Data & Intelligence

Watson Use Cases in Customer Service

According to a recent Forrester report, usage of chat bots and automated customer interaction tools is growing, but the success rate is dramatically low. While more than half of global organizations are using these tools, or planning to use the solutions soon, failure rates are often reported around 70%. One factor contributing to this failure rate is the current capabilities of solutions that rely solely on linear steps, driven only by keyword identification.

As these automated customer service tools incorporate more cognitive analytics and machine learning, the potential to interact with bots in a human-like manner increases, along with satisfaction rates. Leveraging IBM Watson, Perficient has experience implementing cognitive solutions in the customer service space to help with smarter call centers and cognitive chat bots.

The following use cases demonstrate ROI for organizations implementing cognitive and machine learning solutions to transform their customer interactions.

Smarter Call Center

When a patient visits a healthcare provider for a visit, surgery, or any other reason, the provider must call the patient’s health insurance company to gather information on copays, authorization, and referral requirements. To handle these calls and requests for information, one large health insurance provider was using an interactive voice response (IVR) system that was outdated and difficult to use.

On average, a provider could expect to wait eight minutes for one piece of information. Call volumes were approximately 700,000 calls per month and most callers were opting out of the IVR system to speak with a live representative through an outsourced call center – a costly alternative since the insurance provider was responsible for paying by the call. More than 60% of these calls were related to routine pre-service questions with well-defined answers. The organization needed a better way to manage member information inquiries from providers and an alternative to this out-of-date IVR system.

We implemented an innovative, intelligent alternative to the IVR system through development of an IBM Watson-based cognitive agent that can converse naturally with a provider and return accurate member information in a timely and efficient manner.

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This solution first converts speech to text, then leverages advanced machine learning methods to actively learn the meaning behind a provider’s questions and the interaction, determines the appropriate responses, converts those responses into speech, and interacts with the caller through the cognitive agent over the phone in natural dialog.

The following IBM Watson services were central to this solution, driving conversations and providing a cognitive experience to providers:

  • Watson Natural Language Classification
  • Watson Dialog (now Watson Conversation)
  • Watson Text to Speech
  • Watson Speech to Text

Since these inquires and conversations involve protected health information (PHI), we created an application that filtered out sensitive information before interacting with the Watson services. The cognitive agent solution also leverages the insurance provider’s APIs to pull accurate patient information that is relevant to the conversation.

Our IBM Watson solution has delivered many benefits for the organization, including:

  • Drastic reduction in requests for a live agent, leading to substantial cost savings
  • Average call times dropped from eight minutes to three, highlighting the effectiveness of the cognitive agent
  • Enhanced logging capabilities allow for better call metrics and analysis, since Watson can record and report on all conversations

Cognitive Chat Bot

Autodesk makes software for the architecture, engineering, construction, manufacturing, media, and entertainment industries. Customers were being presented with an unfriendly, inefficient user experience when calling in with service requests and questions. We transformed, automated, and improved these interactions by leveraging IBM Watson Dialog and Natural Language Classifier. The solution is designed to understand the intent behind conversation and function as a digital concierge. Approximately 60% of Tier 1 requests are now handled from start to finish by this cognitive agent, resulting in 90% lower support costs and 99% shorter resolution times.

IBM has additional details on this implementation here.

Additional Watson Use Cases

If you’d like more information on these implementations or would like to see additional use cases for cognitive analytics in customer service, check out our recent webinar here.



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