Stimulus and Response - Point of view

1. A joint venture between Moskowitz Jacobs Inc & The Understanding and Insight GroupCreating winning communications and features …in a cost effective, rapid, user friendly way

2. Stimulus and Response - Point of view • Knowledge: Consumers can’t tell you what they want • Perception: But .. They know it when they see it • Empiricism: If you can present them test concepts and get ratings .. You can identify what wins • Structure: If you systematically vary these stimuli you can identify what specific drivers are working

3. What’s the bottom line

4. Five key knowledge benefits • Better data … why settle for guessing or focus groups when you get solid quantitative answers • Clearer results … looking at the results gives you immediate insight and direction • Multi-media….Whether concepts, packages … you get to test many stimuli • Segmentation …you get to see new segments, and what turns them on • Synergies and suppressions .. Identify what works together, what doesn’t

5. Conjoint measurement • Standard research technique – Designed Experiment • Systematically vary the test stimuli • Get responses • Use statistics to link stimulus elements and responses • Build model at individual respondent level showing ‘drivers’ of response

6. What is Conjoint?

7. What is conjoint? A definition… • Conjoint analysis is founded on the statistical method of experimental design (Box, Hunter & Hunter, 1976). By systematically varying the components of the concept and obtaining ratings, the researcher can identify what specific parts of the concept drive the ratings, and what specific parts are irrelevant. • The rationale underlying conjoint is that the consumers may not, in fact, know what is important, if they have to rate each of the components of the product or service on a one by one basis. The goal of all conjoint measurement is to provide a measure for each element or feature in the study. The measure shows the degree to which the presence of the element in a concept drives the rating. (If the rating is interest, then the utility or impact measure shows the ability of the element to increase or decrease interest when the element becomes a part of the concept).

8. Marketing Research Satisfaction Source: PDMA Certification Workshop Mahajan/ Wind 1991

9. Packaging/ Color Red Green White Price $1.25 $1.50 $1.75 Size/Count 6ct-6oz 8ct-8oz 10ct-10oz Product Name Creamy Chunky Crunchy Example of Conjoint Analysis Design Frozen Ice Cream Bar Concept

10. Example of Conjoint Analysis Design Frozen Ice Cream Bar Concept Packaging/ Color Red Price $1.25 Size/Count 6ct-6oz Product Name Creamy

11. Example of Conjoint Analysis Design Frozen Ice Cream Bar Concept Packaging/ Color Red Price $1.50 Size/Count Product Name Creamy

12. The ‘process’User oriented templates Set up the study ‘Fill in the blanks’ Oriented toward simplicity, speed

13. Three different offeringsConcept Screening, Conjoint, Standard Questionnaires

14. Log in to web server

15. Follow guide

16. Set up study parameters

17. Welcome & Orientation

18. Spreadsheet Template‘fill in the blanks’

19. ClassificationSingle, Multiple, Open Ends, Ratings

20. Goodbye Page

21. Launch & Get URL

22. Monitor Topline

23. What the Respondent Sees

24. What the Respondent Sees –Welcome Screen

25. What the Respondent Sees – Concept Screen Category 1 Food Descriptors Category 2 Situational/ Mood Category 3 Emotional Attributes Rating Question Category 4 Brand/ Benefit

26. What the Respondent Sees - Classification Question

27. Getting and Looking at the Data

28. What the Topline looks like

29. Example Total Sample Results – 3 Key Numbers: Base Size, Constant and Element/Utility Scores Base Size Constant Element/Utility Score

30. Classification Questions • For the following set of questions, there may be answer options that are relevant to some beverage categories and not others. Please answer each question based on your idea of what options are relevant for Chicken.

31. The Regression • IdeaMap®.Net uses regression (Y=MX+B) • The constant is B, which refers to the estimated value of Y (interest, persuasion) when X is 0 (X are elements... thus when there areno elements present) • The equation is always developed with a constant • The constant is a mathematical correction factor

32. Two Kinds of Numbers from Regression • Constant – represents the base level of interest in a specified category, before exposure to concept elements; reflects respondent’s prior experience • Element utility – represents the contribution of the element to the overall appeal of the concept; can be positive, neutral, or negative

33. Interpreting Constant Scores • The constant reflects: • Captures any interest in the concept not tied back to any specific element • A respondents previous category experience • Any ingoing level of interest in the overall idea as presented in the positioning statement

34. Norms for the Additive Constant • 0-20 Little base interest • 21-40 Modest base interest • 41-60 Typical base interest • 61-80 High base interest • 81+ Very high base interest

35. Low Additive Constant • If the constant is low, and the elements are low, then.... consumers are not interested in the general idea of the product and no communications tested will enhance their base level of interest. • If the constant is low, and the elements are varied (high...low) then ... the basic idea is fair, but you can increase acceptance by the correct choice of elements

36. High Additive Constant • If the constant is high, but the elements are low... consumers in-going level of interest is high, but none of the tested communications will further enhance their existing level of interest. • We have not seen a high constant and high positive elements

37. Interpreting Element Scores • Each element (concept component) is assigned an impact score that represents the interest contribution of that component. • Positive values indicate that the feature enhances consumer interest. • Scores that are near zero indicate consumers are indifferent to inclusion of that feature. • Negative values indicate that the feature detracts from consumer interest.

38. Norms for the Element Contribution • > 20 = dynamite • 16-20 = excellent • 10-15 = very good, important • 6-10 = good • 0 - 5 = so what • < 0 = detracts

39. Looking Deeper

40. Download Data

41. Data in the form of a spreadsheetIncluding segmentation (automatic)

42. Looking at the DataSee it generate before your eyesUnderstand the sophisticated design behind what you see A trial exercise – 4x3 design

43. Experimental Design – 25 Concepts • Numbers in body of table show which of the 4 elements selected from that category appears in the specific concept • Each concept comprises 5 categories. If a 0 appears, then the concept has no element from that category

44. After the Study Understanding the data that comes back

45. Reports You Receive • Topline report for total sample • Includes base size, elements, and classification • Ranked from highest to lowest element score in each category of the study • Summarized in a single sheet to allow for insights across categories

46. .ELM .QSN .QST .CLS .OP0 .OPC .OPS .OT0 .OTC .OTS .OPEN .SGM Further Files You Can Get

47. Reference Files

48. .ELM file • File with a list of all the elements in the project. • Note: these element keys (E_1, etc.) are used in the following files as column headers to identify utilities for each of the elements. • OP0 • OPC • OPS • OT0 • OTC • OTS

49. .QSN and .QST files QSN file • File with Classification Questions - lists all the classification questions in the project QST file • Text of Rating Question

50. Data Files