مدلی برای پیش بینی ارزش مشتری از دیدگاه جذابیت محصول و استراتژی بازاریابی
|کد مقاله||سال انتشار||تعداد صفحات مقاله انگلیسی||ترجمه فارسی|
|2887||2010||9 صفحه PDF||سفارش دهید|
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|شرح||تعرفه ترجمه||زمان تحویل||جمع هزینه|
|ترجمه تخصصی - سرعت عادی||هر کلمه 90 تومان||10 روز بعد از پرداخت||518,400 تومان|
|ترجمه تخصصی - سرعت فوری||هر کلمه 180 تومان||5 روز بعد از پرداخت||1,036,800 تومان|
Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Expert Systems with Applications, Volume 37, Issue 2, March 2010, Pages 1207–1215
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.
نتیجه گیری انگلیسی
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.