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PHASE 4: INDIVIDUAL PORTION OF GROUP PROJECT KENNETH C HOLMES MGMT600-1502A-01 PROFESSOR HENRIETTA OKORO MAY 11, 2015 CHOICE OF MULTIVARIATE TECHNIQUE AND ITS USES FOR WIDECORP Introduction: Previously I was charged with researching three multivariate techniques: Factor Analysis; Multidimensional Scaling; and Cluster analysis. Now I am charged with choosing which of the three techniques is most suitable for WidgeCorp to apply for its own business purposes, explain why this technique is the most suitable, and how this technique will benefit WidgeCorp. This document explains my findings. The three types of Multivariate Analyses: Factor Analysis: The mission of Factor Analysis is to design the perfect product for the customer base to increase sales. Factor Analysis helps companies determine which marketing efforts to pursue, which need further evaluation, and which to eliminate. Factor Analysis involves changing one variable at a time to determine the results, because there may be many variable, and each variable must be tested independently to determine the outcome. The process is based on obtaining focus group preferences based on comparisons of several products, determining the product they liked best, and why?, enabling the manufacturer to gather information from respondents without making costly changes to the product. Changes can include: improved flavor, more flavor choices, improved quality, improved features, improved performance, more appealing packaging, product size, more color options, enhanced product distribution, product advertising, level of service, etc. Once the favored product has been determined, the manufacturer can then design and manufacture the (ideal) perfect product to place in the market, and increase sales. After the redesigned product has been placed in the market, the manufacturer can then compare the sales before and after the changes, and properly conclude through Factor Analysis the changes made are important to their customer base. The fact is Factor Analysis is not a science, it takes large scale testing to determine the relationship of the variables, and it is best to have an outside market research expert to conduct the Factor Analysis and evaluate the results to ensure accurate cause and effect relationships (Lorette, 2015). Multidimensional Scaling: The mission of Multidimensional Scaling (MDS) is to design the (ideal) perfect product for their customer base. MDS is a statistical techniques commonly used in business marketing and social science used to ascertain consumer attitudes and preferences about product similarities. The process requires respondents to evaluate the similarities between products, compare them to what they see as the perfect product, and then their responses are charted on a perceptual map, a grid with both X and Y axes representing specific product aspects (N.A., Multidimensional Scaling, 2015). Then the manufacturer designs the perfect product for their customer based on analysis of the perpetual map, meaning the features respondents prefer. After the perfect product has been placed in the market, the manufacturer can then determine the success of the product based on sales. Multi Dimensions Scaling is preferred over Factor Analysis because the process relies on respondent’s preferences, and not preset attributes, or the researcher judgements (N.A., Multidimensional Scaling in Marketing, 2015). Cluster Analysis: Cluster Analysis is popular technique widely used to segment customers, products and stores based on patterns. Cluster Analysis is an explanatory data analysis tool that takes similar observations from a larger population and breaks them into smaller groups based on maximal or minimal correlation. Cluster Analysis has many features including: the ability to find concealed patterns and structures in data without a specific assumption; the ability to identify similarities in specific behaviors or parameters; and the ability to reveal patterns in data without explaining why they are there. There are two types of Cluster Analysis including: non-hierarchical which divides larger datasets into smaller data sets, until only one cluster remains (the method divides a larger data set of N objects into M clusters, and K means); and hierarchical which clusters related objects until only one cluster remains, in a hierarchical manner. Which type used is based on the objective of clustering, the type of output desired, the hardware and software facilities available, and the size of the dataset. Clustering is used for several reasons including: performing segmentation from a larger group of data. For example, clustering similar products based on their attributes; anomaly detection. For example, identifying fraudulent transactions; and breaking large data sets into smaller groups for use with other testing techniques (Vohra, 