1. Market Intelligence Class 9

2. Clothing retailers 2

3. Chips 3

4. Sports apparel 4

5. Perceptual Mapping Visual representation of customer perceptions – Shows how target customers view competing alternatives in a Euclidean space representing the market – Pair-wise distances between alternatives indicate how close or far apart the products are in the minds of customers 5

6. Perceptual Mapping Uses of maps – Identify your closest competitors – Suggest repositioning strategies – Suggest advertising themes supporting repositioning – Identify new product opportunities where some segment not well served by current brands 6

7. Sports apparel 7

8. Perceptual Mapping 2 types of maps, based on different ways of measuring similarity between brands: – 1. Similarity-Based Map Based on ratings of overall similarity b/w brands Multidimensional scaling (MDS) to analyze – 2. Attribute-Based Map Based on ratings of brands on various perceptual attributes Brands that are highly correlated on attributes are similar Factor Analysis/Principal Components Analysis to analyze 8

9. Comparison of types of maps 9

10. Similarity Based Map Generate relevant set of brands, products, firms – Relevance: set of competitive products that are relevant for managerial decision making Have respondents rate similarity (e.g. 1-10 pt scale) between every possible brand pairing Can perfectly represent 3 brands in 2 dimensions, but if more than 3, there will be information loss – MDS is a mathematical technique used to analyze similarity perceptions with minimum information loss 10

11. Similarity based map: Soap example 11

12. Similarity based map: Soap example 12 Can aggregate across respondents if interval data

13. SPSS Commands – similarity based Analyze – Scale – Multidimensional scaling (Proxscal) – Select Define – Select variables (brands to include) – Model Proximity transformations: Interval Shape: Upper triangular matrix Proximities: Similarities Dimension: min = 2, max = 2 – Plots Check “common space” 13

14. SPSS Output – similarity based Check fit of model (2 dimensions) Goodness of fit “S-Stress”. Want it less than 0.10 14

15. X, Y coordinates can be plotted 15

16. Similarity Based Map 16

17. Labeling dimensions Not always obvious 3 ways to generate labels – Your own judgment – Have respondents look at dimensions – Run 2 regression with various attributes as predictors: once with X coordinates as DV, then with Y coordinates as DV 17

18. Applications Where are we and competition on key dimensions? Who are Dove’s biggest competitors? Which brand is seen as most different from Dial? Are there clusters of brands (substitution) or are they spread out? Are there gaps in the market? – What would you want to know first? 18

19. Similarity Based Map 19

20. Next step: Plotting ideal points Ask respondents to rate similarity between each brand and their “ideal” on same scale as before Their ideal becomes another “brand” in analysis 20

21. Similarity based map with ideal point 21

22. Mapping ideal points Run analysis separately for each respondent to get individual x,y coordinates for “ideal” 22

23. Similarity map with 1 person’s ideal point 23

24. Final step Create scatterplot with: – Original coordinates (from aggregate data) for each brand – Each respondent’s ideal point coordinates (gotten from separate MDS for each person) 24 + For each person… Based on averages

25. Brands 25 Caress Dial Dove Irish Spring Safeguard Lever 2000 Ivory

26. With ideal points 26 Caress Dial Dove Irish Spring Safeguard Lever 2000 Ivory

27. Applications Are there unmet needs in the market? (any ideal points with no brand close by?) Segments of consumers who want different things? Competitor analysis Repositioning strategy? Brand/line extension opportunities? What should I communicate to customers? 27

28. Perceptual Mapping: Type 2: Attribute-based Based on ratings of brands on different attributes Steps – Generate list of relevant brands – Generate list of key attributes 28

29. Car example Cars Ford Infiniti Cadillac Camero Mercedes Mazda Buick Porsche Kia Audi Attributes Unreliable Roomy Prestige Highquality Lowprofiletires Sporty Powerfulengine Smoothride Tighthandling Poorvalue Attractive Quiet Poorlybuilt Uncomfortable Premiumsound- system 29

30. Perceptual Mapping: Attribute-based Based on ratings of brands on different attributes Steps – Generate list of relevant brands – Generate list of key attributes – Consumers rate each brand on each attribute 30

31. For each brand, ask consumers to rate to what extent each attribute describes the brand Car XStrongly DisagreeAgree 1 2 3 4 5 6 7 8 9 10 Attribute A ____ ____ ____ ____ ____ ____ ____ ____ ____ ____ Attribute B ____ ____ ____ ____ ____ ____ ____ ____ ____ ____ 31

32. SPSS DATA – attribute based map 32

33. Perceptual Mapping: Attribute-based Based on ratings of brands on different attributes Steps – Generate list of relevant brands – Generate list of key attributes – Consumers rate each brand on each attribute – Factor analyze matrix of attribute ratings (use a separate row for each brand for each respondent) 33

34. Factor Analysis Data reduction technique that is useful in mapping. – Identifies number of factors/dimensions that represent the relationships in the larger set of attributes. – For perceptual map: do 2 factors capture a high percentage of the variance in the data? 34

