Reinventing customer care with AI: Increased revenue and happier customers

Today's brands aim to deliver a total experience – seamless customer, employee, and product experiences. But with the demands and challenges contact centers face, it can be hard to balance all three. A joined-up, AI-first approach across the customer care value chain can solve some of the biggest challenges.

Customer expectations

Expectations are high when it comes to contact center interactions. Customers want immediate service and a quick resolution every time. Research shows that more than half of people will leave a company after just one negative customer care experience.

Plus, consumers expect brands to be available everywhere, all the time – chat, email, social media, text, and phone. But being omnipresent and immediate is not enough. Agents must get it right the first time, every time. Customers are nearly 2.5 times more likely to stick with a brand that resolves their problems quickly and 2.7 times more likely to spend additional dollars when companies effectively communicate with them.

Workforce challenges

The connection between the bottom line and customer experience reinforces the need for employee training. Agents must continually refresh their skills and product knowledge so they can quickly address customer concerns. But in an industry with turnover rates as high as 45%, there's a never-ending pipeline of employees to train and onboard – historically an expensive and time-consuming process.

Along with ongoing training needs, contact center leaders are struggling with more work and additional responsibilities. Nearly 60% of customer care leaders anticipate their call interaction volumes growing by as much as one-fifth by 2026. In addition to balancing a bigger workload, they have to generate returns that demonstrate agility – rather than just revenue – such as improved agent response times, higher customer satisfaction scores, and even faster resolution rates.

AI helps brands address these pain points and provide the total experience they seek. Generative AI (gen AI) uses patterns in data to extract, classify, summarize, generate, search, and translate information. The top use cases for gen AI in customer support fall into four categories:

• Service response automation: Responding to customer queries in real time with accurate and informative answers and creating an automated FAQ system for customers to self-serve
• Knowledge management automation: Capturing, managing, and storing resources within a company and providing quick access to information for employees to work more effectively
• Service transaction analytics: Analyzing agent-customer interactions and providing insights to help agents make better decisions
• Learning management automation: Building and organizing employee training programs, automating the onboarding process, and optimizing agent satisfaction with contextual coaching. Today, more than 20% of leaders use AI-based tools to train their customer care teams

Contact center leaders adopting AI-first customer care should look at opportunities before, during, and after customer interactions. Preinteraction, AI can predict customer needs before they arise and personalize interactions by collecting customer data. During interaction, gen AI can offer live assistance to agents and solve queries faster and more accurately. Postinteraction, AI can ensure the customer doesn't have to get in touch again by identifying pain points and opportunities to upsell and cross-sell.

Business and technology leaders should work together to identify how AI can make the biggest impact across the enterprise rather than solving just one problem with a point solution. This means gathering data from customer interactions with all functions to identify where AI can make the biggest impact on the total experience.

By doing this, an AI-first strategy elevates customer service from a functional department to one that impacts every step of the value chain.

There's enormous potential for AI to deliver value across the enterprise, but some companies are taking a measured approach to adopting this technology. These are some of the common challenges to think about before starting to apply AI in customer contact operations.

Leadership and training needs

An AI-first strategy calls for technology and business leaders to work together and articulate the total experience they seek. It also requires training employees on how to use the technology. Without collaboration and training resources, efforts to implement AI will likely stall.

Identifying the right partner

Many leaders lack the experience to implement or manage an enterprise-wide change of this caliber. Brands need relationships with external partners with AI expertise to develop a roadmap, implement, scale, and continuously evaluate the technology's ROI.

Existing technology investments

Most organizations have significant existing investments in digital technologies and want to optimize this spend. Leaders want to know how to integrate AI into their existing systems rather than adopt standalone solutions.

Standards and governance

Headlines about bias, privacy, safety, security, and lack of governance and transparency can cause brands to think twice about using the technology. Contact centers must be able to demonstrate that their AI practices are fair, accountable, and responsible.

With a focus on impacting the wider enterprise and value chain, AI in customer care can support a brand's pursuit of the total experience and strengthen the bottom line with outcomes such as:

• Boosting customer lifetime value by more than 5%, reflecting the additional revenue a customer brings to a business during their lifespan
• Lowering customer effort scores, which show how easily a customer can make their way through an organization and find the information they need
• Increasing self-service resolution rates by more than 40%, enhancing the ability of customers to resolve service issues on their own
• Improving agent retention rates with informed, trained, and happy employees who are likely to stay with the organization longer
• Cutting operating expenses by up to half with enhanced workforce productivity across front, middle, and back offices

Genpact partners with some of the world's most recognizable brands to create AI solutions that enhance customer and employee experience, increase resolution rates, facilitate self-service, and reduce operating costs.

Global technology leader

Time-consuming, manual employee training processes and static customer responses were denting engagement quality, speed of resolution, and customer and merchant experiences. We developed customized learning paths for the global customer service team and created a virtual subject matter expert to help agents quickly resolve issues; customer and merchant experiences are forecast to improve by 3%–6%.

Ecommerce platform

This online retailer wanted to launch its European out-of-warranty service program with omnichannel customer support, self-service options, and integrated invoicing, logistics, and processes. Genpact built an omnichannel user experience with an AI chatbot, self-service tools, texts, and the option to talk with a live agent. The retailer now has new customer care options, tracking requests, and a complete view of customers and services provided.

Global chip manufacturer

Contact center agents were using an internal database to answer technical questions, but it took agents too long to find answers in the thousands of available articles, lowering the speed and accuracy of customer responses. Genpact developed a database for employees to easily search and confirm technical content and generate customer communications. Customer service agents now have faster and more accurate responses to queries.

Collections agency

The company wanted to elevate the debt collection process by providing agents with conversation recommendations based on previous customer interactions. Genpact developed a prompt tuning tool and provided employee training suggestions based on an individual's performance. The company increased its debt recovery rate, optimized agent training, and reduced operational costs.

Theme park

This client wanted to introduce a chatbot that could share real-time information with park visitors while also boosting the operational efficiency of the contact center team. Genpact designed a tool to aggregate chat conversations and built a repository to capture the topics. The language model's total cost of ownership was optimized to less than 75%.

Today's brands face an unprecedented demand for availability, immediacy, and accuracy from customer service interactions. AI holds the key to both meeting this demand and adding more business value by creating a seamless connection point between customers and the organization.

By adopting an AI-first approach that brings together business and technology leaders and working with partners with a combination of customer care, AI, and operational expertise, brands can meet high customer expectations and ensure their contact center teams make an impact at every step of the value chain.