Marketing Analytics: Forecasting Models with Excel

Master Marketing Analytics| Forecasting and Time Series analysis | Sales Forecasting| Build Forecasting models in Excel
4.44 (2171 reviews)
Udemy
platform
English
language
Analytics & Automation
category
Marketing Analytics: Forecasting Models with Excel
145 456
students
6.5 hours
content
May 2025
last update
$19.99
regular price

Why take this course?

了解您想要提供的课程大纲和内容概述,我可以帮助你构建一个结构化的课程框架。以下是基于您提供的信息的一个组织好的课程规划示例:

课程名称: 预测分析与模型:使用Excel进行销售预测

课程目标:

  • 理解时间序列数据中模式的识别。
  • 学会使用简单线性回归模型以及更复杂的多元线性回归模型进行预测。
  • 掌握数据准备的重要步骤,包括数据探索、异常值处理和缺失值填充。
  • 学会处理特殊事件,如假日销售。
  • 识别并估算季节性和趋势,以及如何使用Solver来估算它们。
  • 适应时间发生变化的季节性和趋势。
  • 为新产品预测销售。

课程框架:

第一部分:课程概述

  • 引言(Section 1)
    • 课程结构介绍
    • 预期学习成果

第二部分:基础知识与初步预测

  • 预测分析的基础(Section 2)
    • 预测分析的重要性
    • Excel中简单线性回归模型的创建

第三部分:数据准备

  • 数据准备(Section 3)
    • 业务知识的重要性
    • 数据探索与处理
      • 单变量分析
      • 双变量分析
    • 异常值处理(Outlier Treatment)
    • 缺失值填充(Missing Value Imputation)

第四部分:进阶预测模型

  • 回归模型(Section 4)
    • 多元线性回归模型
    • 模型准确度的量化
    • F统计量及其含义
    • 独立变量中分类变量的解释

第五部分:处理特殊事件

  • 特殊事件(Section 5)
    • 工作日效果
    • 节假日效应
    • 支付日效果

第六部分:季节性与趋势的识别与处理

  • 季节性和趋势(Section 6)
    • 季节性与趋势的概念
    • 使用Solver估算季节性和趋势
    • 移动平均线消除季节性

第七部分:时间变化的季节性与趋势

  • 时间变化(Section 7)
    • 欧洲方法(Winter’s Method)

第八部分:新产品的预测

  • 新产品销售预测(Section 8)
    • S曲线模型
    • 乔丹叙Sa模型

结束语

  • 总结与下一步指导(Section 9)
    • 课程回顾
    • 如何将所学应用于实际工作中
    • 未来学习路径建议

附录:

  • 推荐阅读:《营销分析:原理与应用》(Reading Material)

请注意,这个框架是基于您提供的信息构建的,可能需要根据实际课程内容和学员需求进行调整。确保每个部分都有清晰的学习目标和相关的练习或案例研究,以帮助学员巩固知识并将其应用于实践中。

Course Gallery

Marketing Analytics: Forecasting Models with Excel – Screenshot 1
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Marketing Analytics: Forecasting Models with Excel – Screenshot 2
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Marketing Analytics: Forecasting Models with Excel – Screenshot 4
Screenshot 4Marketing Analytics: Forecasting Models with Excel

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Comidoc Review

Our Verdict

This course offers in-depth knowledge of forecasting models using Excel, making it an ideal resource for anyone interested in marketing analytics. It caters to both beginners and experienced professionals by covering foundational concepts as well as advanced techniques. However, the strong accent might make comprehension difficult for some learners, and there is a need for more engaging content in certain sections. Despite these issues, upon completion of this course, learners will have gained a solid understanding of forecasting methods that they can apply to their professional responsibilities.

What We Liked

  • Comprehensive coverage of various forecasting techniques
  • Hands-on exercises using Excel to build models
  • Covers both simple and advanced forecasting methods
  • Real-world examples to illustrate concepts

Potential Drawbacks

  • Strong accent can make comprehension difficult
  • Lacks engagement in some sections
  • Filler lectures on basic Excel skills for some learners
  • Holts-Winters method formula needs correction
2365872
udemy ID
13/05/2019
course created date
24/10/2019
course indexed date
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