Marketing Analytics using R培训

课程编码

mrkanar

课程时长

21 小时 通常来说是3天,包括中间休息。

要求

The students are expected to be comfortable using R and understand basic marketing concepts.

Students should have access to a recent version of R with the additional packages gbm, caret, and survey installed with their dependencies and suggested packages.

课程概览

听众

Business主(营销经理,产品经理,客户群经理)及其团队;客户见解专业人士。

概观

该课程遵循客户生命周期,从获得新客户,管理现有客户的盈利能力,留住优质客户,最终了解哪些客户离开我们以及为什么。我们将处理来自各行各业的真实(如果是匿名的)数据,包括电信,保险,媒体和高科技。

格式

由五个为期半天的课程进行的讲师指导培训,包括课堂练习和家庭作业。它可以作为教室或距离(在线)课程提供。

Machine Translated

课程大纲

Part 1: Inflow - acquiring new customers

Our focus is direct marketing, so we will not look at advertising campaigns but instead focus on understanding marketing campaigns (e.g. direct mail). This is the foundation for almost everything else in the course. We look at measuring and improving campaign effectiveness including:

  • The importance of test and control groups. Universal control group.
  • Techniques: Lift curves, AUC
  • Return on investment. Optimizing marketing spend.

Part 2: Base Management: managing existing customers

Considering the cost of acquiring new customers for many businesses there are probably few assets more valuable than their existing customer base, though few think of it in this way. Topics include:

1. Cross-selling and up-selling: _Offering the right product or service to the customer at the right time._ - Techniques: RFM models. Multinomial regression. - b. Value of lifetime purchases.

2. Customer segmentation: _Understanding the types of customers that you have._ - Classification models using first simple decision trees, and then - random forests and other, newer techniques.

Part 3: Retention: Keeping your good customers

Understanding which customers are likely to leave and what you can do about it is key to profitability in many industries, especially where there are repeat purchases or subscriptions. We look at propensity to churn models, including - Logistic regression: glm (package stats) and newer techniques (especially gbm as a general tool) - Tuning models (caret) and introduction to ensemble models.

Part 4: Outflow: Understanding who are leaving and why

Customers will leave you – that is a fact of life. What is important is to understand who are leaving and why. Is it low value customers who are leaving or is it your best customers? Are they leaving to competitors or because they no longer need your products and services?

Topics include: - Customer lifetime value models: Combining value of purchases with propensity to churn and the cost of servicing and retaining the customer. - Analysing survey data. (Generally useful, but we will do a brief introduction here in the context of exit surveys.)

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