ارايه‌ي مدلي براي محاسبه‌ي ارزش مشتري (CLV) در صنعت ماشين‌آلات راه‌سازي

OFFERING A MODEL FOR CALCULATING CUSTOMER LIFE TIME VALUE (CLV) IN THE CONSTRUCTION MACHINERY INDUSTRY

در اين نوشتار براي محاسبه‌ي ارزش دوره‌ي عمر مشتريان در صنعت ماشين‌هاي راه‌سازي مدلي ارايه شده است. براي اين كار سعي شده ابتدا مشتريان‌ِ داراي ويژگي‌هاي رفتاري يكسان شناسايي و دسته‌بندي شوند. سپس كتب، مقالات و تحقيقات پيشين بررسي، و مدل‌هاي ارايه‌شده براي محاسبه‌ي ارزش مشتريان در كسب‌و‌كارهاي مختلف شناسايي شد. اين مدل‌ها براساس تناسب‌شان با صنعت ماشين‌آلات راه‌سازي با يكديگر مقايسه ‌شده و مناسب‌ترين الگوي هماهنگ با صنعت ماشين‌آلات راه‌سازي شناسايي و ارايه ‌شد. سپس الگوي مشخصي براي محاسبه‌ي پارامترهاي ورودي ارزش مشتري در روش منتخب براساس دسته‌بندي مشتريان ارايه مي‌‌شود. در نهايت اين روش براي دسته‌بندي و محاسبه‌ي ارزش مشتريان يكي از شركت‌هاي ايرانيِ عرضه‌كننده ماشين‌آلات راه‌سازي به كار برده شده است.

The purpose of this article is to propose a model to calculate customer life time value (CLV) in the construction machinery industry. Researchers have tried to identify and compare different models for calculation of CLV, and, by considering customer behaviour and the nature of the products in the construction machinery industry, have selected the most suitable model. In this study, two sets of CLV models; customer retention-rate based, and customer churn-rate based, were compared with each other and, as construction machinery is a durable product, the set of CLV models based on customer retention-rate was selected for the industry. After that, all the proposed models in this category of retention-rate based models were compared with each other considering the characteristics of the industry, and, finally, the model proposed by Gupta was selected for calculation of CLV. The main challenge in using the Gupta model is calculation of the retention rate parameter for the customers. We applied RFM for customer clustering, and defined the optimum number of customer segments using Matlab programming and K-Means algorithm. In this process, we identified and compared the main parameters in RFM clustering and the most important ones were identified. Retention probability for each segment was calculated using actual customer data in two consecutive periods from a real case study in the industry. After the calculation of the retention rate for each segment, and based on the total purchases and profit margins for each customer, customer life time value for each customer was calculated using the Gupta model. The proposed model for customer segmentation, and calculation of the customer life time value was applied to an Iranian construction machinery production company. In addition to customer life time value, the total value for each segment was also calculated, which can be used in strategic market planning. The identification of key customers was also another outcome of this study.