Contextual Recommendation Engine Using Purchase Lifecycle Segmentation
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Solution Overview
Problem
Conventional collaborative filtering recommendation systems fail to accurately recommend products by not considering the user's current stage in the purchase lifecycle, often suggesting redundant items, overlooking accessories, or presenting complementary items too early, leading to misadvising and reduced sales.
Innovation Solution
The system uses browsing history and purchase data to identify the user's stage in the purchase lifecycle, calculating relationship scores and types to provide contextual recommendations, distinguishing between substitutionary and complementary relationships to offer relevant items at the appropriate time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If collaborative filtering recommendation systems are used to recommend products, then users can receive automated product suggestions, but the recommendations are inaccurate because they do not consider the user's current stage in the purchase lifecycle
Solution Approach 1:
The patent segments the purchase process into distinct stages (awareness, consideration, evaluation, purchase, post-purchase) and provides different recommendation strategies for each stage. This segmentation allows the system to tailor recommendations to the user's current position in the purchase lifecycle, improving accuracy without requiring overly complex algorithms.
Solution Approach 2:
The system performs preliminary classification of users into purchase lifecycle stages before generating recommendations. By pre-segmenting users based on their current stage (e.g., using browsing history, cart status, or purchase patterns), the system can apply appropriate recommendation logic for each stage, improving overall recommendation accuracy.
2Reliability
If recommendation systems provide product suggestions without contextual awareness, then implementation is simple, but they result in misadvising by suggesting redundant items, overlooking accessories, or presenting complementary items too early
Solution Approach 1:
The patent implements dynamic recommendation strategies that adapt to the user's current purchase lifecycle stage. The system dynamically adjusts what types of recommendations are provided (e.g., avoiding redundant product recommendations during the purchase stage, suggesting accessories during the consideration stage) based on real-time user behavior and context.
Solution Approach 2:
The system changes recommendation parameters based on the purchase lifecycle stage. For example, it adjusts the weight given to different product attributes, modifies the timing of complementary item suggestions, and alters the criteria for recommending substitutes versus complements, all based on the user's current stage in the purchase process.
3Productivity
If the system provides contextual recommendations based on purchase lifecycle stage, then misadvising is reduced and user engagement increases, but the system complexity increases due to need for tracking and analyzing user behavior
Solution Approach 1:
The system uses self-service approaches by automatically tracking user behavior and determining purchase lifecycle stages without requiring manual intervention. The system autonomously monitors browsing patterns, cart interactions, and purchase history to classify users into appropriate stages and generate contextually appropriate recommendations, reducing the need for complex manual analysis while improving sales effectiveness.
Data Source
AI summary
A method and a system for making contextual recommendations to users on a network-based system. For example, activity associated with a user interacting with a network-based system is tracked. Based, at least in part, on the tracked user activity on the network-based system, a recommendation relationship type is selected. The recommendation relationship type can be either a substitute relationship type or a complement relationship type. A recommended object can be selected based at least in part on the recommendation relationship type and a first object accessed by the user interacting with the network-based system. A recommendation can be generated for the recommended object for presentation to the user interacting with the network-based system.