Caffeine in Coca-Cola Essay

Design Question: Does the caffeine in Coca-Cola affect blood pressure? Hypothesis: The amount of caffeine in Coca-Cola will cause to the adrenal glands, located on the top of the kidneys, to release more adrenaline which will in turn cause an increase in blood pressure. As the amount of Coca-Cola decreases, so will the difference between initial blood pressure and blood pressure after the consumption of the caffeine. Variables: Vemier Labquest Procedure: In order to test my hypothesis, I decided to use a common caffeinated beverage, Coca-Cola. The amount of caffeine in each bottle is given as 48mg/500mL. So, to keep my calculations clean my initial test was with 500mL of the Coca-Cola. I took my blood pressure with a Vermier Labquest before I consumed any caffeine to have a control variable. It was a manual blood pressure monitor, so I took my own blood pressure. After placing the cuff on my arm, I squeezed the bulb until the cuff pressure reached 170 mm Hg. Then, I released the bulb and let the pressure drop until it was 50 mm Hg and I used the release valve on the bulb to completely release the pressure and stopped the data collection. This data was recorded. Next, I measured the amount of the Coca-Cola used for the current test into a measuring cup and then consumed it. I waited 20 minutes after consumption to take my blood pressure again. Caffeine has been reported to take affect after a time period of 15-45 minutes. I did an identical test every day for five consecutive days with varying amounts of Coca-Cola consumed. On the first day, I consumed 500mL, 400mL on the second day, 300mL on the third day, 200mL on the fourth day, and 100mL on the fifth day. I recorded my blood pressure pre-caffeine consumption on each day. * Spike in blood pressure during the third trial could also be due to the additional consumption of food during the time between initial blood pressure data collection and post-caffeine consumption data collection. No other trials had outside influences on data. Data Processing: The following is a line graph that represents the systolic pressures of each trial before and after caffeine consumption: There are noticeably great differences between the different trials up until the fourth trial when the systolic pressures seem to be nearly the same. The pressure at trial three seems to be an outlier. The following is a line graph that represents the diastolic pressures of each trial before and after caffeine consumption: The correlation between the diastolic pressures does not go along with my hypothesis in showing that the blood pressure will rise as the caffeine intake rises. The spike at trial three is also an outlier here like it was with the systolic pressures. Both of these graphs help to illustrate the change in the pressures visually. Conclusion: Although there is some relation between caffeine intake and a rise in blood pressure, my data was not one-hundred percent accurate in showing that the more caffeine consumed the more of an increase. This would have been shown had the data for trial three been less of an extreme. If the systolic pressure had fallen between 117 and 129 (The systolic pressures for trials 4 and 2) then the data would appear to be much more consistent. The cause of this could be directly due to the fact that I was also eating at the time of the trial whereas with the other four tests, the only thing that I was ingesting at that point in time was the Coca-Cola. I chose to do this experiment on myself for that very reason: I can control my consumption and I am aware of everything consumed that may or may not affect the outcome of the data. If I were to redo this entire experiment, I would also choose to use myself as the test subject. This keeps my variables to a minimum and ensures uniformity. I would also make sure that at the time of the trials that nothing but the actual product, in this case Coca-Cola, was being consumed; especially in the time period between consumption of the test product and then the second blood pressure reading. I would also change my equipment. The blood pressure monitor that I used was manual and at times very unreliable. If given the opportunity to redo this, I would purchase a digital blood pressure cuff that would insure that each reading could not be the product of operator error. This could have also been a factor that led to the outlying data for trial three. Time between consumption and taking a blood pressure reading could also be altered. I used twenty minutes due to the fact that it is a short amount of time and I would not have to worry about controlling possible outside factors for a long period of time. However, if I had let the caffeine be in my system for up to 45 minutes, there may have been more of a noticeable effect to the change in my blood pressure. My hypothesis that the amount of caffeine in Coca-Cola will cause to the adrenal glands, located on the top of the kidneys, to release more adrenaline which will in turn cause an increase in blood pressure. As the amount of Coca-Cola decreases, so will the difference between initial blood pressure and blood pressure after the consumption of the caffeine was somewhat proven to be right. Yes, blood pressure did rise each and every time that I drank the Coca-Cola. However, the latter portion of the hypothesis is still unclear. It is unclear due to the data collected in trial three thus the need to redo trial three for an accurate confirmation of my hypothesis.

