Estimating umbrella-branding spillovers: a retailer perspective
Bibliographic record
Abstract
Purpose The starting conjecture is that the market share of a brand in one category benefits from its performance in another category, and vice versa. The purpose of this paper is to assess the umbrella-branding spillovers by investigating the presence of synergy effect between categories when a retailer and/or a manufacturer decide to adopt/use the same name for his products. In fact, besides the cross-category dependency due to substitutability or complementarity, products can also be linked through their brand name in presence of an umbrella-branding strategy. Design/methodology/approach The authors propose an extended market-share model to account for the spillover effect at the brand level. The spillover is modeled to be generated by the brand's performance and not specific to marketing instruments, as done in the literature. They adopt a multiplicative competitive interaction (MCI) form for the attraction function. Based on aggregated data of two complementary oral-hygiene categories, the authors estimate the umbrella-branding spillover parameters using the iterate three-stage least squares (I3SLS) method. They contrast the results in three scenarios: no spillover, brand-constant spillover and brand-specific spillover. Findings The ensuing results indicate that umbrella-branding spillover is (i) significant and positive, i.e. the brand performance is boosted by its performance in a related category, through the so-called brand-attraction multiplier; (ii) asymmetric, i.e. the spillover is not equal in both directions; and associated to the market strength of each competing brand; (iii) variable across brands. The results show that not accounting for umbrella-branding spillover leads to misestimating the parameters and has a considerable impact on price-elasticities computation. Research limitations/implications Because store brands and some national brands exist in many categories, and thus because consumers make inferences when they face a large number of brands in different categories, spillover effects cannot be labelled as simply complementary or substitution-related. Future research may provide insight about the spillover phenomenon in a more general framework that would consider the spillover occurring between more than two categories. Practical implications Providing accurate assessment for umbrella-branding spillovers governing the competing brands, the results offer a relevant and straightforward method for decision makers to precisely assess the impact of a marketing effort in one category on the retailer's global performance. The findings provide better forecasts of market response in terms of sales and profit, within a cross-category perspective. Originality/value This study develops and estimates a market-share model with the aim of measuring brand-category spillover effects. The literature dealt with cross-category interactions in terms of substitutability or complementarity between the products offered in the two or more categories under investigation. Here, the focal point (and contribution) of the authors is the link at the brand level. Indeed, the authors only require that a minimum of one brand is offered in at least two of the categories of interest. Further, the spillover considered is not specific to marketing instruments, but is generated by the brand performance (attraction or market share), which is the result of both the firms marketing-mix choice and competitors marketing policies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".