Quantitative segmentation is the practice of dividing a broad market into smaller subgroups based on measurable, numerical data. The objective is to move beyond a one-size-fits-all approach by identifying consumer groups through their quantifiable characteristics. By grouping individuals with similar traits, companies can tailor their products, services, and marketing messages more effectively. This strategy allows organizations to allocate resources efficiently and make informed decisions aligned with the needs of each segment.
Key Variables for Segmentation
The foundation of quantitative segmentation rests on the data used to create the groups. This information is organized into four primary categories of measurable variables. These categories are often used in combination to create rich, detailed profiles of the target segments.
Demographic data is one of the most common types of information used because it is straightforward to collect and measure. These variables describe the statistical characteristics of a population. Examples include age, gender, income level, education, marital status, and family size. This data helps to sketch a basic outline of the consumer.
Geographic variables segment the market based on the physical location of consumers. This can be as broad as a country or region, or as specific as a city, zip code, or the climate of an area. A person’s location influences their needs, preferences, and purchasing habits. For instance, a company selling outerwear would analyze climate data to target consumers in colder regions differently than those in warmer ones.
Behavioral data focuses on consumers’ direct interactions with a company or its products. This information is gathered by observing actions rather than asking questions. Metrics include purchase frequency, average order value, specific products viewed or purchased, and loyalty program engagement. These variables are valued because they reflect actual behavior, providing clear insights into how consumers engage with a brand.
Psychographic data delves into the less tangible attributes of consumers, such as their lifestyles, attitudes, and values. While these concepts may seem qualitative, they are quantified for segmentation through specialized tools. Surveys using a Likert scale, which asks respondents to rate their agreement with a statement on a scale, are a common method. This allows analysts to numerically group individuals based on their shared beliefs and motivations.
Data Collection Techniques
Acquiring the necessary data is a foundational step, and companies employ several techniques to gather these quantitative insights. The methods range from direct interaction with consumers to the passive collection of operational data. A comprehensive strategy often involves a mix of these approaches to build a complete picture of the market.
Direct collection involves actively soliciting information from consumers. Online surveys, questionnaires, and customer feedback forms are primary tools. The advantage is the ability to ask precise questions related to the segmentation variables of interest.
A significant amount of data is gathered through observational and transactional collection. This technique involves capturing information generated during the normal course of business. Website analytics tools track user behavior such as clicks and time spent on a page. Customer Relationship Management (CRM) systems record purchase history, and point-of-sale (POS) systems capture transaction details.
Companies may also turn to third-party data acquisition to supplement their internal information. This involves obtaining data from external sources that specialize in compiling consumer information. Government statistical agencies, for instance, provide census data for demographic and geographic segmentation, while specialized data brokers offer datasets to fill gaps in a company’s own data.
Analytical Models and Methods
Once data has been collected, analytical models are applied to transform the raw numbers into meaningful market segments. The methods used can be categorized into two main approaches, which differ based on when the segment criteria are defined. The choice between them depends on the research objectives and the existing knowledge of the market.
The first approach is known as a priori segmentation. In this method, the segments are defined before the data is analyzed. Researchers establish the groups based on pre-existing criteria, for example, segmenting a market by established income brackets or predefined age groups. This technique is simpler and is used when a company has a clear idea of the groups it wants to target.
A more exploratory approach is post-hoc segmentation, where the segments are discovered after the data has been collected and analyzed. This method does not start with predefined groups; instead, it uses statistical techniques to find natural groupings of consumers based on the data itself. This data-driven technique is useful for uncovering new or unexpected market segments.
The primary statistical method used for post-hoc segmentation is cluster analysis. This technique groups data points—in this case, consumers—based on their similarities across multiple variables simultaneously. An algorithm examines the data and identifies consumers who have both a high purchase frequency and high engagement with a mobile app. The result is the formation of distinct segments whose members share a combination of characteristics.