Pega Customer Service Demo
By the time your customer contacts you with a problem, it’s already too late. Pega Customer Service uses Artificial Intelligence that listens to customers when you are not, sensing a customer’s moment of need while taking proactive, preemptive action.
Hi, this is Adam Field, head of technology innovation at Pega. Today we're going to take a look at one of the unified CRM applications from Pega, Pega customer service. Pega customer service is all about improving agent productivity, delivering proactive and preemptive digital service, and empowering field service workers. Let's take a look at a preemptive digital service example. We'll use healthcare for today's example. Our customer, Kerim, had a really bad experience last time with his healthcare provider, U+, so this time when he's moving his home, he decides to go to you U+'s Facebook page and complain. He's letting them know that he's moving again, and he's really not happy with the service he received last time. Of course, this is a common behavior for many customers today. But what Kerim doesn't realize is U+ uses Pega, and Pega is listening in the background and picks up that post, and responds right away, and understands that the sentiment is angry, and that Kerim's complaining about his experience moving.
So it's responding with empathy and driving Kerim to a modern channel, Facebook Messenger, where it's going to service Kerim through what's known as a chatbot. So Kerim says hello, and instantaneously, U+ is able to respond, understanding exactly why Kareem is contacting them, because he's moving. In context of the chatbot, Kerim can provide his new address, and instantaneously, U+, using Pega, will go to all the back end systems and immediately update his address, making this process completely seamless. But while they have him, a next best action is sent his way. U+ understands that Kerim might be interested in a healthy cooking program, so the chatbot will automatically take him through this process of a survey, and as Kerim answers some questions, it's going to begin to understand what customers in the past like Kerim had been interested in. So it's asking Kerim things like if he typically brings a lunch to work, how often he eats at home. But what's really great about chatbots from Pega is they're not static. They can be easily changed.
So here we see a developer logging in and using familiar tools, the same tools that a developer in Pega would use to author standard business processes. We want to ask our user how much they spend on lunch only if they say that they typically buy lunch at work. So we easily drag on a shape, connect them up, and hit save. And now if we go back to the chatbot, we see now that if our customer says that they buy lunch, it automatically now goes down the other path and asks him this contextual question, how much money he spends when he buys his lunch. He answers that question and he moves on. And finally, answering that last question in the survey, Pega now has enough information to make an informed decision, and it's presenting Kerim with those cooking classes that we believe he's going to be most interested in. An example of using artificial intelligence. Kerim decides that he's interested in this particular cooking class and chooses to enroll, and because at the heart, Pega's a case management system, it automatically enrolls him and provides Kerim with a case ID.
Kerim wants to see what else this chatbot's capable of. So we see here some other things that it can do, which typically are only things that a customer service rep can do, but because Pega's omnichannel, we can do it right through the chatbot as well. But we also understand that sometimes, you just need to speak to a human being, and scheduling an appointment at the doctor's office is one of those times. So the chatbot intelligently understands that in order to schedule an appointment, we're going to move him over to the customer service channel. And here we see Pega customer service, and our customer service rep sees a screen pop that Kerim wants to chat and why he wants to chat. Accepting that chat screen pop, we're brought to Kerim's profile, and we can see here that the customer service rep has complete visibility into the chat that Kerim just had with the chatbot, so the rep doesn't need to go and ask the customer why they're contacting today.
Not only is it able to suggest a next best action of scheduling an appointment, but it's able to contextually bring up dialogue which the rep can send to Kerim seamlessly within the same channel. So back on Facebook Messenger, our customer, without losing any context, is able to get that message. And finally, to reinforce the concept of unified CRM, if we were to log in as a sales agent in Pega sales automation and bring up Kerim's profile, we see here that an accident insurance offer was automatically created in the customer service channel when Kerim changed his address. Also, by scrolling down, we're able to see across marketing, sales and service here in the customer service channel that Kerim recently scheduled an appointment on the phone, changes address on chat, complained on Facebook, and also all of the marketing interactions that he's had across channels like that healthy cooking program, completely unified CRM.
Product Area: Customer Service
Topic: AI and Decisioning
Challenge: Customer Service
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