Segmentation has long been a reliable consumer research technique for marketers of all stripes, but with the advent of big data (along with dramatic advances in computing technology), businesses have been able to boost their segmentation capabilities by orders of magnitude.
By leveraging the power of advanced data analytics, marketers can create highly detailed consumer profiles in order to target their intended audiences much more effectively than in decades past.
Below are some of the most common types of customer segmentation techniques in use today — demographic segmentation, behavioral segmentation, and psychographic segmentation — along with their advantages and disadvantages.
1. Demographic Segmentation
Demographic segmentation focuses on classifying groups of people based primarily upon physical or situational factors such as age, gender, income, ethnicity, geography, educational level, etc.
- Demographic variables are typically easier to collect and measure versus those of other segmentation techniques.
- Targeting is typically more straightforward when using demographics as a metric — e.g., you can target a consumer group such as Millennials, or men between the ages of 35 and 45.
- Consumer profiles are easy to understand across the board, which lends itself to an easier strategy development process in terms of internal collaboration between departments (e.g., sales, customer service, management, etc.).
- Similar demographics do not always imply similar needs, values or motivations within a particular demographic group.
- The effectiveness of your marketing message may be hampered by a “one-size-fits-all” approach to the consumers within a given demographic segment.
- Skewed or outdated demographic data within a given region can produce unreliable assumptions, potentially reducing the accuracy of your marketing message and methods.
2. Behavioral Segmentation
As its name implies, behavioral segmentation groups consumers based on their behaviors, such as purchases of products or services, or following up on medical recommendations (e.g., filling prescriptions or getting lab work done). Predictive analytics are largely based on behavioral data, and it’s been proven very useful in the healthcare setting.
- The opportunity exists for brands to build targeted consumer segments based on their responsiveness to certain product categories, promotion types or path-to-purchase preferences.
- Monitoring and understanding the behavior of consumers online has become much more precise and granular due to advances in data collection and tracking technologies (e.g., cookies, beacons, pixels, etc.).
- Brands can now craft their marketing messages based on consumer habits and practices that have already been proven.
- While consumer behavior can be tracked, it is not always easy to pinpoint the motivations behind those behaviors, as they can vary greatly from person to person.
- Behavioral segmentation is often based on complex data constructs that are not always easy to understand.
- Behavioral segmentation only focuses on WHAT people are doing, not WHY they are doing it. Two people can exhibit the same behaviors, but their reasons or motivations for that behavior can be very different.
3. Psychographic Segmentation
Psychographic segmentation focuses on grouping consumers by shared beliefs, values, interests, priorities, emotions, and lifestyles.
- This form of segmentation provides insight into the motivations behind consumer behavior, providing marketers with a more accurate picture of what makes their potential customer “tick.”
- Brands can more easily identify the underlying motives and needs of their target audience, enabling them to customize more compelling marketing messages.
- Psychographic segmentation provides a better overall understanding of the consumer, enabling brands to execute effective emotive marketing to highly responsive segments.
- Psychographic data have traditionally been more difficult to obtain than other data collection methods (e.g., demographics, consumer behavior, etc.).
- It can be difficult to obtain data for consumers in a given population, as participation in a psychographic survey is usually necessary. However, psychographic segments can be statistically projected across a population with predictive modeling, though the accuracy of segment assignment will be significantly less than with a prospective survey.
- Clear rules regarding the interpretation of data must be put in place to ensure consistency of approach among the individuals/departments that engage in customer segmentation analysis.
As with anything else, each consumer segmentation strategy carries distinct advantages and disadvantages, but the overarching goal remains the same: To leverage this data for the purpose of identifying and connecting with highly targeted audiences. By doing so, businesses are far more likely to achieve their marketing goals.