In our multichannel world of intense competition, the pressures on customer service excellence are immense. According to the recent Deloitte Global Contact Center Survey, 93 percent of organizations expect contact volume to remain constant or increase, while 83 percent expect increasing contact complexity. Fortunately, as the digital era comes to fruition, artificial intelligence (AI) technology is delivering practical applications for addressing customer service pain points and enhancing customer experience.
The short story is that AI, and its subset known as machine learning (ML), consist of coded algorithms built into software to detect patterns in large data sets and “learn” over time from previously performed tasks. When imbedded into chatbot programs they can utilize customer data sets consisting of purchase history, spending habits, etc. to offer personalized recommendations, solutions, or multichannel routing.
The AI technology behind customer service processes is helping contact centers handle more engagements faster so that human agents can handle more complex issues and higher-value interactions. These call center solutions deliver cost reductions through lower phone bills and increased agent productivity, while also increasing accuracy and customer retention. A closer look shows how leveraging AI benefits customer service.
How AI Can Transform Customer Service
Call center pain points such as contact volume, data access bottlenecks, time consuming routine info contacts, and other issues can be addressed via AI built into engagement solutions. For example, these solutions can work across multichannel environments to:
- Gather incoming contact details for customer identity validation through customer record data in back-end systems to create a complete customer profile for the agent
- Perform initial screening to determine customer need and either provide the answer or route the contact accordingly
- Direct emails and other multichannel customer contacts to the right person or department, depending on customer need and known data
In addition to handling tasks that often tie up agents, like routine answers and call routing AI can handle complex contact handoffs that allow the agent to provide a solution without asking the customer to repeat the problem. This maximizes human agent effectiveness by minimizing the time spent on tasks that bots can handle, thereby allowing one agent to handle multiple interactions.
The key thing that chatbots need is training through data exposure and a narrow focus, for example answering specific questions with specific answers or call routing based on data. Ultimately, AI can effectively guide customer analytic capabilities as well as optimize tools, applications, and operational processes to engage with customers across every phase of their journey.
Rather than some futuristic technology involving deep experience and technical knowledge, AI and ML solutions are accessible and being proven in customer service today. However, organizations must still prepare for successful integration into customer service.
Preparing Customer Service for AI
The main things holding back organizations from AI adoption are skepticism and understanding, fueled by a lack of real-world examples of how AI and ML can work in customer service. A recent McKinsey Global Institute survey of more than 3,000 AI-aware companies around the world revealed that 30 percent of those using AI in core processes have achieved revenue increases through efficiency.
In order to prepare to integrate AI into customer service, organizations must first understand that data is the lifeblood of machine learning, so results will only be as good as their data. As part of an effective approach to embedding AI technology into the customer experience, businesses must:
- Build on a foundation of high data quality and a strong data management strategy that cuts across silos
- Focus on skillsets and corporate culture during implementation to address employee/ML engagement to smoothly integrate AI with agent workflows
- Quantify the expected results and articulate the business value of machine learning goals to zero in on the most unstructured work patterns and time consuming redundant tasks that can benefit most from automation
- Measure outcomes to continuously reinforce the business case through adapted metrics that look at how ML is increasing first contact resolution (FCR), average handling time (AHT) and other vital key performance indicators (KPIs)
The more organizations know about their customers, the more precisely AI can help tailor their experiences and provide both cost and time savings while bringing top line gains. However, organizations don’t have to traverse the path to AI integration alone. Customer support center services and partnering solution providers can help integrate customized AI technology solutions into call center and customer service processes and business cultures.
Incorporating AI and ML into customer service processes should happen sooner rather than later for business competitiveness. As leaders, agents, and others understand that AI and ML enable jobs rather than circumvent them, these solutions will transform the customer experience while eliminating many customer service pain points.