Cluster Analysis

Chapter 9 Cluster Analysis Learning Objectives After reading this chapter you should understand: – The basic concepts of cluster analysis. – How basic cluster algorithms work. – How to compute simple clustering results manually. – The different types of clustering procedures. – The SPSS clustering outputs. Keywords Agglomerative and divisive clustering A Chebychev distance A City-block distance A Clustering variables A Dendrogram A Distance matrix A Euclidean distance A Hierarchical and partitioning methods A Icicle diagram A k-means A Matching coef? cients A Pro? ing clusters A Two-step clustering Are there any market segments where Web-enabled mobile telephony is taking off in different ways? To answer this question, Okazaki (2006) applies a twostep cluster analysis by identifying segments of Internet adopters in Japan. The ? ndings suggest that there are four clusters exhibiting distinct attitudes towards Web-enabled mobile telephony adoption. In terestingly, freelance, and highly educated professionals had the most negative perception of mobile Internet adoption, whereas clerical of? ce workers had the most positive perception.Furthermore, housewives and company executives also exhibited a positive attitude toward mobile Internet usage. Marketing managers can now use these results to better target speci? c customer segments via mobile Internet services. Introduction Grouping similar customers and products is a fundamental marketing activity. It is used, prominently, in market segmentation. As companies cannot connect with all their customers, they have to divide markets into groups of consumers, customers, or clients (called segments) with similar needs and wants.Firms can then target each of these segments by positioning themselves in a unique segment (such as Ferrari in the high-end sports car market). While market researchers often form E. Mooi and M. Sarstedt, A Concise Guide to Market Research, DOI 10. 1007/978-3-642-1 2541-6_9, # Springer-Verlag Berlin Heidelberg 2011 237 238 9 Cluster Analysis market segments based on practical grounds, industry practice and wisdom, cluster analysis allows segments to be formed that are based on data that are less dependent on subjectivity.The segmentation of customers is a standard application of cluster analysis, but it can also be used in different, sometimes rather exotic, contexts such as evaluating typical supermarket shopping paths (Larson et al. 2005) or deriving employers’ branding strategies (Moroko and Uncles 2009). Understanding Cluster Analysis Cluster analysis is a convenient method for identifying homogenous groups of objects called clusters. Objects (or cases, observations) in a speci? c cluster share many characteristics, but are very dissimilar to objects not belonging to that cluster.Let’s try to gain a basic understanding of the cluster analysis procedure by looking at a simple example. Imagine that you are interested in segment ing your customer base in order to better target them through, for example, pricing strategies. The ? rst step is to decide on the characteristics that you will use to segment your customers. In other words, you have to decide which clustering variables will be included in the analysis. For example, you may want to segment a market based on customers’ price consciousness (x) and brand loyalty (y).These two variables can be measured on a 7-point scale with higher values denoting a higher degree of price consciousness and brand loyalty. The values of seven respondents are shown in Table 9. 1 and the scatter plot in Fig. 9. 1. The objective of cluster analysis is to identify groups of objects (in this case, customers) that are very similar with regard to their price consciousness and brand loyalty and assign them into clusters. After having decided on the clustering variables (brand loyalty and price consciousness), we need to decide on the clustering procedure to form our group s of objects.This step is crucial for the analysis, as different procedures require different decisions prior to analysis. There is an abundance of different approaches and little guidance on which one to use in practice. We are going to discuss the most popular approaches in market research, as they can be easily computed using SPSS. These approaches are: hierarchical methods, partitioning methods (more precisely, k-means), and two-step clustering, which is largely a combination of the ? rst two methods.Each of these procedures follows a different approach to grouping the most similar objects into a cluster and to determining each object’s cluster membership. In other words, whereas an object in a certain cluster should be as similar as possible to all the other objects in the Table 9. 1 Data Customer x y A 3 7 B 6 7 C 5 6 D 3 5 E 6 5 F 4 3 G 1 2 Understanding Cluster Analysis 7 6 A C D E B 239 Brand loyalty (y) 5 4 3 2 1 0 0 1 2 G F 3 4 5 6 7 Price consciousness (x) Fig. 9. 1 Scatter plot same cluster, it should likewise be as distinct as possible from objects in different clusters. But how do we measure similarity?Some approaches – most notably hierarchical methods – require us to specify how similar or different objects are in order to identify different clusters. Most software packages calculate a measure of (dis)similarity by estimating the distance between pairs of objects. Objects with smaller distances between one another are more similar, whereas objects with larger distances are more dissimilar. An important problem in the application of cluster analysis is the decision regarding how many clusters should be derived from the data. This question is explored in the next step of the analysis.Sometimes, however, we already know the number of segments that have to be derived from the data. For example, if we were asked to ascertain what characteristics distinguish frequent shoppers from infrequent ones, we need to ? nd two different c lusters. However, we do not usually know the exact number of clusters and then we face a trade-off. On the one hand, you want as few clusters as possible to make them easy to understand and actionable. On the other hand, having many clusters allows you to identify more segments and more subtle differences between segments.In an extreme case, you can address each individual separately (called one-to-one marketing) to meet consumers’ varying needs in the best possible way. Examples of such a micro-marketing strategy are Puma’s Mongolian Shoe BBQ (www. mongolianshoebbq. puma. com) and Nike ID (http://nikeid. nike. com), in which customers can fully customize a pair of shoes in a hands-on, tactile, and interactive shoe-making experience. On the other hand, the costs associated with such a strategy may be prohibitively high in many 240 9 Cluster Analysis Decide on the clustering variables Decide on the clustering procedureHierarchical methods Select a measure of similarity or dissimilarity Partitioning methods Two-step clustering Select a measure of similarity or dissimilarity Choose a clustering algorithm Decide on the number of clusters Validate and interpret the cluster solution Fig. 9. 2 Steps in a cluster analysis business contexts. Thus, we have to ensure that the segments are large enough to make the targeted marketing programs pro? table. Consequently, we have to cope with a certain degree of within-cluster heterogeneity, which makes targeted marketing programs less effective.In the ? nal step, we need to interpret the solution by de? ning and labeling the obtained clusters. This can be done by examining the clustering variables’ mean values or by identifying explanatory variables to pro? le the clusters. Ultimately, managers should be able to identify customers in each segment on the basis of easily measurable variables. This ? nal step also requires us to assess the clustering solution’s stability and validity. Figure 9. 2 illu strates the steps associated with a cluster analysis; we will discuss these in more detail in the following sections.Conducting a Cluster Analysis Decide on the Clustering Variables At the beginning of the clustering process, we have to select appropriate variables for clustering. Even though this choice is of utmost importance, it is rarely treated as such and, instead, a mixture of intuition and data availability guide most analyses in marketing practice. However, faulty assumptions may lead to improper market Conducting a Cluster Analysis 241 segments and, consequently, to de? cient marketing strategies. Thus, great care should be taken when selecting the clustering variables. There are several types of clustering variables and these can be classi? d into general (independent of products, services or circumstances) and speci? c (related to both the customer and the product, service and/or particular circumstance), on the one hand, and observable (i. e. , measured directly) and un observable (i. e. , inferred) on the other. Table 9. 2 provides several types and examples of clustering variables. Table 9. 2 Types and examples of clustering variables General Observable (directly Cultural, geographic, demographic, measurable) socio-economic Unobservable Psychographics, values, personality, (inferred) lifestyle Adapted from Wedel and Kamakura (2000)Speci? c User status, usage frequency, store and brand loyalty Bene? ts, perceptions, attitudes, intentions, preferences The types of variables used for cluster analysis provide different segments and, thereby, in? uence segment-targeting strategies. Over the last decades, attention has shifted from more traditional general clustering variables towards product-speci? c unobservable variables. The latter generally provide better guidance for decisions on marketing instruments’ effective speci? cation. It is generally acknowledged that segments identi? ed by means of speci? unobservable variables are usually more h omogenous and their consumers respond consistently to marketing actions (see Wedel and Kamakura 2000). However, consumers in these segments are also frequently hard to identify from variables that are easily measured, such as demographics. Conversely, segments determined by means of generally observable variables usually stand out due to their identi? ability but often lack a unique response structure. 1 Consequently, researchers often combine different variables (e. g. , multiple lifestyle characteristics combined with demographic variables), bene? ing from each ones strengths. In some cases, the choice of clustering variables is apparent from the nature of the task at hand. For example, a managerial problem regarding corporate communications will have a fairly well de? ned set of clustering variables, including contenders such as awareness, attitudes, perceptions, and media habits. However, this is not always the case and researchers have to choose from a set of candidate variable s. Whichever clustering variables are chosen, it is important to select those that provide a clear-cut differentiation between the segments regarding a speci? c managerial objective. More precisely, criterion validity is of special interest; that is, the extent to which the â€Å"independent† clustering variables are associated with 1 2 See Wedel and Kamakura (2000). Tonks (2009) provides a discussion of segment design and the choice of clustering variables in consumer markets. 242 9 Cluster Analysis one or more â€Å"dependent† variables not included in the analysis. Given this relationship, there should be signi? cant differences between the â€Å"dependent† variable(s) across the clusters. These associations may or may not be causal, but it is essential that the clustering variables distinguish the â€Å"dependent† variable(s) signi? antly. Criterion variables usually relate to some aspect of behavior, such as purchase intention or usage frequency. Gen erally, you should avoid using an abundance of clustering variables, as this increases the odds that the variables are no longer dissimilar. If there is a high degree of collinearity between the variables, they are not suf? ciently unique to identify distinct market segments. If highly correlated variables are used for cluster analysis, speci? c aspects covered by these variables will be overrepresented in the clustering solution.In this regard, absolute correlations above 0. 90 are always problematic. For example, if we were to add another variable called brand preference to our analysis, it would virtually cover the same aspect as brand loyalty. Thus, the concept of being attached to a brand would be overrepresented in the analysis because the clustering procedure does not differentiate between the clustering variables in a conceptual sense. Researchers frequently handle this issue by applying cluster analysis to the observations’ factor scores derived from a previously car ried out factor analysis.However, according to Dolnicar and Grâ‚ ¬n u (2009), this factor-cluster segmentation approach can lead to several problems: 1. The data are pre-processed and the clusters are identi? ed on the basis of transformed values, not on the original information, which leads to different results. 2. In factor analysis, the factor solution does not explain a certain amount of variance; thus, information is discarded before segments have been identi? ed or constructed. 3. Eliminating variables with low loadings on all the extracted factors means that, potentially, the most important pieces of information for the identi? ation of niche segments are discarded, making it impossible to ever identify such groups. 4. The interpretations of clusters based on the original variables become questionable given that the segments have been constructed using factor scores. Several studies have shown that the factor-cluster segmentation signi? cantly reduces the success of segmen t recovery. 3 Consequently, you should rather reduce the number of items in the questionnaire’s pre-testing phase, retaining a reasonable number of relevant, non-redundant questions that you believe differentiate the segments well.However, if you have your doubts about the data structure, factorclustering segmentation may still be a better option than discarding items that may conceptually be necessary. Furthermore, we should keep the sample size in mind. First and foremost, this relates to issues of managerial relevance as segments’ sizes need to be substantial to ensure that targeted marketing programs are pro? table. From a statistical perspective, every additional variable requires an over-proportional increase in 3 See the studies by Arabie and Hubert (1994), Sheppard (1996), or Dolnicar and Grâ‚ ¬n (2009). uConducting a Cluster Analysis 243 observations to ensure valid results. Unfortunately, there is no generally accepted rule of thumb regarding minimum sampl e sizes or the relationship between the objects and the number of clustering variables used. In a related methodological context, Formann (1984) recommends a sample size of at least 2m, where m equals the number of clustering variables. This can only provide rough guidance; nevertheless, we should pay attention to the relationship between the objects and clustering variables. It does not, for example, appear logical to cluster ten objects using ten variables.Keep in mind that no matter how many variables are used and no matter how small the sample size, cluster analysis will always render a result! Ultimately, the choice of clustering variables always depends on contextual in? uences such as data availability or resources to acquire additional data. Marketing researchers often overlook the fact that the choice of clustering variables is closely connected to data quality. Only those variables that ensure that high quality data can be used should be included in the analysis. This is v ery important if a segmentation solution has to be managerially useful.Furthermore, data are of high quality if the questions asked have a strong theoretical basis, are not contaminated by respondent fatigue or response styles, are recent, and thus re? ect the current market situation (Dolnicar and Lazarevski 2009). Lastly, the requirements of other managerial functions within the organization often play a major role. Sales and distribution may as well have a major in? uence on the design of market segments. Consequently, we have to be aware that subjectivity and common sense agreement will (and should) always impact the choice of clustering variables.Decide on the Clustering Procedure By choosing a speci? c clustering procedure, we determine how clusters are to be formed. This always involves optimizing some kind of criterion, such as minimizing the within-cluster variance (i. e. , the clustering variables’ overall variance of objects in a speci? c cluster), or maximizing th e distance between the objects or clusters. The procedure could also address the question of how to determine the (dis)similarity between objects in a newly formed cluster and the remaining objects in the dataset.There are many different clustering procedures and also many ways of classifying these (e. g. , overlapping versus non-overlapping, unimodal versus multimodal, exhaustive versus non-exhaustive). 