Customer Intent Prediction System Using Probabilistic Latent Semantic Analysis
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Solution Overview
Problem
In e-commerce, it is challenging to anticipate and meet the needs of anonymous online customers due to the lack of personal information, making it difficult for businesses to offer relevant help or recommendations, unlike in traditional brick-and-mortar stores where customer intent can be inferred from behavior and interactions.
Innovation Solution
A method and apparatus that segment online visitors based on characteristics and behavior, using probabilistic latent semantic analysis and machine learning to predict customer intent, enabling personalized recommendations and multichannel support to enhance the customer experience and increase conversion rates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If online customers are served anonymously without personal information, then customer privacy is protected, but customer intent cannot be identified and personalized service cannot be provided
Solution Approach 1:
The patent introduces behavioral data and probabilistic models as intermediaries between anonymous customers and personalized service. Instead of directly accessing personal information, the system uses observed browsing behavior, purchase history, and demographic inferences as mediators to predict customer intent and deliver personalized recommendations while maintaining anonymity.
Solution Approach 2:
The patent replaces the mechanical system of direct personal information collection with an automated information processing system using probabilistic latent semantic analysis and machine learning algorithms. This substitution enables intent prediction through automated analysis of behavioral patterns rather than through direct customer identification.
2Measurement precision
If traditional customer observation methods are used in brick-and-mortar stores, then customer intent can be identified through behavior and interactions, but the same approach cannot be applied to online e-commerce environments
Solution Approach 1:
The patent creates a universal customer intent prediction system that functions across both online and offline channels. By using probabilistic models that analyze behavioral data universally, the system adapts the brick-and-mortar observation approach to work in the online environment where direct observation is not possible, making the methodology channel-agnostic.
Solution Approach 2:
The patent changes the parameters of customer observation from direct physical observation in stores to digital behavioral data analysis online. Instead of observing body language and face-to-face interactions, the system analyzes clickstreams, browsing patterns, time-on-page, and other digital parameters to infer customer intent with comparable or superior precision.
3Productivity
If personalized recommendations are provided based on customer intent prediction, then conversion rates increase, but system complexity and computational requirements increase
Solution Approach 1:
The patent performs preliminary actions by pre-computing probabilistic customer profiles and intent models during off-peak times or in advance. Customer segmentation, behavioral pattern analysis, and recommendation generation are prepared beforehand, reducing real-time computational complexity while maintaining high conversion rates through pre-personalized experiences.
Data Source
AI summary
A method and apparatus enables identification of customer characteristics and behavior, and predicts the customer's intent. Such prediction can be used to adopt various business strategies to increase the chances of conversion of customer interaction to a sale, and thereby can increase revenue, and/or enhance the customer's experience.