35. SPSS Commands – attribute based Note: Lots of alternatives here, a basic example Analyze – Dimension Reduction – Factor – Select variables (attributes to include, do not include the brands here) – Descriptives Initial Solution – Extraction Method: principle components Correlation Matrix Unrotated Factor Solution Extract – Fixed Number of Factors – 2 – Rotation none – Scores Save as variables (regression method) Display Factor Score Coefficient Matrix – Options Sorted by size 35

36. Component Total% of VarianceCumulative %Total % of var. Cum. % 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 7.742 2.800 2.060 1.286.430.385.196.080.021 8.487E-16 5.793E-16 6.083E-18 -4.952E-17 -1.462E-16 -1.901E-16 51.616 18.667 13.733 8.574 2.865 2.568 1.304.530.142 5.658E-15 3.862E-15 4.055E-17 -3.301E-16 -9.749E-16 -1.267E-15 51.616 70.283 84.016 92.591 95.456 98.024 99.328 99.858 100.000 6.979 3.563 46.528 23.755 70.283 Initial Eigenvalues Rotation Sums of Loadings Total Variance Explained The Eigenvalues represent the amount of variance explained by a factor and are scaled such that the sum of the Eigenvalues is equal to the total number of factors. Typically factors with Eigenvalues >1.0 are considered significant. The first 4 factors below meet this cut-off and would capture 92.6% of the total variance. We will keep 2 factors, which explain 70.3% of the variance. Variance Explained

37. Output - Communalities The proportion of variance in each attribute accounted for by the 2-factor solution – Information on “quiet” is not very well captured by the two factor solution. We would need a third or fourth factor to capture the variance in the quiet variable. 37 InitialExtraction Unreliable Roomy Prestige High quality Low profile tires Sporty Powerful engine Smooth ride Tight handling Poor value Attractive Quiet Poorly built Uncomfortable Premium sound- system 1.000.991.780.876.925.562.850.852.875.779.866.565.033.878.232.477 communalities

38. Component Matrix This is the two factor solution (each component is a factor) “f’s” represent correlations between the attributes (rows) and factors (columns) These are the coordinates for where the attributes plot in the factor space – Envision vectors that start at the origin and radiate in the direction of the attribute. – Vectors indicate both magnitude and direction in the Euclidean space. Output - Loadings Component 12 unreliable roomy prestige high quality low profile tires sporty powerful engine smooth ride tight handling poor value attractive quiet poorly built uncomfortable premium sound- system -.908 -.883.652.924.748.579.824 -.688.516 -.925.751 -.040 -.878 -.002.597.408 -.022 -.671 -.269.052.718.417 -.634.716.106.029 -.177.328.482 -.348

39. 39 Output - SPSS Loading Plot

40. Label Factors Now 40

41. Now how to plot brands in this space? 41 Comfort Performance High qualityLow quality

42. Brands SPSS calculates the factor score for each brand which you can plot F1 and F2 are generated in SPSS as new variables 42 F1F2 ford-0.93470.39153 infiniti1.02535-0.50025 cadillac-0.61902-1.76166 camero-0.25632.04001 mercedes1.02643-0.38962 mazda-0.400820.65941 buick-1.10493-0.80225 porsche1.369350.25813 kia-1.127470.17281 audi1.02211-0.0681

43. Now we plot brands 43 ford infiniti cadillac camero mercedes mazda buick porsche kia audi Performance Comfort Low quality High quality

44. Perceptual Mapping: Chicago Beer Example Generate list of relevant beer brands Generate list of attributes 44

45. Chicago Beer Beers Old Milwaukee Budweiser Beck’s Heineken Miller Meister Brau Stroh’s Coors Michelob Coors light Miller light Old Milwaukee Light Attributes Heavy Popular with men Special occasions Dining out Popular with women Less filling Light Pale color On a budget Blue collar Full bodied 45

46. Perceptual Mapping: Chicago Beer Example Generate list of relevant beer brands Generate list of attributes Consumers rate each brand on each attribute (e.g., mild flavor, malty, etc.) Factor analyze matrix of attribute ratings (use a separate row for each brand for each respondent) 46

47. For each beer, ask consumers to rate to what extent each attribute describes the brand BudweiserStrongly DisagreeAgree 1 2 3 4 5 6 7 8 9 10 Mild flavor ____ ____ ____ ____ ____ ____ ____ ____ ____ ____ Sporty ____ ____ ____ ____ ____ ____ ____ ____ ____ ____ 47

48. Perceptual Mapping: Chicago Beer Example Generate list of relevant beer brands Generate list of attributes Consumers rate each brand on each attribute (e.g., mild flavor, malty, etc.) Factor analyze matrix of attribute ratings (use a separate row for each brand for each respondent) 48

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50. Guidelines for interpreting maps The arrow indicates the direction in which that attribute is increasing Length of the line from origin to arrow indicates the variance of that attribute explained by 2D map. Longer the line, greater importance of that attribute in explaining variance Attributes that are relatively important (long vector) and close to 1 of the axes help interpret the meaning of axis 50

51. 51 Questions: 1. How would you label horizontal and vertical axes? 2. Which 2 attributes are most important to describe horizontal axis?