2011). Steps in Cluster Analysis (E. Mooi, 2011): 1) Choose the appropriate variable to cluster: This step is vital because the wrong assumptions and variables, will lead the wrong segmentation, and the wrong marketing strategies. Chart 1 referenced from (E. Mooi, 2011). Chart 1 TYPES AND EXAMPLES OF CLUSTERING VARIABLES General Specific Observable (directly measurable) Cultural, geographical, User status, usage frequency, store demographic, socio-economic and brand loyalty Unobservable (inferred) Psychographics, values, Benefits, perceptions, attitudes, personality, lifestyle intentions, preferences Adapted from Wedel and Kamakura (2000) 2) Determine the clustering method to form the cluster groups: This step is vital because different methods have different decisions before analysis can begin. Clustering methods include both hierarchical and non-hierarchical. 3) Determine the number of clusters needed: A few clusters is easier to understand and determine the necessary marketing strategies, while more clusters helps to determine the differences between the segments, and provides for more marketing strategy options. In other words, the target markets must be large enough to be profitable. 4) Label the final clusters and interpret the solution: This step ensures correct interpretation of the results and determines the appropriate marketing strategies. Real life companies that use Cluster Analysis: SABMiller, a leading international beer brewers and producer of Coca Cola products used Cluster Analysis to: classify their liquids to make communication easier for both marketing and technical staff; help consumers understand the specific differences between beers; determine consumer preferences; identify opportunities for their brands; and to determine the acceptability of their brands in local and international markets (Camo, 2015). ConnectFast Inc. (a cellular telecom company) used Cluster analysis to: segment their customers based on local and international calling habits, age, and income, and then developed advertising and marketing strategies to attract additional customers based on those parameters, and minimize customers changing cellular service providers, all while optimizing their cost. ConnectFast offers: prepaid and postpaid billing; internet plans with 2G, 3G, and 4G data plans; national and international calling; national and international roaming; and national and international data roaming; all to provide options based on their customer segments (Upadhyay, 2013). Travel Alberta (the social media, marketing, and advertising organization for travel to Alberta) used Cluster Analysis to design a profitable advertising and promotion campaign “Travel Alberta Made To Order” by segmenting domestic tourists based on their decision making behavior: their travel, climate, and activity preferences (Ritchie, 2002). Saks Fifth Avenue was facing declining sales, and decreased turnover, and responded by reducing their inventory and improving their fashion content. They used Cluster Analysis to gain a better understanding of customer’s preferences, and their attitudes about buying and shopping through the use of questionnaires’. The information was then used to create marketing and advertising based on those preferences, expand their Men’s accessories collection in their new Beverly Hills store, and then expanded the men’s Accessory shop into other stores. This efforts increased sales, and enhanced their visibility in a market that is frequently ignored because of a lack of expertise in purchasing the products (E. Mooi, 2011). The differences between the techniques: Both Factor Analysis and Multidimensional Scaling (MDS) have the mission to design the perfect (ideal) product for the customer base to increase sales. Both methods use focus groups to compare products to determine their preferred product and why, both methods use the information to determine the favored product without making costly changes, both methods use the information gathered to design and produce the perfect (ideal) product for the customer base and place the new product on the market, and both methods evaluate the new product design based on sales before and after the changes. The differences lie in how they achieve and analyze the responses. Factor Analysis: uses preset attributes to determine the respondent’s favorite; uses the researchers’ judgement to analyze the data and results; uses large scale testing to determine the relationship of the variables; and usually require an outside market research expert to conduct the analysis and evaluate the results to ensure accuracy. MDS: uses respondents preferences instead of preset attributes; has the respondent compare the similarities between products and compare them to their vision of the perfect (ideal) product; charts the responses on a perceptual map (a grid with both X and Y axes representing specific product