4 A practical distinction is the differentiation between hierarchical and partitioning methods (most notably the k-means procedure), which we are going to discuss in the next sections. We also introduce two-step clustering, which combines the principles of hierarchical and partitioning methods and which has recently gained increasing attention from market research practice.See Wedel and Kamakura (2000), Dolnicar (2003), and Kaufman and Rousseeuw (2005) for a review of clustering techniques. 4 244 9 Cluster Analysis Hierarchical Methods Hierarchical clustering procedures are characte rized by the tree-like structure established in the course of the analysis. Most hierarchical techniques fall into a category called agglomerative clustering. In this category, clusters are consecutively formed from objects. Initially, this type of procedure starts with each object representing an individual cluster.These clusters are then sequentially merged according to their similarity. First, the two most similar clusters (i. e. , those with the smallest distance between them) are merged to form a new cluster at the bottom of the hierarchy. In the next step, another pair of clusters is merged and linked to a higher level of the hierarchy, and so on. This allows a hierarchy of clusters to be established from the bottom up. In Fig. 9. 3 (left-hand side), we show how agglomerative clustering assigns additional objects to clusters as the cluster size increases. Step 5 Step 1 A, B, C, D, EAgglomerative clustering Step 4 Step 2 Divisive clustering A, B C, D, E Step 3 Step 3 A, B C, D E Step 2 Step 4 A, B C D E Step 1 Step 5 A B C D E Fig. 9. 3 Agglomerative and divisive clustering A cluster hierarchy can also be generated top-down. In this divisive clustering, all objects are initially merged into a single cluster, which is then gradually split up. Figure 9. 3 illustrates this concept (right-hand side). As we can see, in both agglomerative and divisive clustering, a cluster on a higher level of the hierarchy always encompasses all clusters from a lower level.This means that if an object is assigned to a certain cluster, there is no possibility of reassigning this object to another cluster. This is an important distinction between these types of clustering and partitioning methods such as k-means, which we will explore in the next section. Divisive procedures are quite rarely used in market research. We therefore concentrate on the agglomerative clustering procedures. There are various types Conducting a Cluster Analysis 245 of agglomerative procedures. However, before we discuss these, we need to de? ne how similarities or dissimilarities are measured between pairs of objects.Select a Measure of Similarity or Dissimilarity There are various measures to express (dis)similarity between pairs of objects. A straightforward way to assess two objects’ proximity is by drawing a straight line between them. For example, when we look at the scatter plot in Fig. 9. 1, we can easily see that the length of the line connecting observations B and C is much shorter than the line connecting B and G. This type of distance is also referred to as Euclidean distance (or straight-line distance) and is the most commonly used type when it comes to analyzing ratio or interval-scaled data. In our example, we have ordinal data, but market researchers usually treat ordinal data as metric data to calculate distance metrics by assuming that the scale steps are equidistant (very much like in factor analysis, which we discussed in Chap. 8). To use a hierarchical c lustering procedure, we need to express these distances mathematically. By taking the data in Table 9. 1 into consideration, we can easily compute the Euclidean distance between customer B and customer C (generally referred to as d(B,C)) with regard to the two variables x and y by using the following formula: q Euclidean ? B; C? ? ? xB A xC ? 2 ? ?yB A yC ? 2 The Euclidean distance is the square root of the sum of the squared differences in the variables’ values. Using the data from Table 9. 1, we obtain the following: q p dEuclidean ? B; C? ? ? 6 A 5? 2 ? ?7 A 6? 2 ? 2 ? 1:414 This distance corresponds to the length of the line that connects objects B and C. In this case, we only used two variables but we can easily add more under the root sign in the formula. However, each additional variable will add a dimension to our research problem (e. . , with six clustering variables, we have to deal with six dimensions), making it impossible to represent the solution graphically. Si milarly, we can compute the distance between customer B and G, which yields the following: q p dEuclidean ? B; G? ? ? 6 A 1? 2 ? ?7 A 2? 2 ? 50 ? 7:071 Likewise, we can compute the distance between all other pairs of objects. All these distances are usually expressed by means of a distance matrix. In this distance matrix, the non-diagonal elements express the distances between pairs of objects 5Note that researchers also often use the squared Euclidean distance. 246 9 Cluster Analysis and zeros on the diagonal (the distance from each object to itself is, of course, 0). In our example, the distance matrix is an 8 A 8 table with the lines and rows representing the objects (i. e. , customers) under consideration (see Table 9. 3). As the distance between objects B and C (in this case 1. 414 units) is the same as between C and B, the distance matrix is symmetrical. Furthermore, since the distance between an object and itself is zero, one need only look at either the lower or upper non-di agonal elements.Table 9. 3 Euclidean distance matrix Objects A B A 0 B 3 0 C 2. 236 1. 414 D 2 3. 606 E 3. 606 2 F 4. 123 4. 472 G 5. 385 7. 071 C D E F G 0 2. 236 1. 414 3. 162 5. 657 0 3 2. 236 3. 606 0 2. 828 5. 831 0 3. 162 0 There are also alternative distance measures: The city-block distance uses the sum of the variables’ absolute differences. This is often called the Manhattan metric as it is akin to the walking distance between two points in a city like New York’s Manhattan district, where the distance equals the number of blocks in the directions North-South and East-West.Using the city-block distance to compute the distance between customers B and C (or C and B) yields the following: dCityAblock ? B; C? ? jxB A xC j ? jyB A yC j ? j6 A 5j ? j7 A 6j ? 2 The resulting distance matrix is in Table 9. 4. Table 9. 4 City-block distance matrix Objects A B A 0 B 3 0 C 3 2 D 2 5 E 5 2 F 5 6 G 7 10 C D E F G 0 3 2 4 8 0 3 3 5 0 4 8 0 4 0 Lastly, when working with metr ic (or ordinal) data, researchers frequently use the Chebychev distance, which is the maximum of the absolute difference in the clustering variables’ values. In respect of customers B and C, this result is: dChebychec ? B; C? max? jxB A xC j; jyB A yC j? ? max? j6 A 5j; j7 A 6j? ? 1 Figure 9. 4 illustrates the interrelation between these three distance measures regarding two objects, C and G, from our example. Conducting a Cluster Analysis 247 C Brand loyalty (y) Euclidean distance City-block distance G Chebychev distance Price consciousness (x) Fig. 9. 4 Distance measures There are other distance measures such as the Angular, Canberra or Mahalanobis distance. In many situations, the latter is desirable as it compensates for collinearity between the clustering variables. However, it is (unfortunately) not menu-accessible in SPSS.In many analysis tasks, the variables under consideration are measured on different scales or levels. This would be the case if we extended our set o f clustering variables by adding another ordinal variable representing the customers’ income measured by means of, for example, 15 categories. Since the absolute variation of the income variable would be much greater than the variation of the remaining two variables (remember, that x and y are measured on 7-point scales), this would clearly distort our analysis results. We can resolve this problem by standardizing the data prior to the analysis.Different standardization methods are available, such as the simple z standardization, which rescales each variable to have a mean of 0 and a standard deviation of 1 (see Chap. 5). In most situations, however, standardization by range (e. g. , to a range of 0 to 1 or A1 to 1) performs better. 6 We recommend standardizing the data in general, even though this procedure can reduce or in? ate the variables’ in? uence on the clustering solution. 6 See Milligan and Cooper (1988). 248 9 Cluster Analysis Another way of (implicitly) sta ndardizing the data is by using the correlation between the objects instead of distance measures.For example, suppose a respondent rated price consciousness 2 and brand loyalty 3. Now suppose a second respondent indicated 5 and 6, whereas a third rated these variables 3 and 3. Euclidean, city-block, and Chebychev distances would indicate that the ? rst respondent is more similar to the third than to the second. Nevertheless, one could convincingly argue that the ? rst respondent’s ratings are more similar to the second’s, as both rate brand loyalty higher than price consciousness. This can be accounted for by computing the correlation between two vectors of values as a measure of similarity (i. . , high correlation coef? cients indicate a high degree of similarity). Consequently, similarity is no longer de? ned by means of the difference between the answer categories but by means of the similarity of the answering pro? les. Using correlation is also a way of standardiz ing the data implicitly. Whether you use correlation or one of the distance measures depends on whether you think the relative magnitude of the variables within an object (which favors correlation) matters more than the relative magnitude of each variable across objects (which favors distance).However, it is generally recommended that one uses correlations when applying clustering procedures that are susceptible to outliers, such as complete linkage, average linkage or centroid (see next section). Whereas the distance measures presented thus far can be used for metrically and – in general – ordinally scaled data, applying them to nominal or binary data is meaningless. In this type of analysis, you should rather select a similarity measure expressing the degree to which variables’ values share the same category. These socalled matching coef? ients can take different forms but rely on the same allocation scheme shown in Table 9. 5. Table 9. 5 Allocation scheme for matching coef? cients Number of variables with category 1 a c Object 1 Number of variables with category 2 b d Object 2 Number of variables with category 1 Number of variables with category 2 Based on the allocation scheme in Table 9. 5, we can compute different matching coef? cients, such as the simple matching coef? cient (SM): SM ? a? d a? b? c? d This coef? cient is useful when both positive and negative values carry an equal degree of information.For example, gender is a symmetrical attribute because the number of males and females provides an equal degree of information. Conducting a Cluster Analysis 249 Let’s take a look at an example by assuming that we have a dataset with three binary variables: gender (male ? 1, female ? 2), customer (customer ? 1, noncustomer ? 2), and disposable income (low ? 1, high ? 2). The ? rst object is a male non-customer with a high disposable income, whereas the second object is a female non-customer with a high disposable income. Accord ing to the scheme in Table 9. , a ? b ? 0, c ? 1 and d ? 2, with the simple matching coef? cient taking a value of 0. 667. Two other types of matching coef? cients, which do not equate the joint absence of a characteristic with similarity and may, therefore, be of more value in segmentation studies, are the Jaccard (JC) and the Russel and Rao (RR) coef? cients. They are de? ned as follows: a JC ? a? b? c a RR ? a? b? c? d These matching coef? cients are – just like the distance measures – used to determine a cluster solution. There are many other matching coef? ients such as Yule’s Q, Kulczynski or Ochiai, but since most applications of cluster analysis rely on metric or ordinal data, we will not discuss these in greater detail. 7 For nominal variables with more than two categories, you should always convert the categorical variable into a set of binary variables in order to use matching coef? cients. When you have ordinal data, you should always use distance me asures such as Euclidean distance. Even though using matching coef? cients would be feasible and – from a strictly statistical standpoint – even more appropriate, you would disregard variable information in the sequence of the categories.In the end, a respondent who indicates that he or she is very loyal to a brand is going to be closer to someone who is somewhat loyal than a respondent who is not loyal at all. Furthermore, distance measures best represent the concept of proximity, which is fundamental to cluster analysis. Most datasets contain variables that are measured on multiple scales. For example, a market research questionnaire may ask about the respondent’s income, product ratings, and last brand purchased. Thus, we have to consider variables measured on a ratio, ordinal, and nominal scale. How can we simultaneously incorporate these variables into one analysis?Unfortunately, this problem cannot be easily resolved and, in fact, many market researchers s imply ignore the scale level. Instead, they use one of the distance measures discussed in the context of metric (and ordinal) data. Even though this approach may slightly change the results when compared to those using matching coef? cients, it should not be rejected. Cluster analysis is mostly an exploratory technique whose results provide a rough guidance for managerial decisions. Despite this, there are several procedures that allow a simultaneous integration of these variables into one analysis. 7See Wedel and Kamakura (2000) for more information on alternative matching coef? cients. 