1. Network tools for the analysis of brand image Simona Balbi, Germana Scepi, Agnieszka Stawinoga Università di Napoli “Federico II” EQ(CS)^2 – 2014 Conferenza italiana su eccellenza nella qualità, controllo statistico e customer satisfaction Torino, Campus Einaudi, 18-19 settembre 2014

2. In competitive markets, there is a complex relation between the "image" of a brand and the temporary message communicating the current "idea" Motivations

3.  Here we focus attention on the textual component of advertisement  As language can be modelled as networks of words, we propose a new tool for exploring advertising campaigns in the frame of textual network analysis  We analyse the evolution of the brand image through the different campaigns of a famous brand Aim

4. 164 advertisements: 1890 – 1909 6 1910 - 1919 5 1920 - 1929 17 1930 - 1939 30 1940 - 1949 34 1950 - 1959 16 1960 - 1969 18 1970 - 1979 16 1980 - 1989 6 1990 – 1999 9 Data: Data: advertisements of coca-cola 1890-2000

6. Pre-processing: Corpus: 164 advertisements and 1786 tokens  The corpus is normalized, and cleaned by stop words, showing 1267 types  Lemmatization of selected graphical forms ( 1060 types)  Removing Hapax (terms with occurrence =1) and the most frequent terms (Coca-cola, Coke, refresh, refreshment, pause, ice- cold, taste, delicious, drink) (600 types)  Creation of lexical table (460 terms x 164 documents) (boolean code)

7. BAG OF WORDS Coding system in TM Bag of words: Bag of words: a coding system where a text (such as a document) is represented as an unordered collection of words, disregarding grammar and even word order. LEXICAL TABLE

8. Lexical Table (Boolean code) TERMS LEXICAL TABLE CAN BE TREATED AS AN AFFILATION MATRIX The value of cells indicate the presence or absence of each word in each document. DOCUMENTS

9. From Lexical Table to one-mode network TERMSTERMS DOCUMENTS LEXICAL TABLE DOCUMENTSDOCUMENTS MATRIX “DOC x DOC” DOCUMENTS NORMALIZATION OF CO-OCCURRENCES JACCARD INDEX DOCUMENTS DOCUMENTSDOCUMENTS Jaccard index wkk - the total number of occurrences of object k, wkj - the number of co-occurrences of k and j S J (p xp)

10. According to the Jaccard distribution we choose the threshold value equal to 0.1 Each cells with values >=0.1 go to 1 otherwise go to 0 JACCARD INDEX DOCUMENTS DOCUMENTSDOCUMENTS One mode binary matrix DOCUMENTS DOCUMENTSDOCUMENTS DICOTOMIZATION GRAPH RAPRESENTATION From Lexical Table to one-mode network S J (p x p)A J (p x p)

11. where  E is the number of external ties (ties between nodes belonging to different groups)  I is the number of internal ties (ties between nodes belonging to the same group) This index measures the relative homophily of a group by comparing the numbers of ties within groups and between groups  The E–I index ranges from-1 (all ties are internal) to 1 (all ties are external)  The index can be calculated either for the whole network, for each group or for each individual node E-I index for analyzing homophily of the network When one or more attributes are associated to the network nodes, we can be interested in measuring the strenght of the relations between and within the elements in the groups defined by the attributes In literature, the homophily index (Krackhardt&Stern, 1988) has been proposed:

12. Whole network level E=5, I= 6 E-I=(5-6)/(5+6)= -0.09 Group network level Red: E=5, I= 6 E-I= (5-2)/(5+2)=0.43 Blue: E=5, I= 4 E-I=(5-4)/(5+4)=0.11 E-I index example

13.  With the aim of identifying periods connected with the general image of the brand and periods representing original themes, we use of the relative Krackhardt&Stern’s homophily index  Advertisements are described by the period, considered as a categorical variable associated to nodes. Our strategy Introduzione della nostra rete e attributi dei nodi decade Calcolo del indice nella rete osservata

14.  We compute 1000 values of the E–I index in order to obtain the average value  For each run of the procedure, the value of seed was changed by fixing the random number generator so as to avoid repetition of runs. The ratio between the observed value of the E–I index and its expected value is defined as E-I ratio varies from 0 to 2 A value higher than 1 indicates a common language in the different decades (“brand image”) while a value lower than 1 indicates the presence of different languages in the decades, above and beyond what would be expected by chance  A randomization test was performed with a view to both checking whether the observed value of the E–I index significantly differed from the expected value and avoiding that the values of the index may originate by chance  We randomize by reshuffling the decade of each advertisement. We repeat 1000 permutations Our strategy

15. Observed E–I indexE–I ratio Whole network0.170.66 (prox) 1890 – 19090.260.65 (prox) 1910 – 1919-0.60.21 (prox) 1920 – 1929-0.0240.50 (prox) 1930 – 19390.100.57 (prox) 1940 – 19490.240.64 (prox) 1950 – 19590.570.81 (prox) 1960 – 19690.170.60 (prox) 1970 – 1979-0.060.48 (prox) 1980 – 19890.750.90 (prox) 1990 - 19990.200.62 (prox) Summary results