Research on Supply Chain Advertising Strategies for Big Data-Driven E-Commerce Platforms: Head or Newcomer?

Under the influence of the long-tail effect, market segmentation and personalized demand provide room for small brands to grow. Meanwhile, consumer behavior patterns have also shifted, with increased acceptance of low-priced, highly practical goods. This paper constructs a two-tier competitive supply chain model. The manufacturer invests in big data from e-commerce platforms and decides on the production of products by combining sales data and consumer preferences. The two retailers are a head brand retailer, which is larger, and a newcomer brand retailer, which is smaller, and both consider advertising to expand their markets. The paper distinguishes four types of advertising strategies (NA, R1A, R2A, BA). Secondly, the differential game model is used to discuss the optimal solutions of different advertising strategies under the relevant situations of demand perturbation and demand non-perturbation. Again, empirical analyses are used to verify the robustness of the model by fitting it with the simulation model. Finally, the paper further extends the model to the symmetric domain to explore the optimal retailer capacity in the market, and comes to the following conclusions (1) In the case of non-disturbed demand, the differences in retailer size and competitiveness can promote a more efficient allocation of resources, and the advertisements placed by small brands are the most effective in terms of market share and profitability, which can also improve the overall performance of the supply chain. (2) Demand perturbation makes the unilateral advertisers more susceptible to external disturbances, and the profit is uncertain while the advertisers’ investment increases. (3) In the expansion model, the maximum capacity of small-brand retailers is 3. When retailers exceed 3, it is difficult for other retail brands to enter the market.