Identifying High Consideration E-Commerce Search Queries

TL;DR

This paper presents a scalable method for identifying high consideration e-commerce search queries by analyzing customer engagement signals, enabling targeted user experiences and improving purchase decision support.

Contribution

The study introduces the Engagement-based Query Ranking (EQR) approach that prioritizes query-level behavioral features over popularity signals for identifying high consideration queries.

Findings

High precision (96%) in identifying HC queries.

Effective in improving downstream customer engagement.

Outperforms human-selected queries in commercial deployment.

Abstract

In e-commerce, high consideration search missions typically require careful and elaborate decision making, and involve a substantial research investment from customers. We consider the task of identifying High Consideration (HC) queries. Identifying such queries enables e-commerce sites to better serve user needs using targeted experiences such as curated QA widgets that help users reach purchase decisions. We explore the task by proposing an Engagement-based Query Ranking (EQR) approach, focusing on query ranking to indicate potential engagement levels with query-related shopping knowledge content during product search. Unlike previous studies on predicting trends, EQR prioritizes query-level features related to customer behavior, finance, and catalog information rather than popularity signals. We introduce an accurate and scalable method for EQR and present experimental results demonstrating its effectiveness. Offline experiments show strong ranking performance. Human evaluation shows a precision of 96% for HC queries identified by our model. The model was commercially deployed, and shown to outperform human-selected queries in terms of downstream customer impact, as measured through engagement.