Jingjing Cannon, Verizon
Jingjing Cannon is a data scientist in Verizon. Her career passion resides in advanced data science such as predictive modeling, machine learning and artificial
intelligence. In her current position at Verizon, she is focusing on text analytics projects to provide business insights from voice of customers. Jingjing is also a last
year PhD student at Georgia State University majoring in Computational Neuroscience. Her research interest is in developing Artificial Neuronal Networks to understand how
behaviors are controlled.
Voice of Customers guided New Product Launches
New product launch is not only an exciting but also a risky area for any business. How to assess performance and risk to increase customer satisfaction in a timely manner is a critical question for new products to succeed. We developed a new product risk model to identify and quantify customer pain points throughout customer journey with the new product including learn, buy, get, use, pay and stay/leave (LB-GUPS). Advanced text analytics techniques were applied to enable stakeholders to “listen to” true and real time voice of customers from diverse channels such as customer calls, online chat, survey and social media.
We utilized sentiment analysis, natural language processing and deep learning to classify large scale of customer feedbacks bucketing them into customer journey stages. A quantitative classification approach was applied to uncover the main revenue driving pain points and root causes for actionable business insights. This risk model has been implemented to predict how new products launches impact company revenue and provide early development risk monitoring to guide decision making.