Using open source data in developing competitive and marketing intelligence
Bibliographic record
Abstract
Purpose The paper seeks to show how the increasingly popular use of data and information acquired from open sources (OS) impacts competitive and marketing intelligence (C/MI). It describes the current state of the art in analysis efforts of open source intelligence (OSINT) in business/commercial enterprises, examines the planning and execution challenges organizations are experiencing associated with effectively using and fusing OSINT in C/MI decision‐making processes, and provides guidelines associated with the successful use of OSINT. Design/methodology/approach This is a descriptive, conceptual paper that utilizes and develops arguments based on the search of three unclassified bodies of literature in competitive and marketing intelligence, intelligence processing and marketing analysis. Findings Open sources are useful in marketing analyses because they can be easily accessible, inexpensive, quickly accessed and voluminous in availability. There are several conceptual and practical challenges the analyst faces in employing them. These can be addressed through awareness of these issues as well as a willingness to invest resources into studying how to improve the data gathering/analysis interface. Practical implications Marketing analysts increasingly rely on open sources of data in developing plans, strategy and tactics. This article provides a description of the challenges they face in utilizing this data, as well as provides a discussion of the effective practices that some organizations have demonstrated in applying and fusing open sources in their C/MI analysis process. Originality/value There are very few papers published focusing on applying OSINT in enterprises for competitive and marketing intelligence purposes. More uniquely, this paper is written from the perspective of the marketing analyst and how they use open source data in the competitive and marketing sense‐making process and not the perspective of individuals specialized in gathering these data.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.096 | 0.194 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.025 | 0.032 |
| Science and technology studies | 0.005 | 0.010 |
| Scholarly communication | 0.030 | 0.033 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".