Self-service business scenario

Carmelita Williams, a graduate student and valued U+ Comms customer, has just accepted a six-month internship at a company in France and is going online to suspend her Internet service at home while she's away. She's also been billed 55 USD for a single international phone call and wants to have it credited. Carmelita logs into her U+ Comms service portal and launches the chatbot.

The bot identifies Carmelita's intent to pause her service and proactively asks the questions needed to execute the pause on the right date. The bot uses natural language processing to interpret what she's typed, mapping to the options in the case. For example, "next Friday" is translated to the precise date of June 16th. In only about 30 seconds, Carmelita has completed her pause service request.

Next, the bot connects Carmelita's question about how the pause impacts her billing to a knowledge management article on the topic. All she has to do is just click the link. When she returns to the chatbot and asks about disputing a charge, the bot provides a link to the appropriate Web self-service request section.

Carmelita ends the chatbot session and follows the provided link.

Let's pause the demo for a little background on this next service case: Manage billing issue. The expansion of services and products by U+Comms has led to more complex billing issues and a surge in customer calls. To address this, a billing issue management micro journey in Pega Customer Service application automates the intake process, streamlining questions, and allowing self-service resolution without the need to get the back office workers involved in all cases.

First, Carmelita selects the type of issue she has experienced. Her selection enables the workflow to filter the list of transactions to charges only, making it easy for Carmelita to spot the 55 USD international call charge. She then uses the additional details section to provide contextual information, like the fact that this was an inbound call and she didn't know that it was international.

After the initial intake stage, more complex decisioning is used where Pega leverages process AI to mimic the logic of a back-office billing specialist. Through a combination of predictive and adaptive models, the system lands at Carmelita's aggregate score of 70, well above their 50% threshold to approve the credit. Consequently, she is instantly presented with a positive decision result in the next screen.

Carmelita is happy to have the decision in her favor and that it was made instantly. U+Comms has saved time in both the front office and the back office and kept their customer happy. With Pega's workflow automation and decisioning capabilities, you can intelligently solve customers' billing issues quickly and consistently. Only the most complex billing issues reach billing specialists for review, saving millions of dollars in unnecessary CSR and back-office specialist labor.

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