Is product with a special feature still rewarding? The case of the Japanese yogurt market

Abstract

Manufacturers of packaged consumer goods strive to develop a new product with a special feature that could provide additional value to consumers. However, it is less clear whether such an effort is still rewarding in terms of margin if manufacturers are losing power to retailers as some have argued. To investigate this issue, we conduct an economic analysis in the Japanese yogurt market incorporating strategic interaction between manufacturers and a retailer as well as between manufacturers by extending the framework employed in the earlier literature to suit the retailer Stackelberg game which can reflect the possible power shift from manufacturers to retailers. We find (1) the manufacturers’ margins on special featured brands are larger than those on the others; (2) however, the manufacturer producing such brands is not able to leverage these brands to exert bargaining power over the retailer; and (3) the retailer obtains as large margins as the manufacturers on these brands. In the course of this research, we successfully portray the symmetrical relationship between manufacturer and retailer Stackelberg games, whereby the vertical Nash game is located in the midpoint of those two games.

Keywords:

• consumer heterogeneity
• state dependence in demand
• horizontal strategic interaction among manufacturers
• vertical strategic interaction between manufacturers and retailers
• forward-looking behavior of firms

Public Interest Statement

The retailer power is said to have increased with respect to manufacturers. Then the question arises is whether manufacturers are still willing to innovate because their efforts to develop a new product would end up benefitting only retailers. In this paper, we mathematically formulate an important game reflecting recent trend of increased power of retailers whereby retailers have controls over pricing. We bring this formulation to the Japanese yogurt market to examine if premium brands are still able to command commensurate profit given the power shift from manufacturers to retailers.

Acknowledgements

We thank an editor and two referees for many constructive comments on earlier versions of the article.

Notes

1 These factors largely overlap with those listed by Kadiyali, Chintagunta, and Vilcassim (2000) as the reasons of the similar power shift in the US market.

2 Choi (1991) introduces an RS formulation but the model in that paper assumes linear demand function.

3 Though our data contained sales information in six stores of the same chain, there was only one store where at least of one purchase of the selected brands of yogurt (for our analysis) was recorded everyday. However, if we combined all five other stores, there were enough data for us to be able to calculate the average retail prices of the seven brands of yogurt we used in this analysis.

4 Sudhir (2001) empirically shows that the retailer earns the maximum profits when it engages in category profit maximization pricing, which supports the assumption widely adopted in the literature.

5 The optimal retail price $p_k$ should not be affected by the price of the other brands; else, $p_k$ will no longer be optimal. Thus, $\mathit{\partial }{p}_k/\mathit{\partial }{p}_j$ becomes 0 if $p_k$ is assessed at its optimal level.

6 Note that it is assumed $\mathit{\partial }m{c}_k/\mathit{\partial }{w}_l=0$ for all $k,l=1,\dots ,J$, as wholesale price would not affect marginal cost in general.

7 For convenience in comparison to Che et al. (2007), notations and most definitions are the same as those in that paper.

8 We note that the collusion case can be accommodated by expression (11), namely, by setting $\mathrm{\Omega }$ as an identity matrix.

9 Margins are parametrized as shown since the calculation of margins involves the partial derivative of the logit probability with respect to prices (i.e. $\mathit{\partial }{S}_k/\mathit{\partial }{p}_j$ and $\mathit{\partial }{S}_k^2/\mathit{\partial }{p}_j\mathit{\partial }{p}_h$), since $S_k$ is the market share of brands which is essentially ${Pr}_{ij{t}_i}^s$ times market size.

10 We cannot disclose the name of the bacilli as it would identify the product.

11 The combined market share of the seven selected brands is 44.5%, excluding box-type yogurt. The number is relatively small because there existed 300 brands during the study period and market share of each brand was small. We chose top selling seven brands because the minor brands had many missing daily price information.

12 We increased the number of segments to minimize Akaike Information Criteria (AIC). Although AIC was lower for the five-segment model than for the four-segment model, we chose the latter because the size of fifth segment became 0.7% in the five-segment model, as targeting a segment size less than 0.7% out of a sample size of 183 does not make much sense.

13 “Agar Usage” is whether the yogurt contains agar or not. Agar is used to produce so-called “hard-type” yogurt. We also tested “Raw Milk Usage” (i.e. the proportion of raw milk in yogurt; three levels—none, some, and all) and “Fat Level” (i.e. the amount of fat in yogurt; three levels—less than 3%, between 3% and 4%, and more than 5%) as candidates for the attributes, but they were found to be nonsignificant.

14 We also tested (1) a multinomial logit model without state dependence and (2) a model with state dependence but without forward-looking behavior, in addition to the forward-looking model, but the Vuong test statistics showed that the VN-collusion forward-looking model fitted the data best.

15 The results are available on request.

Additional information

Funding

We are grateful for financial support from the Japan Society for the Promotion of Science under Grant-in-Aid for Scientific Research (B) 20310081, (B) 15H03333 and Challenging Exploratory Research 25590051. The CCL-CAFɮdata employed in this manuscript for illustration purpose are provided by CUSTOMER COMMUNICATIONS, Ltd., through Professor Tadahiko Sato of the Graduate School of Business Sciences, University of Tsukuba. His work is supported by Grant-in-Aid for Scientific Research (A) 21243030. We are very grateful to CUSTOMER COMMUNICATIONS, Ltd. and Professor Tadahiko Sato for this generous offer.

Notes on contributors

Tomohito Kamai

Tomohito Kamai is a PhD candidate in Graduate School of System and Information Engineering, University of Tsukuba. He holds MBA with emphasis in marketing, at William E Simon School of Business, University of Rochester. His interest is in pricing in a differentiated product market.

Yuichiro Kanazawa

Yuichiro Kanazawa is Professor of Statistics at the Faculty of Engineering, Information, and Systems, University of Tsukuba. He holds a PhD in statistics from Yale University. He has publication in the area of applied microeconomics, industrial organization, organizational behavior, and quantitative criminology as well as in statistics proper.