Marketing Analytics using R курс

Код курса

mrkanar

Длительность

21 часов Обычно 3 дней, включая паузы

Технические требования

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.

Обзор

Аудитория: Владельцы бизнеса (менеджеры по маркетингу, менеджеры по продуктам, менеджеры клиентской базы) и их команды; профессионалов в области клиентских знаний Обзор: Курс следует жизненному циклу клиента от приобретения новых клиентов, управления существующими клиентами для обеспечения прибыльности, сохранения хороших клиентов и, наконец, понимания того, какие клиенты оставляют нас и почему Мы будем работать с реальными (если анонимными) данными из разных отраслей промышленности, включая телекоммуникации, страхование, средства массовой информации и высокотехнологичные технологии Формат: Инструктированное обучение в течение пяти полуденных занятий с упражнениями на занятиях, а также домашнее задание Он может быть доставлен как классный или дистанционный (онлайн) курс ,.

Machine Translated

Программа курса

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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