Southwestern Conquistador Beer, Secondary Data, Measures, Hypothesis Formulation, Chi-Square

1. Southwestern Conquistador Beer, Secondary Data, Measures, Hypothesis Formulation, Chi-Square Market IntelligenceJulie Edell Britton Session 2 August 8, 2009

2. Today’s Agenda • Announcements • Southwestern Conquistador Beer Case • Backward Market Research • Secondary data quality • Measure types • Hypothesis Testing and Chi-Square

3. Announcements • National Insurance Case for Sat. 8/22 • Download National.sav from platform • SPSS on machines in MBA PC Lab and see installation direction on the platform on how to install on your machine • Do tutorial to familiarize with SPSS • Use handout in course pack to answer questions: 1-6 • Stephen will do a tutorial on Friday, 8/21 from 1:00 -2:15 in the MBA PC Lab and be available on 8/21 from 7 – 9 pm in the MBA PC Lab to answer questions • Submit slides by 8:00 am on Sat. 8/22 3

4. SWCB Objectives • Feasibility decisions • Problem formulation, information needs • Role of secondary data • Role of research and time budgets • Quality, cost, speed 4

5. SWCB Questions • What should Mr. Gomez do? • Consumer behavior? • What information do we need to make decision? • Which reports allow that information to be estimated? • What decision do these reports suggest? 5

6. SWCB Conclusions • Feasibility studies need data on: industry demand, market share, investment, costs, margins. Break even analysis common. • Conceptualize data before doing research • Effort at problem formulation stage reduces later costs of doing research • Secondary data is the place to start 6

7. SWCB Conclusions (cont.) • Cost of information is real; research budget typically constrained • Cheap info may not be most economical if it is unreliable • Just because budget has funds does not mean you should conduct extraneous research. 7

8. Today’s Agenda • Announcements • Southwestern Conquistador Beer Case • Backward Market Research • Secondary data quality • Measure types • Hypothesis Testing and Chi-Square

9. Backward Market Research • Obvious? Psychology of why so hard to do. • Imagine the end of the process: • What will the final report look like? DUMMY TABLES • What decision alternatives might be implemented? • What analyses can support a choice between alternatives? • Where to get the data for analysis? • Do they already exist? • If not, may need to commission a study. • Design the study (“need-” vs. “nice-to-know”) • Analyze data & make recommendation

11. Analysis Dummy Table Ad Score = .25 UpF +.20 Claims + .15 AAd + .40 AB Action Standard - Run the Ad with the Higher Ad Score

12. Research Process Fig 3-1, p.49 • Marketing Planning & Info System. • Agree on Research Purpose AmEx • Research Objectives (hypotheses, bounds) • Value of Information (the clairvoyant, p. 59) • Design Research • Collect Data & Analyze • Report Results & Make Recommendations

14. Research Process Fig 3-1, p.49 • Marketing Planning & Info System. • Agree on Research Purpose AmEx • Research Objectives (hypotheses, bounds) • Value of Information (the clairvoyant, p. 59) • Design Research • Collect Data & Analyze • Report Results & Make Recommendations

15. American Express Marketing Research Brief(To Be filled out by End User) • Marketing Background - Describe the current information or environment – what are the issues that precipitated the need for the research? What business units will be impacted? • Business Decisions - What decisions will be made and what actions will be taken as a result of the research? (If appropriate, specify alternatives being considered). What other data or business considerations will impact the decision? • Information Objectives - What are the key questions (critical information) that must be answered in order to make the decision? • Relevant Populations - Who do we need to talk to and why? • Timing - When must the research be completed to make the marketing decision? • Budget – How much money has been budgeted for this research? To what budget line will it be charged? • Requested by ________________ Manager • Requested by ________________ Director • Requested by ________________ Vice President

16. American Express Marketing Research Brief(To Be filled out by Marketing Research) Job # __ Project Title _________ Budget Line ___ Business Unit___ • Marketing Background • Business Decisions To Be Made • Research Objectives • Research Design • Action Standards • Existing Sources of Information Consulted (e.g. syndicated and/or previous research) • Research Firm • Timing • Cost • Market Research Department Travel Cost • Approval ________________ Vice President • Approval ________________ if between $100,000 and $500,000 - Sr. VP • Approval ________________ if over $500,000 - Exec. Committee Member

17. American Express Marketing Research Actionability Audit (To Be filled out by End User) • Project Name • End User Name • What Decisions or Actions were taken or are planned as a result of this research? If none, explain why. • Were any Actions Taken or are any actions being considered that are in conflict with the research learning? If so, why? • In retrospect, is there anything that could have been done differently to improve the actionability of the research investment? If so, what? • Relevant Populations - Who do we need to talk to and why?

