Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 226

The DOI matches its Crossref record.

Planning Marketing-Mix Strategies in the Presence of Interaction Effects

Naik, Prasad A., Raman, Kalyan, Winer, Russell S.·2005·Marketing Science

Abstract

Companies spend millions of dollars on advertising to boost a brand's image and simultaneously spend millions of dollars on promotion that many believe calls attention to price and erodes brand equity. We believe this paradoxical situation exists because both advertising and promotion are necessary to compete effectively in dynamic markets. Consequently, brand managers need to account for interactions between marketing activities and interactions among competing brands. By recognizing interaction effects between activities, managers can consider interactivity trade-offs in planning the marketing-mix strategies. On the other hand, by recognizing interactions with competitors, managers can incorporate strategic foresight in their planning, which requires them to look forward and reason backward in making optimal decisions. Looking forward means that each brand manager anticipates how other competing brands are likely to make future decisions, and then by reasoning backward deduces one's own optimal decisions in response to the best decisions to be made by all other brands. The joint consideration of interaction effects and strategic foresight in planning marketing-mix strategies is a challenging and unsolved marketing problem, which motivates this paper. This paper investigates the problem of planning marketing mix in dynamic competitive markets. We extend the Lanchester model by incorporating interaction effects, constructing the marketing-mix algorithm that yields marketing-mix plans with strategic foresight, and developing the continuous-discrete estimation method to calibrate dynamic models of oligopoly using market data. Both the marketing-mix algorithm and the estimation method are general, so they can be applied to any other alternative model specifications for dynamic oligopoly markets. Thus, this dual methodology augments the decision-making toolkit of managers, empowering them to tackle realistic marketing problems in dynamic oligopoly markets. We illustrate the application of this dual methodology by studying the dynamic Lanchester competition across five brands in the detergents market, where each brand uses advertising and promotion to influence its own market share and the shares of competing brands. Empirically, we find that advertising and promotion not only affect the brand shares (own and competitors') but also exert interaction effects, i.e., each activity amplifies or attenuates the effectiveness of the other activity. Normatively, we find that large brands underadvertise and overspend on promotion, while small brands underadvertise and underpromote. Finally, comparative statics reveal managerial insights into how a specific brand should respond optimally to the changes in a competing brand's situation; more generally, we find evidence that competitive responsiveness is asymmetric.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1287/mksc.1040.0083

Cites

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cited by

See all 226

Metadata

Authors
Naik, P. A., Raman, K., & Winer, R. S.
Venue
Marketing Science
Published
2005-02
Volume
24
Issue
1
Pages
25-34
ISSN
  • 0732-2399 (ISSN-L)
  • 1526-548X
OpenAlex
W2138116946
Cited by
226
References
0
FWCI
48.32
Type
journal article
Language
en
Open access
closed

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

How to cite

Cite CoChat for the checks on this page, and the paper for its own findings.

This page
CoChat. (2026). Citation check: Planning Marketing-Mix Strategies in the Presence of Interaction Effects. https://app.cochat.ai/papers/10.1287/mksc.1040.0083
The paper
Naik, P. A., Raman, K., & Winer, R. S. (2005). Planning Marketing-Mix Strategies in the Presence of Interaction Effects. Marketing Science. https://doi.org/10.1287/mksc.1040.0083

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.