Marketing Analytics with R-Pacific (TRA31) Rescheduled Oct 7-11, 2013
Saturday, July 20, 2013 at 9:00 AM - Wednesday, July 24, 2013 at 1:00 PM
San Francisco, California
London, United Kingdom
This is a 5-day class that runs from 9am-1pm Pacific Time. In addition to the contact hours, please estimate homework assignments that might take 4 hours per day.
Marketing Analytics using R
This course will give the participants an overview of the key techniques used in direct marketing for analysing customer data. The focus is on getting to the business results with just enough technical and mathematical detail to allow us to get there reliably. Our approach is what is usually called scientific marketing: it is about getting the facts and letting the data speak as opposed to relying on our ‘gut’ instincts.
Prerequisites: The students are expected to be comfortable using R and understand basic marketing concepts.
Target audience: business owners (marketing managers, product managers, customer base managers) and their teams; customer insights professionals.
Tools: 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.
The course follows the customer life cycle from acquiring new customers, managing the existing customers for profitability, retaining good customers, and finally understanding which customers are leaving us and why.
We will be working with real (if anonymous) data from a variety of industries including telecommunications, insurance, media, and high tech.
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.
Measuring and improving campaign effectiveness
a. The importance of test and control groups. Universal control group.
b. Techniques: Lift curves, AUC
c. Return on investment. Optimizing marketing spend.
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.
Cross-selling and up-selling
Offering the right product or service to the customer at the right time
a. Techniques: RFM models. Multinomial regression.
b. Value of lifetime purchases.
Understanding the types of customers that you have
a. Classification models using first simple decision trees, and then random forests and other newer techniques.
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.
Propensity to churn models
a. Logistic regression: glm (package stats) and newer techniques (especially gbm as a general tool)
b. Tuning models (caret) and introduction to ensemble models.
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?
Customer lifetime value models
a. 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
a. Techniques: Handling missing data; imputation.
b. Techniques: Combining survey answers to single score(s), e.g. net promoter score.
c. Techniques: simple survey analysis and complex survey analysis (survey package)
We have the right to cancel the event for any reason at any time. Revolution Analytics will refund all monies paid for ticket sales in full in the event of a cancellation. We are not responsible for any travel related expenses incurred by attendees for this event. This includes but not limited to transportation, hotel accommodations or any other travel related expenses secured by the attendee, due to a cancellation on our part.
30 days from event date Full refund less 10% of the paid ticket price
21 days from event date 50% of paid ticket price
Within 15 days of event date Non refundable
- Discount offers cannot be combined
- A student ID Number is not a proof of full time university enrollment to get the student’s discount. Proof of enrollment in 9 units or more on a current academic registration document will be required to receive the student's discount.