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Design of Experiments for Direct Marketing

Course Details
Code: DOEF42
Tuition (USD): $1,600.00 • Classroom (2 days)

This course deals with the concepts and techniques that are used in the design and analysis of experiments. The course primarily focuses on direct marketing applications, but it is also relevant for someone interested in designing experiments in the fields of physical, chemical, biological, medical, economic, social, psychological, and industrial sciences; engineering; or agriculture. This course teaches you how to design efficient marketing experiments with more than one factor, analyze the results that your experiments yield, and maximize the information that is gleaned from a marketing campaign. Factorial and fractional factorial designs are discussed in greater detail.

Skills Gained

  • Determine the appropriate sample size for your tests.
  • Apply the principles of balance, orthogonality, randomization, replication, block designs, factorial designs, and fractional layouts.
  • Build efficient experimental designs that generate as much information as possible for minimum cost.
  • Identify challenges associated with analyzing experimental designs.
  • Test as many factors as possible in each campaign.
  • Apply well-known experimental design practices to direct marketing efforts.

Who Can Benefit

  • Business analysts, market researchers, and anyone interested in designing, conducting, and analyzing experiments specially for marketing campaigns

Prerequisites

  • Before attending this course, you should
  • Have a strong interest in experimentation.
  • Have at least an introductory-level familiarity with statistics and regression modeling. You can gain this experience by completing the course Statistics II: ANOVA and Regression or equivalent training.

Course Details

Introduction to Experimentation

  • Designed experiments: Why they matter.
  • Designed experiments: What they are.

Simple Designs: Testing a Single Factor

  • Hypothesis testing.
  • Design of experiments: terminology.
  • Power and sample size.

Complex Designs: Testing Multiple Factors

  • Two 2-level factors.
  • Orthogonality.
  • Blocking.

More Complex Designs: Too Many Treatments

  • Fractional factorials and orthogonal arrays.
  • Optimal designs.
  • Augmenting designs.
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