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

Rhetorical Analysis on Ads in Magazines Essay

Magazines are gaining in popularity nowadays as a tool not only to provide information, but also to advertise ads on products that are available on the market. Since magazines gain readers with different kinds of interest, what are the rhetorical strategies used by advertisers to market similar products to different target audiences of similar culture? Capturing the target audiences’ attention requires understanding about the audiences which open new avenues for many strategies to be used by advertisers to advertise an ad in order to make sure that the ad can actually capture the target audience. To describe or analyze the strategies used by advertisers, a variety of analytical tools, such as determining who the target audience is, describing the details in the ad, studying the Aristotelian appeals used by the advertisers, and also the angle of vision involved in the ad are needed to examine these strategies. Describing the details on an ad could be a good starting point to be gin the analysis. Kraft ad for its Macaroni & Cheese in Oct 2009 issue of Good Housekeeping magazine shows a girl standing on her right feet, with a smile carved on her face and an umbrella in her left hand. Beyond her are two boxes of macaroni and cheese of the same size, one of Store Brand and another one of Kraft, sitting right next to each other with the Kraft’s splashing a massive amount of cheese out of the box. What appears right above the bottom line, with all letters capitalized, is â€Å"KRAFT HAS MORE CHEESE THAN THOSE OTHER GUYS. SO GO FOR THE CHEESIEST† and with a bigger font right above it, is â€Å"THE MAC WITH MORE CHEESE†. Emphasis is on the color of the Kraft macaroni, the splashes of cheese, the girl’s boots, and the umbrella, as they stunningly boast the same magnificent orange color, unlike the one that is being used on the macaroni of the Store Brand. Sunchips came up with an ad in May 2010 issue of Women’s Health magazine on page 109. The focus in the ad is on a lady with a black hair and brown skin smiling as she is about to make her bite on a chip that she is holding. Right above her head is a sentence saying â€Å"THERE ARE MANY WAYS TO HELP THE PLANET. HARVEST CHEDDAR IS ONE†. Few steps beyond the lady, happily playing with the fresh water of in the river, are two kids. There is even rock face by the river’s edge with 4 people on it watching the kids. Nothing can beat the feel of having river water flows through our fingers and touches our body as we immerse ourselves in the water. It is amazing of how the water never stops flowing, not even a single second. SUNCHIPS claims that such beauty of nature can be sustain with the use of its plant-made bags that is compostable. Happiness is possible as we can enjoy every single bite of the chips and at the same time, taking care of the nature. Such joy can be seen on the face of the lady that is about to make her bite. Determining who the target audience is should be the next step after describing the details in the ad. Kraft appeared in the Good Housekeeping magazine, targeting middle class and a wide range of age of married women that care about the health of the their family, interested in learning home cooking for the family and care about environment. An article on â€Å"Cook like a Chef† provided some interesting food for the family and how to make it. This article is targeting married women in the middle class who would like to learn how to cook some interesting meal that is affordable for the family. Dixie Ultra came up with an ad showing a picture of a family happily having breakfast by using their paper plates. This ad targets on those who care about the environment. Fresh Step came up with an ad for its product, which is a healthy food for cats. This ad is clearly targeting those who care about the health for the family by promoting a healthy product for pets in the family. It is clear that based on the evidences found in the magazine, the advertiser for the ad for Kraft is basically targeting a wife and a mother that puts family above all else. The ad for Sunchips appeared in Women’s Health magazine, which targets middle-aged up to old women with middle-class income that care about staying in a healthy lifestyle as a consumer. â€Å"Run Less / Lose More†, an article in the magazine provided the information on how do women, ranges from middle-age up to old, lose fat in order to obtain a healthy and nice looking body. Another article that showed the reason for this type of target audience is â€Å"Lose your Fear of Lifting†, which gave some encouragement to women to get a perfect bodyline. The magazine came up with an ad, â€Å"Metabolic Max Program† by Jenny Craig that showed the target audience is those in pursuit of healthy lifestyle and at the same time concern about how much they will have to spend on such program. Another ad showed a lineup of affordable branded cosmetics products for the women. This ad is targeting middle class women that care about their beauty appearances. Different from the ad for Kraft, the advertiser for Sunchips’ ad targets any women, whether married or not, that put physical appearance and health as the top priorities in their life. Now that the details in the ads and the target audience have been figured out, making way for rhetorical analysis for both ads is needed to show how the strategies used by the advertisers to connect the details in the ads with the target audience and how do they give impacts to the audience. In order to attract the targeted audience, Kraft uses a strategy called pathos, one of the Aristotelian appeals, on its ad. The use of orange color in high contrast tends to attract the targeted audience as it can show the amount and quality of cheese that is being used to make the product. Cheese is well known for its delicious taste and good for the health, so this detail will surely attract the target audience, as they would want to choose a delicious and healthy food for the family. A picture of a girl standing on one leg with joy also brings the same strategy. The emotion that is being expressed by the girl tends to catch the targeted audience’s attention because they would definitely want to see their children having the same emotion. These two strategies tend to be related to pathos because they are attracting the audience’s emotions and values. The same strategy, pathos, is being used by Sunchips in order to steal the targeted audience’s attention. Pathos can be seen through the use of a picture of a lady that is about to make her bite on the chips. Such pleasant smile showed by the lady can attract the audience by creating a desire of having the same smile among the audience. Besides pathos, logos is also being used in the ad too. The message in the ad, about what are the bags of the chips made off, gives a clear reason to the targeted audience. For the consumers that care about their health, they will definitely give attention to this message because it helps them maintain a clean environment. Living in a clean environment is another way of having a healthy lifestyle. In this ad, the strategy of delivering emotions to the targeted consumers shows pathos. Logos can be seen by the reasons showed in the ad for the targeted consumers. Another strategy for the rhetorical analysis is the angle of vision, which is the use of important details to be focused on and the omitting of other details that may distract the audiences’ attention, is being involved in the ad too. For Kraft ad, the advertisers focuses on the massive amount of cheese spilling out from the box that contains Kraft’s macaroni and cheese product. The reason why the advertisers did this is because that they wanted to show how large is the amount of cheese present in the product. The advertisers include a text that suggests the macaroni product of Kraft has more cheese and a picture of a girl that is happily standing on one leg, for the audiences to focus on. The angle of vision presents in these details attracts the target audience by showing the quantity of the cheese and how appetizing the Kraft’s macaroni and cheese is. These would probably be the things that the target audience would want to focus on when it comes to choosing f ood that their kids will enjoy. However, there is a scientific detail that the advertisers try to omit from the audiences which is the ingredients used to make the product. Since the target audience wants healthy food for their families, this detail is being omitted because without doing so, the ingredients will show how unhealthy the food is. The advertisers for Sunchips ad focus on the image of a lady that is smiling while holding a chip on her hand and the use of the beautiful scene of a riverbank beyond the lady. Such beautiful smile of the lady and how wonderful is the environment shown in the ad are as if that the secrets lie behind the chips. This would attract the target audience as they would want to see such beauty in themselves. The same detail as in the Kraft ad, which is the nutritional information, is being omitted from the audiences because of not healthy. This is done because the advertisers know that the targeted audiences care about health in their life. The rhetorical strategies used by advertisers to advertise an ad are simply not just strategies. They are a step-by-step method to deliver the message in the ad to the target audiences; from determining whom the target audiences for the ad are, to how to give an impact to those audiences by using Aristotelian appeals and angle of vision in the ad to so that the target audiences could get a clear picture on what is being delivered by the ad. Analyzing the strategies used to deliver what the advertisers wanted to through the ad could actually open up new avenues on how to communicate with the audiences through an ad by just connecting the details available in the ad. In fact, it is not only helpful to gain audiences for an ad, but also to gain readers for our writing work. Works Cited Sunchips. Advertisement. Women’s Health May 2010: 109. Print. Kraft Macaroni and Cheese. Advertisement. Good Housekeeping Oct. 2009: 108. Print.

