Audience Segmentation Models
Published: September 21, 2009
Understanding the people who will ultimately engage with a product or service provides the foundation for user experience design. Modeling those people and segmenting our models into meaningful groups lets us explore different clusters of needs, then address our solutions to meeting the needs of people belonging to specific clusters.
Audience segmentation models come in many shapes and sizes. So far, the practice of UX design has focused primarily on the persona as the model of choice. This article explores alternative ways of segmenting audiences and the design research we need to derive each type of model.
In April 2009, I wrote a column for UXmatters titled “User Research for Personas and Other Audience Models,” in which I focused primarily on the research techniques we can use to generate personas, a particular type of audience segmentation model. Personas are archetypal representations of audience segments, or user types, which describe user characteristics that lead to different needs and behaviors. Where the characteristics of users overlap, we build up an archetype, or persona, that represents those users collectively. Where the characteristics of users differ, we must create other personas that represent different audience segments.
However, the discussion around personas tends to focus more on process and communication rather than the type of audience segmentation we’re undertaking. So let’s take a look at some different types of audience segmentation models and how we might go about deriving each of them—that is, what we’re looking for and what research to do.
Audience Segmentation Models
Designing a Web site or application, product, or service for a large, heterogeneous audience can be a daunting task. Typically, we know some members of such an audience are more important to our company than others. To help us design for our most important audience segments, we need some methods of identifying and representing them.
There are many different methods we can use to think about our audience segments, focusing on differences that can help us prioritize and design the features that best meet the needs of each. The type of segmentation we should choose depends on what characteristics would elicit the most meaningful segments.
I’ve already touched on personas and discussed the type of segmentation they produce, but there are many other useful segmentation models, like the following:
- market segmentation—Within the business and marketing communities, this has been the most commonly used type of audience segmentation for decades.
- experience lifecycle—This segmentation model shows the end-to-end lifecycle of the customer experience.
- mental models—This model comprises an affinity diagram of user behaviors surrounding a particular topic.
- capability level—This segmentation model indicates the stages of capability our audiences go through over time.
- mood—This type of segmentation draws on a concept Will Evans put forward, using mood maps.
- game-play style—While this method of segmentation is somewhat specific to the world of online games, this type of segmentation takes into account the way players want to play games—solo or multiplayer.
A market segment is a group of people or organizations that share one or more characteristics, causing them to have similar product or service needs. A true market segment meets all of the following criteria:
- It is distinct from other segments. Different segments have different needs.
- It is homogeneous within the segment. People belonging to a market segment exhibit common needs.
- It responds similarly to a market stimulus.
- It can be reached by a market intervention.
The term market segmentation is also used when a company divides consumers with identical product or service needs into groups, so they can charge them different amounts of money.
This description of a market segment applies equally well to the other segmentation types I’ll discuss later—particularly the needs for a segment to be distinct from other segments and for all members of a segment to exhibit common characteristics, needs, and behaviors.
Surveys and other forms of analytic data generally drive market segments. Such data might include information about purchasing intent and behavior, demographic information, and frequency of activity in a problem domain.
The primary research tools for market segmentation are, therefore, surveys, interviews—usually closed-question surveys—and focus groups. The analysis process focuses on identifying clusters of characteristics that define distinct segments of an audience—tending heavily toward the multivariate statistical forms of analysis. However, it is possible to derive market segments using fairly simple tools like Excel—though this capability is advanced for such software.
Experience lifecycle is a generic term that represents the start-to-finish series of interactions a customer has with an organization. For example, LEGO uses an experience wheel like that shown on Customer Experience Matters, depicting the end-to-end experience of a frequent flyer traveling to New York from London.
With this form of segmentation, the goal is to target the design of a product or service to meet the needs of consumers at different stages in their experience. On a recent project that I described in an article on the Johnny Holland Magazine, I broke down the accommodation lifecycle for a backpacker during his or her travels. Different lifecycle stages presented very different sets of requirements and needs for the same person.
You can build experience lifecycles based on a wide range of design research techniques, including observational research, interviews, surveys, analytics, purchasing behavior, and ethnographic studies. The aim here is to understand both the overall structure of the experience—as a lifecycle comprising stages with certain experiential characteristics—and the component tasks of each stage in the lifecycle.
