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The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.

Attributing Conversions in a Multichannel Online Marketing Environment: An Empirical Model and a Field Experiment

Li, Hongshuang (Alice), Kannan, P.K.·2014·Journal of Marketing Research

Abstract

Technology enables a firm to produce a granular record of every touchpoint consumers make in their online purchase journey before they convert at the firm's website. However, firms still depend on aggregate measures to guide their marketing investments in multiple online channels (e.g., display, paid search, referral, e-mail). This article introduces a methodology to attribute the incremental value of each marketing channel in an online environment using individual-level data of customers’ touches. The authors propose a measurement model to analyze customers’ (1) consideration of online channels, (2) visits through these channels over time, and (3) subsequent purchases at the website to estimate the carryover and spillover effects of prior touches at both the visit and purchase stages. The authors use the estimated carryover and spillover effects to attribute the conversion credit to different channels and find that these channels’ relative contributions are significantly different from those found by other currently used metrics. A field study validates the proposed model's ability to estimate the incremental impact of a channel on conversions. In targeting customers with different patterns of touches in their purchase funnel, these estimates help identify cases in which retargeting strategies may actually decrease conversion probabilities.

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Metadata

Authors
Li, H. (., & Kannan, P. K.
Venue
Journal of Marketing Research
Published
2014-02
Volume
51
Issue
1
Pages
40-56
ISSN
  • 0022-2437 (ISSN-L)
  • 1547-7193
OpenAlex
W2064636232
Cited by
437
References
4
FWCI
53.58
Type
journal article
Language
en
Open access
closed

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.