Market Segmentation

No corporate out there is capable of meeting everyone’s needs in this world. Corporates always caters or in other words good in catering to a sub set of this world – we can call them market segments. Market segments are nothing but group of individuals who are similar to one another. There can be different ways of making these groups – by product, service, price, psychographics, behaviors, needs, attitudes, etc. Once these groups are determined along with their description, size and market potential – these segments can be used very smartly by marketers to make critical business decisions, right and timely.

There are four key Market Segmentation Methods:

Factor Segmentation: This technique assigns segments to the respondents in a way that each respondent is assigned to one segment only and followed by segmenting on a non-mutually exclusive basis to examine the overlap among segments. Clusters of respondents yielded from this technique are with very similar attitudes and perceptions.

K-Means Cluster Analysis: K-means cluster analysis attempts to identify relatively similar groups of respondents based on selected characteristics, using an algorithm that can handle large numbers of respondents. This procedure attempts to identify similar groups of respondents based on selected characteristics.

2-Step Cluster Analysis: It is a next step or enhancement to the K- Means Cluster Analysis. It has several desirable features that differentiate it from traditional k-means clustering techniques: the handling of categorical and continuous variables, and automatic selection of the number of clusters. This procedure can automatically determine the optimal number of clusters by comparing the values of a model choice criterion across different segment solutions.

Latent Class Cluster Analysis: Clustering techniques mentioned so far are uncontrolled. Latent class cluster analysis is a solution that that optimizes the number of clusters and the fit of the segmentation model to the data. This analysis takes multiple dependent variables into account as a function of segment membership. Latent class cluster analysis can also introduce secondary variables (brand usage, demographics, etc.) as covariates that correspond with needs, attitudes, and behaviors. Respondents are assigned to the cluster to which they have the highest probability of belonging.

Latent Class Choice Modeling: With the help of a conjoint exercise survey respondents are exposed to multiple product concepts and are asked to select their most and least preferred sets and rate the influence of different product attributes (features, benefits, price, etc.) on the purchase decision. Latent class choice modeling classifies customers into segments based on their preferred product attributes. This type of segmentation is ideal for customizing product offerings or bundles to match segment preferences, enabling the firm to maximize business performance.

Knowledge Excel Services has helped a big CPG company identify country clusters to target their marketing campaigns better.

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