Acquisition and retention · Empirical predictive-modeling study. Phase 1 uses nearest neighbors from current customers; after noncustomer feedback, phase 2 compares logistic regression, decision trees, and neural networks; phase 3 combines models into a weighted prospect list.
Model-supported business-to-business prospect prediction based on an iterative customer acquisition framework
Jeroen D’Haen, Dirk Van den Poel (2013). Model-supported business-to-business prospect prediction based on an iterative customer acquisition framework. Industrial Marketing Management. https://doi.org/10.1016/j.indmarman.2013.03.006
Plain-English take
The iterative framework produces a higher-than-average prospect-to-lead conversion in preliminary tests and can be implemented without human interaction in a web application.
How much weight to give this
2/5 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.
Empirical predictive-modeling study. Phase 1 uses nearest neighbors from current customers; after noncustomer feedback, phase 2 compares logistic regression, decision trees, and neural networks; phase 3 combines models into a weighted prospect list.
Business-to-business customer-acquisition setting using customer-base information and feedback on companies that did not become customers; preliminary tests assess prospect-to-lead conversion quality.
Review the paper for comparison conditions and analysis.
Publisher record checked; detailed methods were not verified.
Double-blind? Blinding was not established from the publisher page.
Research question
What the authors investigated
What does model-supported business-to-business prospect prediction based on an iterative customer acquisition framework show about customer behavior or marketing outcomes?
Design
Empirical predictive-modeling study. Phase 1 uses nearest neighbors from current customers; after noncustomer feedback, phase 2 compares logistic regression, decision trees, and neural networks; phase 3 combines models into a weighted prospect list.
Sample and setting
Business-to-business customer-acquisition setting using customer-base information and feedback on companies that did not become customers; preliminary tests assess prospect-to-lead conversion quality.
Reported result
What the study found
The iterative framework produces a higher-than-average prospect-to-lead conversion in preliminary tests and can be implemented without human interaction in a web application.
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 accessible publisher page indicates a limitations/further-research section but does not expose its full text; no additional limitation is asserted here. Full-text methods and correction history were not independently checked.
Possible founder application
Use this study as a bounded example related to model-supported business-to-business prospect prediction based on an iterative customer acquisition framework; evaluate whether its customers, intervention and outcome match your own context.
Evidence access
Publisher page; full-text availability varies and was not established.
Study metadata and reported results were cross-checked against a direct publisher-page retrieval; detailed full-text methods were not reviewed.
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.