Streaming Help Display Personalization Using ML Intent Prediction
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Solution Overview
Problem
Existing digital content streaming platforms face inefficiencies in customer service solutions, leading to wasted computing resources and user frustration as users search for help through self-service libraries and wait in call queues.
Innovation Solution
A help intent prediction system that uses a machine learning model to analyze user navigation events and features to predict the type of help needed, dynamically customizing the help display with personalized solutions before the user asks for assistance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If users search through self-service libraries and wait in call queues, then customer service channels are accessible, but computing resources are depleted and user experience deteriorates
Solution Approach 1:
The system performs preliminary analysis of user navigation events and features using machine learning to predict help intent before the user actually requests assistance. This allows the system to proactively generate and display personalized help content, preventing users from wasting time searching through self-service libraries and avoiding the depletion of computing resources associated with handling generic support requests.
Solution Approach 2:
The system enables self-service by automatically analyzing user behavior patterns and providing personalized help solutions without requiring manual intervention. The machine learning model processes navigation events and account features to generate customized help displays, allowing users to receive targeted assistance autonomously rather than waiting in queues or searching through generic resources.
2Ease of operation
If generic help displays are provided to all users, then system complexity is reduced, but user experience and help relevance deteriorate
Solution Approach 1:
The system applies local quality by customizing help displays according to individual user characteristics and navigation patterns. Instead of providing a uniform help interface for all users, the system analyzes each user's specific navigation events, account features, and predicted intent to generate personalized help content, thereby improving relevance and user experience while managing complexity through targeted personalization.
Solution Approach 2:
The system changes parameters dynamically by adjusting help display content based on real-time analysis of user navigation events and features. The machine learning model processes various input parameters (navigation events, account features, streaming features) to generate customized help responses, allowing the system to adapt its behavior based on user-specific conditions rather than using a static generic interface.
3Measurement precision
If the system analyzes user navigation events and features to predict help intent, then help relevance is improved, but processing time and computational resources increase
Solution Approach 1:
The system applies partial action by analyzing only the most relevant navigation events and features necessary for accurate help intent prediction, rather than processing all possible user data. The machine learning model selectively processes navigation events from the previous predetermined time period and key account features, achieving sufficient prediction accuracy without excessive computational expenditure or processing time.
Data Source
AI summary
A computer-implemented method for predicting a user's help intent in relation to a digital streaming system and dynamically customizing a help display based on the predicted help intent. For example, embodiments discussed herein train a help intent machine learning model to generate help intent predictions based on various types of inputs. The embodiments discussed herein further leverage the generated help intent predictions to dynamically update a help display such that when a user lands on that display, predicted solutions that are customized to the user's most likely problem are immediately presented. Various other methods, systems, and computer-readable media are also disclosed.