Customer behavior and adoption · Scalable user-profile inference from online surfing records; empirical customer-base/display-ad analysis and economic simulation.
Crumbs of the Cookie: User Profiling in Customer-Base Analysis and Behavioral Targeting
Zainab Jamal, Kartik Hosanagar, Avi Goldfarb (2016). Crumbs of the Cookie: User Profiling in Customer-Base Analysis and Behavioral Targeting. Marketing Science. https://doi.org/10.1287/mksc.2015.0956.
Plain-English take
Profiles were interpretable; internal-only data could be biased; search engines covered less activity but had higher data quality and could recover profiles; simulation showed targeting gains. For your marketing: Use as a behavioral mechanism or experiment-design reference, not a direct forecast for a SaaS funnel.
How much weight to give this
2.5/5 Evidence needs cautious interpretation
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.
Scalable user-profile inference from online surfing records; empirical customer-base/display-ad analysis and economic simulation.
Online activity records across search engines/advertising networks; applications to customer-base analysis and display advertising.
Judged from the publisher-described design and comparison; inspect the paper for complete analysis.
Publisher page and abstract were retrieved directly; full text was not assumed open.
Double-blind? Blinding was not established from the retrieved publisher record.
Research question
What the authors investigated
Can online surfing data infer useful profiles for customer analysis and behavioral targeting?
Design
Scalable user-profile inference from online surfing records; empirical customer-base/display-ad analysis and economic simulation.
Sample and setting
Online activity records across search engines/advertising networks; applications to customer-base analysis and display advertising.
Reported result
What the study found
Profiles were interpretable; internal-only data could be biased; search engines covered less activity but had higher data quality and could recover profiles; simulation showed targeting gains.
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
Profile inference and economic gains are data/model dependent; simulation is not a randomized ad experiment.
Possible founder application
Use as a behavioral mechanism or experiment-design reference, not a direct forecast for a SaaS funnel.
Evidence access
Publisher record and abstract retrieved; consult the linked paper for full methods and tables.
Publisher abstract explicitly reports empirical analysis, data-quality comparison, temporal limits and simulation. Direct publisher-page retrieval was recorded on 2026-10-09; this profile uses the publisher abstract/metadata and does not claim independent review of unavailable full text.
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.