What is Cluster Analysis?
Cluster Analysis is a process of grouping similar attributes together based on their properties towards different dimensions. These groups are called segments—each segment sharing a particular property, which is typical for the group.
Cluster Analysis is a powerful statistical method that provides clearly defined segments of groups, thereby using the marketing team to analyze and target different clusters/ segments with tailored, targeted communication/ marketing strategies.
Cluster Analysis in Market Research
We use Cluster Analysis in Market Research for the analysis of the survey data. As the process helps researchers by generating different segments of the population who have provided the survey. This allows us to derive various inferences of their existing or potential customer data and present an ability to divide the population into different segments for targeted marketing strategy.
Creating segmentation and targeted marketing has proven results in the growth of businesses and new customers’ acquisition. Clustering similar attributes together give an understanding of the customer insights related to that segment, thereby enabling researchers to custom create plans to achieve business goals.
Market Segmentation is based on various attributes of the customer. It ranges from his demographics to his purchasing approaches. Dividing the customer base into different segments helps create inferences that benefit the business in identifying growth opportunities and creating a product development plan that can target different personas of the customer.
Attributes which influence Market Segmentation
- Operating Variable.
- Purchasing Frequency.
- Personal Characteristics.
- Situational Factors.
What is SSE in Cluster Analysis?
A Sum of Squared Error(SSE) is a metric that helps researchers choose the best number of segments that they can use in segmentation. It does this by performing repeated iterations of calculation to bring the clusters together/ closer. If the customers matched the segment scores exactly then, the SSE would be 0, i.e., no error or a perfect match.
A perfect match scenario is not ideal for real-world data. Thus a low SSE value is considered as the choice of a cluster. We do not choose a cluster with the lowest SSE score, but we choose one of the low SSE.
Clustering is measured using:
- Intracluster distance (Homogeneous): It is the distance between the data points inside the cluster. When it is a strong clustering effect, this should be a smaller value.
- Intercluster distance (Heterogenous): It is the distance between data points in different clusters. When a strong clustering exists between the two clusters, these should be a large value.
BY looking at SSE, segment size, and heterogeneity of the clusters, we can decide to target a segment substantial for our business. The segment’s homogeneity will also make it easier to understand the type of marketing and promotional activity ideal for the segment. For example, if we come to the inference that our segment is price sensitive, we should offer discounts, bundles, etc. Or if we see that the price is not an issue, but the segment is prone to brand switching if the quality of the service or product is inconsistent, then we will focus on better services.
Cluster analysis is an excellent way to understand the market to choose the target segment and gives insight into the ideal marketing mix for catering that segment.