Welcome to the first module of using analytics to improve decision making in business. This is the third course in the introduction to business analytics specialization. In this course, we transition from the descriptive and predictive models that we introduce in the second course, to the prescriptive models that we will discuss here. We start this transition by answering the question, what is cluster analysis? As discussed in the two previous courses, there are three types of analytics models, descriptive, predictive, and prescriptive. Cluster analysis is one of those, so called, data mining tools. These tools are typically considered predictive, but since they help managers make better decisions, they can also be considered prescriptive. The boundaries between descriptive, predictive and prescriptive analytics are not precise. However, as we move from descriptive to predictive and then prescriptive models, it is fair to say that the analysis tends to be a bit more complex. But it's very useful if it is done well, cluster analysis refers to a technique for identifying groups in which the elements of each group are similar. The approach is often used in marketing in order to understand differences among customers. The main idea of this application of cluster analysis is to create homogeneous market segments to target product offerings. In this graphical representation we show how in practice is not possible to find perfectly homogeneous groups. However, the groups resulting from cluster analysis are similar in some way. For example, the orange cluster includes round objects. Not all of them are of the same color or size, and some are crossed out, but they are all round. The objects in the green and blue clusters are also similar among themselves, but they are not exactly the same. What is important is to be able to describe each cluster. In this simple example, the orange cluster may be described as a group of multi-colored round objects. The green cluster may be characterized as a group of square objects. And the blue cluster consists of triangular and diamond-shaped objects. Now, let's take a look at a real world example. Consumers around the world continue to embrace natural and organic products. Consumers have become increasingly concerned about what is in their products they buy. They have created an industry that, in 2012, the market intelligence firm, Information Resources Inc., estimated to be $81 billion in the United States. Information resources survey 5,000 customers and match the participants answers with the actual purchasing behavior. The analysis of the data focus on segmenting the US market by considering relationships between shoppers and natural, organic, and eco-friendly products. The market segmentation was assigned to help manufacturers and retailers to achieve new levels of growth through a better understanding of these shoppers needs. The cluster analysis identified seven consumer segments based on lifestyle, history of purchasing organic and natural products, attitudes towards these products, and demographics. These segments were labeled as True Believers, Enlightened Environmentalist, Strapped Seekers, Healthy Realists, Indifferent Traditionalists, Struggling Switchers and Resistant Non-believers. Detecting the segments that are good targets for natural and organic markets was one of the main outcomes of the analysis. True Believers and Enlightened Environmentalists were identified as a key segments. They represent 18% of the population but together, they drive nearly half of the total sales. True Believers are passionate about staying fit and healthy, they are focused on trying new things and serving as a strong role models for their children. They are strong believers in the benefit of natural and organic products, their median income is highest among all segments. Their average age is 40 and they have at least a college education. Enlightened Environmentalists are passionate about the environment and about making good choices to preserve it. These shoppers are committed to make healthier choices and go out of their way to shop at stores that carry natural and organic products. Enlightened Environmentalist are older than True Believers averaging 63 years old, but have a slightly lower median income. It is interesting to note that the analysis show that both of these segments shared the same top priorities regarding food, home care, and personal care. The importance of this cluster analysis, is that it was able to identify groups of manufacturers and retailers who can motivate to learn more about organic and natural products. For example, the analysis show that the Strapped Seekers and Healthy Realist are a strong target, driving 24% of sales, or representing 25% of the consumers. Strapped Seekers believe and seek natural products, when their budget allows it. They realize the benefit of natural and organic products and have the potential of becoming True Believers as their income increases. Healthy Realists are passionate about being fit but find it difficult deciding whether to buy healthy or traditional products. Closer analysis show that not all segments are worth pursuing. Indifferent Traditionalists lead a simple life with few passions. They are not likely to buy natural/organic products because they don't see the reason to change. Struggling Switchers know that they should be eating healthier and exercising more but focus on staying within their budgets. Resistant Non-believers got very little desire to support all their options for things to buy, so they stay loyal to the products they know. Market segmentation is not the only application of cluster analysis in business. Clustering is also used in market strategies such as product and price segmentation. Because applications in areas such as human resources and operations management. In product segmentation, closer analysis identifies groups of consumers who want different benefits or levels of functionality of a product category. The results are used to offer different versions of a product, in order to match consumers needs more precisely. Coffee producers, for example, offer regular and decaffeinated versions of the same brand. In brand segmentation the same product is offered at different prices to different groups. Higher prices are offered to customers in groups that are willing to pay for extras or pay for better quality packaging, or for additional customer services. In human resources, clustering could identify characteristics of successful employees in order to improve recruiting and hiring practices. Grouping customers by geographical areas is a typical application of cluster analysis in operations management. We have shown how cluster analysis can be very useful as a market segmentation tool. In this module, we will explore additional clustering applications, but first, we will learn how to perform the analysis and how to interpret results.
