Paid media and measurement · Individual-level attribution model validated in a field study
Attributing Conversions in a Multichannel Online Marketing Environment: An Empirical Model and a Field Experiment
Li, H. (Alice), & Kannan, P. K. (2014). Journal of Marketing Research, 51(1), 40–56.
Plain-English take
Attribution based on estimated channel effects differed significantly from common metrics. For your marketing: This is a useful framework for considering channel sequence, carryover, and retargeting harm when last-click reports over-credit later touches.
How much weight to give this
2.5/5 Limited or context-dependent evidence
The 1–5 evidence-check score is a practical reading aid, not a formal peer review or a probability that a conclusion is true. It combines design strength (2 points), sample reporting (1), comparison/control clarity (1), and how much of the paper could be checked (1). Access affects how confidently this directory can assess a study, not the study's underlying quality. Blinding is reported separately because it is not relevant to every design.
The accessible description does not establish a strong causal design.
A usable sample size is not stated in the profile's accessible source.
Comparison or control details are limited in the reviewed source.
This profile could be checked against an abstract or metadata, not the full paper.
Double-blind? Not applicable in the same way as a participant-treatment experiment.
Research question
What the authors investigated
How can channel contribution be estimated while accounting for customer consideration, visits, carryover, and cross-channel effects?
Design
Proposes an individual-level multichannel model and validates estimated incremental channel effects with a field study.
Sample and setting
Individual customer touchpoint histories and subsequent website purchases; the accessible abstract does not give sample size or business category.
Reported result
What the study found
Attribution based on estimated channel effects differed significantly from common metrics. The model also identified cases where retargeting could decrease conversion probability.
This is a paraphrase of the source. Check the original paper for definitions, estimates, statistical uncertainty and analysis details.
Scope and transfer
Limitations to keep in view
The abstract provides limited detail on the validation sample and assumptions. Attribution depends on the model and available touchpoint data and should not be treated as a randomized causal result for every channel.
Possible founder application
A useful framework for considering channel sequence, carryover, and retargeting harm when last-click reports over-credit later touches. Pair modeled attribution with holdouts where feasible.
Evidence access
Publisher abstract; full text may require access
SAGE publisher abstract retrieved with Firecrawl; full text and further design details were not inspected.
If only an abstract or metadata was available, the profile is limited to claims supported there. A paper's paywall does not change its evidentiary status, but it limits what we can substantiate from accessible text.