aspects); relies on the results instead of the researcher’s judgement; and for all those reasons is preferred over Factor Analysis (N.A., Multidimensional Scaling, 2015 and N.A., Multidimensional Scaling in Marketing, 2015). Cluster Analysis is widely used to segment customers, products and stores. Cluster Analysis does not use focus groups to determine customer preferences. Cluster Analysis is used to: reduce a larger data set of similar observations into smaller clusters based on level of correlation; can be used to cluster data in a hierarchical manner; identifies specific behaviors or parameters; reveals patterns in data without explaining why they are there; can be used to create new products for the customer based on consumer patterns identified; can be used for anomaly detection including fraudulent transactions; and for breaking large data sets into smaller groups for use with other testing techniques (Vohra, 2011). In conclusion, Factor Analysis and Multidimensional Analysis are used to design and produce the perfect (ideal) product based on respondents preferences, while Cluster Analysis is used to segment customers, products, and stores based on specific parameters including: gender; ethnicity; age; income; frequency of use; price of the product they use; type of product they use; the variety of product a store carries; the sales of the products the store carries; and then create advertising and marketing strategies, and new or improved products for each segment of the customer base. Business Clustering: Clustering is more than just categorizing your customer base by various parameters. Regarding business and industry: clusters are connected firms and institutions in related or unrelated industries; they can be the competition or complementary businesses; they are located in the same industrial park or area; and usually use the same supporting services. Supporting service usually include: parts, component, and raw material suppliers; tech services; and shipping and delivery services. The combined force of these businesses enables resource and solution sharing to create and take advantage of market opportunities. Examples include: London’s fashion district and industry; London’s film industry including the BBC: Los Angeles and the film, music, and fashion industries; Miami’s fashion and film industry; New York City’s fashion district; New York City’s financial district, including brokerage houses and banking; New York City’s SoHo art district; Paris’s fashion district and industry; Paris’s art district; and many more (Advameg Inc., 2015). Business clustering benefits include (Advameg Inc., 2015): Productivity increases related to specialization, the sharing of information, coalitions, and public goods access. Can create a competitive advantage through cooperative purchasing of products, supplies, and services. Encourages product innovation due to competition and research cooperatives. Encourages expansion of business into new markets and territories. Encourages local economic growth and development, and employment recruitment. Attracts investors both domestic and foreign. Encourages relationships with the local chambers of commerce. The chosen Multivariate technique: What technique was selected and why: The group as a whole determined Cluster Analysis to be the most useful and practical for WidgeCorp to use. This conclusion is based on extensive research of the subject, well thought out comparisons of the three techniques, the purposes of each technique, case studies from various industries, and the next step for WidgeCorp based on their situation. Since they are a market leader in the snack food industry with a recently added cold beverage line, we determined Widgecorp needs to know their customer bases, have a full understanding of the products they are purchasing, and to create effective marketing and advertising campaigns, and all of this requires separating and identifying the market segments to make the process cost effective. The application of Factor Analysis or Multidimensional Scaling would only slow down progress, since both techniques have the mission of designing and producing the perfect product, both require trending data on markets we have just entered, and that we are lacking trending data on. At this point WidgeCorp needs to establish advertising and marketing campaigns to create more exposure for their snack food line, and encourage consumers to try our cold beverage line. The combined force of the snack food and cold beverage lines, with an effective advertising and marketing strategy have the potential to make WidgeCorp a highly profitable and worthy competitor in the snack and beverage industry, and potentially make WidgeCorp a snack food and cold beverage GIANT. Once we have produced a successful marketing and advertising campaign, then we can take that information and concentrate our efforts on