Cluster Analysis
Cluster analysis is one of the crucial techniques undertaken by business organizations for the purpose of analyzing the trends and characteristics of a marketplace. Cluster analysis is the process in which different cases or observations, and entities that are having homogeneous characteristics are undertaken in a group. This group is called as cluster. All the entities included in a cluster show similar trends and activities, while dissimilarities in two different clusters can be traced quite easily. This cluster analysis is useful for companies, as it reduces the workload of conducting marketing research. Through this measure, rather than observing individual customers, the company observes a specific set of homogeneous customers or clusters for the purpose of tracing a specific trend (Romesburg, 2004). There are a number of different fields such as market prediction, forecasting, pattern cognition, information retrieval, trend analysis, in which intensive use of cluster analysis is taken into account (Everitt, Landau, Leese & Stahl, 2011).
Practical Implications of Cluster Analysis
For business organizations, cluster analysis has some crucial utility as it makes the marketing research more efficient and explicit. A number of different crucial multinational companies have started to undertaken cluster analysis as one of their important strategic analysis ools (Everitt, Landau, Leese & Stahl, 2011).. The example of Autonomy Corporation, a UK based multinational enterprise software company, can be the most suitable. The company has been using its IDOL engine for many years in order to undertaken cluster analysis for predicting marketing trends. In this process through IDOL engine, the company provides an interactive platform to its customers, on which they can provide their feedbacks and suggestion. The company deals in different products as well as Own special purpose products. On the basis of usage of different types of product, the company categorizes its customers into different clusters. Increasing usage of social media by people has also provided an intensive way for the company to collect some essential information and views of different customers on the basis of which, different clusters are prepared by the company (HP Autonomy, 2013). A cluster is considered as a separate entity by the company and at the time of pilot testing, needs, requirements, and demands of every individual cluster is taken into account.
Another crucial example of the implication of cluster analysis is WiseWindow, a leading marketing research company and private data providers. The company utilized cluster analysis technique for the purpose of conducting market analysis of Music industry for one of its client. Under this technique, the company analyzed about 25 million comments on about top 500 musical concert and acts within the US in a month. The collective trend of the market, predicted on the basis of analysis of different clusters showed 84 different aspects of an artist, such as his brand positioning, value, stage presence, music sense, which can be regarded as indicators of success and failure of the show. In this way, it can be reflected that company designed its future policies on the basis of past and collective information from different clusters (Woods, 2010).
Not only business organization, but different firms operating in marketing research work also, use case studies and customer testimonials retrieved from their websites for the purpose of dividing its overall customer base into different segments. Their all the predictions and forecasting regarding the trends of external market directly depends upon different types of information retrieved from various clusters (Everitt, Landau, Leese & Stahl, 2011). In this way, it can be revealed that cluster analysis techniques have some crucial practical implementation for business practices.
References
Everitt, B.S., Landau, S., Leese, M. & Stahl, D. (2011). Cluster Analysis. John Wiley & Sons.
HP Autonomy (2013). Access and understand all your data. Retrieved September, 20, 2013, from http://www.autonomy.com/products/idol
Romesburg, R. (2004). Cluster Analysis for Researchers. Lulu.com.
Woods, D. (2010). The Predictive Power of Social Media. Retrieved September, 20, 2013, from http://www.forbes.com/2010/07/26/cluster-analysis-predictions-technology-social-media.html
