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Spotlighting Customers' Visual Attention at the Stock, Shelf and Store Levels with the 3S Model

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Published on: May 24, 2019

Shopper intent prediction from clickstream e-commerce data with minimal browsing information.

Borja Requena¹, Giovanni Cassani², Jacopo Tagliabue³

¹ICFO - Institut de Ciencies Fotoniques, The Barcelona Institute of Science and Technology, Av. Carl Friedrich Gauss 3, 08860, Castelldefels, Barcelona, Spain.

Scientific Reports

|October 13, 2020

Summary

Predicting user intent from e-commerce clickstream data is achievable using k-gram statistics or deep learning. Purchase prediction is reliable even with short user observation windows.

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Area of Science:

Computational intelligence
Data science
E-commerce analytics

Background:

User intent prediction from clickstream data is crucial for e-commerce personalization and optimization.
Existing methods often require detailed data, which may not always be available or practical.
Symbolic representation of clickstream data offers a way to simplify analysis while retaining key information.

Purpose of the Study:

To develop and compare two distinct approaches for user intent prediction from coarse-grained e-commerce clickstream data.
To evaluate the effectiveness of hand-crafted feature-based and deep learning-based classification methods.
To assess the performance of these methods for both arbitrary and limited-length trajectory classifications, including imbalanced datasets.

Main Methods:

Development of a proprietary, coarse-grained clickstream dataset to create symbolic trajectories.
Implementation of a hand-crafted feature-based classification using k-gram statistics and visibility graph motifs.
Benchmarking and improvement of deep learning models, specifically a proposed Long Short-Term Memory (LSTM) architecture, against state-of-the-art (SOTA) methods.

Main Results:

K-gram statistics with visibility graph motifs demonstrated fast and accurate classification performance.
Purchase prediction was found to be reliable even with extremely short observation windows using the feature-based approach.
The proposed LSTM architecture achieved improved classification accuracy compared to SOTA models on the new dataset.

Conclusions:

Both hand-crafted feature engineering and deep learning are viable for user intent prediction from simplified clickstream data.
The feature-based approach offers efficiency and accuracy, particularly for early purchase prediction.
The deep learning approach, specifically the LSTM model, provides enhanced accuracy, with a thorough analysis of trade-offs for industry applications.