An Advanced Tool to Optimize Product Characteristics and to Study Population Segmentation
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1 OP&P Product Research Utrecht, The Netherlands May 16, 2011 An Advanced Tool to Optimize Product Characteristics and to Study Population Segmentation John M. Ennis, Daniel M. Ennis, & Benoit Rousseau The Institute for Perception Phone: (804)
2 Landscape Segmentation Analysis LSA first unfolds liking and creates a space relevant to consumer acceptability The closer a consumer is to a product, the more he/she likes it Descriptive data is then added by regressing the attributes on the map Some attributes can be fit on the map and are drivers of liking Others can t and are less relevant to consumer acceptability Optimum product locations and profiles can also be estimated Crunchy P 1 P 6 Berry Consumers P 3 P 4 P 2 Sweet P 5 Smooth Vanilla 2
3 Unfolding Liking
4 Unfolding Liking
5 ideal Ideas behind LSA Momentary perception Momentary ideal Dislike Extremely Neither Like Nor dislike Like Extremely The similarity estimate will be used by the model to optimize: Product locations Product variances Individual ideal locations Individual biases 5
6 LSA map generation process 6
7 Fracturability Dry Competitor 6 Competitor 7 Current Prototype 1 Competitor 2 Competitor 3 Stress Sweet Competitor 1 Hardness Competitor 5 Competitor 4 Prototype 2 Bitter 7 7
8 Example: Liking of 25 Products 8
9 Liking of 25 Products 280 consumers 25 beverages Liking ratings on 9-point hedonic scale Dislike extremely Neither like nor dislike Like extremely 9
10 25 Product Liking Distributions Product 7 Product 17 Product 18 Product Ratings Product "1" "2" "3" "4" "5" "6" "7" "8" "9" Mean
11 LSA Contour Plot Ratings Product "1" "2" "3" "4" "5" "6" "7" "8" "9" Mean
12 Example: Child and Adult Food Preferences 12
13 Child and Adult Food Preferences Preference and liking for 20 foods by 150 adults and 150 children (8-12 years old) Apple sauce Chocolate milk Fruits Orange juice Soda Bottled water Cookies Hamburger Pizza Soup Carrot sticks Cup cakes Ice cream Popsicle Spaghetti Chicken French fries Iced tea Sandwich Tossed salad Only names given, no actual tasting of the foods Adults liking and preference for foods for their children Landscape Segmentation Analysis on liking ratings 13
14 Child and Adult Food Preferences Children Cup cakes Adults Hamburger Soda French fries Cookies Ice cream Pizza Popsicle Chocolate milk Chicken Spaghetti Soup Sandwich Iced tea Tossed salad Fruits Bottled water Orange juice Apple sauce 14
15 Example: Motivations for Product Consumption 15
16 Fruit-Based Beverages with Medicinal Properties A company manufactures fruit-based beverages Company would like to assess the motivators for product use among a representative sample of consumers Six hundred (600) heavy users of the product respond to eight statements dealing with possible motivators I drink this product because: I like the flavor I like it It reduces back pain It is thirst quenching It is healthy for me It tastes good It is good for urinary health I like the tangy taste
17 Back Pain Urinary Health Health Thirst Quenching Tangy Taste Tastes Good Like It Like the Flavor 17
18 Example: Blind/Branded Investigations 18
19 Blind/Branded Study: Scenario Winery wants to introduce new chardonnay wine products in the premium category Conducts a study to investigate acceptability of its own products by casual/novice and experienced/knowledgeable wine drinkers 10 chardonnay wines: 4 premium brands 4 value brands 2 new products 500 consumers 400 casual/novice wine drinkers 100 experienced/knowledgeable wine drinkers 19
20 Blind/Branded Study: Blind Evaluation Value 1 Value 1 Premium 2 Premium 2 Premium 1 Value 4 Premium 1 Value 4 Own 2 Own 2 Premium 3 Premium 4 Value 2 Own 1 Premium 3 Premium 4 Value 2 Own 1 Value 3 Value 3 No segmentation is visible Own products well accepted Some value products well accepted also Casual wine drinker Experience wine drinker Novice and knowledgeable wine drinkers spread throughout the map without any particular structure 20
21 Blind/Branded Study: Branded Evaluation Premium 1 Premium 3 Premium 1 Premium 3 Premium 4 Own 1 Premium 2 Value 1 Premium 4 Premium 2 Value 1 Value 4 Value 2 Own 2 Own 1 Value 4 Value 2 Own 2 Value 3 Value 3 Little segmentation is visible Premium products migrate to the north close to highest consumer density Own and value products migrate to the south Casual wine drinker Experience wine drinker Product migration can be attributed to the high ratings of the knowledgeable consumers for the premium products 21
22 Blind/Branded Study: Blind vs. Branded Value 1 Value 1 Premium 1 Premium 3 Premium 2 Premium 2 Premium 1 Premium 4 Premium 1 Own 1 Premium 2 Premium 4 Value 4 Own 2 Own 1 Premium 4 3 Value 4 Own 2 Value 1 Own 1 Premium 3 Value 4 Value 2 Own 2 Value 2 Value 2 Value 3 Value 3 Value 3 On a blind basis, the company s products perform well over the whole population The branded LSA illustrates the power of the brands in this set of 10 products Results indicate that the company should focus on improving the products image rather than their sensory profiles as the latter are close to being optimal 22
23 Further Capabilities of LSA 23
24 Finding Optima An LSA map can be used to estimate locations of optimally placed products P 3 P 3 P 2 P 2 Crunchy P 1 Crunchy P 1 Vanilla Vanilla Optimal Product If scales have been regressed onto an LSA map then product profiles for optima can be generated 24
25 Profile Placement Locations of prototypes can also be estimated using the profiles of prototypes on regressed scales Prototype Vanilla Crunchy Prototype P 3 P 3 P 2 P 2 Crunchy P 1 Crunchy P 1 Vanilla Vanilla Prototype 25
26 Example: ASTM E-18 Image Appropriateness 26
27 Image Appropriateness Study 27
28 Image Appropriateness Study 80 respondents evaluated 29 images in order to determine best image or collection of images Each respondent rated each image on a 9-point appropriateness for brochure inclusion scale: Any of the images could be used to represent what we do as sensory evaluation professionals. Please judge each image in terms of the degree to which it appropriately represents our profession. Results were analyzed using Landscape Segmentation Analysis (LSA) 28
29 Image Appropriateness Means (1/3) Image Appropriateness Mean
30 Image Appropriateness Means (2/3) Image Appropriateness Mean
31 Image Appropriateness Means (3/3) Image Appropriateness Mean
32 Image Appropriateness Partial LSA Map (1/3) 32
33 Image Appropriateness Partial LSA Map (2/3) 33
34 Image Appropriateness Partial LSA Map (3/3) 34
35 Image Appropriateness One Optimum 1 35
36 Image Appropriateness Two Optima
37 Image Appropriateness Three Optima
38 Image Appropriateness Conclusions LSA Results Slight segmentation apparent Selected images covered space adequately but not perfectly One, two or three optima can be used to cover space Recommendations For a single image: For two images: For three images: 38
39 OP&P Product Research Utrecht, The Netherlands May 16, 2011 An Advanced Tool to Optimize Product Characteristics and to Study Population Segmentation Presented By: John M. Ennis The Institute for Perception Phone: (804)
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