Six Ways Generative AI Enhances Customer Experience and Journey Orchestration

This is an excerpt from our AI eBook: Achieve CX Excellence with AI-Powered Journey Orchestration

Generative AI models are only as good as the data they have access to. While large language models are very powerful, they require fine-tuning with an organization’s specific knowledge, workflow & automation meta-data, and data to provide real value for the specific questions that they receive. Opportunities to improve the models are abundant but training and supervision is required. 

With that said, there are six key applications where generative AI can supercharge the customer experience and customer journey orchestration:

1. Extract
2. Summarize
3. Suggest
4. Answer
5. Improve
6. Insights

Extract

Businesses can train generative AI models to extract relevant information from customer case data in customer support tickets, chat transcripts, and survey feedback. Automating this process will help time, reduce effort, and ensure accuracy in data capture. Specific customer details for extraction would include:

• Historical Interactions: Generate actionable insights to optimize the customer journey with a goal to understand the context, topics and sentiments expressed in past interactions. 
• Issues and Challenges: Gain a comprehensive understanding of customer pain points and take proactive measures to address them by using generative AI to identify and categorize key problem areas and challenges faced by customers across various data sources.
• Touchpoints: Better visualize the end-to-end customer journey by training generative AI models to identify touchpoints that customers interact with, including the optimal sequencing of touchpoints to create a more engaging experience. 
• Expressed Sentiments: Gauge customer satisfaction and improve business actions by analyzing and extracting customer sentiment across reviews, feedback, chats to determine whether their sentiments are positive, negative or neutral. 

Summarize

Generative AI models can be trained to summarize large sets of data across multiple related objects into simple and clear summaries. In doing so, companies can save significant time as well as equip teams with the context they need to provide better experiences to end-customers. These can include summaries of:

• A Single Engagement: Get context and insight of the engagement quickly, including details from notes, customer conversations, events timeline and steps taken towards resolution. 
• Multiple Engagements: Summarize multiple connected cases to capture bigger issues that span multiple interactions, teams and, ideally, external organizations.
• Customer Journeys: Generate text summaries of any active or completed customer journey, including all relevant cases, tasks, automations, emails, and chats. 

Suggest

Building off of generative AI’s summarization capabilities, organizations can train models to suggest relevant answers to internal teams and users. Employees receive content directly versus having to search for information across legacy enterprise systems, thereby saving time and effort.

• Knowledge Search: Find answers to questions quickly by delivering specific generated answers, versus links to knowledge articles, to both internal teams and customers looking for information to self-serve. 
• Knowledge Suggestions: Deliver specific generated answers to employees working on a customer query and ensure quality control by offering employees the ability to review and approve answers before responding to a customer. 
• Conversation Response Suggestions: Generate responses as quick replies in real-time based on the customer context and conversation history, including tailoring suggestions to better fit the communication type (email, chats, SMS). 
• Similar Cases: Uncover patterns or issues that need to be addressed across similar customer interactions to provide relevant real-time recommendations and decrease resolution time.

Answer

As Generative AI models begin to perform well, businesses can increase their focus into delivering answers directly to end-customers in omni-channel environments. 

• Chat Conversational Fallback: Resolve a higher number of cases without escalating to humans by training AI models to generate consistent fallback responses to common questions.  
• Email Responses: Automate email responses with high quality and proper monitoring to save time and free up teams to work on more complex problems that require their attention. 
• Customer-Facing Knowledge: Add generated answers to the customers’ search queries within different available knowledge libraries in snippet form. 

Improve

With ongoing training of the generative AI models, there is an evergreen opportunity to improve experiences. One key way is through Q&A Response Generation. 

Q&A Response Generation: Businesses can generate both questions and answers for customers' top questions, including whether teams are responding accurately to the queries. Combining this data with additional relevant context, such as the interaction rating, journey status sentiment, customer product information, and more will allow teams to provide finely-tuned Q&As to organizations. 

Insights

Outside of empowering customers and customer-facing teams with generative AI, we believe there is also a large use case for empowering organizational leaders to quickly and easily make better decisions with their data without the need for complex data analysis, specialist tools, or dedicated analysts. 

Specifically, organizations have the opportunity to leverage generative AI to augment visual dashboards to answer quick questions and provide flexible insights that could only previously be unlocked with a lot of time, effort and technical expertise. Some critical insights that can be gleaned include:

1. What are the most common customer problems?
2. What teams have the biggest queue?
3. Which journeys have the least churn?
4. What are the most escalated cases?
5. Which teams have to escalate the most interactions?
6. How can we optimize a specific Journey?
7. Which ecosystem partner resolves cases the fastest?
8. What are the overall sentiments of the customers?
9. How can I improve the overall Health Score of my customers?

Generative AI is a game-changer for businesses across industries.The capacity to automate content generation, optimize touchpoint and analyze large volumes of data can help businesses dramatically improve customer engagement and satisfaction. With generative AI, businesses can uncover valuable insights and take charge of customer and employee outcomes at scale.