مدلی برای پیش بینی ارزش مشتری از دیدگاه جذابیت محصول و استراتژی بازاریابی

This paper proposes a model for customer relationship management (CRM) using iThink®, which incorporates the concept of system dynamics. The proposed CRM model consists of module 1: a customer purchasing behavior model, module 2: a Markov chain model, and module 3: a financial returns model. By considering the marketing activities and product attractiveness to the customer, the probability that a customer will (re)purchase can be modeled in module 1. The probabilities are then fitted into module 2 for the calculation of customer lifetime value (CLV). The estimated CLV for each customer is inputted into module 3 to predict the firm’s return on investment in the long term. By defining the parameters on the attractiveness of a product and on user responses from historical marketing campaigns, a firm can easily evaluate its business strategy from both marketing and product development perspectives, thereby refining those parameters and adopting the best strategy for creating customer value and yielding the maximum profit. A case study of a listed firm in Hong Kong is employed to illustrate our model, which not only gives insights into the product development, but can also support the decisions related to marketing activities.

Customer relationship management (CRM) is increasingly important in all kinds of businesses to sustain long-term growth in today’s competitive environment. CRM is a philosophy that anticipates customer needs with the purpose of providing the target customers with the right product, at the right time, in the right place (Yourdon, 2000). Firms are therefore motivated to adopt CRM in order to respond rapidly to market needs. This is due to the fact that the acquisition cost of new customers exceeds the retention cost of existing customers by a substantial margin (Dyche, 2002). Moreover, satisfying customer needs can build up customer retention and customer loyalty, and enhance market share and business position. Importantly, CRM would result in significant financial returns (Chen et al., 2004, Ip et al., 2006 and Rust and Zahorik, 1993). In fact, developing customer relationships is the key value creation activity in today’s business strategy, while pursuing long-term customer relationships is the ultimate goal of many firms. Being customer-focused to create and deliver value to customers is therefore essential to enable a firm to remain competitive. The CRM system in this study includes parameters from both marketing strategy and product attractiveness. Nowadays, marketing strategy, including advertising, promotion, membership programs, etc., is an essential complement to the sales of newly launched or existing products. Moreover, the development and delivery of customer-driven products are of paramount importance for value generation in a customer. This system addresses the increasing customer demand for superior quality, pleasing design, and reasonable price. In terms of management, a CRM system would help a firm to plan its new product development and to design appropriate marketing strategies. This study is thus designed to propose a system dynamics model, which will not only provide a comprehensive picture to better understand the interactions among all elements in product development and marketing, but will also be practical for predicting the financial returns of a product. There are two major parameters being considered in product development: (a) the attractiveness of a product, which depends on its design, quality and price; (b) the user experience of a product in terms of user satisfaction and brand loyalty; these parameters are identified by a customer survey of a particular product. In marketing activities, the historical marketing data of a firm is acquired to address these two major parameters; (c) adoption from advertising; and (d) adoption from marketing, which are determined by advertising effectiveness and marketing effectiveness respectively. In the next section, a study approach to develop the system dynamics model and the relevant methodology will be described. To illustrate the design, development and application of the system dynamics model, as well as the calculation of CLV using the Markov chain model, a case study will be conducted and will be presented in Section 3. The implication of the proposed model and suggestions for extending this study will be given in the conclusion section.

4.1. Summary of findings and implications In this fiercely competitive market, firms have to place more emphasis on customer acquisition and even retention primarily by means of marketing functions and product development. Through analyzing the effects of different marketing activities and product attributes on customer purchasing behaviors, firms can predict the outcomes in terms of market share and sales value captured from the customers in order to achieve better business strategy formulation. It is, however, hard to find any model taking account of this issue. The system dynamics model developed above (see Fig. 3 and Fig. 4) using the Markov chain analysis for estimating the CLV is therefore of paramount significance to commercial practice. It provides a full picture of the dynamic behavior of customers, as well as of the flow of value created, delivered, and captured by firms (see Fig. 1), while also enabling firms to easily evaluate their business strategy from both marketing and product development perspectives through the model simulation and Markov chain model advocated (see Fig. 2). In fact, a firm can take great advantage of this model by simply altering the input values of the model according to different marketing activities and product development, so as to forecast the return on investment of the tentative business strategy, and to get insight into further improvement in terms of product ideas and marketing campaigns. This study is significant in that it can help firms to evaluate the outcomes in advance, as well as to adopt the best strategy for achieving them. 4.2. Future extension Further investigation can be conducted to study the impact of other influencing factors on customer purchasing behavior, such as competition, product development cost, and technological readiness. Second, exploring a way to perceive the optimal business strategy (in terms of product design and quality, marketing strategies and expenses, etc.) for market expansion and profit yielding would also be interesting. This may require extensive and abundant data collection, including customer feedback on different kinds of products, expenses incurred for marketing campaigns against products, return on investment from different combinations of product and marketing activities, etc. Through a number of simulations, followed by result comparison and analysis, it may be possible to discover the optimal parameter values for maximizing the market penetration and profitability of a particular type of product. For example, if the optimal value of the parameter “attractiveness of product design” is 0.85 compared with its existing value of 0.8, a firm may need to place much focus on the product design. This is because improving the product design to approach its optimal value signifies that the outcomes of the model (including the initial and repeat purchase rates, and gross profit) are enhanced in terms of market expansion and profitability. Alternatively, if the optimal value of the parameter “marketing effectiveness” is 0.05, and it is found that promoting a particular category of product through TV channels often leads the parameter value to be optimal or close to optimal, a firm may effectively perceive this as the most appropriate marketing campaign against the product to stimulate customers to purchase. Further research into the optimal values in this model would therefore be fascinating and beneficial in supporting the decision-making pertaining to marketing strategy and product development.