Direct Marketing Research and Experimentation

1. Direct Marketing Researchand Experimentation Chapter 11 Contemporary Direct Marketing Chapter 11

2. Research Serves Direct Marketers • Fact-Finding • Information Gathering • Problem-Solving • Decision-Making Contemporary Direct Marketing Chapter 11

3. Need for Marketing Research • Managers must have good, accurate, timely information with which to make decisions. • Marketing research helps to gather the needed information. • The results of research can be quantitative and/or qualitative. • Valid research measures results … not opinions. Contemporary Direct Marketing Chapter 11

4. Problem Structure • How much advertising is needed? • How will the direct marketing mix be selected? • How will our resources be utilized? Contemporary Direct Marketing Chapter 11

5. The Nature of Research Surveys vs. Experiments: • A survey looks at things the way they are. A mailed questionnaire, for example, may attempt to profile respondents to a product offer or promotion strategy. It may seek to anticipate future buying intentions; to determine product or service preferences; to guide pricing decisions; or to measure attitudes, interests, opinions. • An experiment is designed to measure the effect of change. What is the effect of a product change? What happens when a price level is raised or lowered? What is the result of selective promotion to specific market segments? What is the response influence of one promotion strategy relative to others? Contemporary Direct Marketing Chapter 11

6. Databased Research and Analysis “My mind’s made up. Don’t confuse me with facts.” A characteristic of database-driven and directed marketing is measurement and accountability for actions. Decisions are based on facts, not opinions. Direct marketers build databases from facts, relying not so much on responses derived from survey, but more on conclusions derived from experimentation. Contemporary Direct Marketing Chapter 11

7. What Do Direct Marketers Test? • Products and Services • Media • Offers/Propositions • Copy Platforms • Creative Formats • Timing/Seasonality Contemporary Direct Marketing Chapter 11

8. TEST THE BIG THINGS Contemporary Direct Marketing Chapter 11

9. Sourcing & Collecting Information • Secondary Data • Primary Data Contemporary Direct Marketing Chapter 11

10. Contemporary Direct Marketing Chapter 11

11. Primary data, collected via survey, can yield information about: • Behavior • Intentions • Knowledge • Socioeconomic Factors • Attitudes and Opinions • Motivations • Psychological Traits Contemporary Direct Marketing Chapter 11

12. Contemporary Direct Marketing Chapter 11

13. How to Design An Experiment • Control • Randomization • Statistically-valid sample size Contemporary Direct Marketing Chapter 11

14. How to Track Responses Key Codes Contemporary Direct Marketing Chapter 11

15. Response rate & break-even analysis • Control vs. experimental packages • Direct marketers test or experiment with different offers and campaign themes to determine which one generates the greatest response rate Contemporary Direct Marketing Chapter 11

16. Promotion Cost ------------------------ = Break-even Number of Sales Unit Profit per Sale Contemporary Direct Marketing Chapter 11

17. Contemporary Direct Marketing Chapter 11

18. Samples & Estimations • Random sample designs • Simple Random Samples • Systematic Random Samples • Stratified Random Samples • Cluster Samples • Replicated Samples • Sequential Samples • Determination of sample size Contemporary Direct Marketing Chapter 11

19. Let’s first define the four terms – confidence level (z-value) limit of error, expected (or actual) response rate and sample size -- that enter into the calculation: Confidence Level (Z-value): This is the value from a normal distribution that corresponds to the chosen confidence level. For example, the 90%, 95%, 99% confidence levels correspond to z-values of 1.65, 1.96, 2.58 respectively. ·Limit of Error: The number of percentage points by which the researcher is allowed to miscalculate the actual response rate. A 20 percent limit of error, assuming a 1 percent response rate, for example, could result in a range of actual response as low as 0.8 percent to as high as 1.2 percent; or, 1%  20% of 1%. ·Expected (Actual) Response Rate: The number of times, in percentage, that response is expected to occur. ·Sample Size: The number of observations in the experiment, or test. This is, for example, the number of pieces mailed in a test from which the response is to be determined. Contemporary Direct Marketing Chapter 11

