Connecting Marketing and Sensory through Means-End Chain Analysis

1. Connecting Marketing and Sensory through Means-End Chain Analysis Market Research Methods

2. Overview... What are Means-End Chains Data - collection and analysis Examples Exercise

3. Means-End Theory Framework to explain how : products provide consumers with personal benefits products assist them to realise personal values

4. Three levels of abstraction 

5. Means-end chain Links elements attributes, consequences and values Structure of chain associations between elements

6. Means-end chain value hierarchy terminal values instrumental values psychosocial consequences functional consequences abstract product attributes concrete product attributes Self-esteem self control social acceptance slimming fewer calories low fat

7. Laddering - data collection process Elicit product attributes influencing choice and differentiating between brands in a product category Series of why-questions why product attributes are important in reaching personal values

8. A possible laddering sequence Q: Why do you buy apples Q: Why is a crunchy taste important to you Q: Why is freshness important to you Q: Why is eating fresh foods every day important Q: Why is it important to stay healthy A: I like their crunchy taste A: I enjoy crunching it and I feel it indicates it is fresh Because I like to eat fresh foods every day Because I want to stay healthy Because I want to look after my kids at least until they are 19

9. Another laddering sequence Q: Why do you buy apples Q: Why is being British important to you Thinking about eating Cox apples - what is important to you Why is eating apples with a good flavour important to you A: Because I eat Cox apples and they are British Because I am proud to be British and we should not be importing apples I like their flavour Because it reminds me of my childhood when everything tasted better.

13. Forming a means-end chainCount number of times a connection was madeIn this table rows are lower element and columns are higher.

16. Role of Means-End theory in AdvertisingThe MECCAS modelMeans-End Conceptualisation of the Components of Advertising Strategy MEC values Consequences Attributes Driving Force Leverage Point Consumer benefit Message elements Executional framework

17. Opportunities M-E-C provides a tool to connect sensory properties with deeper reasons for purchase and enjoyment Means end chains in different ethnic groups to optimise segment promotion M-E-C in different cultures (Germany, UK) to get sensory and advertising connected

18. M-E-C assessment Data collection tool is rather imprecise (other methods have been proposed but not satisfactory) A need for more applications to test validity Appears useful in cross-cultural application

19. Summary Means-end chain analysis Cross-cultural meat preference Improving promotion campaigns

20. References Gutman, J. (1982) A means-end chain model based on consumer categorization processes. Journal of Marketing , 46, 60-72 Gutman, J. (1991) Exploring the nature of linkages between consequences and values. Journal of Business Research, 22, 143-149 Olson, J.C. (1989) Theoretical Foundations of means-end chains. Werbeforschung &Praxis, 5, 174-178 Reynolds, T.J. and Gutman, J. (1988) Laddering theory, method, analysis, and interpretation. Journal of Advertising Research, 11-31

21. Exercise Choose a product category (eg Coffee, Fruit or the packages) Do a laddering exercise on the product with your partner thinking about three examples of each category Try to note down the links At the end repeat with your self as the respondent Analyse the results for attributes, consequences and values

22. Multidimensional scaling and perceptual mapping. Hal MacFie Reading Agric Econ

23. Overview Hierarchy of methods Preference mapping - review Multidimensional scaling Correspondence Analysis

24. A hierarchy of methods

28. Tell me which one is coming next

29. To superimpose sensory attributes into plot calculate correlations with the two preference dimension scores Pref 1 Pref 2 Toughness -0.9 0.9 Juiciness 0.8 0.8 breaks easily 0.4 -0.7 Large bits -0.7 -0.4

30. Internal Preference Mapping: Summary Collect data from a single attribute -eg liking into a subjects by samples matrix Scale each row to zero mean and unit variance Do a PCA (eigenvalue analysis) Plot subjects and samples plots Superimpose sensory correlations

31. Preference mapping in SPSS

32. A hierarchy of methods

33. What is Multidimensional Scaling Technique to obtain maps of objects when only pairwise similarities (or distances) between objects are given Non-metric MDS can handle simply ranks of the similarities or distances. Technique produces 1,2,3,4 dimensional solutions and experimenter decides dimensionality

34. Simple example of distances between towns

37. Rankings of distances between towns

39. Example of use of Multidimensional scaling Market researcher has done pairwise preference tests on a round robin basis (eg all possible pairs given singly to say 40 families.)Wishes to obtain map.

40. Transform to a scale representing dissimilarity by expressing as absolute distance from 0.5

43. Ordinary Multidimensional scaling Recovers maps from distances Non-metric will handle ranks and other non-linear forms (ratios or counts) Can input variables as well as interdistances Dimensionality determined by inspection Can look at maps of cases as well as variables in SPSS

44. Individual Differences Scaling - Indscal Complete similarity sets from different consumers or groups Forms a map and gives weights for each individual on to each dimension calculated Similar to preference mapping but with difference data

45. Perception of electrical stimuli Created 9 electrical stimuli varying in strength and frequency 12 consumers scored each pair for similarity

46. Perception of electrical stimulieach person produces a 9 by 9 triangular matrix of pairwise distances

47. Perception of stimuli (Indscal ) analysis

48. Individual Differences Scaling Gives similar output to prefmap from distances Can be used in a non-metric version

49. A hierarchy of methods

50. Correspondence Analysis Used to relate rows and columns of a frequency table in a joint space. The nearer a row and a column are in the joint space the more they are associated with each other. Most often used for brands and attributes Uses similar mathematics to factor analysis

51. HATCO Correspondence example HATCO wished to identify the perceptions of itself and its 9 major competitors from representatives from 18 companies that represented their potential client base. One task for the clients was to pick any firms characterised by a particular attribute.

52. Cross-tabulated frequency data of attribute descriptors for HATCO and competing firms

54. Interpreting Correspondence Analysis Firms A,F,E, I & HATCO form a group associated with manufacturer�s image,delivery speed, price level and service The rest are rather scattered and not heavily associated with many attributes.

55. Correspondence Analysis Suitable for two way tables of frequencies A true bi-plot in the sense that nearness of rows and columns implies association More used in France than anywhere else.

56. A hierarchy of methods