250 9 Cluster Analysis First, we could compute distinct distance matrices for each group of variables; that is, one distance matrix based on, for example, ordinally scaled variables and another based on nominal variables. Afterwards, we can simply compute the weighted arithmetic mean of the distances and use this average distance matrix as the input for the cluster analysis. However, the weights hav e to be determined a priori and improper weights may result in a biased treatment of different variable types.Furthermore, the computation and handling of distance matrices are not trivial. Using the SPSS syntax, one has to manually add the MATRIX subcommand, which exports the initial distance matrix into a new data ? le. Go to the 8 Web Appendix (! Chap. 5) to learn how to modify the SPSS syntax accordingly. Second, we could dichotomize all variables and apply the matching coef? cients discussed above. In the case of metric variables, this would involve specifying categories (e. g. , low, medium, and high income) and converting these into sets of binary variables. In most cases, however, the speci? ation of categories would be rather arbitrary and, as mentioned earlier, this procedure could lead to a severe loss of information. In the light of these issues, you should avoid combining metric and nominal variables in a single cluster analysis, but if this is not feasible, the two-ste p clustering procedure provides a valuable alternative, which we will discuss later. Lastly, the choice of the (dis)similarity measure is not extremely critical to recovering the underlying cluster structure. In this regard, the choice of the clustering algorithm is far more important.We therefore deal with this aspect in the following section. Select a Clustering Algorithm After having chosen the distance or similarity measure, we need to decide which clustering algorithm to apply. There are several agglomerative procedures and they can be distinguished by the way they de? ne the distance from a newly formed cluster to a certain object, or to other clusters in the solution. The most popular agglomerative clustering procedures include the following: l l l l Single linkage (nearest neighbor): The distance between two clusters corresponds to the shortest distance between any two members in the two clusters.Complete linkage (furthest neighbor): The oppositional approach to single linka ge assumes that the distance between two clusters is based on the longest distance between any two members in the two clusters. Average linkage: The distance between two clusters is de? ned as the average distance between all pairs of the two clusters’ members. Centroid: In this approach, the geometric center (centroid) of each cluster is computed ? rst. The distance between the two clusters equals the distance between the two centroids. Figures 9. 5–9. 8 illustrate these linkage procedures for two randomly framed clusters.Conducting a Cluster Analysis Fig. 9. 5 Single linkage 251 Fig. 9. 6 Complete linkage Fig. 9. 7 Average linkage Fig. 9. 8 Centroid 252 9 Cluster Analysis Each of these linkage algorithms can yield totally different results when used on the same dataset, as each has its speci? c properties. As the single linkage algorithm is based on minimum distances, it tends to form one large cluster with the other clusters containing only one or few objects each. We can make use of this â€Å"chaining effect† to detect outliers, as these will be merged with the remaining objects – usually at very large distances – in the last steps of the analysis.Generally, single linkage is considered the most versatile algorithm. Conversely, the complete linkage method is strongly affected by outliers, as it is based on maximum distances. Clusters produced by this method are likely to be rather compact and tightly clustered. The average linkage and centroid algorithms tend to produce clusters with rather low within-cluster variance and similar sizes. However, both procedures are affected by outliers, though not as much as complete linkage. Another commonly used approach in hierarchical clustering is Ward’s method. This approach does not combine the two most similar objects successively.Instead, those objects whose merger increases the overall within-cluster variance to the smallest possible degree, are combined. If you expect s omewhat equally sized clusters and the dataset does not include outliers, you should always use Ward’s method. To better understand how a clustering algorithm works, let’s manually examine some of the single linkage procedure’s calculation steps. We start off by looking at the initial (Euclidean) distance matrix in Table 9. 3. In the very ? rst step, the two objects exhibiting the smallest distance in the matrix are merged.Note that we always merge those objects with the smallest distance, regardless of the clustering procedure (e. g. , single or complete linkage). As we can see, this happens to two pairs of objects, namely B and C (d(B, C) ? 1. 414), as well as C and E (d(C, E) ? 1. 414). In the next step, we will see that it does not make any difference whether we ? rst merge the one or the other, so let’s proceed by forming a new cluster, using objects B and C. Having made this decision, we then form a new distance matrix by considering the single link age decision rule as discussed above.According to this rule, the distance from, for example, object A to the newly formed cluster is the minimum of d(A, B) and d(A, C). As d(A, C) is smaller than d(A, B), the distance from A to the newly formed cluster is equal to d(A, C); that is, 2. 236. We also compute the distances from cluster [B,C] (clusters are indicated by means of squared brackets) to all other objects (i. e. D, E, F, G) and simply copy the remaining distances – such as d(E, F) – that the previous clustering has not affected. This yields the distance matrix shown in Table 9. 6.Continuing the clustering procedure, we simply repeat the last step by merging the objects in the new distance matrix that exhibit the smallest distance (in this case, the newly formed cluster [B, C] and object E) and calculate the distance from this cluster to all other objects. The result of this step is described in Table 9. 7. Try to calculate the remaining steps yourself and compare your solution with the distance matrices in the following Tables 9. 8–9. 10. Conducting a Cluster Analysis Table 9. 6 Distance matrix after ? rst clustering step (single linkage) Objects A B, C D E F G A 0 B, C 2. 36 0 D 2 2. 236 0 E 3. 606 1. 414 3 0 F 4. 123 3. 162 2. 236 2. 828 0 G 5. 385 5. 657 3. 606 5. 831 3. 162 0 253 Table 9. 7 Distance matrix after second clustering step (single linkage) Objects A B, C, E D F G A 0 B, C, E 2. 236 0 D 2 2. 236 0 F 4. 123 2. 828 2. 236 0 G 5. 385 5. 657 3. 606 3. 162 0 Table 9. 8 Distance matrix after third clustering step (single linkage) Objects A, D B, C, E F G A, D 0 B, C, E 2. 236 0 F 2. 236 2. 828 0 G 3. 606 5. 657 3. 162 0 Table 9. 9 Distance matrix after fourth clustering step (single linkage) Objects A, B, C, D, E F G A, B, C, D, E 0 F 2. 236 0 G 3. 06 3. 162 0 Table 9. 10 Distance matrix after ? fth clustering step (single linkage) Objects A, B, C, D, E, F G A, B, C, D, E, F 0 G 3. 162 0 By following the single linkage proce dure, the last steps involve the merger of cluster [A,B,C,D,E,F] and object G at a distance of 3. 162. Do you get the same results? As you can see, conducting a basic cluster analysis manually is not that hard at all – not if there are only a few objects in the dataset. A common way to visualize the cluster analysis’s progress is by drawing a dendrogram, which displays the distance level at which there was a ombination of objects and clusters (Fig. 9. 9). We read the dendrogram from left to right to see at which distance objects have been combined. For example, according to our calculations above, objects B, C, and E are combined at a distance level of 1. 414. 254 B C E A D F G 9 Cluster Analysis 0 1 2 Distance 3 Fig. 9. 9 Dendrogram Decide on the Number of Clusters An important question we haven’t yet addressed is how to decide on the number of clusters to retain from the data. Unfortunately, hierarchical methods provide only very limited guidance for making th is decision.The only meaningful indicator relates to the distances at which the objects are combined. Similar to factor analysis’s scree plot, we can seek a solution in which an additional combination of clusters or objects would occur at a greatly increased distance. This raises the issue of what a great distance is, of course. One potential way to solve this problem is to plot the number of clusters on the x-axis (starting with the one-cluster solution at the very left) against the distance at which objects or clusters are combined on the y-axis.Using this plot, we then search for the distinctive break (elbow). SPSS does not produce this plot automatically – you have to use the distances provided by SPSS to draw a line chart by using a common spreadsheet program such as Microsoft Excel. Alternatively, we can make use of the dendrogram which essentially carries the same information. SPSS provides a dendrogram; however, this differs slightly from the one presented in F ig. 9. 9. Speci? cally, SPSS rescales the distances to a range of 0–25; that is, the last merging step to a one-cluster solution takes place at a (rescaled) distance of 25.The rescaling often lengthens the merging steps, thus making breaks occurring at a greatly increased distance level more obvious. Despite this, this distance-based decision rule does not work very well in all cases. It is often dif? cult to identify where the break actually occurs. This is also the case in our example above. By looking at the dendrogram, we could justify a two-cluster solution ([A,B,C,D,E,F] and [G]), as well as a ? ve-cluster solution ([B,C,E], [A], [D], [F], [G]). Conducting a Cluster Analysis 255 Research has suggested several other procedures for determining the number of clusters in a dataset.Most notably, the variance ratio criterion (VRC) by Calinski and Harabasz (1974) has proven to work well in many situations. 8 For a solution with n objects and k segments, the criterion is given by: VRCk ? ?SSB =? k A 1 =? SSW =? n A k ; where SSB is the sum of the squares between the segments and SSW is the sum of the squares within the segments. The criterion should seem familiar, as this is nothing but the F-value of a one-way ANOVA, with k representing the factor levels. Consequently, the VRC can easily be computed using SPSS, even though it is not readily available in the clustering procedures’ outputs.To ? nally determine the appropriate number of segments, we compute ok for each segment solution as follows: ok ? ?VRCk? 1 A VRCk ? A ? VRCk A VRCkA1 ? : In the next step, we choose the number of segments k that minimizes the value in ok. Owing to the term VRCkA1, the minimum number of clusters that can be selected is three, which is a clear disadvantage of the criterion, thus limiting its application in practice. Overall, the data can often only provide rough guidance regarding the number of clusters you should select; consequently, you should rather revert to pr actical considerations.Occasionally, you might have a priori knowledge, or a theory on which you can base your choice. However, ? rst and foremost, you should ensure that your results are interpretable and meaningful. Not only must the number of clusters be small enough to ensure manageability, but each segment should also be large enough to warrant strategic attention. Partitioning Methods: k-means Another important group of clustering procedures are partitioning methods. As with hierarchical clustering, there is a wide array of different algorithms; of these, the k-means procedure is the most important one for market research. The k-means algorithm follows an entirely different concept than the hierarchical methods discussed before. This algorithm is not based on distance measures such as Euclidean distance or city-block distance, but uses the within-cluster variation as a Milligan and Cooper (1985) compare various criteria. Note that the k-means algorithm is one of the simplest n on-hierarchical clustering methods. Several extensions, such as k-medoids (Kaufman and Rousseeuw 2005) have been proposed to handle problematic aspects of the procedure. More advanced methods include ? ite mixture models (McLachlan and Peel 2000), neural networks (Bishop 2006), and self-organizing maps (Kohonen 1982). Andrews and Currim (2003) discuss the validity of some of these approaches. 9 8 256 9 Cluster Analysis measure to form homogenous clusters. Speci? cally, the procedure aims at segmenting the data in such a way that the within-cluster variation is minimized. Consequently, we do not need to decide on a distance measure in the ? rst step of the analysis. The clustering process starts by randomly assigning objects to a number of clusters. 0 The objects are then successively reassigned to other clusters to minimize the within-cluster variation, which is basically the (squared) distance from each observation to the center of the associated cluster. If the reallocation of an object to another cluster decreases the within-cluster variation, this object is reassigned to that cluster. With the hierarchical methods, an object remains in a cluster once it is assigned to it, but with k-means, cluster af? liations can change in the course of the clustering process. Consequently, k-means does not build a hierarchy as described before (Fig. . 3), which is why the approach is also frequently labeled as non-hierarchical. For a better understanding of the approach, let’s take a look at how it works in practice. Figs. 9. 10–9. 13 illustrate the k-means clustering process. Prior to analysis, we have to decide on the number of clusters. Our client could, for example, tell us how many segments are needed, or we may know from previous research what to look for. Based on this information, the algorithm randomly selects a center for each cluster (step 1). In our example, two cluster centers are randomly initiated, which CC1 (? st cluster) and CC2 (second clu ster) in Fig. 9. 10 A CC1 C B D E Brand loyalty (y) CC2 F G Price consciousness (x) Fig. 9. 10 k-means procedure (step 1) 10 Note this holds for the algorithms original design. SPSS does not choose centers randomly. Conducting a Cluster Analysis A CC1 C B 257 D E Brand loyalty (y) CC2 F G Price consciousness (x) Fig. 9. 11 k-means procedure (step 2) A CC1 CC1? C B Brand loyalty (y) D E CC2 CC2? F G Price consciousness (x) Fig. 9. 12 k-means procedure (step 3) 258 A CC1? 9 Cluster Analysis B C Brand loyalty (y) D E CC2? F G Price consciousness (x) Fig. 9. 13 k-means procedure (step 4) epresent. 11 After this (step 2), Euclidean distances are computed from the cluster centers to every single object. Each object is then assigned to the cluster center with the shortest distance to it. In our example (Fig. 9. 11), objects A, B, and C are assigned to the ? rst cluster, whereas objects D, E, F, and G are assigned to the second. We now have our initial partitioning of the objects into two c lusters. Based on this initial partition, each cluster’s geometric center (i. e. , its centroid) is computed (third step). This is done by computing the mean values of the objects contained in the cluster (e. . , A, B, C in the ? rst cluster) regarding each of the variables (price consciousness and brand loyalty). As we can see in Fig. 9. 