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54. Adding Customer Ideal Points Introduce an “ideal” brand as an additional stimulus rated (on attributes) by consumers Plot location of ideal brand for each consumer Cluster analysis to get segments Represent size of segment by size of circle around ideal point Use brand and segment info to forecast shares. 54

55. Ideal Points – Hypothetical Individuals

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57. Chicago Beer Market Base Shares Positioning and Segmentation can yield market share estimates – For illustrative simplicity, give 100% of each segment to brand closest to its center. – Exact ties split 50-50 – Commercial uses -> Probabalistic allocation of share. Forecast preference for each segment Forecast brand shares by adding up brand shares at segment level 57

58. Forecasting tool 58

59. 59 Forecasting tool

60. 60 Forecasting tool

61. 61 Forecasting tool

62. 62 Forecasting tool

63. 63 Forecasting tool

64. Chicago Beer Market Repositioning Reposition by reformulating product or advertising. What- if share calculations. Expense = f (distance moved). Sustainability - who else can do it cheaper? Repositioning Questions – How should you reposition if you were Miller? Who is hurt, would there be a countermove? – If you were Beck’s? 64

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66. Chicago Beer New Product Intro Where are gaps? Badly served segments? If I introduce, who is hurt? Can they respond by repositioning without losing other, more valuable business? As market matures, more brands. Viability of straddling segments? 66

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68. Questions to ask when you are presented with a perceptual map What were data inputs used to generate map? (similarity vs. attribute) If similarity-based, how were brands chosen? How did you generate labels for axes? Was MDS used for analysis? What was s-stress? If attribute-based, what were attributes? How were dimensions labeled? Was factor analysis/PCA used? What was goodness of fit? 68

69. Simulated Test Market Relevant for new product development Comes at a relatively late stage of process – Have developed the product and the positioning – Want to know how much $ you will make from this product. Estimate comes from 2 pieces Sales from Initial Trial of product (do they like the concept?) Sales from repeat purchase (once they try, does it deliver on promise?) 69

70. TWO KINDS OF STMS True STM (e.g., ASSESSOR, LITMUS) – Trial Rate: Consumers come to laboratory store. Exposed to half hour sitcom with finished ads for new product, competition. Then led to mini-store where new product, competitors available for purchase w/ own $. Concept Test STM (Nielson BASES). Cheaper. – Trial Rate: Consumers exposed to 2-paragraph concept, not final ads. Adjusted “Top Box” (Definitely Would Buy…), usually using web panels Concept Tests more popular b/c accuracy not that different 70

71. Why do a BASES concept test? Cost: about $100K for full STM with projections for 3 alternative marketing plans Secret from Competitors; no sabotage Accurate -- when assumptions implemented – 1000’s validations of BASES forecasting accuracy; average forecast falls within 9% of actual sales; 91% fall within 20% of actual sales. 71

72. Part 1: Trial Step 1: ________ Trial rate (%) – use deflators for likelihood ratings Step 2: ________ Adjusted trial rate: multiply by awareness (%) and availability/(ACV%) Step 3: ________ Trial households: Multiply by number of households Step 4: ________ Trial unit sales: Multiply by transaction size (how many units bought at trial) Step 5: ________ Trial dollar sales: multiply by unit price (and by factory price adjustment) 72 80% of Definitely Buy 30% of Probably Buy

73. Part 1: Trial *Note: If brand extension, do steps 1-4 below separately for current users and non-users of brands Step 1: ________ Trial rate (%) – use deflators for likelihood ratings Step 2: ________ Adjusted trial rate: multiply by awareness (%) and availability/(ACV%) Step 3: ________ Trial households: Multiply by number of households Step 4: ________ Trial unit sales: Multiply by transaction size (how many units bought at trial) _______________ *If brand extension, add trial units for users and non-users now Step 5: ________ Trial dollar sales: multiply by unit price (and by factory price adjustment) 73

74. Part 2: Repeat Step 6: ________ Repeat rate (%) Step 7: ________ Repeat households: multiply by # of trial households Step 8: ________ Repeat unit sales: multiply by repeat purchases per household and repeat transaction size Step 9: ________ Repeat dollar sales: multiply by unit price (and by factory adjusted price) -------------------------------------------------------------------------------------------------------- Total sales in units: ________________ Add Step 4 and Step 8 Total sales in dollars: ________________ Add Step 5 and Step 9 74

75. What happened? TruEarth did launch Pizza product Sales (volume, trial, & repeat), share, and profit performance were well below expectations – Inaccurate inputs Penetration lower Market support lower – Change in product Topping spoilage 60%, changed product to cheese and cheese & pepperoni only – not what was rated in BASES – Bad luck: price & promotion war b/w Dominos and Pizza Hut, delivery now same price as Cucina Fresca