Designing the Content of Advertising in a Differentiated Market (CEIBS Working Paper, No. 006/2020/MKT)
Bibliographic record
Abstract
In many markets, consumers use detailed attribute information to assess the value they expect from purchasing a product or service. Markets that Öt this description include LED monitors, wine, some OTC healthcare products, mattresses and automobile tires. In these markets, quality di§erences exist yet many di§erences are horizontal in nature: the consumer is interested in Önding a product that meets her unique tastes. Beyond ensuring that consumers know the brand, the category and the price; in these markets, it seems advertising should provide consumers with detailed attribute information. However, a signiÖcant proportion of advertising does not provide it. In fact, within the same category, competitors respond to messages that emphasize detailed attribute information with messages that are devoid of attribute information. These messags are uniformative about product attributes. We explore how competition in a di§erentiated market is a§ected by the ability of a Örm has to choose uninformative messages. We construct a model to investigate the factors that a§ect a Örmís decision to use advertising with detailed attribute information or advertising that does not provide attribute information. The model demonstrates that content decisions about advertising are a§ected by the di§erences between products, the range of heterogeneity in consumer tastes and the degree to which costs increase as a function of the quantity of information in advertising. Surprisingly, even when the cost to increase the quantity of information in advertising is low, uninformative campaigns can be more proÖtable than campaigns with detailed attribute information. The analysis also demonstrates that Örms may be more likely to provide detailed attribute information when there are less consumers that are attribute-sensitive. Finally, the model shows that uninformative messages can create "artiÖcial di§erentiation" in some conditions.
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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 | 0.008 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.019 | 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".