Dynamic Effectiveness of Advertising and Word of Mouth in Sequential Distribution of New Products
Bibliographic record
Abstract
Firms in many industries release new products in sequential stages. They also launch separate advertising campaigns at each distribution stage. Thus, communication mix elements—advertising and word of mouth (WOM)—can play important, distinct, and yet interdependent roles in stimulating new product demand. Their effectiveness may fluctuate within and across stages and spill over from earlier to later stages. Thus, the authors construct a dynamic linear model to study the dynamic effects of advertising and WOM on demand for heterogeneous products across stages. They further apply the model to examine a canonical example, the theater-then-video sequential distribution of motion pictures, and estimate the parameters using Kalman filtering/smoothing and Markov chain Monte Carlo methods. The results show that advertising and WOM exert dynamic, yet diverse, influences on demand for new products. For example, while increased ad spending is more effective at an earlier stage due to repetition wear-in and synergy with WOM, increased WOM activities at a later stage could become more powerful in driving demand. Subsequent optimization exercises suggest that films of varied characteristics can potentially re-allocate their advertising budgets and reap additional revenues.
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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 | 0.003 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".