12, both clusters’ centers now shift into new positions (CC1’ for the ? rst and CC2’ for the second cluster). In the fourth step, the distances from each object to the newly located cluster centers are computed and objects are again assigned to a certain cluster on the basis of their minimum distance to other cluster centers (CC1’ and CC2’). Since the cluster centers’ position changed with respect to the initial situation in the ? st step, this could lead to a different cluster solution. This is also true of our example, as object E is now – unlike in the initial partition – closer to t he ? rst cluster center (CC1’) than to the second (CC2’). Consequently, this object is now assigned to the ? rst cluster (Fig. 9. 13). The k-means procedure now repeats the third step and re-computes the cluster centers of the newly formed clusters, and so on. In other 11 Conversely, SPSS always sets one observation as the cluster center instead of picking some random point in the dataset. Conducting a Cluster Analysis 59 words, steps 3 and 4 are repeated until a predetermined number of iterations are reached, or convergence is achieved (i. e. , there is no change in the cluster af? liations). Generally, k-means is superior to hierarchical methods as it is less affected by outliers and the presence of irrelevant clustering variables. Furthermore, k-means can be applied to very large datasets, as the procedure is less computationally demanding than hierarchical methods. In fact, we suggest de? nitely using k-means for sample sizes above 500, especially if many clusterin g variables are used.From a strictly statistical viewpoint, k-means should only be used on interval or ratioscaled data as the procedure relies on Euclidean distances. However, the procedure is routinely used on ordinal data as well, even though there might be some distortions. One problem associated with the application of k-means relates to the fact that the researcher has to pre-specify the number of clusters to retain from the data. This makes k-means less attractive to some and still hinders its routine application in practice. However, the VRC discussed above can likewise be used for k-means clustering an application of this index can be found in the 8 Web Appendix ! Chap. 9). Another workaround that many market researchers routinely use is to apply a hierarchical procedure to determine the number of clusters and k-means afterwards. 12 This also enables the user to ? nd starting values for the initial cluster centers to handle a second problem, which relates to the procedureâ €™s sensitivity to the initial classi? cation (we will follow this approach in the example application). Two-Step Clustering We have already discussed the issue of analyzing mixed variables measured on different scale levels in this chapter.The two-step cluster analysis developed by Chiu et al. (2001) has been speci? cally designed to handle this problem. Like k-means, the procedure can also effectively cope with very large datasets. The name two-step clustering is already an indication that the algorithm is based on a two-stage approach: In the ? rst stage, the algorithm undertakes a procedure that is very similar to the k-means algorithm. Based on these results, the two-step procedure conducts a modi? ed hierarchical agglomerative clustering procedure that combines the objects sequentially to form homogenous clusters.This is done by building a so-called cluster feature tree whose â€Å"leaves† represent distinct objects in the dataset. The procedure can handle categoric al and continuous variables simultaneously and offers the user the ? exibility to specify the cluster numbers as well as the maximum number of clusters, or to allow the technique to automatically choose the number of clusters on the basis of statistical evaluation criteria. Likewise, the procedure guides the decision of how many clusters to retain from the data by calculating measures-of-? t such as Akaike’s Information Criterion (AIC) or Bayes 2 See Punji and Stewart (1983) for additional information on this sequential approach. 260 9 Cluster Analysis Information Criterion (BIC). Furthermore, the procedure indicates each variable’s importance for the construction of a speci? c cluster. These desirable features make the somewhat less popular two-step clustering a viable alternative to the traditional methods. You can ? nd a more detailed discussion of the two-step clustering procedure in the 8 Web Appendix (! Chap. 9), but we will also apply this method in the subseque nt example.Validate and Interpret the Cluster Solution Before interpreting the cluster solution, we have to assess the solution’s stability and validity. Stability is evaluated by using different clustering procedures on the same data and testing whether these yield the same results. In hierarchical clustering, you can likewise use different distance measures. However, please note that it is common for results to change even when your solution is adequate. How much variation you should allow before questioning the stability of your solution is a matter of taste.Another common approach is to split the dataset into two halves and to thereafter analyze the two subsets separately using the same parameter settings. You then compare the two solutions’ cluster centroids. If these do not differ signi? cantly, you can presume that the overall solution has a high degree of stability. When using hierarchical clustering, it is also worthwhile changing the order of the objects in y our dataset and re-running the analysis to check the results’ stability. The results should not, of course, depend on the order of the dataset. If they do, you should try to ascertain if any obvious outliers may in? ence the results of the change in order. Assessing the solution’s reliability is closely related to the above, as reliability refers to the degree to which the solution is stable over time. If segments quickly change their composition, or its members their behavior, targeting strategies are likely not to succeed. Therefore, a certain degree of stability is necessary to ensure that marketing strategies can be implemented and produce adequate results. This can be evaluated by critically revisiting and replicating the clustering results at a later point in time. To validate the clustering solution, we need to assess its criterion validity.In research, we could focus on criterion variables that have a theoretically based relationship with the clustering variabl es, but were not included in the analysis. In market research, criterion variables usually relate to managerial outcomes such as the sales per person, or satisfaction. If these criterion variables differ signi? cantly, we can conclude that the clusters are distinct groups with criterion validity. To judge validity, you should also assess face validity and, if possible, expert validity. While we primarily consider criterion validity when choosing clustering variables, as well as in this ? al step of the analysis procedure, the assessment of face validity is a process rather than a single event. The key to successful segmentation is to critically revisit the results of different cluster analysis set-ups (e. g. , by using Conducting a Cluster Analysis 261 different algorithms on the same data) in terms of managerial relevance. This underlines the exploratory character of the method. The following criteria will help you make an evaluation choice for a clustering solution (Dibb 1999; Ton ks 2009; Kotler and Keller 2009). l l l l l l l l l l Substantial: The segments are large and pro? able enough to serve. Accessible: The segments can be effectively reached and served, which requires them to be characterized by means of observable variables. Differentiable: The segments can be distinguished conceptually and respond differently to different marketing-mix elements and programs. Actionable: Effective programs can be formulated to attract and serve the segments. Stable: Only segments that are stable over time can provide the necessary grounds for a successful marketing strategy. Parsimonious: To be managerially meaningful, only a small set of substantial clusters should be identi? ed.Familiar: To ensure management acceptance, the segments composition should be comprehensible. Relevant: Segments should be relevant in respect of the company’s competencies and objectives. Compactness: Segments exhibit a high degree of within-segment homogeneity and between-segment h eterogeneity. Compatibility: Segmentation results meet other managerial functions’ requirements. The ? nal step of any cluster analysis is the interpretation of the clusters. Interpreting clusters always involves examining the cluster centroids, which are the clustering variables’ average values of all objects in a certain cluster.This step is of the utmost importance, as the analysis sheds light on whether the segments are conceptually distinguishable. Only if certain clusters exhibit signi? cantly different means in these variables are they distinguishable – from a data perspective, at least. This can easily be ascertained by comparing the clusters with independent t-tests samples or ANOVA (see Chap. 6). By using this information, we can also try to come up with a meaningful name or label for each cluster; that is, one which adequately re? ects the objects in the cluster.This is usually a very challenging task. Furthermore, clustering variables are frequently unobservable, which poses another problem. How can we decide to which segment a new object should be assigned if its unobservable characteristics, such as personality traits, personal values or lifestyles, are unknown? We could obviously try to survey these attributes and make a decision based on the clustering variables. However, this will not be feasible in most situations and researchers therefore try to identify observable variables that best mirror the partition of the objects.If it is possible to identify, for example, demographic variables leading to a very similar partition as that obtained through the segmentation, then it is easy to assign a new object to a certain segment on the basis of these demographic 262 9 Cluster Analysis characteristics. These variables can then also be used to characterize speci? c segments, an action commonly called pro? ling. For example, imagine that we used a set of items to assess the respondents’ values and learned that a certain segm ent comprises respondents who appreciate self-ful? lment, enjoyment of life, and a sense of accomplishment, whereas this is not the case in another segment. If we were able to identify explanatory variables such as gender or age, which adequately distinguish these segments, then we could partition a new person based on the modalities of these observable variables whose traits may still be unknown. Table 9. 11 summarizes the steps involved in a hierarchical and k-means clustering. While companies often develop their own market segments, they frequently use standardized segments, which are based on established buying trends, habits, and customers’ needs and have been speci? ally designed for use by many products in mature markets. One of the most popular approaches is the PRIZM lifestyle segmentation system developed by Claritas Inc. , a leading market research company. PRIZM de? nes every US household in terms of 66 demographically and behaviorally distinct segments to help ma rketers discern those consumers’ likes, dislikes, lifestyles, and purchase behaviors. Visit the Claritas website and ? ip through the various segment pro? les. By entering a 5-digit US ZIP code, you can also ? nd a speci? c neighborhood’s top ? ve lifestyle groups.One example of a segment is â€Å"Gray Power,† containing middle-class, homeowning suburbanites who are aging in place rather than moving to retirement communities. Gray Power re? ects this trend, a segment of older, midscale singles and couples who live in quiet comfort. http://www. claritas. com/MyBestSegments/Default. jsp We also introduce steps related to two-step clustering which we will further introduce in the subsequent example. Conducting a Cluster Analysis 263 Table 9. 11 Steps involved in carrying out a factor analysis in SPSS Theory Action Research problem Identi? ation of homogenous groups of objects in a population Select clustering variables that should be Select relevant variables that potentially exhibit used to form segments high degrees of criterion validity with regard to a speci? c managerial objective. Requirements Suf? cient sample size Make sure that the relationship between objects and clustering variables is reasonable (rough guideline: number of observations should be at least 2m, where m is the number of clustering variables). Ensure that the sample size is large enough to guarantee substantial segments. Low levels of collinearity among the variables ?Analyze ? Correlate ? Bivariate Eliminate or replace highly correlated variables (correlation coef? cients > 0. 90). Speci? cation Choose the clustering procedure If there is a limited number of objects in your dataset or you do not know the number of clusters: ? Analyze ? Classify ? Hierarchical Cluster If there are many observations (> 500) in your dataset and you have a priori knowledge regarding the number of clusters: ? Analyze ? Classify ? K-Means Cluster If there are many observations in your datas et and the clustering variables are measured on different scale levels: ? Analyze ? Classify ?Two-Step Cluster Select a measure of similarity or dissimilarity Hierarchical methods: (only hierarchical and two-step clustering) ? Analyze ? Classify ? Hierarchical Cluster ? Method ? Measure Depending on the scale level, select the measure; convert variables with multiple categories into a set of binary variables and use matching coef? cients; standardize variables if necessary (on a range of 0 to 1 or A1 to 1). Two-step clustering: ? Analyze ? Classify ? Two-Step Cluster ? Distance Measure Use Euclidean distances when all variables are continuous; for mixed variables, use log-likelihood. ? Analyze ? Classify ?Hierarchical Cluster ? Choose clustering algorithm Method ? Cluster Method (only hierarchical clustering) Use Ward’s method if equally sized clusters are expected and no outliers are present. Preferably use single linkage, also to detect outliers. Decide on the number of clu sters Hierarchical clustering: Examine the dendrogram: ? Analyze ? Classify ? Hierarchical Cluster ? Plots ? Dendrogram (continued) 264 Table 9. 11 (continued) Theory 9 Cluster Analysis Action Draw a scree plot (e. g. , using Microsoft Excel) based on the coef? cients in the agglomeration schedule. Compute the VRC using the ANOVA procedure: ? Analyze ?Compare Means ? One-Way ANOVA Move the cluster membership variable in the Factor box and the clustering variables in the Dependent List box. Compute VRC for each segment solution and compare values. k-means: Run a hierarchical cluster analysis and decide on the number of segments based on a dendrogram or scree plot; use this information to run k-means with k clusters. Compute the VRC using the ANOVA procedure: ? Analyze ? Classify ? K-Means Cluster ? Options ? ANOVA table; Compute VRC for each segment solution and compare values. Two-step clustering: Specify the maximum number of clusters: ? Analyze ? Classify ? Two-Step Cluster ?Numbe r of Clusters Run separate analyses using AIC and, alternatively, BIC as clustering criterion: ? Analyze ? Classify ? Two-Step Cluster ? Clustering Criterion Examine the auto-clustering output. Re-run the analysis using different clustering procedures, algorithms or distance measures. Split the datasets into two halves and compute the clustering variables’ centroids; compare ce