enhancing the lines with fabulous new snacks and beverages, while being cost efficient in the process. I must also add that Cluster Analysis is useful for segmenting customer, products, and stores, and for clustering a business based on the industry and region or regions of operation. The benefits are immense, and the results will make a big difference on: sales, profits, and valuable business connections and arrangement with the industry supply chain and the competition. Benefits of using Cluster Analysis for WidgeCorp: Segmenting their retailer base by types and quantities of products sold to maximize the availability and distribution of those products, and drive sales. Segment their consumer base by: age; gender; income; marital status; family status (have children); ethnicity; and dietary habits (vegetarian or non-vegetarian, or healthy snacks and beverages vs. traditional snacks and beverages). Segment their product base according to product purchases and frequency. Use their market segmentation, and information from their consumer hotline to create additional products specifically for their market segments. Create advertising and marketing campaigns to target specific segments of their consumer base, and drive sales. Create advertising to educate their consumers regarding WidgeCorp’s line of healthy and traditional snacks and beverages, all to encourage existing and new customers to try their products, and drive sales. Applications of business clustering for WidgeCorp: Increase their productivity by sharing industry and consumer preference research information. Create a competitive edge over the competition through the use of cooperative purchasing agreements with cooperative competitors and support services. Increase their innovation, and expand their product line through competition. Expanding their business into untapped national and international markets. They can use their success to attract investors from around the world. Develop a competitive edge through strong relationships with the local chambers of commerce, and local businesses and schools to create brand loyalty to WidgeCorp and their product line. Marketing ideas for WidgeCorp: Create a magazine and television advertising campaign using a series of advertisements depicting women, men, children, and families from each market segment with both the traditional and healthy lines of snacks and beverages, and drive sales. Create a series of in store promotional boards depicting women, men, children, and families with both the traditional and healthy lines of snacks and beverages, and drive sales. Create magazine, television, and internet advertising campaigns promoting WidgeCorp’s participation in the Student Healthy Lunch Program, to create positive publicity for WidgeCorp, potentially establish new clients, and drive sales. Product innovation ideas for WidgeCorp: WidgeCorp could use customer segmentation to expand their product lines by using products purchased by that segment as a reference, without the need for additional research, and cost effectively. WidgeCorp could add prepackaged snack and lunch combinations (snack, sandwich and beverage), both traditional and healthier versions, to make lunch choices for kids easier, and to make product purchasing easier for the consumer. Conclusion: Based on extensive research and detailed case studies, Cluster Analysis would provide the most benefits for WidgeCorp. By using Cluster Analysis: Widgecorp will be able to segment their customer base, product line, and retailer base; determine which products are purchased by what segment; the volume of the purchases; and enable advertising and marketing to create campaigns geared toward those segments. In addition to those benefits Widgecorp will also be able to determine: if adjustments are needed for production and shipping of their products; if replenishment levels are acceptable or need adjustments; if the current advertising and promotion of their products need adjustments; and develop invaluable relationships with business and institutions to create the brand loyalty essential for a competitive edge. WidgeCorp will also be able to educate the consumer about their product line, and the difference between theirs and the competition’s, and enhance their strategy on meeting consumer demand and incorporate customer feedback. In conclusion, addressing these questions will help WidgeCorp address any issues, add new snack and beverage products, increase product exposure, increase their brand loyalty, increase sales, and achieve the most results for their money. REFERENCES Advameg Inc. (2015). Clusters - advantage, benefits, Benefits of clustering. Retrieved from Reference for Business: EncyclopediafFor Business: www.referenceforbusiness.com › Bo-Co Camo. (2015). SABMiller: Bringing science to the art of brewing better beers . Retrieved from Case Studies: Multivariate Data Analysis-Camo: