Customer Engagement Prediction with K-Nearest Neighbor Embeddings
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Solution Overview
Problem
Current customer engagement prediction systems rely on single-user modeling and require constant manual tuning due to the decay of models with time, failing to leverage the activity and attributes of similar users, and are costly in terms of resources and efficiency.
Innovation Solution
A method using a K-nearest neighbor approach in latent space with variational autoencoder embeddings to predict user engagement by generating feature vectors, encoding variables into weight-of-evidence values, transforming into embeddings, and clustering users based on proximity in a multidimensional vector space for automated engagement prediction and classification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If single-user classification modeling is used to predict engagement, then the system can provide engagement predictions, but the model requires constant manual scaling and re-tuning due to decay over time
Solution Approach 1:
The system uses unsupervised learning algorithms that automatically adapt to changing customer preferences and data patterns without requiring manual intervention. The model self-updates by continuously learning from new data, eliminating the need for constant manual scaling and re-tuning while maintaining prediction accuracy.
Solution Approach 2:
The system implements continuous learning mechanisms where engagement outcomes are fed back into the model to automatically adjust and improve predictions over time. This feedback loop enables the model to adapt to decay and changing patterns without manual intervention.
2Measurement precision
If traditional classification modeling is used, then engagement predictions can be made, but the system does not leverage activity and attributes of similar users
Solution Approach 1:
The system merges individual user analysis with similar user pattern recognition by integrating unsupervised learning algorithms that identify and leverage behaviors, attributes, and engagement patterns from similar users to improve individual engagement predictions.
Solution Approach 2:
The system introduces similarity-based pattern matching as an intermediary mechanism that connects individual user predictions with collective user behaviors. By identifying similar users and their patterns, the system enriches individual predictions with relevant group-level insights.
3Productivity
If companies focus resources on high likelihood customers only, then resource efficiency improves, but the system cannot identify customers with personalized needs that may lead to attrition
Solution Approach 1:
The system segments the customer base into different groups based on unsupervised learning patterns, identifying distinct customer personas and behaviors. This enables targeted engagement strategies for different segments, including at-risk customers who may require different approaches than high-value customers.
Solution Approach 2:
The system applies different engagement strategies and resource allocation to different customer segments based on their specific patterns and needs. Rather than uniform treatment, each segment receives customized approaches optimized for their particular characteristics and risk profiles.
Data Source
AI summary
Methods and apparatuses are described for automated customer engagement prediction and classification. A server generates a feature vector comprising variables corresponding to historical user activity data for a user. The server encodes, for each feature vector, each variable in the feature vector into a corresponding weight-of-evidence value. The server transforms each encoded feature vector into an embedding in a multidimensional vector space. The server generates, for each user, a user engagement probability value by identifying embeddings of other users in proximity to the user embedding using a similarity measure and determining an engagement outcome for the identified embeddings. The server assigns each user to an engagement probability cluster based upon the engagement probability value for the user. The server generates instructions for a remote device to initiate communications to each user based upon the assigned engagement probability cluster.