Paleolithic to Neolithic change over time Essay

During the sixth century in India, an Indian prince named Siddhartha Gautama renounced his status and wealth in order to become enlightened. After becoming enlightened he announced the principals of what he believed was going to be the new way of life, Buddhism. Some many years later, Buddhism found its way into China. And while many peasants saw a positive impact in the incoming way of life, many people with higher statuses did not. Many peasants and people of lower class supported this new teaching because it gave them something more to believe in, like an afterlife, rather than Confucianism which was stricter and did not have an afterlife. According to tradition, the Four Noble Truths explain how in ones life you can take the suffering you are born with and make something out of it. This is a positive outlook because it teaches people that there is a way to end the sorrow in ones life. (doc. 1) Along with peasants, many Chinese scholars believed in Buddhism. When asked their opinion on Buddhism, some of them replied with the opinion that Buddhism exceeded any other teachings like Confucianism. In document 3 a scholar says â€Å"To compare the sages to Buddha is like comparing a white deer to a unicorn.† This comparison made is clearly a positive outlook in that compared to an ordinary deer, a unicorn, which is representing Buddhism, is so marvelous and exotic. This same scholar also shows in his response that Buddhism is a simple way of life, and to some (mostly monks) things like wives and property are the luxuries of the world not needed to complete ones life. This proves that Buddhism is an easy-going, simple way of living that could appeal to many. (doc. 3) In the early 9th century, a Buddhist scholar by the name of Zong Mi expressed his opinion that all of the sages (Confucius, Laozi, and Buddha) were all perfect teachings for the time being. In his opinion all3 teachings led to an orderly society. I agree with Zong Mi in saying that each time period has different needs and demands, so a new teaching for each time period is wise in that they each meet the specific demands of that time period. (doc. 5) Although many lower class people supported Buddhism, many of the higher people did not because the fear that people would break away from Confucianism was present. Rulers want to be in complete power, and a new teaching that is severing that order could cause chaos in the rulers mind.  Also, Buddhism compared to Confucianism is very laid back and easy-going. Confucianism’s a lot stricter and if people do not live in fear of this strict code what is going to prevent them from revolting? An official in the Tang imperial court by the name of Han Yu expressed his opinion on Buddhism to be very negative. He demands that Buddhism is to be â€Å"rooted out, and late generations spared this delusion.† He also views Buddhism as being no more than a cult of barbarians, because it was not here in ancient times. I do not agree with his statement in that many things were not here in the past but are here today. For example, the cell phone. The cell phone was not here 50 years ago, but is here today and can be used for emergencies and better our safety in this world. Just because something is new does not mean it is evil or barbaric. However I do agree with the statement made that says â€Å"The Buddha†¦who did not speak Chinese and who wore clothes of a different fashion†¦and the Buddhas manner of dress did not conform to our laws.† I agree with this because Buddhism is a way of life, these new teachings would impact peoples lives daily. So if this Buddha is an alien, a person who is not from here how could he possibly understand our laws and fit our needs? Although its not bad to try new things, I understand the hesitance of people to believe in something so foreign. (doc. 4) Supporting an earlier statement that rulers did not want a change, Emperor Wu, in the edict of Buddhism explains how Buddhism has â€Å"Injured mankind†. He states that â€Å"Buddhism wears out the peoples strengths, pilfers their wealth, causes people to abandon their lords and parents for the company of teachers, and severs man and wife with its monastic decrees. In destroying law and injuring humankind indeed nothing surpasses this doctrine!† This shows that he thinks Buddhism has many negative impacts on everyone’s lives and this evil should be eradicated. (doc. 6) An additional document that would help me prove positive and negative points in this essay would most likely be something in the point of view of a peasant. Seeing a view of a peasant in this time period would help support my theory that peasants liked Buddhism, or help prove me wrong in that they did not. Seeing a document like this would also help me understand why people would choose a rough and strict teaching like Confucianism over an easy way of life like Buddhism. Everyone is entitled to their own opinion, neither is wrong nor right. However in the sixth century China when Buddhism was first introduced there was a split mindset of how things should be run. Most people of lower class appealed to this new teaching of Buddhism while many people of higher classes believed that Buddhism should be eradicated because of its true barbaric and evilness. Neither opinion is right, yet neither opinion is wrong. Change Over Time Courtney Morelli Mrs. McCaffrey period 3 Between the years 10,000 and 3,000 BCE many changes occurred. The way of life was altered for many people with new ideas, technologies and ways of life. But along with these changes some things remained the same through this time period. Just like todays society, it is changing in many ways but still keeping in touch with old ways or traditions. Before the Neolithic Age occurred, there was a time period called the Paleolithic Age. During the Paleolithic Age (2500000 BCE – 8,000 BCE) the way of life was hunting and gathering. People relied on hunting and gathering as their food source, therefore they moved from place to place never really settling in one spot. The men usually had the job of hunting for food, and the women had the role of gathering plants and berries and taking care of the children. Therefore, the women had an equal role to the men and were treated just the same. The people of the Paleolithic age expressed their life in not words, but art. They painted along the walls of caves showing things like hunting, or different animals that were around. After a long period of time the climate of places all over the world changed drastically, allowing the change from Paleolithic to Neolithic happen. The Neolithic age lasted from 10,000 BCE to 3,000 BCE, and many changes occurred. The Neolithic man started to plant crops now that the climate was  warmer and therefore they settled instead of moving from place to place. Now that the people were planting crops they were able to have a good amount of food, and never go hungry through the winter thanks to wheat. Since the people of the Neolithic era no longer needed women to gather foods, they lost their role in society. They were no longer considered equal to men and usually stayed home caring for the children. One of the biggest changes between these time periods is the change from hunting to domesticating animals. Instead of hunting down your next meal people domesticated and raised their own animals like goats and pigs. Keeping these animals close by made it easier for people to eat. Although, the agricultural way of life was much more complicated than hunting and gathering. Farmers had a much harder lifestyle because they had to work all day as to a hunter would only work until he got what he needed. Along with the many changes over this time period there were also many things that remained the same. One thing that remained the same was in both time periods people relied on animals as their main food source. Hunting was a very important part of the peoples meal, and is still a big part in our diet today. Also, although people in the Neolithic age had more food, the nutritional value was still as low as it was during the Paleolithic age. The men in the Paleolithic age had the job of hunting the animals and providing food for their families which was a big responsibility. Even during the Neolithic age this responsibility for men never changed because they still worked in the fields or domesticated and slaughtered animals for their food. Eventually, over many years every society and culture evolves. Some things change for the better of the people, and come along with new technologies and ways of life just like today’s culture and people. But along with these new things are also the same values or ways that have been going on for years and years. The Neolithic revolution was one of the biggest turning points in history, because of the many things that changed a way of life for many people. But even though so many things changed, some of the values and traditions always remained the same.

What evidence is there to support the prescribing of exenatide for Literature review

What evidence is there to support the prescribing of exenatide for adults patients who are already prescribed insulin with type - Literature review Example In insulin dependent patients with type 2 diabetes, especially with obesity, control of glycemia is a challenging issue (Hood et al, 2006). Intensification of insulin therapy to achieve target levels of glycosylated hemoglobin leads to further weight gain. Infact, one of main anxieties with insulin therapy in this population is poor weight gain (Nayak et al, 2010). In several developed countries like UK, there are recommendations for obesity surgery, along with exercise, diet and drug control of diabetes. However, obesity surgery is associated with significant risk. Exenatide, when given as an adjunct to insulin therapy, has been proven to not only achieve better control of blood glucose levels, but also decrease the chances of gaining weight. Infact, some studies have demonstrated weight loss with exenatide therapy. In this article, evidence to support the prescription of exenatide, as an adjunct to insulin therapy will be discussed through review of suitable literature. Understanding the pathophysiology and treatment basis of diabetes type-2 Diabetes mellitus can be defined as a group of clinical syndromes characterized by hyperglycemia arising as a result of absolute or relative insulin deficiency (Edwards et al, 2002). There are basically 2 types of diabetes mellitus. While type-1 is due to absolute insulin deficiency as a result of pancreatic beta-cell destruction, there is relative insulin deficiency in type-2 as a result of combination of peripheral resistance to insulin action and an inadequate secretory response by the beta cells (Kumar et al, 2007). Type 2 diabetes is the most common form of diabetes constituting 90% of diabetic population (Ramachandran et al, 2002). In a classic definition, type 2 diabetes has been defined as a triad of 3 etiologies, namely, resistance to insulin, progressive failure or exhausion of beta cells, and increased gluconeogenesis at liver. However, there is another pathophysiologic abnormality that is worth mentioning and that is decreased activity of GLP-1 (Jellinger, 2011). The imp aired insulin secretion in type-2 diabetes is due to beta cell dysfunction (DeFronzo, 1997). The beta cells fail to adapt themselves for the long-term demands of peripheral insulin resistance and increased insulin secretion (Kumaret al, 2007). In type-2, this dysfunction is both quantitative and qualitative. There is loss of normal pulsatile, oscillating pattern of insulin secretion and the rapid first phase of insulin secretion which is a normal response to elevated plasma glucose is attenuated. There is also decrease in beta cell mass, islet degeneration and deposition of islet amyloid (Kumaret al, 2007). Infact, studies have established the onset of insulin resistance much before the manifestations of hyperglycemia (DeFronzo, 1997). The pancreas beta-cell function declines gradually over time already before the onset of clinical hyperglycaemia (Stumvoll et al, 2005). The factors which probably lead to insulin resistance are increased non-esterified fatty acids, inflammatory cytok ines, adipokines, and mitochondrial dysfunction for insulin resistance, and glucotoxicity, lipotoxicity, and amyloid formation for beta-cell dysfunction (Stumvoll et al,

To read a story and write about it Essay Example | Topics and Well Written Essays - 500 words

To read a story and write about it - Essay Example To him, life became unbearable when his he even decided to attend new meetings to improve on the knowledge he had, as the unemployment benefits were running were almost ending, the cost of living was rising up in Washington. It was after searching from town to town of Pennsylvania, New York, Kentucky among others, in different companies that one call came from a headhunter and said it would be a perfect job for him, only to be asked why he never wrote about his bachelor’s degree. The author said that he never did one, and was told it is one requirement that would have made him prefect. At that moment, it came clear why the writer got rejections and at times no responses from his resumes. He realized he was not self-sufficient and was living a lie. To him, he had known that he had all what it takes in terms of knowledge and skills to get the jobs, but his thinking was shattered. It then clicked in his mind that he needed to put things right, by first looking for ways to improve himself, by furthering his education, changing his thinking process and ideas on education. He decided to tie it all together after following some adverts he once saw on TV and the internet about Phoenix university, and went back to continue his education. Over the learning period, the writer discovered three things that people say, never fear failure, and honesty is the best policy. They term it so because they believe that failure gives room for learning opportunities for growth. He quotes, â€Å"Thomas Edison failed thousand times to create the filament for light bulbs.† He later succeeded after not giving up. To be a failure means giving up and accepting that one cannot succeed. But the â€Å"I say† part of the writer is that he believes that if he wakes up in the morning, people can make a difference in the world in way or another. He says that people’s perception of failure is taken to be, that failure is bad, people look at what was done and what was left undone. But where they

Student's Musical Experience in the EKU Center Essay - 2

Student's Musical Experience in the EKU Center - Essay Example One song the reporter chooses is â€Å"Shortcut Home† by Dana Wilson composed in 1946. The tempo was very fast with a strong influence of the brass instruments. Heavy percussion was included. The tune then switched to something a little softer but then picked up speed again. It continued to go back and forth but the underlying tempo maintained a fast speed while some of the other instrument sections played over the top of it. The end was very powerful and most of the time it was played very loudly. Toward the end, the song had a sinister twist. There was more use of bells and the triangle at the end. â€Å"Shortcut Home† is a fanfare style song and each ensemble had its own special portion where it was featured within the song. The style was made of several different jazz styles. The music is profound in some areas and cascades until the final C Major chord. Hearing this song in person versus hearing songs in class was very arousing. There was just a vision that he coul d associate with how it made me feel. Since it is called â€Å"Shortcut Home† I think that it is a piece about someone who is frantic to get home and all of a sudden they find a shortcut, go scurrying, and then, at last, they are at home. The other song he chooses is â€Å"Cloudburst† by Eric Whitacre in 1970. It was interesting because it had audience participation. The music started out with the horns blaring at a slow pace, followed by woodwinds and then a short brief silence until all of the ensembles came together to perform. The sound of the cymbals would lead the first section to a climax. Then it was almost a dark thundercloud was lingering as the deeper brass instruments began to play their low haunting tune. Based on the slow tempo and the crashing cymbals, the reporter thinks that â€Å"Cloudburst† was about a thunderstorm. It was very soft and mellow at many points and then there would be a light crashing of the cymbals again.