www.camo.com/resources/casestudies.html E. Mooi, a. M. (2011). Cluster Analysis. Retrieved from Springer.com: www.springer.com/cda/content/document/cda_downloaddocument/... Lorette, K. (2015). Importance of Factor Analysis in Marketing. Retrieved from smallbusiness.chron.com › … › Importance of Marketing N.A. (2015). Multidimensional Scaling. Retrieved from www.allbusiness.com/barrons_dictionary/dictionary-multidimensional... N.A. (2015, May 3). Multidimensional Scaling in Marketing. Retrieved from Econonomic Expert.com: www.economicexpert.com/a/Multi:dimensional:scaling:in:marketing.htm Ritchie, S. H. (2002, July 8). Understanding the domestic market using cluster analysis: A case study of the marketing efforts of Travel Alberta . Retrieved from Sage Publications: jvm.sagepub.com/content/8/3/263.abstract Upadhyay, R. (2013, November 10). Customer Segmentation & Cluster Analysis – Telecom Case Study (Part 1). Retrieved from YOU CANanalytics: ucanalytics.com/blogs/customersegmentation-cluster-analysis... Vohra, G. (2011, February 23). Cluster Analysis for Business. Retrieved from ezinearticles.com › Business Peer evaluations: Appendix 1 Peer Evaluation Form Kenneth C Holmes/Group #3 My Group Members: (not including me) NAME (First, Last) 1 Nicole, Paulson 2 Jean Renel, Casimir 3 Joni, Allen 4 Jeanette, Pitchford 5 Marlon, Ashley 6 Irina, Knight 7 Xin Hui, Li 1.Timeliness 18/20 18 /20 20/20 18/20 0/20 20/20 18/20 2.Neatness 20/20 20/20 20/20 20/20 0/20 20/20 20/20 3.Correctness 20/20 18/20 20/20 18/20 0/20 15/20 20/20 4.Cooperativeness 20/20 20/20 20/20 20/20 0/20 20/20 20/20 5.Contributed his/her Fair Share 20/20 20/20 20/20 20/20 0/20 20/20 20/20 98/100 96/100 100/100 96/100 0/100 95/100 TOTAL My experience with group 3: Since I was the first to communicate with the entire group, I was by default chosen as the group leader. The group discussion board started out with two posts by 5/5/15, but by 5/7/15 the last three participating group members posted their research, and the communication flowed rather well. The participating members include: Joni Allen, Kenneth C Holmes, Irina Knight, Xin Hui Li, Jean Renel Casimir, Nicole Paulson, and Jeanette Pitchford. The final responses regarding the consensus arrived on Saturday 5/10/15, which prompted me to post the final consensus. This delay annoyed my because I hoped for an earlier consensus, but it worked out. I scored the members as follows. Joni Allen received a (100%), because her posts were on time, neat and easy to understand, her research is accurate and her case studies are excellent, she was cooperative and a big help with gaining consensus, and she contributed her fair share. Irina Knight received a (95%), because her posts were on time, neat and easy to read, her research was accurate: but she only discussed Cluster Analysis and used an excellent case study, she was very cooperative, and contributed her fair share. Xin Hui Li received a (98%), because her posts were timely: except she waited till Saturday evening to post her consensus, her posts were neat and easy to read, her research was accurate and used some good case studies: except for the last one, where I do not know what they do or used it for, she was cooperative, and she contributed her fair share. Jean Renel Casimir received a (96%), because her posts were timely: except she also waited until Saturday night to post her consensus, her posts were neat and easy to read, her research was accurate: but she did not provide a case study for MDS, she was cooperative, and she contributed her fair share. Nicole Paulson: received a (98%), because her post was late: she posted on Sunday night and I understand why, her posts were neat and easy to read, her research was accurate and she used good case studies, she was cooperative, and she contributed her fair share. Jeanette Pitchford: received a (96%), because her post was late: she also posted on Sunday night and I understand why, her posts were neat and easy to read, her research was accurate and she used examples of how the methods are used: but she did not discuss an example of Cluster Analysis, she was cooperative, and she contributed her fair share. Since we were missing one member there results are as follows. Marlon Ashley received a (zero) because he did not participate. He emailed me asking what his part of the project is, and that he will do it on the weekend. I emailed him all relevant information regarding his part, and we have no posts from him. Overall it was a good experience. I am just not a fan of waiting for responses from others. I am the type who wants to get things done, and does not like delays. For future reference, I think you would get a much better mix of subjects if we were able to choose what technique we 98/100 wanted to write about. That would avoid problems and delays in consensus, and provide you a larger mix of techniques to read about.