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Statistics And Predictive Modeling
For Direct Marketers Seminar
Learn How To Better Understand And Evaluate Direct Marketing Statistical Techniques - and Better Manage Your Modeling Projects
You know how important your customer database is. Now get up-to-speed on essential direct marketing statistical techniques that will enable you to amplify its power. This seminar is a mainstay of the DMA education series. Since 1986, hundreds of companies have sent employees to DMA’s Statistics & Modeling for Direct Marketers Seminar. Many think of it as their own internal training course that prepares staff to make informed decisions and better manage modeling projects. This seminar, taught by DSA's hands-on analytical staff.
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Benefits of Attending
Get Answers to All Your Important Questions, Including:
- What are the most effective statistical techniques and how can we use them?
- What’s the best way to determine optimal promotion quantities?
- How can we tell if differences in test results are significant or due simply to chance?
- Which is better – multivariate response analysis or traditional direct marketing RFM cell analysis?
- What do we need to know to choose the correct multivariate techniques for a particular problem?
- Is there a way to evaluate the benefits of modeling in dollars and cents?
Course Overview
DAY 1
Basic Statistical Review and Applications
- How “chance” affects the results from DM list and package tests
- How to adjust for these “chance” effects (normal distributions etc.)
- How to build forecasts for the results from your next promotion
- How to determine your needed sample size
- How to test if two list or package results are “statistically different”
- Differences between the two types of modeling procedures
- How they relate to each other
- When to use each
- How to read scatter diagrams and plots of residuals
- How to read a correlation matrix, and what it tells you to avoid
- When to use a multiple regression program
- How to analyze data prior to regression analysis and why it’s imperative to perform this step
- How to model the non-linear response patterns so common in direct marketing response analyses
- How to use “dummy variables” in regression analysis
- How to perform simple regression analyses using Excel™
- How to interpret the output of an AID/CHAID program
- Why an AID/CHAID analysis is not used for scoring
- How AID/CHAID can be used to choose variables for a regression model
DAY 2
Factor Analysis & Principal Components Analysis
- When to use factor analysis prior to using regression
- How to interpret the output of a factor analysis
- How to use factor analysis to analyze your marketing research data
- How to use a cluster analysis to create your own customer target market groups
- How to decide on the “right” number of clusters to include in a cluster analysis
- The similarities and the differences between discriminant analysis and regression analysis. When to use each.
- What it is
- Why it is the recommended method to be used when performing response analysis
- How it is different from ordinary regression and/or discriminant analysis
- What hardware and software you need
- How to determine if you should use consultants, service bureau, or do it in-house
- What you can expect to gain in the first year