• Corpus ID: 201689244

Predictive Analytics of E-Commerce Search Behavior for Conversion

@inproceedings{Niu2017PredictiveAO,
  title={Predictive Analytics of E-Commerce Search Behavior for Conversion},
  author={Xi Niu},
  year={2017}
}
This study explores online customer search behavior on a large e-commerce website—Walmart.com. In order to more accurately predict customer purchase conversion based on their search behavior, we adopt a modern machine-learning technique, random forest, as well as logistic regression to develop two computational models. We also integrate information retrieval literature to propose metrics to quantify online consumers’ search behavior. Results show that the random forest model performs better… 
aisel.aisnet.org

Figures and Tables from this paper

References

SHOWING 1-10 OF 35 REFERENCES
On the Depth and Dynamics of Online Search Behavior
TLDR
The results suggest that more-active online shoppers tend also to search across more sites, and that this consumer characteristic largely drives the dynamics of search that can easily be mistaken as increases from experience at the individual level.
Search With Learning for Differentiated Products: Evidence from E-Commerce
This article provides a method to estimate search costs in a differentiated product environment in which consumers are uncertain about the utility distribution. Consumers learn about the utility…
Online Search and Buying Behaviour in Consumer Markets
TLDR
It is shown that online research and online purchasing are negatively correlated with shop density, and the importance of branding and advertising to improve the likelihood of inclusion in online consideration sets is outlined.
Using information scent and need for cognition to understand online search behavior
TLDR
Evaluating system effectiveness based on the first page of results, even for tasks that require the user to view multiple documents, and varying interface features based on NFC are suggested.
Consumer information search behavior and the internet
There is no question that the Internet has, and will continue to have, a major impact on consumer information search behavior. However, under what conditions, and how, that impact will be felt is not…
Exploring and exploiting user search behavior on mobile and tablet devices to improve search relevance
TLDR
There are significant differences between user search patterns in these three platforms, and therefore use the same ranking system is not an optimal solution for all of them, and a framework is proposed that leverages a set of domain-specific features, along with the training data from desktop search, to further improve the search relevance for mobile and tablet platforms.
Improving Web Search Ranking by Incorporating User Behavior Information
TLDR
It is shown that incorporating user behavior data can significantly improve ordering of top results in real web search setting, improving the accuracy of a competitive web search ranking algorithms by as much as 31% relative to the original performance.
A large-scale evaluation and analysis of personalized search strategies
TLDR
It is revealed that personalized search has significant improvement over common web search on some queries but it also has little effect on other queries, and even harms search accuracy under some situations.
Analyzing the interaction patterns in a faceted search interface
TLDR
Whether faceted search improves the interactions between searchers and library catalogs and sheds light on ways that facets are used during a library search are clarified and the results indicate that most searchers were able to understand the concept of facets naturally and easily.
Good abandonment in mobile and PC internet search
TLDR
An analysis of abandoned internet search queries across two modalities (PC and mobile) in three locales is presented, to approximate the prevalence of good abandonment, and to identify types of information needs that may lead to good Abandonment, across different locales and modalities.
...
...