Marketing Data Analysis

Transcription

1 Marketing Data Analysis Rian BEISE-ZEE From Marketing Metrics, 2nd Ed. by Paul W. Farris, et. al. (ISBN: ) Copyright 2012 Pearson Education, Inc. All rights reserved.

2 Perceptual Mapping

3 Positioning is establishing a specific customer perception about an existing product or brand.

4 Positioning Happens in the mind of the customers. We can position the same product differently. Defined relative to competitors, therefore can keep a product/service different from competitors products in the mind of the customer even if it is physically the same

5 Positioning Principles

6 Same Product - Different Perception

7 Mouthwash

8 Perceptual Mapping (or: Multidimensional Scaling) We can think of a variety of product attributes that possibly differentiate a product; however, consumers are unlikely to keep many attributes in mind. We can position a brand, but we don t know if consumers are really buying it. 1. Perceptual mapping is a tool to measure what dimensions consumers use to differentiate products. 2. For marketers two or three dimensions allow a graphic representation, which makes strategy building a lot easier (but it does not mean that the world is really that simple).

9 Perceptual Map The outcome of a multidimensional scale is called perceptual map, a visual representation which helps to derive strategic conclusions about: What main dimensions are used by consumers to compare Brands? (but not its importance!) Who are our competitors and how similar are they to our Brand? What product clusters do consumers perceive? Are there holes in the market that are not yet covered?

10 Example: Detergent

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13 Methods There are several methods Attribute-based: we start with many attributes and try to reduce them to 2 or 3 main dimensions (by means of factor analysis or discriminant analysis) Comparison-based: We start with comparisons between two brands and end up with interpreting a distance field

14 Attribute based MDS Rate each beer brand s association Heavy Action Good Value Budget Relaxation Popular with women Sporty Popular with men Premium Special Occasion Sex Appeal Not descriptive at all Totally descriptive

15 From Moore and Pessemier (1993).

16 Comparison-based MDS Beer Chang vs. Singha Beer Totally similar Extremely dissimilar

17 Expl: Similarity ratings of 4 Objects (for example products or companies) A B C D A B 3.2 C D

18 A Two Dimensional Solution of Empirical Distances

19 Dimensionality How many dimensions a perceptual map has depends on the complexity of the similarity data. Often, two dimensions are sufficient. More than three are difficult to interpret. But one dimension is also quite possible. Two dimensions are often selected because it looks better. The correct method to decide on the number of dimensions is the stress value and the scree plot.

20 Labeling the axes (= interpreting the dimensions) MDS only delivers the map but no clue about how to interpret the dimensions Based on individual judgment of the researcher, e.g. by known objective attributes Based on additional questions on the Brands attributes Based on objective vector algorithms with additional attribute information

21 Interpreting the Dimensions: Soft Drinks Example

22 Perceptual Map for Beer in Thailand

23 Interpreting the Dimensions

24 Problems with MDS Different methods lead to different maps (use several and analyze the differences) Different consumer groups have different perceptual maps (e.g. users/non-users) but MDS assumes homogeneity Different users consider different attributes and attach different importance to attributes Less than 4 objects per dimensions lead to misleading solutions but a large number of comparisons often overexert respondents.

25 Correspondence Analysis Another method to derive a perceptual map based on the relationship between objects (brands, products) and nominal attributes (gender, age group, lifestyle) A perceptual map can be derived from any cross-tabulation (frequency count)

26 Expl. Cross-tabulation Young adults (>35) Product Sales A B C Total Middle age (36-55) Mature (55+) Total

27 Process of Correspondence Analysis Expected cell count under independence = (column total/total * Row total/total)*total Chi-quare value for each cell = actual cell count expected cell count expected cell count Chi-sqare is used as a measure of association (similarity) between row and column : The greater chi-square the higher the similarity The more negative the higher the dissimilarity

28 Derived Perceptual Map

29 The Ford Ka

30 Which of these cars is more similar to the Ford Ka?

31 VW Polo

32 VW Polo Ad

33 VW Polo Ad

34 Renault Twingo Ads

35 Toyota Rav4

36 Fiat 500 (Cinquecento)

37 Opel Tigra

38 One Dimension Solution of all Respondents Object Points Common Space FIAT500 MICRA POLO P106CORSA FIESTA TWINGO RAV4 KA TIGRA Dimension 1

39 Dimension 2 Two dimension solution of all respondents Object Points Common Space FIAT500 MICRA CORSA TWINGO KA P106 FIESTA TIGRA -.4 POLO RAV Dimension 1

40 Dimension 2 Solution for Respondents with Ka Preference Object Points Common Space POLO P106 FIESTA RAV4 TIGRA MICRA -.4 FIAT CORSA 0.0 TWINGO.5 KA Dimension 1

41 Dimension 2 Solution for Respondents with low Ka-liking Object Points Common Space.8.6 KA.4 FIAT500 MICRA FIESTA TWINGO CORSA RAV4 TIGRA -.4 P106 POLO Dimension 1