18. Research Process Fig 3-1, p.49 • Marketing Planning & Info System. • Agree on Research Purpose AmEx • Research Objectives (hypotheses, bounds) • Value of Information (the clairvoyant, p. 59) • Design Research • Collect Data & Analyze • Report Results & Make Recommendations

19. Overview of Research Design • Exploratory • Generate ideas on alternatives & criteria to evaluate the alternatives • Descriptive • 1-way: frequencies, proportions, means, medians • 2-way: correlations, crosstabs • Causal • Assess cause-effect relationships

20. Today’s Agenda • Announcements • Southwestern Conquistador Beer Case • Backward Market Research • Secondary data quality • Measure types • Hypothesis Testing and Chi-Square

21. 3 Key Skills • Backward market research (1, 2) • Getting data and judging its quality • Secondary data (2) • Exploratory research (3) • Descriptive research (4,5) • Causal research (6) • Analysis frameworks for classic marketing problems (7-10)

22. Primary vs. Secondary Data • Primary -- collected anew for current purposes • Secondary -- exists already, was collected for some other purpose • Finding Secondary Data Online @ Fuqua • http://library.fuqua.duke.edu

23. Primary vs. Secondary Data

24. Evaluating Sources of Secondary Data • If you can’t find the source of a number, don’t use it. Look for further data. • Always give sources when writing a report. • Applies for Focus Group write-ups too • Be skeptical.

25. Secondary Data: Pros & Cons • Advantages • cheap • quick • often sufficient • Disadvantages • there is a lot of data out there • numbers sometimes conflict • categories may not fit your needs

26. Types of Secondary Data *IRI = Information Resources, Inc. (http://us.infores.com/)

27. Secondary Data Quality: KAD p. 120 & “What’s Behind the Numbers?” • Data consistent with other independent sources? • What are the classifications? Do they fit needs? • When were numbers collected? Obsolete? • Who collected the numbers? Bias, resources? • Why were the data collected? Self-interest? • How were the numbers generated? Exter: • Sample size • Sampling method (Sessions 5&6) • Measure type • Causality (MBA Marketing Timing & Internship)

28. It is Hard to Infer Causality from Secondary Data

29. Evaluating Sources of Secondary Data • If you can’t find the source of a number, don’t use it. Look for further data. • Always give sources when writing a report. • Applies for Focus Group write-ups too • Be skeptical.

30. Be Skeptical MBA’s May Be A Marketing Liability… “A master of Business Administration degree is not only worthless, it can work against a marketer, according to a survey of marketing executives from 32 consumer-products companies by consulting firm Ken Coogan & Partners...Marketing executives from 18 underperforming companies – which had sales grow 7% less than their categories on average in the last two years ended August 2005 – were twice as likely to have been recruited out of MBA programs than marketing executives from out-performing companies, which averaged growth 6.2% faster than their categories over the two years.” Source: AdAge.com, March 21, 2006

31. Today’s Agenda • Announcements • Southwestern Conquistador Beer Case • Secondary data quality • Measure types • Hypothesis Testing and Chi-Square

32. Measure Types • Nominal: Unordered Categories • Male=1; Female = 2; • Ordinal: Ordered Categories, intervals can’t be assumed to be equal. • I-95 is east of I-85; I-80 is north of I-40; Preference data • Interval: Equally spaced categories, 0 is arbitrary and units arbitrary. • Fahrenheit temperature – each degree is equal • Ratio: Equally spaced categories, 0 on scale means 0 of underlying quantity. • $ , Age

