bots, answering questions for supervisors and translating text from one language to another
Download “5 ways leading brands use AI” to learn how these organizations have improved the customer and employee experience while reducing costs.
Source: Genesys Cup of G edition.
Adventus Solutions partnered with Genesys has the experience and use cases for helping companies implement contact center project and AI solutions — across all market segments, company sizes. To get more information about Genesys Cloud contact center platform look for product description or feel free to contact us and we will happy to help.
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