Financial analysis Essay Example | Topics and Well Written Essays - 1000 words

Financial analysis - Essay Example The startup of the whole food market venture in Canada will involve different foods supermarket chains that specialize in both the organic foods and natural foods. This venture aims at expanding in to the global markets within three years. The Canadian whole food will generally operate in the Canadian markets in one segment thus the organic foods supermarkets and the natural foods supermarket. The venture will operate in different stores across the Canada markets. The stores will be averagely 38,000 square feet depending with the location of the venture (Petusevsky, 2002). The venture will not be limited to grocery, produce and floral, bakery, coffee, tea, nutritional supplements, prepared foods and catering, whole fields and whole family of brands (Petusevsky, 2002). Opening the business venture in Canada will most definitely be a challenging prospect with issues ranging to exchange risks and settling in the new market ventures. In most cases, issues to do with financial risks tend to affect the business if not well taken care of (Rush, 2012). Foreign exchange risks in this case will exist when the financial transaction will be denominated in the currency, which is not part of the base currency, and in this case, the business venture. Alternatively, the foreign exchange risk will exist when the foreign lesser of the global venture maintains financial statements that is in the currency which is not part of the reporting currency and in this case the consolidated entity. For the case of the Canada whole foods venture, which operates abroad, there is high possibility that it would lose too much money even if the global venture is not prepared to crystalize its assets. Some of the common types of challenges that would most likely affect the venture due to foreign exchange risk include; translation exposure, economic exposure, and transaction exposure (Rush, 2012). Dealing

