UC Berkeley

My thesis research explicitly emphasizes integrating marketing and operations management (hereafter OM) perspectives in the formulation of strategy. Shortened product life cycles, technological advancements in products and processes, globalization of markets, consumerism, and the rapidity of change have only exacerbated the perceived need to link Marketing/OM strategies. My primary research interest follows the trend and explores the science of delivering value to customers from an integrated view of marketing and OM with special emphasis on the timing of introducing product lines (Chapter 1) and the role of social contagion in forming customer lifetime value (Chapters 2 and 3).

In Chapter 1, we study the problem of when to introduce a line extension of a product with an existing version in an integrated inventory (supply) and diffusion (demand) framework. The launch of a new product with successive (and differentiated) versions always commands a large commitment of resources in production and marketing, thus the introduction strategy often requires careful planning. A key element in the introduction strategy is the introduction time. There is yet a formal model to quantify the impact of inventory cost on product line introduction timing decisions considering the demand dynamics in product life cycle and substitution among versions. This paper takes a first step towards filling this gap. On the demand side, we consider the demand dynamics of both versions during product life cycle, in marketplaces where repeated industry practices are observable to customers. Based on the Bass model, we propose a splitting Bass-like diffusion model to describe the adoption processes for the two successive (and differentiated) versions of one product, taking into account the role of customer expectation in shaping purchase choices. On the supply side, we model the impact of inventory holding cost that arises from a simple ordering policy. We show there exists a unique optimal time to introduce the line extension in the planning horizon. We quantify the optimal launch-time and both versions' sales trajectories. In contrary to the existing optimal policy in the literature (i.e., ``Now or Never''), we find that the optimal introduction can happen anytime from ``Now'' to ``Never'', depending upon the characteristics of different products. We show that when inventory holding cost is small and the ordering cycle is short, the optimal introduction time is indeed ``Now '' or ``Never''. However, as inventory holding becomes substantial, the firm might choose to delay the introduction when the line extension is more profitable than the existing version, or to accelerate the introduction when the existing version generates more profit. Our integrated model sheds light on the necessity of coordinating marketing and operations management decisions.

In Chapters 2 and 3, we incorporate social contagion into customer lifetime value analysis. Prior research has assumed that a customer's lifetime value (LV) only depends on her own purchase history. The rise of Internet and viral marketing casts doubt on this assumption. In the Web 2.0 economy, social contagion is so integral to customer's shopping process that purchase behaviors are frequently interdependent. We investigate how social contagion might influence a customer's lifetime value beyond her own purchases. We posit that a customer's total lifetime value (LV) is a sum of her total purchase value (PV) (accounting for others' influence on her purchases) and her total influence value (IV). Specifically we have: LV = PV + IV. Consequently a customer can still have a high lifetime value even if she has a low PV as long as she has a high IV.

Chapter 2 presents a model with homogeneous population. Building on the classical Bass diffusion model, we show that PV, IV, and LV decrease in the convex manner with adoption time. Hence a customer who adopts earlier is much more valuable than a customer who adopts later. While PV increases with the innovation parameter, IV decreases with it. Early adopters have their LV decrease with innovation parameter while later adopters have their LV increase with it. Interestingly, PV decreases with the imitation parameter and IV increases with it for early adopters and decreases with it for late adopters. LV increases with the imitation parameter if the timing of adoption is below a cutoff value and decreases with it if it is above the cutoff. We then examine how a firm might improve its overall customer LV by accelerating purchase made possible by offering introductory price discounts to a subset of customers. We characterize the optimal size of the targeted customers in terms of level of discount, innovation as well as imitation parameters and demonstrate that the firm can significantly increase its total customer LV by purchase acceleration. We also analyze the impact of purchase deceleration in a make-to-stock supply chain environment and a make-to-order supply chain environment respectively. We show that an out-of-stock phenomenon that occurs earlier in a product's life cycle always leads to a significantly greater loss in total customer LV. We also demonstrate even a small lead time leads to a big loss in total customer LV.

Chapter 3 presents a model with heterogeneous population. We propose a four-segment model which considers the ex ante heterogeneity among customers in the tendency to be in tune with new developments and the tendency to influence (or be influenced by) others. Specifically, we segment customers into four types: type 1 customers are both innovators and global influencers, type 2 customers are both innovators and local influencers, type 3 customers are both imitators and global influencers, and type 4 customers are both imitators and local influencers. We characterize the closed-form expressions for adoption rate of each customer type. Based on them, we derive closed-form expressions for the customer PV, IV and LV as a function of product adoption time. We also investigate how PV, IV and LV vary with the adoption time and the innovation parameters. Finally, we analyze the impact of purchase acceleration on customer LV, and propose an algorithm based on the LV of marginal customers to optimally allocate free samples among customers.