In industries where companies depend on customer service excellence, success is driven by the right people, processes, and a cultural commitment to customer satisfaction.
But in today’s empathy economy where customer expectations are higher than ever, companies need to leverage technology to enable and empower front-line agents in their customer service operation. Increasingly we see companies turning to artificial intelligence tools in order to, enable and drive an effective and friendly customer service operation.
Gartner recently found that by 2020, 55 percent of major brands will have some kind of AI initiative in place, many of which will be focused on customer service. One industry constantly flooded with inquiries is the airline sector. Brands like KLM Royal Dutch Airlines, have already found ways to leverage AI to power their service operations and drive friendlier and more personal customer support interactions.
But how exactly are airline companies and others stepping up their customer service game with AI? And how is technology changing the game in terms of operational efficiency, agent morale, and support center profitability?
Automated Classification & Routing
Few industries have more volume of customer service data than airlines. That’s simply due to the number of customer touch-points that naturally exist when it comes to air travel. Flyers shop, browse and add to their shopping carts online, check in electronically, check into their flight at a specific time, and utilize their frequent flyer miles for future trips and rewards. This leaves a trail of data-points that hasn’t been utilized to the benefit of customer service.
With proper applications of machine learning and AI, contact centers can finally put that data to full use. Unlike scripted chatbots, which usually provide simple, automated responses based on predefined rules or scripts, proper AI tools can pull together and analyze a variety of historical conversations. As a result, AI models can automatically classify the nature of incoming support inquiries and route them intelligently to the best support channel. If, for example, a certain passenger sends an inquiry via email about lost luggage, AI can analyze their request and route them accordingly, while automatically pre-filling all the details around the case, so agents don’t have to waste valuable time clicking around the screen.
Improvement in Service Metrics
Across industries, every contact center is normally driven by the need to achieve certain metrics to maintain an acceptable level of customer satisfaction and retention. And the metrics they use are specific to the needs and issues of their target customer. Typical service KPIs for contact centers include: Average Handling Time (AHT), First Response Time (FRT), First Contact Resolution (FCR), Customer Satisfaction (CSAT) and Employee Satisfaction (ESAT).
Some companies also focus on industry-specific KPIs, based on their business model and customer base. The advantage of using AI in contact centers is that these tools can be pointed towards tackling issues that address specific KPIs. Let’s say an airline’s biggest concern is addressing urgent inquiries quickly. It can use a machine learning algorithm to ensure that all customer issues that fit the definition of “urgent” can be elevated above the rest of the message traffic.
Over time, the AI will learn and iterate its responses to achieve whatever improvement in hard service metrics are being sought after, freeing up more time for agents to handle the high-level conversational and empathetic functions of service interactions.
Boosting Agent Morale
One of the most critical aspects of providing outstanding customer support is having front-line support agents that are satisfied with their roles, and don’t feel overburdened or inundated. But that can be a difficult task. Especially when 30-60% of a customer service agent’s day involves repetitive tasks which neither stimulate nor inspire.
This is why AI has become an effective tool when it comes to enabling frontline agents. Staff now work alongside AI, training it to perform the mundane tasks they least enjoy and unlocking valuable time to work on difficult cases and engaging in more personal interactions than they otherwise could.
Boosting frontline agents’ self-reported Employee Satisfaction (ESAT) by implementing AI reduces turnover (and the costs associated with it) while driving more pleasant interactions with customers. When someone reaches out about a lost bag, for instance, the AI models may have already located helpful information for the agent before they start working on the case. This reduces both the amount of work and stress agents experiences with each interaction, enabling them to resolve the issue in a shorter amount of time and drive high-level passenger service that’s becoming a standard in the travel industry.
At the core of great customer service are empowered agents and satisfied customers Integrating AI into contact centers is freeing up agents’ time, boosting morale, and letting them focus on the most important emotional aspects of each conversation.
The success stories of leveraging AI in customer service operations is just beginning. As more companies realize how AI can be implemented to improve their service metrics, along with boosting agent morale, the sky will literally be the limit for what their service teams can achieve.