论文标题
RMM：用于基于客户选择的R包，用于销售交易数据的收入管理模型
RMM: An R Package for Customer Choice-Based Revenue Management Models for Sales Transaction Data
论文作者
论文摘要
我们使用Cho等人中引入的强大需求估计（RDE）方法开发R take rmm来实现条件logit（CL）模型。 （2020），一种基于客户选择的$ \ textbf {r} $ evenue $ \ textbf {m} $ anagement $ \ textbf {m} $ odel。在业务中，重要的是要了解客户的选择行为和偏好，当产品价格随着时间和各种客户而变化时。但是，由于不可观察的无购买客户（即需求问题），很难估计需求。使用RDE方法拟合的CL模型可以启用具有频繁产品价格变化的更通用的公用事业模型。它不需要将销售数据汇总到时间窗口中以捕获每个客户的选择行为。这项研究使用真实的酒店交易数据来介绍R软件包RMM，以提供功能，使用户可以使用RDE方法以及选择概率的估计值，无购买客户的大小及其标准错误来适应CL模型。
We develop an R package RMM to implement a Conditional Logit (CL) model using the Robust Demand Estimation (RDE) method introduced in Cho et al. (2020), a customer choice-based $\textbf{R}$evenue $\textbf{M}$anagement $\textbf{M}$odel. In business, it is important to understand customers' choice behavior and preferences when the product prices change over time and across various customers. However, it is difficult to estimate demand because of unobservable no-purchase customers (i.e., truncated demand issue). The CL model fitted using the RDE method, enables a more general utility model with frequent product price changes. It does not require the aggregation of sales data into time windows to capture each customer's choice behavior. This study uses real hotel transaction data to introduce the R package RMM to provide functions that enable users to fit the CL model using the RDE method along with estimates of choice probabilities, size of no-purchase customers, and their standard errors.
