Manage contacts for Customer Service

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- [Auto-Attendant] All of our representatives are currently busy. Please stay on the line and your call will be answered by the next available representative. Today's customer hates to wait for service. When I call up a brand or a service provider, I expect to get my questions answered quickly and to my satisfaction. In the world of customer service, the challenge is to stay a step ahead of customer expectations and ensure that every brand interaction is not just positive, but delightful for the customer. My name is Manish Goyal and I'm a Senior Partner in IBM Consulting, leading our AI analytics business globally. The goal of my team is to help our clients apply AI analytics and automation to create insights that drive better decisions. And one of the areas I spend a lot of time in is advising clients on how AI analytics can help deliver delightful customer experiences. This could be through self-service experiences or through aiding human agents with tools and insights so that they can deliver a great experience to the customer. Now, enterprises spend billions of dollars on customer service every year. Good customer service can turn one-time clients into long-term brand champions. And the lifetime value of an NPS promoter can be 10 times more than an NPS detractor. On the other hand, around 80% of consumers say they would rather do business with a competitor after more than one bad experience with a brand. The contact center, the hub of most customer service operations, has come a long way in the past couple of decades. Tools such as interactive voice response (IVR), agent assist, robotic process automation and chat bots have already made customer service agents more productive. However, at most enterprises, the use of these technologies is fragmented instead of seamless. At the same time, customer expectations continue to be more and more demanding these days, especially coming out of the pandemic. Customers expect seamless access and speedy resolution to their queries across digital and voice channels. This in turn puts pressure on businesses to deliver a frictionless customer experience at every stage, from discovery to purchase, through service and retention. Now with generative AI, this experience can be taken to the next level. Large language models have the power to significantly expand what can be automated, performing critical customer service tasks that are far beyond the capacities of earlier technologies. These models are trained on vast amounts of data and can recognize, classify, and create sophisticated text and speech with speed and precision. To see how generative AI can significantly improve customer service, let's look at three key areas. The first is self-service, wherein you give the tools to customers to serve themselves. Virtual agents or chatbots serve the purpose here. And over the years, they have become very good at being able to direct customers down a predetermined journey. To make these journeys, you first analyze what people are asking about, you understand their intent and then handcraft the dialogue flows to direct them down the right journeys. In the past, creating these flows took time, but now with generative AI, you can deliver much richer self-service experiences. These experiences are more natural and conversational. They're more resilient to variations and digressions, and the tooling to create these flows is also now being augmented with generative AI, so that the process area and domain area experts can describe the journeys in natural language and AI generates the necessary underlying flows. The second area, where generative AI can significantly improve customer experience is by augmenting the human agent, whether in the contact center or in the field. A lot of time is spent by agents in the contact center searching knowledge bases to resolve the queries for customers. Generative AI can dramatically improve the retrieval of this information from the knowledge bases and present it back to agents in a summarized way, to help resolve the customer query quickly. This cuts down the time that the customer is on hold, improving their experience, while also allowing the agent to handle more calls during their shift. Similarly, field service agents can be armed with generative AI based solutions that help them troubleshoot problems in the field faster and more accurately. Another good example is helping agents draft email responses automatically, based on the context or query, allowing them to review and edit before responding to the customer. AI augmented emails have shown to have a higher satisfaction score by customers. And the third area is in contact center operations. Let's say you have a call center of a thousand or maybe ten thousand agents. Gaining insights into what's happening across all the conversations taking place between agents and customers was difficult or expensive before. With generative AI, you can go through the transcripts of every call made and continuously gather insights on how and why agents are taking a long time to handle certain types of calls, or understanding granular classification of complaints on products or services. This insight that a application of generative AI provides can allow your operations leaders to find the root cause of a problem faster and resolve them, if in the servicing function, or alert the product or marketing teams, if they need to take remedial action. There's also a lot of time spent by agents, after each call with a customer, documenting a summary of their conversations and actions taken. During that after call work time, they're unavailable to attend a new call. Again, with generative AI, you can transcribe in real time the conversation that they're having and generate a draft of the summary that agents can then edit and feed back into the CRM system. Not only does this drive consistency in capturing details of each conversation with customers, but it also saves time and drives productivity for the agents. Today, as the cost of building these solution comes down with foundation models and the ROI becomes justifiable, there's a renewed focus on the ways generative AI can be used for customer service. And it's not difficult to imagine why! Customer service has always been complex. Just think of the last time you called in for customer service. Chances are you wanted to address a problem you were facing with a purchase or a product or a service you have; and if you have a problem, you are generally unhappy, which is why enabling service journeys that anticipate and deliver delightful omnichannel experiences, whether as a self-service function or a human assisted one is critical. Secondly, in some companies, there are a lot of employees who can influence the experience customers have with the brand or service. If you can augment their skills and drive productivity across this large population who front your brand, it'll be a huge win for your enterprise. And given the capabilities of AI and the rate at which it is evolving, you can expect significant gains in productivity with the right deployment. And there's more! As businesses focus on building omnichannel experiences for their customers, AI can power interactions or conversations irrespective of the channel customers come in from. Which means a customer request that originated in one channel can be completed in another channel, seamlessly. Combining traditional AI with generative AI enterprises can drive proactive outreach, helping avoid problems or help resolve them faster. If you were to look closely at how these companies achieve high levels of coordination amongst their channels, you will discover a five step approach driving the execution. The first step, as you kick off, is to have a clear idea of the experience you want to deliver. Next is to understand your customers well. What is the demographic, preferences, digital or voice? And now that you know your audience, you need to determine how you want to serve them, decide what channels you want to direct them to. And then, look at the best tools that can support those channels. So what is your platform? Do you go with the cloud-based contact center solution? Will it be on-premises for other reasons, or something else? Once you have your tool chain sorted, the final step is to design the journey end-to-end, so it delivers on the service strategy, the experience you had defined when you started. My favorite story, and it really warms my heart whenever I speak of this, is the work that we have been doing with the US Veterans Affairs since 2019. Before we came in, it used to take a really long time for veterans to get their benefits. We applied analytics and automation to help support faster claim creation and response to veterans. Just a few numbers: 3 million packets processed end-to-end, and these packets have lots of documents in them. 280 different document types, 24 distinct mail type processes, 100% automation of all mail intake. We are processing 220,000 documents per week. We then took that process further, by applying sophisticated AI to analyze the medical records. We are now helping the claims adjudicator make decisions faster, so that the veterans who fought for our country get the help they need without the long wait. And since 2023, we have processed over 125,000 claims. We have been expecting AI to make a big difference for quite some time now. And I think the capabilities have finally caught up with the hype. What's more, the innovation rate has also accelerated dramatically, with a new announcement almost every week. It is becoming increasingly evident now that the faster you add the capabilities of generative AI to your organization, the sooner you can make use of the unlimited opportunities it opens up. Imagine the next time your customer runs into a problem and needs help: You'll have all the capabilities to turn a possibly adverse situation into a positive experience, without making them wait, and perhaps, even before they pick up the phone. So the question for enterprises looking to fold AI into their customer service is no longer, "Why?" But "When?"