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Advertising is considered as an important strategic tool to promote product and improve sales. Extensive research has been devoted to advertising strategies and their effect on product sales. Chiefly because aggregate-level sales data are easy to collect, the prior studies predominately develop and analyze aggregate advertising models which relate product sales to advertising spending under a known sales response function. Nowadays, however, the emergence of Internet, e-commerce and data analytics approaches has made collecting data on individual consumer behavior and real-time sales feasible. Therefore, studying more sophisticated advertising models which can exploit these data is necessary and meaningful. In this dissertation, we consider two dynamic advertising models, one incorporates customer satisfaction and customer switching behavior and the other involves dynamic sales learning. / The first model focuses on the markets of experience goods whose quality levels are unobservable to the buyers. The buyers make the purchase decisions based on their past usage experience of the goods and the advertising outlays of the sellers. We first consider the competitive market where there are multiple brands planning their advertising campaigns. We derive the long-term steady-state equilibrium advertising strategies and market shares of the brands. We study how customer reaction to their past usage experience of the product (satisfaction) affects the sellers’ advertising strategies and market shares. We further analyze the monopoly market, where the focus is on the question of whether the monopolist should use even-level advertising or pulsing advertising strategy. / In the second model, we study the dynamic advertising budget allocation problems, in which the relationship between the advertising expenditure and the product sales is unknown to the retailer and the retailer can only learn this information through observing realized sales. We propose nonparametric advertising budget allocation policies for both single- and multi-product problems. We show that such policies are asymptotically optimal. In particular, for the single-product problem, by constructing a lower-bound instance, we show that our policy achieves near-best asymptotic performance. / 广告预算的确定和分配是企业运营中一个极为重要的决策。而广告资金的动态支出策略已经被研究了数十年。由于以前可获取的数据往往仅限于市场的一些宏观数据。传统文献主要用一个已知的销售响应函数从宏观层面刻画广告支出和销售量之间的关系。现今，随着互联网，电子商务，社交媒体和数据分析方法的出现，使收集有关消费者行为和实时销售量的数据成为可能。合理地利用这些数据可以大大提高企业的广告效率。这也给广告策略的研究带来了新的契机。本论文研究两个动态广告支出策略模型，一个模型涉及顾客满意度和顾客转换行为，另外一个涉及销售响应函数的动态学习。 / 本文的第一个模型考虑体验商品市场中卖家的广告支出策略。在市场中，顾客没有办法观测到产品的真实质量，他们的产品选择受到自己之前的产品体验和卖家的广告支出的影响。我们首先考虑有竞争的市场，市场中有多个品牌同时进行广告支出决策。我们推导了市场的长期稳态平衡。研究了不同的客户满意度与顾客转换行为的关系对卖家的广告支出策略和市场份额的影响。然后，我们分析了垄断市场中垄断卖家的长期广告支出策略。此外，我们还讨论了该卖家应该使用持续的广告投入策略还是周期性脉冲广告策略的问题。 / 本文的第二个模型研究是的动态广告预算分配问题。我们假设卖家起初并不知道广告支出的销售响应函数，他只能通过观察实时销售数据来对销售响应函数进行学习。我们分别考虑了单产品和多产品的问题，并提出了相应的非参数动态广告预算分配策略。我们证明了所提出的动态预算分配策略是渐进最优的。对于单产品的问题，通过构造出一类“最坏”的响应函数，我们证明了所提出的动态预算分配策略的渐进绩效已基本接近最优。 / Yang, Chaolin. / Thesis Ph.D. Chinese University of Hong Kong 2015. / Includes bibliographical references (leaves 133-140). / Abstracts also in Chinese. / Title from PDF title page (viewed on 12, October, 2016). / Detailed summary in vernacular field only. / Detailed summary in vernacular field only. / Detailed summary in vernacular field only.

Liu, Xing. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2011. / Includes bibliographical references (p. 80-87). / Abstracts in English and Chinese. / Abstract --- p.i / Acknowledgement --- p.iv / Chapter 1 --- Introduction --- p.1 / Chapter 2 --- Literature Review --- p.10 / Chapter 3 --- The 2-Promotional Level Model --- p.15 / Chapter 3.1 --- Average Profit Function --- p.17 / Chapter 3.2 --- Auxiliary Profit Function --- p.19 / Chapter 3.3 --- Characterizing the Auxiliary Function --- p.21 / Chapter 3.4 --- Display Optimization --- p.22 / Chapter 3.5 --- Ordering Policy Optimization --- p.27 / Chapter 3.6 --- "An Algorithm for Finding an Optimal (r, Q, d, D) Policy" --- p.28 / Chapter 3.7 --- Optimality Verification --- p.30 / Chapter 4 --- Multiple Promotional Level Model --- p.36 / Chapter 4.1 --- Analysis --- p.38 / Chapter 4.2 --- "An Algorithm for Finding an Optimal (r,Q,dn, .,d1,D1,...,Dn)Policy" --- p.46 / Chapter 5 --- Extension to Random Demand Size --- p.49 / Chapter 6 --- Numerical Examples --- p.63 / Chapter 7 --- Concluding Remarks --- p.75 / Bibliography --- p.80