Generative AI is Revolutionizing How Banks Approach Customer Experience

For instance, 8.4 out of 10 is a fine average per 1,000 queries, providing positive reinforcement for the GenAI responses. Then compare these scores with satisfaction ratings for human-generated equivalents. This means that we will increasingly see them used to deal with routine inquiries.

Real-World Examples

• When feedback is batched and shared with agents weekly, its effectiveness in improving customer satisfaction levels is limited, explained Gladly’s Ansanelli.
• In fact, over 70% of CX leaders say they struggle to design projects that increase customer loyalty and achieve results, according to Gartner.
• 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.
• Use single queries requesting feedback scores on a scale of 1-10, then divide the sum of those CSAT scores per 1,000.
• AI can identify trends and patterns by analyzing vast amounts of customer data.

This transformation is evident across various industries, with businesses adopting AI-driven solutions to enhance customer interactions and streamline support operations. Brian Slepko, SVP of global service delivery for Rimini Street, has leveraged artificial intelligence to generate CSAT scores. Automated CSAT surveys provide better support to internal teams on the front lines with clients and reduce pressure on the workforce by improving customer satisfaction measurements.

Customer Service: How AI Is Transforming Interactions

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.

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.

However, if you can integrate that feedback into the service experience — allowing employees to see the feedback results of an interaction in real-time — agents can use that information to inform future conversations. A CSAT score isn’t there to make you feel bad about your brand or discipline your team. The point of gathering feedback is to make tangible improvements to your business that affect your customer satisfaction levels. If you can improve customer satisfaction, you can boost your entire customer experience program. Creating more personalized customer experiences is an opportunity for financial institutions, and most want to move quickly.

Additionally, the increasing cost of labor has pushed some businesses to explore more cost-effective solutions, some of which might use AI. Furthermore, a growing emphasis on customer experience can make AI an attractive option. AI-powered personalization and full-time availability can benefit customer satisfaction. In a support context, this means it can quickly analyze large volumes of tickets or inquiries, categorizing them according to the sentiment of the customer. This could even take place in real-time, for example, by guiding human agents on how to respond during person-to-person interactions. A customer satisfaction survey typically focuses on specific transactions or interactions, which may not provide a comprehensive view of the overall customer experience.

If you can turn an unhappy customer into a satisfied one, there are a lot of benefits. Research from Bain & Company shows that increasing retention rates by just 5% can boost profits by 25% to 95%. The CX framework needs to include all inputs from all channels—behavioral, attitudinal, and inferred—from a company’s physical shop to its sites and apps and contact center engagements.

The Pros and Cons of Measuring CSAT

Those who master this integration will not just satisfy customers but anticipate their needs, creating experiences that transform satisfaction into genuine loyalty. A survey by Salesforce found that 63% of service professionals think generative AI will help them serve customers faster. The report also indicated a strong correlation between agentic AI adoption and improved customer satisfaction (CSAT) scores.

I do, however, believe that professionals in the field who prepare themselves for the AI revolution will increase their chances of remaining useful and valued. So, let’s explore the ways in which I believe the day-to-day work of customer support agents will be disrupted. I’ll also take a look at how professionals in the field can adapt to ensure they stay relevant in the AI-powered business landscape of the near future. Rather than replacing human agents, AI will act as a co-pilot, offering live suggestions and contextual support to human representatives to streamline resolution processes. At the same time, ethical AI practices and data privacy standards will become more critical than ever. To address this, we implemented an AI-driven triage system that identified intricate cases and seamlessly escalated them to technical support engineers.

As attention spans continue to shorten, marketers have the difficult task of not only capturing attention but keeping it. I encourage other business leaders to approach AI implementation with a strategic mindset. Start with clear objectives and identify areas where AI can add the most value.