The Roles of Marketing Breadth and Competitive Spread on Product Life Cycle (CEIBS Working Paper, No. 015/2020/MKT, 2020)
Bibliographic record
Abstract
Understanding the patterns of demand evolution for a new innovation is critical for firms to effectively manage capacity planning, market and service operations, and research and development. The objective of this paper is to analyze how marketing at the industry level affects the evolution of primary demand in different stages of the product life cycle. We empirically analyze the growth and different types of marketing spending for product categories in the pharmaceutical industry across 7 countries. Our literature review leads to the identification of two constructs that characterize the pattern of competitive marketing spending over time: marketing breadth and competitive spread. The first construct reflects the spread of spending across different marketing instruments at the industry level, and the second construct reflects the spread of spending across different firms. Even though both construct measures a certain kind of spending spread, we find that they have qualitatively different (opposite) impact on market growth. An econometric model making use of the hierarchical nature of time observations within countries is estimated for each category. First, we find that high degrees of spending breadth impede market growth when the number of competitors is small (the category is young) but accelerate market growth when the number of competitors is higher (the category is maturing). Second, we find that high levels of competitive spread decrease category growth when spending levels are relatively low. However, as spending levels increase, the negative effect of competitive spread on demand growth all but evaporates.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 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".