Measuring and Understanding Brand Value in a Dynamic Model of Brand Management
Bibliographic record
Abstract
We develop a structural model of brand management to estimate the value of a brand to a firm. In our framework, a brand’s value is the expected net present value of future cash flows accruing to a firm due to its brand. Our brand value measure recognizes that a firm can change its brand equity by investing in advertising. We estimate quarterly brand values in the stacked chips category for the period 2001–2006 and explore how those values change over time. Comparing our brand value measure to its static counterpart, we find that a static measure, which ignores advertising and its ability to affect brand equity dynamics, yields brand values that are artificially high and that fluctuate too much over time. We also explore how changing the ability to build and sustain brand equity affects brand value. At our estimated parameterization, if brand equity were to depreciate more slowly, or if advertising were more effective at building brand equity, then brand value would increase. However, counterintuitively, we find that when the effectiveness of advertising is sufficiently high, increasing the rate at which brand equity depreciates can increase the value of a firm’s brand, even as it reduces the value of the firm overall. Data and the online appendix are available at https://doi.org/10.1287/mksc.2016.1020 .
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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 | 0.008 |
| 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 | 0.003 | 0.005 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".