QUEEN CHRISTINA IN THE LIGHTS OF GERMAN EXPRESSIONISM Essay

QUEEN CHRISTINA IN THE LIGHTS OF GERMAN EXPRESSIONISM - Essay Example The movie shows us Queen Christina's personal desire for love and happiness and her affair with the Spanish Ambassador, Don Antonio De Pimentel. However, ultimately he dies leaving their love unfulfilled. Another aspect of the movie, which is of significance, is that Queen Christina wanted to be a human and not just some object, like women were treated in that time. Queen Christina displays German Expressionism, which was a kind of film movement and refers to the numerous interrelated artistic movements that had come about in Germany. Interweaving German Expressionist in the movies of the 1930's was an upcoming and new style, which was widely shown in many movies. Most of the developments, which took place in Germany, were due to this movement. A number of movies, including Queen Christina, served an important role in transporting the private emotions of a person in the open and thus, is intimately related to the concepts and ideas of the German Expressionism. As emphasized by German Expressionism the movie gives us emotional and extremely personal reactions. This movie uses many dark and light contrasts with tilted angles, exaggeration and dreamy atmospheres. In the early 19th century, most of the German films were copied from foreign films or were made for commercial usage. However, due to its intense success many other movies displaying expre ssionist style started appearing. After the First World War Germany faced confusion and unrest and there was hysteric misery everywhere. Social pressure created an atmosphere of terror. Queen Christina completely thrives on the continuous and always present fantastic, gruesome and mysterious element of terror. In addition, the film industry in Germany suffered a lot when the German economy was recovering. As inflation grew in Germany, films were very cheap and were easily sold in the foreign markets. However, with these upcoming Expressionist movies their budget raised making them a competition for the foreign movies. (Lamb, 2004) This movie also contains a number of elements of German Feminism. Throughout history, the story and lives of especially the women have been neglected to a larger amount. Their roles in various political matters, cultural and social changes have often been ignored. Queen Christina is feminist in the solid views that it has given. It gives us various views about bisexuality, female nobility and homosexuality. Queen Christina is one of the most appreciated movies of all times and gives us an insight to the personal and delicate struggle that women go through while trying to surpass the feelings she has towards a person whom society does not approve of. It shows us a delicate character of a very strong yet emotional woman facing many difficulties. Since it has portrayed bisexuals, the movie has often been criticized and disputed by a number of people. Queen Christina was one of the greatest movies in the wonderful era of silent movies. Although the movie contains elements of that time, it is amazingly ahead of time. It displays qualities of rich art at several levels. Queen Christina's secret love affair with the Spanish Ambassador served as a catalyst bringing up questions about Queen Christina's perspective, duty and also her

Zara Case Study Example | Topics and Well Written Essays - 500 words

Zara - Case Study Example The company has created a niche in the global retail industry through its aggressive and carefully planned out business model. The unique points of their business model that has helped them grow in relatively short time are primarily two folds. One, their turn around time that is as aggressive as it can be; where in, Zara believes in staying with the trend and comes up with entirely new chain of clothing style with in two weeks of time. They have always maintained and encouraged employees to come with clothes that are trendy and can attract customers for a certain amount of duration before going in for a complete revamp as soon as the trend is out of fashion. Secondly, its Just-In time inventory system, where it believes in having just the required amount of clothing stock. This not only helps them keeping the cost low, but also come up with entirely new style of clothing inspired from customers’ feedback in rather quick time.   The company spends almost a negligible amount in advertising as the Zara management has always maintained that what’s important is to give the customers that they want rather than force them to buy what you have. This principle has been the backbone of the Zara retails and has been the major point of success. Before launching their business in any new region, the management does a careful planning about the outlet positioning. Zara stores invariably are situated in main commercial areas and usually attract shoppers with its attractive and trendy display of stylish clothes. At the point of billing, customer service staff take extensive feedback from customers to identify the needs and the expectations of customers, this information is strictly followed while deciding on newer products. Surveys and customer feedback also allows Zara management to keep the prices competitive and within the range that the customers are happy to pay for and yet ensuring profits for the company. Th e large scale volume of business also allows Zara to

Globalization topic research paper Example | Topics and Well Written Essays - 2750 words

Globalization topic - Research Paper Example Hence, globalization has just made it simple. It is the change of thinking of the locals to open up their borders with a wider outlook of interconnections and interdependence to the rest of the world. (Baylis, Smith and Owens, 2013) They can exchange their capital goods at will while on the other hand; movement of labor is not prohibited. The measurements nonetheless, if not controlled can hurt the less developed economies. Thus, these seem not to bother the two countries as their economies are at par and have nothing to lose but just to maintain their superiority. The census from both countries shows that trade is the significant mutual benefits that the two countries enjoy. In fact, approximately 21% of the United States imports are from China making them the biggest trade partner in the early 21st century. A survey done by Yingyi, Qingguo,Chong’en, and Jisi, in (2014),in China on international politics and economy shows that both nations have enormous amount of capital goods and will be willing to exchange at their free will. China, on the other hand, has its primary essential from America that comprise of about 9% of their total imports, such is indeed a mutual relationship between the two countries. Baylis, Smith, and Owens, (2013) an educational study indicates that these ties will be long term benefits and is unlikely to end soon. It observes China has the largest population in the world providing the ready market for goods and services for the America economy. The population in China can help in the labor production that is requ ired in the America. Hence, Chinese are more than willing to partner the Americans since it provides for job destination to its millions of citizens reducing unemployment. Thus, the interdependence is just but the beginning of what mutual happenings are bound to happen between the two world economic giants. However, the interdependence between the