CBI and QNB Assignment Example | Topics and Well Written Essays - 1750 words

CBI and QNB - Assignment Example This assignment also highlights the implemented strategy that may increase its portfolio and reputation in their respective markets as compared to others. Furthermore, the implemented strategy will also be evaluated so as to analyze its effectiveness and to recommend the most effective strategy is also for the organizations. The Commercial Bank International (CBI) of United Arab Emirates is a one of the budding commercial bank of United Arab Emirates. It is one of the reputed local brands established in the year 1991 offering a wide range of financial benefits such as auto loans, vehicle loans, credit cards enhancements and many more to its customers. This helped the organization to improve its market share and demand in the market of United Arab Emirates as compared to many other rival contenders. In-spite of being a public share holding company, it enhanced its reliability and loyalty within the minds of the local customers for its value-added services. The organization always tries to offer high valued products to its customers so as to improve their commitment and reliability. This is done in order to increase the sustainability and relationship with the customers that may amplify its brand value and profitability (Commercial Bank International, 2014). Similarly, Qatar National Bank (QNB) is established in the year 1964 as one of the first owned commercial bank of Middle East. It mainly offers a wide range of investment banking value-added services to its corporate and institutional clients with the help of its subsidiaries. This helped the organizations to expand it-self in numerous locations that amplified its dependency and reputations as compared to others (Qatar National Bank, 2014). The mission of Commercial Bank International (CBI) is to offer highly value-added and simple products and services to its target

History of Christian Thought - Final Exam Questions Essay

History of Christian Thought - Final Exam Questions - Essay Example They were concerned with the future consequences of the kingdom. They identified themselves as â€Å"The Community of the Poor† and their social philosophies always favored the poor people (Frend, 27-28). Jesus Christ was given a violent death by his antagonists who crucified him onto a cross. The Jewish religion believed that the prophets usually sacrificed their lives as a martyr, and Jesus death occurred in a similar circumstance. Of course, being the â€Å"Son of God†, he resurrected himself within three days of his dying. Thus, his followers came to regard him as â€Å"the true and faithful martyr† who sacrificed his life for the salvation of mankind (Frend, 54). Paul, a religious genius, shifted the Christian ideology away from Palestinian Judaism to the Jewish cultural centers in Europe and Asia Minor. According to him, although Christianity was a reform movement within Judaism, one could become a Christian only through a formal process of baptism to the r eligion. However, Paul had not respected the Christian followers at Jerusalem and they naturally opposed his philosophies (Frend, 89). 2. During the 2nd century, Rome emerged as the leading center in Christianity. According to the account in Clement I, the Roman Church was governed by presbyter bishops, instead of a single authoritative bishop. Hermas’ account suggests that different religious officials were responsible of carrying out different tasks: Clement was in charge of the foreign correspondence of the Church while other bishops or overseers were asked to monitor the area of hospitality and other charitable activities of the institution. During this time, Rome also started implementing beneficial activities for communities living beyond the city (Frend, 130). During 130-180, the Christian religion experienced the advent of the Gnostic movement. The movement advocated a form of Gentile Christianity, which encouraged its followers to encompass all kinds of knowledge and experience in their ultimate aim of achieving salvation centering around the divinity of Christ. Basilides, Valentinus, and Heracleon were three of the pioneering teachers of the movement, who working in Alexandria, spread its influence to Rome, Italy, Asia Minor and the Rhone valley. The Gnostic philosophy laid the foundation for the Alexandrian school of theology and Christian Platonism, which flourished in the subsequent centuries (Frend, 195). During this time, the Christian religion was retained its presence although in a smaller scale. During the second century, Christians had become almost a minority in certain places of the western world. By this time, new religious movements were also emerging which differed from Christianity in their basic ideals. Religious fanaticism had reached such a peak that, Christians being a minority began to be persecuted at different places of the Roman Empire. 3. During the 2nd and the 3rd centuries, the Roman Empire started to witness evidence of religious syncretism among its citizens. During the ancient time, the empire had been under the pagan influence after which the Christian religion had become popular among the people. Now, influences of other religions had started percolating into Christianity and the people had started to include these new practices within their existing

The Grapes of The wrath Essay Example | Topics and Well Written Essays - 750 words

The Grapes of The wrath - Essay Example He is in his late twenties and is highly respected by his family. The story starts off with Tom being released from the prison. He was imprisoned for four years for killing a man in a fight. "Homicide," he said quickly. "That's a big word—means I killed a guy. Seven years. I'm sprung in four for keepin' my nose clean" (Steinbeck 13). This shows that he obviously has certain violent tendencies. He cannot keep his temper in check, can lose his senses at it being crossed. He also seems to be quite powerful physically. The amount of time spend in jail has, of course, changed his personality but he cannot possibly turn a new leave completely and as it is shown later, these former traits come out at different parts of his journey. Tom is also quite blunt; he does not seem to be embarrassed about people guessing about him having a criminal record. He seems to be proud of himself as a person and very sure of himself. He is also determined and wills things to go his way. He does not st rike to be as an educated person and, in fact, he is not. He cannot read or write though he asserts that he could if he wanted to. Tom is sly enough and manages to convince a driver to drop him off near his home, easily keeping up with the conversation. There he comes across a preacher called Jim Casy. They get reacquainted, building a new friendship. Casy is no longer the believer of the things as he was earlier. Later in the story, Casy is shown to be a great influence on Tom with certain repercussions attached to it. The two get closer and Casy makes Tom realise what unfairness and prejudice people are suffering through. "Well, you and me got sense. Them goddamn Okies got no sense and no feeling. They ain't human. A human being wouldn't live like they do. A human being couldn't stand it to be so dirty and miserable. They ain't a hell of a lot better than gorillas" (Steinbeck 221). On one of such arguments with the police, Casy turns violent and is arrested. When he is released, T om goes back to him. Casy is shot dead by a policeman in front of Tom. To avenge the death of a friend, Tom shows his loyalty by killing an officer. Here, the murderer reemerges. On the way to his home, Tom picks up a stray turtle. "An old turtle," he said. "Picked him up on the road. An old bulldozer. Thought I'd take 'im to my little brother. Kids like turtles" (Steinbeck 21). This shows that despite the tough personality he is intent on showing to others, though he has a volatile temperament, he still is in touch with his human side. He has enough affection for his siblings to want to take something for him to make him happy. In the different areas of the text, Tom is shown as fondly remembering his family. He obviously loves them quite a bit. At reaching the house, Tom is informed by Muley Graves the neighbor that his family have left and gone to Uncle John’s, planning to pack up and migrate to California in search of jobs. He finds them, time and time again shows his fon dness for his family. On their road trip to California, his grandparents pass away. Life is not easy in the new city until Noah, the oldest Joad child, gives in and leaves his family. Tom now officially becomes the head after his father. He assumes the responsibilities and is respected by them. It is during these times that Tom starts to look at the conditions in the long run, decides to do something for the others and not just himself or his family. "I climb fences when I got fences to climb," said Tom. Casy

Weeks vs. Southern Bell Research Paper Example | Topics and Well Written Essays - 1000 words

Weeks vs. Southern Bell - Research Paper Example Mrs. Weeks have also appealed that her employer, Southern Bell should return the position to Mrs. Weeks along with compensation for damages inflicted for the activities of discrimination of sex. Mrs. Weeks have also appealed for necessary action so that Southern Bell should refrain from such unlawful practices of employment in future. The detailed records of the case indicate that Mrs. Weeks had applied for the post of switchman in South Bell on 17th March, 1966. Southern Bell refused the application of Mrs. Weeks on 18th April, 1966 citing the reason that the position of switchman and the duties and responsibilities associated with the post is not fit for women. Post this refusal, Mrs. Weeks filed an unsworn charge with the Equal Employment Opportunities Commission and a representative of the Commission obtained a sworn charge from Mrs. Weeks on 30th July, 1966. The Commission carried out investigations on the charges brought about by Mrs. Weeks on her employer Southern Bell and fou nd there was no scope of judgment looking at the duties and responsibilities of switchman in the company that women are not fit for such positions (Staleup, 2005). On 19th April 1967, Mrs. Weeks was informed by the Commission that the conciliation procedure with Southern Bell has proved to be a failure and that Mrs. Weeks was provided a time period of 30 days to file the case against Southern Bell. The Commission appointed a counsel for Mrs. Weeks who filed the case against Southern Bell on her behalf on 18th May, 1967. In reply to this alleged unlawful practice of sex discrimination in the field employment in context to Mrs. Weeks, the company cross-appealed saying that as per the requirements of the code of law, there was no sworn charge filed by Mrs. Weeks within three months of the alleged unlawful practice. As per the codes of jurisdiction, the refusal of the application for employment occurred on 18th April, 1966 and that the sworn charge should be filed within 90 days, i.e. b y 30th July, 1966. The company highlighted that there was error on the part of the District Court to overrule this aspect and based on these points, the company applied for dismissal of the charges filed by Mrs. Weeks against them. The District Court validated the actions of the commission in this case saying that the amendments allow the Commission to charge cases filed beyond the time period of 90 days. The District also emphasized that irrespective of whether its is a sworn charge, any written complaint against the offender or the employer by their employee or the victim that identifies the parties involved in the case and the alleged unlawful practices subject to court’s judgment is deemed to be valid under the codes of jurisdiction. Southern Bell has held the view that that Commission only has the right to receive complaints from the aggrieved parties and take part in the administrative processes and not in any juridical process. Thus the commission has the right to take part in the process of settlement through conciliation, conference, etc. The Commission has no power to enforce juridical matters as it has done through engagement of the counsel on behalf of Mrs. Weeks (Robertson, 2006). In the context of this case, the legislative history is, however, silent on the matter regarding the requirement of the charges to be filed by the aggrieved parties. The charge irrespective of its nature whether it is a written complaint or sworn charge is viewed to be the stimulant that initiates the proceedings against the alleged lawful practices like the case of sex based discrimination of employment

Explain the Buddhist concept of nirvana. What is its connection to Assignment

Explain the Buddhist concept of nirvana. What is its connection to Anatman - Assignment Example That is why many Western theologians criticized Buddhism for being a pessimistic and nihilistic religion. Though the notion or concept of Nirvana exists in multiple Eastern traditions, yet the Sanskrit term Nirvana is intimately associated with Buddhism. In Pali it is known as Nibbana (Hawkins 117). Nirvana is the eventual goal of pursuing the Buddhist way of life in most of the Buddhist traditions. Since it is a Sanskrit word, in a literal sense Nirvana means extinguishing or getting extinguished. In a thematic context it means the way to the cessation of suffering owing to the extinguishing of the three poisons of ignorance, hatred and desire, which eventually leads to the cessation of rebirth and suffering. Nirvana as per Buddhism leads to the final settlement of all karmic debts of an individual. The thing that needs to be understood is that as per Buddhism, Nirvana no way means a final annihilation or merging with some higher Brahman. Rather it means passing into a superior state of consciousness, of which there is no parallel that could be mentioned. His holiness the Dalai Lama defines Nirvana as â€Å"a state of freedom from a cyclical existence or Samsara (Lama 84).† It is an eventual unhinging of the state of mind from an array of defilements pervading the Samsara. It frees an individual from the effects and counter-effects of Karma and eventually liberates an individual from the never ending cycle of life and death. The concept of Nirvana is intimately related to the Buddhist notion of Anatman. As per Buddhism there are five Skandhas or states of existence that are forms, sensations, perceptions, mental formations and consciousness (Hawkins 118). Thereby, according to Buddhism individuals are devoid of any self possessing self and this doctrine of no-self is referred to as anatta or Anatman (Hawkins 42). The individual self or what is known as ego is a

Data and Assumption on New Technology Innovation Essay Example for Free

Data and Assumption on New Technology Innovation Essay Yesterday at the stroke of midnight a series of tragic events came to a closing when Othello, Venices most eminent and respected Moorish general, killed himself in his wifes bedchamber after smothering the young bellenone other than Venices coveted Desdemonawith a pillow. According to several witnesses of the bloody suicide, the mentally tormented general was under the notion that Desdemona had been illicitly tupping his first-in-command, Michael Cassio, a lie fed to him by the ironically misnamed Honest Iago. Iago, the villain responsible for the murder of his own wife as well as a Venetian gentleman, has been taken by Cyprian officials for questioning and possible torture. However, it is known that he orchestrated a plan to create conflict between General Othello and Lieutenant Cassio surrounding Desdemona that he hoped would result in the death of both Cassio and the lady. Witnesses to the blood bath describe the scene gravely and painfully. I walked into the room and there was Othello, with his wife Desdemona slain on the bed. It was really shocking. Her face was very pale, and though she was evidently dead, it seemed she was trying to say something, says Gratiano. Another witness describes Othellos suicide with great distress. He was very calm, but there was a wondrous rage in his face, like a monster. I had never seen him like that before. He took his sword and drove it into his chest before anyone could stop him. The only survivor of the discord is Lieutenant Cassio, who suffered a major injury in his leg from Iagos sword. When asked whether he was ever involved with lady Desdemona, Cassio responded, We were dear friends, and it pains me greatly that she is gone. But we never shared more than the touching of hands or a brief brush on the shoulder. As for the man who caused this, I will see to it that he pays for his cruelty with his own suffering. A funeral will be held in three days near the town square. Mourners are welcome, including former suitors of Desdemona. Michael Cassio asks that all that attend bring memorabilia of the lost ones such as locks of hair, clothing, letters, or embroidered handkerchiefs.

