Asapp's Newest Dataset Tracks Dialogue to Improve Customer Experiences

Customer service AI company Asapp has announced a new dataset designed to accurately track customer service dialogue context, in order to more accurately address customer inquiries.

The new dataset, Action-Based Conversations Dataset (ABCD), uses over 10,000 historical human conversations to mark points in conversations that can be used to predict customer requests. The dataset can also interpret context in conversations, which speeds up customer inquiries by avoiding additional explanations from customers. The company intended to create a dataset that was specifically focused on customer service dialogue, differing from competitors that use more generic conversations and information, to create a more effective customer experience with AI.

“Models relying on DST as a measure of success have little indication of performance in real-world scenarios, and discerning customer experience leaders should look to other indicators grounded in the conditions that actual call center agents face,” the company wrote in a press release. “We can’t wait to see what the community creates from this dataset. Our contribution to the field with this dataset is another major step to improving machine learning models in customer service.”

The need to create a better AI experience within the customer service realm has been a major point of focus in recent years. In fact, research conducted by Vontage’s NewVoiceMedia discovered that nearly 25% of participants said that they prefer chatbots and other self service options for inquiries. AI has a reputation within the customer service space for being inaccurate with customer responses, so creating a reliable system with detailed datasets is a crucial move for improving experiences.

Edited by Maurice Nagle