Make one up Essay Example | Topics and Well Written Essays - 750 words - 2

Make one up - Essay Example Religion has turned out to be one of the most influential parties in the issue of abortion. The laws of the land cannot also be ignored as they try to harmonize social issues and accommodate or reject abortion. On the same note, the pagans’ say on the matter has as well counted. All these aspects combined make abortion a complex issue across all known divides. The most important aspect in this case, however, is what the Goddess says about abortion. The Goddess holds abortion as one of the many forms of sacrifice. In this case, sacrifice practices are seen as a way of choosing between life and death. Most importantly, sacrifices are important to the Goddess. The more sacrifices humans make to the Goddess, the happier and satisfied she becomes. Those who sacrifice are also viewed as persons who can make essential decisions in life. The Goddess accepts sacrifices of many forms, and those made through abortion are just part of the sacrifice system. Abortion is all about love. Women who engage in abortion make sacrifices to the Goddess. This is a sign of love to the Goddess. On the other hand, women abort for varied reasons, all of which seem to conform to the Goddess’s system of taking sacrifices. In doing so, these women show their love in the death of the fetus. They have predetermined reasons to do so, a phenomenon that is held to be better for the unborn. As a result, abortion in this case becomes a way of expressing love to the unborn by women who abort. While religion would not hold any of the above to be the truth, the religion of the Goddess holds that some religions do not do women any good. This is evidenced by â€Å"the militant wing that identified patriarchal religion as the root of the problem of women subordination† (Stewart 280). The Goddess upholds the freedoms and rights of women, thereby countering the problem of subordination of women that is said to exist in patriarchal religion. Expression of love to

Discuss the failure of business journalism in reporting the great Essay

Discuss the failure of business journalism in reporting the great market crash of 1929 - Essay Example Furthermore, banks invested customers’ money in stock markets. The upward bound in the stock market was highly promising, and the great market crash in October 1929 hit everyone by surprise. Nevertheless, there had been warning signs like the mini-crash in March 25th, 1929 when prices began falling, but with the assurance of Charles Mitchell of the continued lending of money; the panic was suppressed (King 2000, p. 67). The spring of 1929 also gave more signs of a serious setback to the economy due to the slowing down of steel production, car sales and house constructions. During this time, some few individuals warned of impending serious crash in the stock markets, but they were cautioned, ignored and labelled as pessimists. Most economists believe in cycling of the overall economic activities between the expansion and contraction of the economic periods. The economic growth alternates with depressions and recessions. Analysis of the great depression indicates efforts by econ omists and journalists and their determination of the causes of depression (Burgan 2002, p.78). Discussion Business journalism requires that the journalists know exactly what is required, and the content should be critically analyzed before distribution to the audience. Some business journalists do not know the appropriate sources for their information to back up a story or an event. Others do not understand the principles of economics and the importance of stock markets. Some business reporting lack contextualization, which helps readers understand the meaning of the economic situation. The major goal of business reporting is to enhance more customer cover up and retention. This is especially notable since many people have shifted to the media for them to know the prevailing economic conditions. People simply want better business coverage (King 2000, p. 67). Business journalism in reporting during the great market crash The business journalists knew about the great depression, but their ignorance of the economic history was massive. Ignorance in expressing their opinions rendered everything wrong during their reporting on the great depression. For instance, in 1920, the forecasting reported on flourished economy and failure to recognize the coming depression, a factor that totally put them out of business. J. K. Galbraith’s reporting on The Great Crash 1929 relates to the forecasts of the Harvard Economic Service who failed in warning the business of impending depression. Galbraith wrote in November 1929 that the U.S. was not facing a protracted liquidation and that severe depression like that which was in 1929-1931 was less likely to be experienced. This, according to Wood, is shallow, misleading and lacks permanent value, and that any journalist who relies on ‘great market crash’ account by Galbraith deserves the sack (Ewing et al 2007, p. 1123-128). Business journalism failed to recognize the warnings from some economists of the impendi ng depression. For instance, Ludwig von Mises, in summer of 1929, refused a job offer in Kreditanstalt Bank since he saw the coming of the depression and feared to be associated with it. Furthermore, Mises warned that the loose money policies by central banks would have caused the depression. Also, Friedlich von Hayek warned of the impending depression in US. Writing from the Institute of Economic Research in