Uniqlo Clothing Analysis

Uniqlo Clothing Analysis Introduction Japanese clothing brand, Uniqlo, specialises in stylish casual-wear which is both high quality and affordable. It is the predominant brand of the Japanese company Fast Retailing Company Limited and accounts for 90 per cent of companys total sales (UNIQLO UK). The first Uniqlo store opened its doors in Japan in 1984 and there are now over 760 stores across the country. Now a global brand, Uniqlo began international expansion with the opening of the first UK store in 2001, and it is Uniqlo UK which will be the focus of this report. After introducing itself to the UK, it was not long before Uniqlo had 23 stores across England. However, the brands simple designs did not prove popular and within two years 18 stores were closed (Marketing, 2007). Since its relaunch in 2007, Uniqlo UK has been gaining momentum, but it still has a long way to go towards becoming a leading fashion retailer in Britain. Consequently, this report aims to further this momentum through the development of a new integrated marketing communications campaign which would be implemented in 2010. Summary Context Analysis Customer Context Segment Characteristics Uniqlo currently targets a very wide audience, describing its key demographics as: Male and female; Fashion-conscious; Cost-aware; and aged between 16 and 96! (UNIQLO UK) Calgary Avansino, Executive Fashion Editor of Vogue magazine, has personally experienced the wide appeal of the Japanese brand. She said, â€Å"the store is for a young customer but at the same time I went with my mum who is 60 and she bought the skinny jeans in taupe and burgundy† (The Independent, 2009). However, the lack of focus which comes with having such broad segmentation results in an unclear brand identity, thus making it harder for Uniqlo to stand out. Brand Awareness, Perception Attitudes Awareness Every single one of Uniqlos 14 UK stores are concentrated in and around London. Current marketing communications are therefore focussed mainly on the London area, and as a result awareness of the brand outside this region is low. Include brand awareness pie chart + sentence about where theyre from. Perception Uniqlo is generally perceived as â€Å"deliver[ing] true value quality and style at a fair price† (Inside Retailing, 2009). Attitudes A random selection of Uniqlo shoppers on Oxford Street, London, were asked their opinions of the brand earlier this year. Eighteen-year-old student Rafaela commented on the versatility of the designs, stating they are â€Å"easy to mix and match†, and twenty-year-old textile design student Holly similarly said, â€Å"you can make the clothes look your own with more individual accessories. I love the massive range of colours.† Twenty-seven-year-old visual effects artist Nick liked â€Å"the fact that there are no naff slogans or logos†, and twenty-year-old textile student Iona appreciated Uniqlos â€Å"simplicity† (The Independent, 2009). Level of Involvement Involvement is relatively low when it comes to Uniqlos products. This is because it offers basic, inconspicuous clothing designs, none of which visibly feature the Uniqlo logo, and prices are reasonable which lowers risk. Business Context Market Positioning â€Å"Clothes that can be worn by anyone, any day†; â€Å"Uniqlo †¦ focuses on quality and value with a broad demographic appeal, though it has aimed at attracting young trendsetters by collaborations with designers and artists.† (Mintel, 2008) Current Marketing Mix Product Basics Simple, plain designs for both men and women, in a wide variety of colours. Items include t-shirts, outerwear, knitwear, jeans, trousers, and accessories, as well as dresses and skirts for women. HEATTECH Uses unique material, developed by Uniqlo and Toray Industries, which retains body heat. +J Collection made especially for Uniqlo by designer Jill Sander, released 1st October 2009. In the words of Calgary Avansino, â€Å"Her look is classic and simple, but also cutting edge in the way she cuts and presents her clothes† (The Independent, 2009). UT Uniqlo T-shirts (a sub-brand); Limited edition collections of t-shirts created by designers and artists from all over the world; UT has a different image to the rest of Uniqlos products and the t-shirts are promoted and sold separately. It will therefore not be included in the IMC campaign this report seeks to create. Price Basics Jeans roughly  £25, t-shirts  £8- £14, cashmere around  £25- £45 (Mintel, 2008); HEATTECH From  £6.99 (UNIQLO UK, 2009); +J Outerwear  £49.99- £99.99, bottoms  £24.99- £29.99, shirts  £24.99, jersey and sweats  £14.99- £29.99, knitwear  £19.99- £29.99, cashmere  £59.99-99.99, accessories  £14.99-79.99 (F.Tape, 2009). Place Physical stores: Currently 14 Uniqlo stores in the UK, including global flagship store on Oxford Street in London. The other stores are also all situated in the south of England, in and around the capital. Online store: Uniqlos comprehensive UK online store is an important distribution channel, allowing the rest of the country access to the brand. Promotion Advertising Uniqlo UK generally avoids overt TV advertising, but it has recently released a TV commercial for its HEATTECH range (see Appendix ?). Uniqlo mainly invests in press (newspapers and magazines), and outdoor (billboards, in the subway, and on buses). Look to Appendix C for a table detailing Uniqlos advertising spend, Appendix? for examples of Uniqlos recent print advertising and Appendix ? for examples of their outdoor advertising. This summer Uniqlo launched a global ad campaign for their new casual sportswear, featuring well-known models Agyness Deyn, Luke Worrall and Gabriel Aubry (see Appendix ?). Public Relations UT Cannes Lions Grand Prix 2010 Part of the Cannes Liones International Advertising Festival; Annual t-shirt design competition held by Uniqlo; The top 20 designs will be made into Uniqlo products, and the overall winner gets $10,000 (US); Semi-finalists announced November 2009, finalists announced February 2010. Uniqlo Jump Collection of photographs of Uniqlo employees from around the world (UK, USA, Japan, China and South Korea) mid-jump, and wearing Uniqlo clothing. Uniqlo Paper (Uniqlos online and in-store magazine) Articles in magazines/newspapers Sales Promotion Partnerships with specialised discount websites (www.vouchercodes.co.uk, www.hotukdeals.com); Since January 2008, Uniqlo has been involved in an affiliate programme with LinkShare which has resulted in a partnership between Uniqlo and Marie Claire magazine. The Japanese brand received editorial exposure on Marie Claires website, which featured ten exclusively discounted Uniqlo products (Revolution, 2009). Interactive Marketing Communications Uniqlock Online clock which can be set to any time zone; Features continuous rhythmic music, short clips of Japanese dancers wearing Uniqlo clothes every five seconds, and extended dance sequences every hour; Users can post a mini version of the clock to Facebook, Bebo, Myspace or their personal blog, or they can download it as a screensaver or as an iPhone/iPod Touch application. Uniqlo Calendar (similar idea to Uniqlock) Uniqlo Introduction Flash page attached to uniqlo.com; Gives a sense of Uniqlos brand identity and the range of clothing available. In short, Uniqlo currently has a wide and varied marketing communications mix, and a lot has been done below-the-line to engage the consumer. However, the communications are far from integrated: the advertising campaigns are all rather individual, could quite easily be for HM or Zara, and do not match the quirky, fun and distinctly Japanese character of many of the below-the-line communications. Competitor Analysis Competition within the clothing retail market is fierce, especially given the recent economic downturn, and Uniqlo has many well-established and similarly-positioned competitors to contend with. See Appendix C for a table comparing Uniqlos annual sales total with those of its competitors. According to this table, Uniqlos three most significant competitors are American clothing brand GAP, Spanish Zara and Swedish Hennes Mauritz (HM). Gap â€Å"Clean, classic, American designs† with mid-market stance (Mintel, 2009); Has lost its distinctiveness and sales are falling (Mintel, 2008). Zara â€Å"Fast-moving fashions at affordable prices, inspired by the catwalks† (Mintel, 2009). HM â€Å"Quality at the best price† (Mintel, 2008); â€Å"[S]omething for everyone from modern basics to cutting-edge fashion †¦ Although the UK customer base is heavily skewed to 15-24-year-olds (and women), it †¦ appeals across the socio-economic spectrum.† (Mintel, 2009); Number of high profile designer collaborations in the past, plus recent release of shoe collection from Jimmy Choo; â€Å"Very strong identity in face of competition from other young fashion brands and potential for expansion.† (Mintel, 2008). External Context Stakeholders See Appendix A for a diagram of Uniqlos main stakeholders. One of Uniqlos most significant stakeholders is their suppliers. All Uniqlo products are made in the Far East, mainly in China. Fast Retailing does not own any factories but works closely with the same manufacturers year after year, thus developing loyalty and ensuring its products are of the highest quality (The Independent, 2009). PEST Analysis Political Factors Globalisation has brought the UK and Japan closer together and, as a result, the two countries are increasingly acknowledging mutual interests and concerns (Embassy of Japan in the UK, 2000). Economic Factors After the recession, UK customers have become much more price-conscious. Customers are thus more likely to look for clothes of better value with greater longevity. Socio-Cultural Factors Japanese culture is very different to that of the UK, but elements of Japanese culture, particularly food, fashion and design, have already had a great impact in the West. It has been said that, in general, â€Å"Japan is increasingly the epicentre of cool with Tokyo its style capital† (Inside Retailing, 2009). Technological Factors E- and M-commerce are both growing rapidly, and the internet is becoming increasingly central in the worlds of business and marketing. Internal Context Organisational Identity Fast Retailing, the parent company of Uniqlo, strives for excellence and efficiency in all it does, and, above all, it always put the customer first. See Appendix B for the corporate statement, mission statement, values and principles which constitute the details of Fast Retailings organisational identity. Marketing Expertise After the store closures in 2003, Uniqlos Marketing Chief at the time, Dominic Chambers, lost his job. In 2006, the former Merchandising Director for Dorothy Perkins joined Uniqlo and Marketing Manager, Amy Howarth, as part of a new team (Marketing, 2007). SWOT Analysis The following SWOT analysis has been constructed in order to highlight and summarise the main issues within the Context Analysis. Strengths High quality clothing; Clothes are easy to mix and match; Sophisticated textile technology; Comprehensive online store; Japanese identity; Reasonable prices. Weaknesses Low brand awareness among target market in UK; Brand identity inconsistent and does not stand out against competitors; Uninspiring website design. Opportunities Popularity of Japanese culture; High growth of E-commerce and M-commerce. Threats Economic recession (consumers with less disposable income); Numerous strong competitors. Objectives Business Objectives Open more stores across UK; Before the store closures in 2003, Uniqlo (UK) had branches in the North West of England, the Midlands, Manchester, Liverpool and Coventry, and original plans had been to have 50 UK stores by 2004 (Times Online, 2003). Despite its increasing success, Uniqlo still only has 14 stores in the UK, and they are all located in or around London. In 2007, Chief Operating Officer Simon Coble stated, â€Å"We would like to be trading in all the major towns and cities in UK† (Marketing, 2007). Of course it would not be wise to try and open too many new stores too quickly as this approach failed the first time around, so a realistic target might be to have one or two new stores open in 2011, perhaps in Leeds and Glasgow. Increase UK online stores sales volumes; Currently the Uniqlo website is the only access Scotland, Wales, Northern Ireland and most of England have to the brands wares. Therefore, until more stores are opened across the UK, the online store is vital. A 20% increase in online sales by 2011 would be a realistic target. Increase market share. Because the market is saturated, this will involve taking market share from competitors. A realistic target would be a 1.5% market share by 2011. The reason this percentage is so low is because Uniqlo does not even appear in Mintels table of estimated market shares of leading retailers in womenswear from 2002 to 2007. Further, this table shows that Uniqlos strongest competitor, HM, only had a 1.2% share of the womenswear retailing market in 2007 (Mintel, 2008). Communication Objectives Increase consumer awareness and engagement among target audience across UK; The brand is currently relatively unknown beyond London. In terms of the DRIP Model, this objective fits with the key task of informing the consumer. Of course, awareness is not enough on its own: the consumer needs to be engaged. Uniqlo already engage their consumers through such means as Uniqlock, but engagement efforts need to be furthered as awareness increases. Differentiate from competitors through development of distinctive brand identity. Analysts concluded that Uniqlos initial losses after coming to the UK were due to the brand â€Å"fail[ing] to offer clothes that were sufficiently distinctive from rivals such as Gap, Zara, HM and the recently revitalised Marks Spencer† (Times Online, 2003). In terms of the DRIP Model, this objective fits with the key task of differentiating in the mind of the consumer. Marketing Communications