Clickstream-Based Consumer Interest Modeling for E-commerce Targeting
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Solution Overview
Problem
E-commerce companies face challenges in effectively targeting and marketing to anonymous online consumers due to limitations in current data management and analytical technologies, which fail to accurately model consumer interests and purchasing patterns from vast amounts of clickstream data, leading to suboptimal marketing strategies and reduced revenue.
Innovation Solution
A system and method that derive a multi-dimensional, multi-resolutional, de-normalized interaction table to model consumer interests and purchasing patterns from clickstream data, calculating affinity scores to predict buying probabilities and generate targeted marketing lists, thereby optimizing product offerings and advertising campaigns.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional demographic and psychographic data are used for customer targeting, then customer segmentation can be performed, but the resolution into nuanced interests of customers to specific products is poor and only a small population can be targeted
Solution Approach 1:
The patent changes the fundamental parameter for customer segmentation from demographic/psychographic attributes to behavioral clickstream data attributes. By analyzing actual browsing behavior, time spent on pages, navigation patterns, and interaction sequences, the system achieves high-resolution customer interest profiling that applies to all online visitors regardless of whether they have provided demographic information, thereby simultaneously improving measurement precision and expanding targetable population size
Solution Approach 2:
The patent introduces clickstream data as an intermediary that bridges the gap between anonymous online visitors and meaningful customer segmentation. Instead of relying directly on demographic data that is sparse and unreliable, the system uses behavioral clickstream data as a mediator to infer customer interests, preferences, and purchasing intent, enabling effective targeting of the vast majority of anonymous online shoppers
2Reliability
If explicit preference data provided during account registration is used, then customer interests can be classified, but the data is sparse and unreliable in determining true shopping interests
Solution Approach 1:
The patent implements feedback by continuously monitoring and analyzing actual customer browsing behavior and clickstream data to infer and update customer interests and preferences. Rather than relying on static self-reported data from registration, the system dynamically adjusts customer profiles based on observed behavior patterns, time spent on product pages, navigation sequences, and interaction history, thereby improving reliability and reducing information loss about true shopping interests
Solution Approach 2:
The system enables customers to effectively provide their own preference data through their browsing behavior itself. By analyzing what customers actually view, click on, spend time on, and navigate to, the system automatically generates accurate preference profiles without requiring customers to explicitly fill out preference forms, thereby capturing complete and reliable preference information that customers may not be able to articulate
3Productivity
If historic purchasing activity data is used for targeting, then repeat purchase rates can be predicted, but response rates drop precipitously on second and future campaigns as natural buying thresholds are exceeded
Solution Approach 1:
The patent applies preliminary action by analyzing clickstream behavior and browsing patterns that occur before customers reach their natural buying thresholds. By identifying customers who are actively browsing relevant product categories, spending time on product pages, and showing engagement patterns indicative of purchase intent, the system can trigger targeted marketing campaigns at the optimal moment, thereby maintaining high response rates across multiple campaigns rather than waiting for historic purchase cycles to complete
Solution Approach 2:
The system transitions from static historic purchase-based targeting to dynamic real-time behavioral targeting. By continuously monitoring current clickstream activity and adjusting targeting criteria based on present browsing behavior rather than past purchase patterns alone, the system adapts to changing customer interests and maintains campaign effectiveness over time, preventing the precipitous drop in response rates that occurs with traditional periodic re-targeting
4Measurement precision
If vast amounts of clickstream data are collected for analysis, then consumer interests can be modeled, but unwieldy volumes requiring high storage and processing capacity are required
Solution Approach 1:
The patent extracts and focuses on the most critical and informative features from vast clickstream data, rather than attempting to store and process all raw data. By identifying and extracting key behavioral indicators such as time spent on pages, navigation sequences, click patterns, and category browsing hierarchies, the system achieves accurate consumer interest modeling while significantly reducing storage and processing requirements compared to analyzing complete raw clickstream datasets
Solution Approach 2:
The system segments clickstream data into meaningful behavioral categories and patterns that can be analyzed efficiently. By organizing raw clickstream events into structured segments such as browsing behavior, product interaction, category exploration, and purchase journey stages, the system enables precise consumer interest modeling using manageable data structures that reduce computational complexity while preserving the essential information needed for accurate targeting
Data Source
AI summary
A system and methods which enable modeling of end consumer interests based on online activity and producing e-commerce reports is described. The method includes scoring and classifying interests and preferences of consumers in relation to various items being offered as function of time and utilizing such scores to predict purchasing activity and revenue yield for n-dimensional combinations of interest for generation of consumer lists for target marketing and merchandising. The method also includes converse modeling of the performance and behavioral profile of items offered as a function of consumer activity. This Abstract is provided for the sole purpose of complying with the rules that allow a reader to quickly ascertain the subject matter of the disclosure contained herein. This Abstract is submitted with the explicit understanding that it will not be used to interpret or to limit the scope or the meaning of the claims.