Jazz Music in America from 1900 to 1920 Research Paper

Jazz Music in America from 1900 to 1920 - Research Paper Example As the discussion stresses  originating in the United States among African American communities, jazz has played a powerful role with respect to the development of popular music within the 20th century. This form of music was originally the result of a type of synthesis between African and European/American musical instruments/styles. Key elements that help to differentiate jazz from other forms of music are concentric upon the fact that jazz incorporates the following components: swing tones, improvisation, syncopation, blue notes, and poly-rhythms. Additionally, jazz has also incorporated elements of American popular music, further proving it to be one of the most syncretic types of music. As a direct result of the change that has been presented with respect to jazz music, it has continued to evolve and currently represents one of the most dynamic musical genres.  From this paper it is clear that  the transatlantic slave trade can at least be partially understood to have cont ributed heavily to the influx of jazz instrumentation, style and culture from parts of West Africa. During this time, roughly half a million sub-Saharan Africans were taken to the Americas. These slaves were mainly from West Africa and the majority of them originated from the Congo River basin. With them, they brought strong musical traditions.  Understanding concerning jazz music cannot be wholly separated from an understanding of unique societal patterns and/or the politics of a particular era.

Offshore Outsourcing in Service Sector Essay Example | Topics and Well Written Essays - 2000 words

Offshore Outsourcing in Service Sector - Essay Example This essay stresses that  firms face complex decisions as they outsource reorganize the value chains as offshore outsource some activities. The firms consider the cost reduction, standardisation of product quality, human capital and risks involved in reorganizing their value chains. The reorganization of value chains have tended to retain the knowledge oriented skills in the industrialised nations while the lower value added functions are transferred to developing or less advanced countries. Certain industries demonstrate ‘path dependent’ tendencies towards concentration of the processes and people in certain localities of the advanced countries such as the Silicon Valley in California.As the paper highlights  the geographical concentration is driven by the access to talented software engineers and programmers. The proximity to large pool of sophisticated users also driven the locality concentration of such firms. The firms have realised the economic gains of geograp hically separating the programming functions to lower cost countries overseas while the introduction of networked computer terminals have added more impetus for relocation of the routine functions.  Offshoring software development in Ukraine is driven by the culture of research and availability of highly talented personnel due to reliance of IT technologies. The increased competitiveness has forced firms to reengineer in order to seek new knowledge and arbitrage costs.