20. The sample size formula is as follows: N = (R)(1-R)(Z)2 E2 To illustrate the use of the above formula, one can determine the sample size required to be mailed as a test when the expected response rate is 1%; the desired limit of error is  0.2%; at a confidence level of 95%. Thus: R = 1% ... 0.01, expressed as a decimal 1 - R = 99% ... 0.99, expressed as a decimal Z = 1.96, corresponding to a 95% confidence level E = 0.2% ... 0.002, expressed as a decimal N = to be determined Substituting the above values into the formula for the determination of sample size, provides this solution: Contemporary Direct Marketing Chapter 11

21. Contemporary Direct Marketing Chapter 11

22. Contemporary Direct Marketing Chapter 11

23. Measurement of Differences • Hypothesis testing • Types of errors in hypothesis testing • Statistical evaluation of differences Contemporary Direct Marketing Chapter 11

24. Hypothesis Testing Hypotheses are typically stated in negative terms; that is, a null hypothesis (H0) versus an alternative hypothesis (Ha) in a form such as the following: H0: Direct mail response from the test promotion is at or below direct mail response from the control promotion. Ha: Direct mail response from the test promotion is above direct mad response from the control promotion. The null hypothesis, then, states that direct mail response will not be better than the control. Our measurement sets out to disprove this null hypothesis. Contemporary Direct Marketing Chapter 11

25. Types of Errorin Hypothesis Testing Type One: • Results when the decision-maker rejects the null hypothesis even though it is, in fact, true ... i.e., taking an action when one shouldn't Type Two: • Results when the decision-maker accepts the null hypothesis when, in fact, not true ... i.e., not taking an action when one should. Contemporary Direct Marketing Chapter 11

26. Assume that a sample has been properly selected and is of an adequate size. Assume further that an experiment has been designed and implemented in a valid manner. It’s now remains for the direct marketer to be able to recognize the difference in the response rate from a test and that from a control, with some degree of confidence and within an acceptable limit of error. Contemporary Direct Marketing Chapter 11

27. Test Control Totals Response A C A + C Non-response B D B + D Total mailed A + B C + D A + B + C + D = N The statistic 2 is computed as follows: 2 = N [ (A x D) - (C x B)  - N/2]2 (A+B) x (C+D) x (A+C) x (B+D) Here is a sample calculation: Test Control Totals Response 200 100 300 Non-response 800900 1700 Total mailed 1000 1000 2000 2 = 2,000 x [ 180,000 - 80,000 - 1,000]2 1,000 x 1,000 x 300 x 1,700 2 = 38.4 ... which is significant at the 99++% level since it exceeds the critical value in the X2 table for one degree of freedom for a significance level of 0.001, given as 10.83 Contemporary Direct Marketing Chapter 11

28. Contemporary Direct Marketing Chapter 11

29. Easier Approximation Formula = ∑[(actual – Expected)2 /Expected] for all internal cells Contemporary Direct Marketing Chapter 11

30. Easier Approximation Formula = ∑[(actual – Expected)2 /Expected] for all internal cells (con’t) Contemporary Direct Marketing Chapter 11

31. Calculation using easier formula is as follows using the Easier Approximation Formula: = ∑[(actual – Expected)2 /Expected] for all internal cells = [(200-150)2/(150)] + [(100-150)2/(150)] +[(900-850)2/(850)] + [(800-850)2/850] = (50)2/(150) + (-50)2/(150) +(50)2/(850) + (-50)2/(850) = 2500/150 + 2500/150 + 2500/850 + 2500/850 = 16.667 + 16.667 + 2.941 +2.941 = 39.216 vs. 38.4 by actual formula Contemporary Direct Marketing Chapter 11

32. Contemporary Direct Marketing Chapter 11

33. STRUCTURING and EVALUATING AN EXPERIMENT • State the hypothesis • Develop, by a priori analysis, the assumptions required and compute the appropriate sample size • Structure and perform the experiment • Develop, by a posteriori analysis, statistics for judging hypothesis validity • Make the decision Contemporary Direct Marketing Chapter 11