Path Data in Marketing: An Integrative Framework and Prospectus for Model Building

@article{Hui2009PathDI,
  title={Path Data in Marketing: An Integrative Framework and Prospectus for Model Building},
  author={Sam K. Hui and Peter S. Fader and Eric T. Bradlow},
  journal={Mark. Sci.},
  year={2009},
  volume={28},
  pages={320-335}
}
Many data sets, from different and seemingly unrelated marketing domains, all involve paths---records of consumers' movements in a spatial configuration. Path data contain valuable information for marketing researchers because they describe how consumers interact with their environment and make dynamic choices. As data collection technologies improve and researchers continue to ask deeper questions about consumers' motivations and behaviors, path data sets will become more common and will play… 

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References

SHOWING 1-10 OF 121 REFERENCES

Path Data in Marketing: An Integrative Framework and Prospectus for Model-Building

Many datasets, from different and seemingly unrelated marketing domains, all involve "paths" - records of consumers' movements in a spatial configuration. Path data contain valuable information for

An Integrated Model of Grocery Store Shopping Path and Purchase Behavior

As behavioral hypotheses about in-store decision making become more common in the marketing literature, there is a growing need for richer, more complete datasets in order to test them more

Spatial Models in Marketing

By generalizing the notion of a map to include demographic and psychometric representations, spatial models can capture a variety of effects that impact firm or consumer decision behavior.

Dynamic Conversion Behavior at E-Commerce Sites

The proposed model of conversion behavior that predicts each customer's probability of purchasing based on an observed history of visits and purchases offers excellent statistical properties, including its performance in a holdout validation sample, and also provides useful managerial diagnostics about the patterns underlying online buyer behavior.

The Third Wave of Marketing Intelligence

During the last 25 years, marketing research in retail settings has been transformed by technological change. The first wave of change occurred when retailers adopted point-of-sale (POS) systems with

Virtual Shopping: Breakthrough in Marketing Research

Publication languages: Data set: Elsevier Source The Journal of Product Innovation Management > 1996 > 13 > 6 > 558-559 Unfortunately, this is not a research report, but an advocacy presentation. A

Modeling Browsing Behavior at Multiple Websites

A stochastic timing model of cross-site visit behavior is developed to understand how to leverage information from one site to help explain customer behavior at another and shows that a failure to account for both sources of association not only leads to poor fit and forecasts, but also generates systematically biased parameter estimates.

Modeling Online Browsing and Path Analysis Using Clickstream Data

This work shows how path information can be categorized and modeled using a dynamic multinomial probit model of Web browsing and finds that purchasers can be predicted with more than 40% accuracy, which is much better than the benchmark 7% purchase conversion prediction rate made without path information.
...