Tesla Motors, Inc. Financial Analysis Essay Example for Free

Tesla Motors, Inc. Financial Analysis Essay Tesla Motors, Inc. was founded in early 2003 by a group of Silicon Valley engineers, incorporated in Delaware on July 1 of the same year, and is now headquartered in Palo Alto, California (â€Å"Tesla- Investor†). The company designs, develops and manufactures electric vehicles and electric vehicle powertrain components. Tesla also provides the same services and powertrain components to other manufacturers of electric vehicles (Analysts Corner 2). Tesla Motors is best known for producing the Tesla Roadster, an all electric sports car released in 2008, with outstanding performance results (â€Å"About Tesla†). Tesla has developed a unique marketing plan that differs from the traditional automotive industry by marketing and selling its products over the internet and through a global network of 32 stores that are owned and operated by Tesla. The company has 2,964 employees and has electric vehicles on the road in 37 countries around the globe (â€Å"About Tesla†; Analysts Corner 2). On June 29, 2010 Tesla Motors Inc. became the first American car company to go public since the Ford Motor Company in 1956. The company offered 11.8 million shares priced at $1 above the initial offering at $17. The IPO was so successful that Tesla offered an additional 20% equity in the company by increasing the offering to $13.3 million shares to meet demand. The company raised a total of $226 million, the stock price soaring up 41% to $25 a share on opening day (Andrejczak). Today, Tesla Motors Inc. (TSLA) is traded on the NASDAQ stock exchange at a price of more than $180 a share. The stock has grown over 800% since its IPO and now Tesla has a market capitalization of over $22 billion. This impressive increase in stock price may come as a surprise to many investors due to the company’s earnings, or lack thereof. Given the current economic conditions, the relatively new market and existing competition; the Tesla stock price is grossly overvalued. Tesla produces an outstanding product with an outstanding price tag. The new Tesla S was designed to compete in the luxury sports car arena along with BMW, Mercedes and Audi. Tesla has estimated that 21,000 Model S will roll out of its production facility in 2013 at a sticker price between $70,000 and $100,000 (Seetharaman; Finger). This year, Mercedes will sell 25,000 luxury sedans in that price range to U.S. consumers and BMW just slightly less than Mercedes (Finger). Tesla has estimated it will produce 40,000 Model S next year, almost twice the number of BMW’s sold in that price range. In the post recessionary economic climate of 2013, there is not a strong demand for vehicles in this price range, even with the $7500 tax credit offered to consumers. The Tesla Model S is still out of reach for most Americans. There are more reasonably priced alternatives in the EV market such as the Nissan Leaf and the Chevy Volt, but sales have been sluggish (Stammers; Alpert). General Motors recently â€Å"announced a $5,000 formal price cut for the Chevrolet Volt plug-in hybrid† and Chrysler has opted to stay out of the EV market until â€Å"dragged there by consumers† (Buss). Since the key to the future profitability of Tesla Motors is in the mass production of an affordable EV, demand for the product becomes paramount. As Dale Buss, an automotive industry journalist explains; It’s one thing for Tesla to sell nearly 1,500 Model S a month at about $70,000 apiece in the U.S. market these days; when Elon Musk’s startup attempts to penetrate the lower part of the EV market with its own model, success will be a lot harder to come by. (Buss) Tesla intends to meet that challenge with the 2016 release of the Gen. III., a Tesla EV with the range of a Model S, but half the price. The 200 mile range of a Tesla EV is what gives it a competitive advantage over other more affordable EVs, but at the cost of additional batteries (Alpert). The added cost of the batteries makes the target price next to impossible to reach, but necessary in order to avoid the fate of other affordable EVs, such as the Volt. So, rather than realize their goal †to accelerate the world’s transition to electric mobility with a full range of increasingly affordable electric cars†(â€Å"Tesla Improves†), Tesla Motors is â€Å"helping create a highly bifurcated segment where only expensive EVs can achieve a feasible volume† while pushing affordable EVs and its own profitable future further out of reach (Buss). Tesla Motors CEO, Elon Musk, maintains that there is still adequate demand for the Model S and that the company has received 10,000 orders in North America alone. The truth of the matter is that the true demand for EVs remains to be seen. In May of 2013, Tesla shocked analysts when it â€Å"stopped disclosing its end-of-quarter order backlog which might have shed light on the issue after previously trumpeting a 15,000-unit reservation list† (Alpert). Many of Tesla’s orders were cancelled due to the company’s price increases on options for the Model S, which added an average 8%-9% to the overall price and stirred up numerous customer complaints on the Tesla website blog (Finger; Blanco). Many consumers are interested in reducing CO2 and reducing our dependence on fossil fuels, but simply cannot afford the more expensive alternatives. In an attempt to make Tesla vehicles more affordable and increase sales; Tesla Motors has partnered with U.S. Bank and Wells Fargo Bank to provide financing to qualified Model S customers that includes longer terms, lower payments and the Tesla resale guarantee. â€Å"Buying a Model S through the Tesla financing offering now comes with a guarantee that the resale value will be higher than that of BMW, Audi, Mercedes, Lexus or Jaguar† (â€Å"Tesla Improves†). The guarantee is personally backed by the CEO of Tesla Motors â€Å"to give owners complete peace of mind about the long term value of the product† (â€Å"Tesla Improves†). Elon Musk, Tesla’s chief executive described the financing program during an interview; If our car was chiefly available for purchase and not by financing, I think thats maybe accessible to roughly 1 million US households. A financed product with the right financing-fully optimized financing, I think its probably accessible to the top 10 million households. (Seetharaman) Musk went on to report that since the finance program was implemented, the company has experienced â€Å"a meaningful increase in demand† that he estimates to be about 30,000 cars a year in North America (Seetharaman). There is little doubt that the Tesla financing program has boosted sales. â€Å"In the second quarter 30 percent of sales fell into this category† (Finger). Amazingly, against generally accepted accounting principles (GAAP), Tesla