Targeting a segment of the market can be a powerful strategy. It’s the concentration of marketing effort to dominate a market niche. Market segmentation is the process of identifying and targeting groups of individuals who are similar to one another. Markets can be segmented in many different ways: by product or service needs, by sensitivity to price, by geographic area, by demographic segment, or by psychographics and lifestyles. Successful segmentation depends on understanding what consumers need, how groups of consumers differ from one another, and how consumers decide among products.

Optimize Data Analytics’ Advanced Analytics Group searches for and identifies patterns in the data. Rigorous analytic techniques (including factor analysis, discriminant analysis, k-means and hierarchical clustering, latent class segmentation) are used to organize consumers into groups with similar attitudes, needs, and desires. The size and market potential of each customer segment is determined, along with the positioning and appeals that should be employed to reach each segment.

Market Segmentation Methods

• 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.
• TwoStep Cluster Analysis. This procedure is relatively new. It uses hierarchical cluster analysis and is designed to handle very large data sets. The algorithm employed by this procedure 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. By comparing the values of a model-choice criterion across different clustering solutions, the procedure can automatically determine the optimal number of clusters.
• Latent Class Cluster Analysis. Latent class cluster analysis produces an objective segmentation solution that optimizes the number of clusters and the fit of the segmentation model to the data. This model can predict patterns in multiple dependent variables (such as attitudes, needs, and behaviors) as a function of segment membership. It easily incorporates data from different types of questions and different types of scales (e.g., yes/no answers, multiple choice questions, various rating scales, and even volumetric data) without the need for rescaling or normalizing the data. Latent class cluster analysis can 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. Survey respondents select their most and least preferred sets of product benefits and rate the influence of these benefits on the purchase decision. Latent class choice modeling classifies customers into segments based on their preferred product benefits. This type of segmentation is ideal for customizing product offerings or bundles to match segment preferences, enabling the firm to maximize business performance.

Scoring Model

Once a market segmentation model has been produced, a scoring model (a set of equations) may be developed to allow additional respondents to be classified using the same segmentation scheme. Discriminant analysis is usually used to develop the model, although other forms of regression may also be employed. This analysis identifies the questions that are the most important in determining segment membership.

Database Customer Segmentation

Most companies have multiple databases containing information about their customers’ attitudes, preferences, and buying behaviors, but rarely are these databases fully linked and integrated. Linking the information in these various databases enriches the value of each database. Our Advanced Analytics Group has developed unique ways of linking segmentation solutions based on attitudinal and/or preference data with transactional databases. They “bend” the segmentation solution in a way that optimally finds and creates “hooks” into each database, while maintaining the basic structure of the segments. We can apply these “bending” techniques to identify high-potential customer segments that merit special marketing attention.

Insight Into Target Segments

Segmentations are only useful if they can be applied. Once a few target segments have been identified, further analytic work can answer these strategic questions for each segment:

• What factors (drivers) impact the outcome of interest, such as purchase intent, intent to prescribe, or intent to use? Key Driver Analysis
• Which key drivers are important? Strategic Attribute Mapping
• Which important drivers are opportunities or risks for the brand? Opportunity/Risk Analysis
• What is the market potential? Volumetric Analysis