|
所在平台: Coursera |
课程主页: https://www.coursera.org/learn/forecasting-models-marketing-decisions
课程评论：没有评论
课程名称：市场决策的预测模型 课程概述：本课程旨在帮助学员预测客户未来的行为，评估产品和服务的需求及库存管理。除了单纯的预测客户行为外，市场营销人员还需要了解自己的行动如何能影响未来的客户行为。在“使用Excel开发预测工具”模块中，学员将学习预测模型的基本组成部分，构建自己的预测模型，以及评估这些模型的表现。所有的学习都基于Microsoft Excel，确保学员能够将所学技能应用于实际业务问题中。 课程大纲： 1. 预测模型基础 - 描述：本模块将讨论如何根据历史数据中的模式识别预测模型所需的必要组成部分。同时，您还将学习如何使用样本内和样本外指标评估预测模型的表现。 2. 客户分析：预测个别客户行为 - 描述：“有意义的市场营销洞察。”这一部分的内容对于完成第一门课程的学员来说将是熟悉的，建议把这部分视为复习。 3. 管理客户资产：将客户分析与客户价值关联 - 描述：本模块将讨论客户资产管理、客户获取与保留、市场价值及客户估值。您将学习如何将客户价值分解为其基础组成部分。 4. 市场营销组合建模 - 描述：在开发预测模型时，一个常见的任务是利用这些模型来做出有关市场营销组合活动的决策。通过市场营销组合模型，组织可以评估不同市场营销行为的效果。本模块包含一个受欢迎的冷冻食品类别的数据样本。除了每周销售和定价信息外，我们还拥有关于产品是否出现在店内广告（例如报纸宣传）和产品是否在商店展示的信息，以及竞争对手的定价信息。在这个模块中，我们将构建一系列回归模型来评估品牌及竞争对手行为的影响。 通过这些模块的学习，学员将具备在市场营销决策中应用预测模型的能力。
Name:Basics of Forecasting Models
Description:This module will discuss how to identify the necessary components of a forecasting model based on patterns in the history data. You will also be able to evaluate the performance of a forecasting model using both in-sample and out-of-sample metrics.
Name:Customer Analytics: Predicting Individual Customer Behavior
Description:"Meaningful Marketing Insights," This content will be familiar for learners who completed the first course; please think of this portion of the class as a review.
Name:Managing Customer Equity: Linking Customer Analytics to Customer Value
Description:This module will discuss managing customer equity, acquisition, retention, & market value, and customer valuation. You will learn how to decompose customer value into its underlying components.
Name:Marketing Mix Modeling
Description:A common task in developing forecasting models is to use them to make decisions regarding the marketing mix activity. With a marketing mix model, organizations can assess the efficacy of different marketing actions. Included is a sample of data for a popular frozen food category. In addition to weekly sales and pricing, for the focal brand we have information on whether the product was featured in the store’s advertising (e.g., newspaper circular) and if the product was on display in the store. We also have pricing information from competitors. In this module, we will build a series of regression models to evaluate the impact of the brand’s actions and competitors’ actions.
How will customers act in the future? What will demand for our products and services be? How much inventory should we order for the next season? Beyond simply forecasting what customers will do, marketers need to understand how their actions can shape future behavior. In Developing Forecasting Tools with Excel, learners will develop an understanding of the basic components of a forecasting model, how to build their own forecasting models, and how to evaluate the performance of forecasting models. All of this is done using Microsoft Excel, ensuring that learners can take their skills and apply them to their own business problems.