33. Meaningful Statistics & Permissible Transformations

34. The Interval/Ordinal Distinction • The mean is a meaningless statistic when a variable is ordinal or nominal. • That is because different permissible transformations lead to different conclusions • Example on next slide: Male and female speed to finish quiz (lower # means faster finish) • Measure 1 implies males faster, but measure 2 implies females faster. • In contrast, median is meaningful for ordinal data, because different permissible transformations lead to same conclusion • Median female faster than median male in measure 1, measure 2, or any permissible transform

35. Means and Medians with Ordinal Data

36. Ratio Scales & Index Numbers

37. Today’s Agenda • Announcements • Southwestern Conquistador Beer Case • Backward Market Research • Secondary data quality • Measure types • Hypothesis Testing and Chi-Square

38. MBA Acceptance Data A. Raw Frequencies B. Cell Percentages

39. C. Row Percentages D. Column Percentages

40. Rule of Thumb • If a potential causal interpretation exists, make numbers add up to 100% at each level of the causal factor. • Above: it is possible that gender (row) causes or influences acceptance (column), but not that acceptance influences gender. Hence, row percentages (format C) would be desirable.

41. Hypothesis Hypothesis: What you believe the relationship is between the measures. Theory Empirical Evidence Beliefs Experience Here: Believe that acceptance is related to gender Null Hypothesis: Acceptance is not related to gender Logic of hypothesis testing: Negative Inference The null hypothesis will be rejected by showing that a given observation would be quite improbable, if the hypothesis was true. Want to see if we can reject the null.

42. Steps in Hypothesis Testing • State the hypothesis in Null and Alternative Form • Ho: There is no relationship between gender and MBA acceptance • Ha1: Gender and Acceptance are related (2-sided) • Ha2: Fewer Women are Accepted (1-sided) • Choose a test statistic • Construct a decision rule

43. Chi-Square Test • Used for nominal data, to compare the observed frequency of responses to what would be “expected” under some specific null hypothesis. • Two types of tests • Contingency (or Relationship) – tests if the variables are independent – i.e., no significant relationship exists between the two variables • Goodness of fit test – Compare whether the data sampled is proportionate to some standard

44. Chi-Square Test With (r-1)*(c-1) degrees of freedom Expected number in cell i under independence Observed number in cell i number of columns number of cells number of rows = Column Proportion * Row Proportion * total number observed

45. MBA Acceptance Data Contingency A. Observed Frequencies B. Cell Percentages C. Expected Frequencies

46. Chi-Square Test With (r-1)*(c-1) degrees of freedom =(140-111)2/111 + (860-890)2/890 + (60-89)2/89 + (740-710)2/710 = 19.30 So? 3. Construct a decision rule

47. Decision Rule • Significance Level - • Degrees of freedom - number of unconstrained data used in calculating a test statistic - for Chi Square it is (r-1)*(c-1), so here that would be 1. When the number of cells is larger, we need a larger test statistic to reject the null. • Two-tailed or One-tailed test – Significance tables are (unless otherwise specified) two tailed tables. Chi-Sq is on pg 517 • Ha1: Gender and Acceptance are related (2-sided) Critical Value = 3.84 • Ha2: Fewer Women are Accepted (1-sided) Critical Value = 2.71 • Decision Rule: Reject the Ho if calculated Chi-sq value (19.3) > • the test critical value (3.84) for Ha1 or (2.71) for Ha2 Probability of rejecting the Null Hypothesis, when it is true

48. Chi-Square Table

49. Chi-Square Test • Used for nominal data, to compare the observed frequency of responses to what would be “expected” under some specific null hypothesis. • Two types of tests • Contingency (or Relationship) – tests if the variables are independent – i.e, no significant relationship exists • Goodness of fit test – Compare whether the data sampled is proportionate to some standard

50. Goodness of fit – Chi-Square Ho: Car Color Preferences have not shifted Ha: Car color Preferences have shifted Data Historic Distribution Expected # = Prob*n Red 680 30% 750 Green 520 25% 625 Black 675 25% 625 White 625 20% 500 Total(n) 2500 Do we observe what we expected?