Build It, Buy It, or Both? Rethinking the Sourcing of Advertising Services
Bibliographic record
Abstract
This paper provides an update on the current state of in-house agencies. Whereas traditional consideration of internalizing advertising services was framed as a binary choice of build or buy, today’s advertisers frequently pursue hybrid policies of build and buy to procure the customized bundle required to develop, produce, and implement relevant, resonant promotional campaigns. Increasing numbers of advertisers are discovering that the demand for advertising and marketing services is best served through the coordination and integration of resources from both inside and outside the company, rather than assuming that these options are mutually exclusive. A review of advertising industry history reveals why internal agencies have long operated in the shadows of their external counterparts and how the former organization form has evolved over time. The core competencies underlying the contemporary in-house agency model are analyzed and the competitive position that in-house agencies presently occupy in relation to external providers is assessed. Two case examples of successful internal/external agency collaboration are presented. Finally, recommendations are offered for advertisers seeking to bring their internal and external agency resources together and arrive at a more-collaborative operating model for advertising services.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.016 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.006 | 0.019 |
| Scholarly communication | 0.034 | 0.027 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".