Cluster Analysis is a data processing method that organizes responses into segments or clusters based on how closely they are associated. This method finds subject groups that are closely matched, measuring the overall set of characteristics. The cluster analysis method is typically used when there are no assumptions about possible similarities within a data set and highlights those associations and patterns.
Cluster analysis is most commonly used for classification where respondents are put into groups with similar respondents. Cluster analysis can also identify variables such as social status, age, geographical location, and more in market research. It is also used for segmentation where groups can be identified and targeted with the most relevant message that they will respond to. For example, in a pet shop survey, cluster analysis may group segments containing young persons who place very high importance on their pets and aren’t sensitive to price. With this information, the business can target that subset of customers with advertisements focusing on higher-end pet products but focus on the benefits of the product to their pet. Though that strategy may work for that younger group, the cluster analysis may also pick up on an older group consisting of persons who place high importance on their pets but are very sensitive to price because they may be retired persons living off a pension. The business would then know to target these people through advertising sales and coupons.
When analyzing marketing data, it is nearly impossible to look at each customer individually. Though it is important to receive as much data as possible from individual customers, it is impossible to analyze these records all at once. To make strategic level decisions, the company must implement a statistical method for processing the data so that the customer’s voice can be adequately heard. This challenge can be rectified by segmenting customers through cluster analysis.