Strategy Segmentation Targeting Look to Appendix E for a table of possible market segments for Uniqlo to target. This IMC campaign will use Segment 2 as its primary target. Because Positioning Positioning Strategy This IMC campaign will be based on a pull-positioning strategy, as objectives are to increase awareness and encourage involvement, and messages are to be directly targeted at end-user customers. A consumer-driven campaign fits with the value Fast Retailing places on approaching issues from the customers perspective. Key Message The key message will be that Uniqlo provides the consumer with fashionable but simple clothing which is easy to mix and match and therefore allows each customer to create their own unique look. Uniqlo does not simply reproduce catwalk trends which result in everyone wearing the same thing: Uniqlo encourages individuality. The brand already promotes a similar idea on their rather isolated online introduction, which can only really be stumbled upon as it is not linked directly from the main website. It states: â€Å"Were not a brand that dictates a total lifestyle. To us, clothes are items the individual chooses to express a personal lifestyle† (UNIQLO INTRODUCTION). The aim of this campaign is therefore to make more of a feature of this message, and make it consistent across the rest of their marketing communications. It will be called the YOUniqlo campaign. Communications Mix The tag-line which will appear throughout our campaign is Uniqlo. Mix it up. It is concise, funky, and vague enough to apply to all our communications tools. It conveys the idea of doing things differently and, more specifically, relates to the fact Uniqlos clothing is easy to mix and match. Advertising Advertising will significantly contribute to a rise in brand awareness and the development of a more definite and unique brand identity. It should also drive further business to the online store from across the UK as well as to the actual shops in and around London, and ultimately increase Uniqlos market share. It is important to remember, however, that our target audience is advertising and marketing literate and will not be patronised by clichà ©s and blatant selling techniques. The YOUniqlo campaign must avoid the overt, and instead offer quirky and entertaining ads which engage the consumer and, furthermore, project a consistent brand identity. Print: Magazines Magazine advertisements are good for creating impact and demanding readers attention. Further, the sheer quantity of different genres and titles means it is easy to target a specific audience (Fill, 2009: 715). Judging by primary research, the chosen target market segment tend to read magazines such as Vogue, Cosmopolitan and Elle (). Particularly since these magazines contain extensive style advice, it would be wise to put print advertisements in them. The YOUniqlo print campaign will use unknown, friendly-looking models who will be much more relatable than the likes of Agyness Deyn. This should bring the brand and the consumer closer together. The adverts will illustrate the mix it up message through featuring one model superimposed three times in the same image. Each version of the model will be in a different pose and wearing a contrasting outfit. The outfits will all look very different but there will be some overlap in individual items, thus demonstrating the mix and match aspect. Appendix ? shows a mock-up of the womenswear ad, though the real ad would be portrait rather than landscape and have a plain white background. There will also be a version for menswear. Outdoor: Transit Shelter Poster The age group being targeted by the YOUniqlo campaign, in particular the students living in and around city centres, are unlikely to have cars. Therefore, advertising at bus stops would be a good idea. Ten classic, relatively unisex Uniqlo items will be selected to make a series of transit shelter posters, with one item being used on each. They will each be nicely photographed front-on and look as though they are being worn, and will be shown life-size on a transparent background, positioned to match where they would sit on an average-sized person. Some interesting and amusing accessories will also be shown in order to create humour appeal. See Appendix ? for mock-ups of the posters. Involvement will then occur as people can pose behind the poster and have it look as though they are wearing the items featured. Since only one Uniqlo item will be shown on each poster, individuals will all look different as the items mix with their own outfits. This therefore expresses the mix it up message. Bus stops on central streets and around university campuses in major cities will be ideal locations for reaching the target audience and attracting attention. Interactive Marketing Communications Website As acknowledged in the SWOT analysis, one of Uniqlos strengths is their comprehensive online store. It is a great functional tool with easy navigation and simple checkout process. However, it currently gives no sense of brand identity, with the main page launching straight into new releases and current offers. Earlier this year Uniqlo collaborated with 4Ps Marketing, a search engine marketing agency, and through customised Search Engine Optimisation and Pay Per Click campaigns Uniqlos only revenue has since increased by 30.76% (4Ps Marketing, 2009). It therefore seems wise to mould and expand the website into a demonstration of the brand as well as its actual clothing. This would add greater interest and individuality to the site, thus helping to differentiate the brand in the mind of the consumer. It would further engage the consumer, and ultimately help fulfil the business objective of increasing online sales volumes. As stated by Terence A. Shimp, websites â€Å"can be considered the centrepiece of companies online advertising efforts† (Shimp, 2007: 443). The general feel of the website should match that of the physical stores, which have a very streamlined look and give a firm but not clichà ©d sense of Uniqlos Japanese identity (see Appendix ?). There should also be the option of signing up to a members section, which will add users to a mailing list and give them access to extra features and exclusive deals. Sponsored Facebook Group Many retailers around the world already use sponsored Facebook groups as a way of directly marketing towards Generation Y, a new type of consumers highly influenced by the internet. Such online groups give marketers the ability to target a particular audience by using a ‘member list to message people and inform them about new collections or events. As part of the YOUniqlo campaign, the sponsored Facebook group will operate in line with the PR activities, and messaging can be tailored depending on the location of the user in relation to that of the event. Consumers can also become more involved through use of the discussion board. Public Relations Organisation of a large-scale PR event would greatly help fulfil the brand awareness and engagement objective. It would not only grab the target audiences attention, but also that of the media, thus generating further publicity for the brand. The YOUniqlo event will revolve around a nation-wide art competition, called Uniqlo Masterpiece appropriate as Uniqlo clothing appeals to artistic, creative people. Consumers will be attracted to a Competition section of the re-launched website, where they can use a Flash application to create their own artwork using small graphics of Uniqlo clothing items in a wide variety of colours (see Figure 1). This will emphasise Uniqlos wide range of items and colours, and it fits with the Mix it up concept as it encourages users to mix Uniqlo clothes to create something truly unique. Entrants must be: Aged 18-25; UK residents; Signed up as member of the Uniqlo website. After the competition closes, twenty semi-finalists from across the UK will be chosen by Uniqlo. Website members will then be able to vote for their favourite from the final twenty, thus creating further involvement and engagement. After the vote closes, five finalists will be revealed and invited down to London for the main event (travel expenses paid), where they will create a real version of their artwork using Uniqlo clothes on one of five big white canvases. The event will take place in the courtyard in front of Somerset House (see Appendix), and big screens will be present to show how the artworks look from above. Each finalist can compete as part of a group of up to four people. The event will have a DJ, be hosted by a local celebrity, and have a judging panel present to decide the winner at the end of the afternoon. PRIZES? + Donation of clothes to Oxfam? which adds CSR into the mix. Scheduling and Implementation Table 1: Media schedule for 2010 YOUniqlo campaign Taking the workings of the fashion industry into consideration, the print and outdoor campaigns will run in accordance with the release of new ranges at the beginning of each season. Pre-Event 01/02/10 Re-launch of Uniqlo website together with opening of Uniqlo Masterpiece competition. 01/02 31/03/10 Online entry using Flash application. 07/04 30/04/10 Online public vote from list of 20 semi-finalists. 07/05/10 Top 5 announced. Event 05/06/10 Final event in London. Table 2: Timetable for Uniqlo Masterpiece competition The competition has been timed so that the main event lands comfortably on Saturday 5th June. Nice weather is important for an outdoor event, and most students will have finished their exams by then. Publicity for competition on website and through sponsored Facebook group. + Use Bluetooth on day of event to attract passers by. Resources The proper execution of the YOUniqlo campaign relies on Uniqlos resources. This resource has evolved to become a strategic function that perceives the association between talented people and the success of an organization. Uniqlo needs to have the strength of extensive man power to establish a successful marketing campaign. The human resource involved in the campaign includes the professional team which comes up with innovative print ads, the efficient PR team which has the ability to host an event by capturing the audiences attention and a tech savvy team which creates breakthrough in internet advertising. The proper execution of a campaign depends on the financial resources available to support it. Uniqlo‘s most recent annual report available (2006) depicts that it has sufficient funds to carry out an extensive media campaign. The costs involved in conducting a campaign include the venue hiring costs, the cost of publishing print ads in magazines and the cost of publicizing the event through various websites. Uniqlo must treat this expenditure of conducting a campaign as an investment to create awareness about their brand and its uniqueness. Justify cost of PR event + prizes by fact that theyve already paid for supermodel Agyness Deyn + various celebs at Uniqlo flagship store opening. Evaluation, Control Feedback Precisely, several post-testing tools will be applied (tracking studies and likeability test) to create reliable and validated feedback (Fill, 2009). Deciding on the scope of the evaluation, it is logical to refer back to the communication objectives and assess to what extent they were achieved. Tracking studies will give the company a good view of communication campaign impact on brand awareness in the targeted regions of the UK. Interviewing a large number of people in these areas on a regular basis will also provide significant data on how their attitude and perception of Uniqlo has being influenced by all marketing efforts of the company. The awareness of the Uniqlo.co.uk will be easier to track by the number of hits from UK users. However, awareness on its own is not so important for the brand performance, unless level of engagement is also high. Youniqlo campaign was designed to create customer engagement with the brand and likeability analysis will aim to assess its success. The method will examine the level of entertainment/enjoyment people received from, for example, PR event or bus stop advertisements. A good showing here will also be the number of pictures uploaded to the company website. The likeability analysis will also demonstrate how well company has managed to differentiate itself from the competitors in consumers minds. QUALITATIVE. Recall tests. Conclusion Corporate Statement Changing clothes. Changing conventional wisdom. Change the world. Group Mission To create truly great clothing with new and unique value, and to enable people all over the world to experience the joy, happiness and satisfaction of wearing such great clothes; To enrich peoples lives through our unique corporate activities, and seek to grow and develop our company in unity with society. Values Approaching issues from the customer perspective; Embracing innovation and challenge; Respecting and supporting individuals to foster both corporate and personal growth; Committing to ethical standards and correctness. Principles Do everything possible for our customers; Pursue excellence and aim for the highest possible level of achievement; Achieve strong results through the promotion of diversity and teamwork; Move speedily and decisively in everything we do; Conduct business in a very real way based on the current marketplace, products and facts; Act as global citizens with ethics and integrity. (Fast Retailing, 2008) Appendix C: Uniqlos Advertising Spend, 2006-7 Total Spend Cinema Direct mail Internet Outdoor Press Radio TV  £m % % % % % % % 0.3 0.0 0.0 0.0 25.3 74.5 0.2 0.0 Uniqlos total advertising spend and media used, year to end May 2007. Source: Nielsen Media Research/Mintel (Mintel, 2007). Appendix D: Total Annual Sales of Uniqlo and Its Competitors Company Name (Flagship Brand) Country Fiscal Year End Sales ( ¥ Billions) GAP USA Jan. 2008 1723.7 INDITEX (ZARA) Spain Jan. 2008 1517.5 HM Sweden Nov. 2007 1342.1 Limited Brands USA Jan. 2008 1108.2 NEXT UK Jan. 2008 666.2