records the entire amount of the payment it receives from the bank as  revenue. The bank pays the full amount of the car price to Tesla, but after the 3 year lease agreement ends, Tesla pays the outstanding balance of around $46,000 when it takes the car back as part of the buy-back guarantee. According to GAAP, the $46,000 would be a debt until the bank has been paid. This method of recording debt as revenue makes Tesla look more appealing on the books. Meanwhile, Tesla builds up an expensive and costly inventory of used Model S sedans as it works hard to convince consumers that the model S should be replaced, which effectively lowers the value of their own used inventory. The buy back guarantee has the potential to become a serious liability for the new car company. In an effort to promote the use of cleaner technology and improve the air quality in the state of California, law makers there have implemented a program whereby automobile producers can earn credits for every zero-emission-vehicle (ZEV) and partial-zero-emission-vehicle (PZEV) brought to or sold in the state. Manufacturers can only sell a certain amount of vehicles that don’t fall into this category. The manufacturers then have the option to purchase more credits from other companies in order to continue selling vehicles in California. Tesla earns credit every time the company sells a Model S and these credits are sold to competitors. It has been estimated that for every Model S sold, Tesla receives between $25,000 to $35,000 worth of these credits and could contribute about $188 million in revenue for 2013 (Isidore). This year Tesla Motors Inc. reported its first profit in the first quarter and â€Å"better than projected earnings† for the second quarter (Isidore). A closer look at the source of that revenue reveals that it did not come from sales of vehicles, but actually from the selling of zero-emissions-credits. Tesla sold a total of $68 million worth of the credits, 12% of its total revenue in the first quarter (O’Brien). Despite the source of the income, Tesla Motors stock price rose 17% when the quarterly report was released (Seetharaman). Another contributing factor to the run-away stock price is that historically Tesla stock has been a favorite amongst investors for shorting. One analyst at Barron’s reported that as much as 45% of Tesla outstanding shares were shorted until the first quarter earnings were released (O’Brien). When the  stock began its climb, the short sellers were forced to buy back at higher prices than they had hoped to, which effectively drove the market price higher. Investors also responded favorably to the news that the Model S won The Car and Driver Magazine’s Car of the Year for 2013 and again when the Model S earned the highest score ever given to an automobile by Consumer’s Reports (O’Brien, Finger). The forward price earnings ratio for Tesla Motors is in the neighborhood of 170. A high price-earnings ratio suggests that investors are expecting future growth and earnings. An exceptionally high P/E is indicative of a speculative bubble and overvaluation. Market Capitalization for Tesla Motors Inc. has doubled within the last couple months and is now over $22 billion. The value of an enterprise for profit is dependent on what it can produce or profit from moving forward into the future, and in doing so increase the wealth of the ownership. Tesla Motors will produce roughly 20,000 cars this year and plans on doubling that output for 2014. That makes every car Tesla produced this year worth $1.1 million of stock. Compare that number to â€Å"luxury automaker BMW that has a market cap of$52.79 billion on global sales of 1.85 million cars or $28.53 thousand per car. Mercedes Benz produces cars at $43.4 thousand per car using the same calculation (Finger). Tesla will have to produce hundreds of thousands of vehicles to support a stock price even half of what it is currently. With the limited amount of demand at the current price, the stock can soar as high as the market will allow it, but the price will have no foundation and will eventually crumble down. The over-valuation leaves Tesla poised for a buyout or takeover by a larger manufacturer. Tesla Motors is obviously good at what it does. The technology developed by the company is being used by other more established automobile makers, such as Toyota and Daimler. The problem with specialization is that it leaves a company, especially a large one, vulnerable to changes in technology, market shifts and consumer sentiments. Tesla has all of its eggs in one basket. A significant shift in the market, a radical change in technology or even consumers warming to the hybrid concept before taking to the electric one; would be the end for the new car company. It seems that Tesla has nothing to fall back on, no contingency or alternate plan. Although Tesla has taken the electric vehicle to new heights, the technology is not break-through and the concept is not mind blowing. The whole package is a winning combination, in a small segment of the market. The company may be headquartered in California and founded by geeks, but it is still an automobile producer. The automotive industry is dominated by an oligopoly of corporations that historically have been successful at weeding out smaller companies just like Tesla Motors. The competition is fierce and the pockets are deep, economies of scale are a reality in automobile manufacturing. The patents and proprietary technology that Tesla holds right now will be meaningless in a matter of a few years, or several months. If the demand for electric cars increases significantly, Tesla will be forced to compete. Without the differentiation that Tesla has now, the company doesn’t have much of a chance. Tesla has a challenging future; in order to survive it must lower its costs and crank up production. The niche market of wealthy movie stars that want to be seen in a Tesla Roadsters isn’t going to get them there. The high price people are willing to pay for their stock isn’t going to either. Tesla Motors Inc. produces amazing all electric vehicles that are sporty, luxurious, and state of the art. The company’s CEO and spokesperson, Elon Musk is an innovative billionaire, who is enthusiastic, confident, and as cool as the Tesla Roadster. The company has turned the automotive industry upside-down and investors want a piece of it. The stock price is just waiting for another promising news story or SEC filing to soar even higher.†Tesla is, according to all the critics, an incredible car, but it is a company held together with financial bubblegum† (Finger). The only question is when will the bubble burst? Works Cited About Tesla Motors. Teslamotors.com, 2013. Web. 23 Sep 2013. . Alpert, Bill. Recharge Now!. Online.barrons.com, 2013. Web. 24 Sep 2013.