Generative AI KPI Playbook: Measure What You Manage

A new report highlights the transformative impact of “agentic AI” on customer service, revealing significant improvements in efficiency and customer satisfaction for companies embracing the technology. Generative AI goes beyond traditional AI by creating new content based on existing data. This includes generating responses, creating personalized recommendations and producing content that aligns with customer preferences. For instance, generative AI can craft email responses and generate product recommendations. It can simulate human-like conversations, which can make customer interactions more dynamic and engaging.

Generative AI can also be used to draft automated but personalized responses to email inquiries, making sure that messages carry a consistent tone while providing customers with advice relevant to their specific issues. This is where the Dell AI Factory comes into play, helping organizations first identify priority use cases informed by their AI strategy. These use cases are supported by high-quality data, a modern infrastructure, and an open ecosystem of partners with deep experience deploying AI systems. Customer satisfaction metrics help you determine what you’re doing right and what you’re doing wrong, allowing you to focus your efforts on the areas that matter most. It’s just one key in becoming a customer-centric business that people rave about. This process also helps agents who engage with the same customer in the future, added Ansanelli.

Predictive Analytics And Sentiment Analysis

• Then compare these scores with satisfaction ratings for human-generated equivalents.
• For example, if a number of users are having difficulty accessing a service, then other users who are likely to want to use the service could be warned beforehand, enabling them to make alternative arrangements.
• Your top KPI must gauge employees’ active engagement with GenAI tools for their work.

Companies must therefore unify their digital experience stack if they want a full picture of the customer journey. There are solutions dedicated to helping close these gaps, including platforms that integrate digital experience analytics (DXA), digital experience monitoring (DEM), product analytics (PA), and voice of customer (VoC) data in one place. Between analytics, product, IT, and customer service, there’s no shortage of data, but that data often lives in silos, making it hard to get a full picture of customer sentiment and behavior across touchpoints and over time.

What Is CSAT (Customer Satisfaction Score)?

The AI agent can autonomously perform certain defined tasks, such as reconciling financial statements or drafting detailed responses to customer questions. Agentic AI represents a significant evolution in artificial intelligence for customer experience. Unlike traditional AI, which primarily focuses on answering questions or guiding users through pre-defined pathways, agentic AI possesses the ability to autonomously make decisions and take actions on behalf of the customer. The data that supports why this is so critical is clear—“digitally disciplined” teams that systematically test and refine their CX are outperforming the competition. Benchmark data shows they’ve reduced load time frustration by 22 percent, minimized rage clicks by nearly 5 percent, and cut friction 4.5x more effectively than their peers. Looking forward, organizations must prioritize key technologies when developing a unified CX strategy, ensuring they integrate with existing systems by investing in flexible CRM and CDP solutions that connect, not compete.

Since generative AI exploded onto the scene with the release of ChatGPT (still less than two years ago, unbelievably), we’ve seen that it has the potential to impact many jobs.

• You can deploy a customer satisfaction survey at any point along your customer’s journey to gain insight into how happy they are with your brand.
• Initially met with skepticism, the AI system soon demonstrated its value by reducing response times by 40% and improving customer satisfaction scores.
• By unifying data feeds up front, the CX platform can provide a comprehensive, analytical view of customer interactions in real-time.
• I encourage other business leaders to approach AI implementation with a strategic mindset.

Kevin Daly is the global head of Verint’s Experience Management Business, which is focused on using big data, machine learning, and SaaS software to help clients compete through superior customer experience. However, the more human-like and nuanced AI agents become, the more reliant customers will become on AI agents. Even still, financial institutions need to remain human-centric, especially for emotionally fraught transactions such as buying a first home or investing for retirement. Our sister community, Reworked, gathers the world’s leading employee experience and digital workplace professionals. And our newest community, VKTR, is home for AI practitioners and forward thinking leaders focused on the business of enterprise AI. DestinationCRM.com is dedicated to providing Customer Relationship Management product and service information in a timely manner to connect decision makers and CRM industry providers now and into the future.

More Resources on Customer Experience Quality

If your business meets customer expectations most of the time, you’re more likely to retain customers. Gathering CX metrics like CSAT provides decision-makers with quantitative and qualitative actionable data at key interaction points. Organizations can’t make measurable progress on satisfaction or meet business goals by playing CX whack-a-mole—i.e., chasing one problem after another hoping to hit on the right formula.

Build a strong data foundation, invest in talent and foster a culture of experimentation and learning. By carefully considering these factors, businesses can begin using AI while mitigating potential risks. They can be continuously kept up-to-date with the latest developments in best practices so that human agents will always have access to the most current information and insights. Finally, I foresee that AI will help onboard and train support engineers more efficiently by analyzing interactions and providing feedback, ensuring teams stay up to date with best practices. As customers grow increasingly concerned about data security, AI implementations will need to be more transparent. Companies will prioritize AI solutions with built-in privacy features and robust security measures to ensure compliance with global regulations and maintain customer trust.

In the past, most of us will probably have experienced the frustration of dealing with slow, clumsy and far-from-intelligent voice recognition and automated customer support technology. Today, thanks to the application of chatbots built on LLMs, bots can have conversations that are close to being as dynamic and flexible as those of humans. Making money is the holy grail for companies seeking to validate tech investments; it’s no different with AI.

Have you listened to customer feedback and improved your score over the past year or two? That can be done with unification tools that map the data-source context, extending analysis capabilities while housing all sources of data within one platform. By unifying data feeds up front, the CX platform can provide a comprehensive, analytical view of customer interactions in real-time. Despite these benefits, Content Guru’s research shows the journey to AI adoption is not without challenges, especially employee hesitancy. Only 15.5% of organizations reported that their employees were already using AI tools before formal rollout, and 37.9% of organizations’ employees were hesitant about adopting AI tools.

These solutions need to work together to understand and activate customer data. Customer service is evolving quickly thanks to the power of artificial intelligence. To me, AI is not just a trend; it’s redefining how businesses connect with customers.

A report by Harvard Business Review found that of 13 essential tasks involved in customer support and customer service, just four of them could be fully automated, while five could be augmented by AI to help humans work more effectively. After all, chatbots are a flagship use case for generative AI, and the process of transitioning from human agents to automated systems began long before the emergence of language models (LLMs). Another major development will be the increased personalization of customer interactions. AI will leverage customer history and behavioral patterns to create hyper-personalized experiences, tailoring responses and recommendations to individual needs. This deeper level of customization will not only enhance user engagement but also improve overall satisfaction, making interactions feel more intuitive and meaningful. Your CSAT score can be disproportionately influenced by very negative or very positive experiences, potentially skewing your understanding of general customer sentiment.