Mental models provide an excellent way of understanding how users approach a context for which we’re designing a product. They let us gain an understanding of collections of activities that make sense to a person—and communicate that understanding to others.
A range of observational research techniques can drive mental models, as well as interviews and, occasionally, focus groups. The key determinant is that the research technique must provide qualitative insights into the ways people approach a problem space, as well as how they cluster tasks into activities and activities into an overall experience.
People can have very different requirements during their early engagement with a product or service versus the requirements they may have once they’ve become familiar with it, then, subsequently, an expert. This is a common requirement for many systems. A user’s early engagement with a system such as a game or social network often requires a great deal more guidance and hand-holding than is necessary once the user has overcome his initial learning hurdles.
To understand the needs of novice versus expert users, it may be necessary to undertake research that is more akin to alpha testing, drawing on techniques from education and technical communication for the solution design. For all its faults, Basecamp handles this progression well, incorporating instructional material into the user interface to help novice users over their initial exposure to the service that disappears over time with use.
Understanding the needs of users with different levels of expertise can involve an analysis of support inquiries. After reviewing support tickets for recurring themes, you can address the problems users are encountering through iterative design and implementation of your solutions.
In a recent article, “Design Ethnography & Mood Maps,” Will Evans proposed the use of mood maps as a way of describing the various states people go through as they undertake an activity. We can take this approach and modify it somewhat to create a segmentation model that is based on a person’s mood—or frame of mind. For example, an insurance company with an informal brand language may want to use a different style of error message in its online quoting system from the one it uses for, say, its claims application for life insurance policyholders. A segmentation that is based on mood can help highlight such distinctions and remind the design team to take them into consideration. Here is an excerpt from the article that describes mood mapping:
“The [mood map] describes the emotional ups and downs identified by study participants as part of the design exercise conducted during in-home visits with participants. Note that the location of the study is less relevant than the importance of observing the participants in the most likely context in which they will engage in their experience with the brand’s product or service. During the exercise, participants are asked to name each of the phases they went through, from framing their problem through exploration and finally (hopefully) problem solving, and to then assign a corresponding emotion to each phase.”—Will Evans
Contextual studies are key to mood mapping. When seeking information about a person’s frame of mind or mood, neither surveys, interviews, or focus groups, nor Web analytics or other quantitative methods can provide the insights that are available to an observer watching a person as he goes about a task.
Joe Lamantia recently wrote about learning from games. People approach games in different ways—as a personal escape, a way to socialize, a form of mindless entertainment that lets them immerse themselves in a storyline, or a way of challenging their skills against others. A game like Eve Online covers these different styles of game-play well, providing rich storyline content and opportunities for solo play with cooperative opportunities for groups numbering in the hundreds.
A large-scale, multiplayer online game should incorporate design features that combine each of these styles and approaches. Understanding the combinations of features that support different game-play styles can require a design researcher to incorporate observational research with interviews, ethnographic, and sociological research techniques. Such combinations of features tend to be fixed, but the expectations of players within each segment can vary—and it is these variations we need to understand.
Segmentation As Framing
When we adopt a particular audience segmentation model, we also adopt a particular perspective on a problem space that, in turn, shapes our understanding of the solution. This framing of the problem allows us to focus our efforts, but can also leave us blind to other, potentially significant characteristics of a potential solution. For example, if we use market segmentation and focus on the differences in people’s purchasing triggers, we may miss opportunities to provide features that customers have not requested, but would offer significant value. Alternatively, a focus on the lifecycle of the customer experience can hide differences in audience needs within a particular stage.
In the same way that we can overcome shortcomings in one research method by undertaking several, complementary forms of research, combining or using multiple audience segmentation models can help us overcome such structural blind spots, arising out of a particular perspective on a problem.
The use of a variety of different audience segmentation models can inform the design of products and services in different ways. Perhaps just as important, we can apply the underlying research techniques we use for one audience segmentation model to the construction of other models. The key differences between the audience segmentation models I’ve described in this column lie in the goals of the research and the characteristics we want to explore. Thus, we can build on the work we do to produce one type of audience segmentation to develop other segmentation models—and continually expand our toolkits.