Abstract: Potential customers may underrate the real value of a new product. However, the expected value can be affected by the other customers’ ratings and the online ratings are the most convenient information which customers can achieve, especially for the online products. So the enterprises want to gain a considerable amount of online reviews in a short time to increase the expected value of a new product. Seeding—giving away free new products—has been a very popular program to enhance the expected value. Choosing a proper type of consumers can help enhance the quality of the ratings. However, seeding programs not only decrease the demand especially for the one-purchase product but also increase cost. So the optimal seeding strategies need to be studied.
Based on the hoteling model and the effect of online ratings, this paper explores three seeding patterns(no seeding, union seeding and seeding the highly-matched customers) and conducts the optimal seeding strategy, including the optimal seeding size—the percentage of the market to seed—and pricing strategy to maximizing profit. The results help enterprises choose the optimal seeding pattern and the corresponding seeding strategies (seeding size and pricing strategies), and then give the adjustment seeding strategies when the initial expected value of a new product or the travel cost is changed. The results show that when the expectation of the potential customers is high, seeding is not the best choice; when the initial expected value of a new product is at upper middle level, the enterprise can choose union seeding; when the initial expected value of a new product is low, seed the highly-matched customers. When the initial expected value of a new product or the travel cost is changed, the adjusted seeding strategies are showed in the end of the paper. Meanwhile, according to the adjusted seeding strategies, we find that improving initial expected value of a new product and unit travel cost both contribute the enterprises’ profit.

Key words: target seeding strategy, online ratings, hoteling model, pricing strategy

摘要： 定向市场播种,即选择特定的消费群体对其免费发放新产品,由此获得的在线评论对其他潜在消费者的购买决策有较大的影响作用,但还没有 相关研究。以在线销售的新产品为研究对象,基于Hoteling模型,加入在线评论对消费者预期值的影响作用来构建企业最优收益模型,研究垄断厂商最优的定向播种策略。研究结果揭示了(1)最优播种目标与消费者对产品的初始预期值相关:预期值较高时,可不播种;预期值处于中等水平,可选择均匀播种;预期值较低时,应选择高匹配用户播种;(2)三种播种方式下各自的最优播种比例及其定价策略;(3)当消费者初始预期值与产品不匹配成本发生变化时,三种播种策略下最优的播种比例和定价策略的调整方案。研究结果为探究在线评论对播种策略的影响提供了新的研究方向和理论依据。

关键词: 定向播种策略, 在线评论, Hoteling模型, 定价策略