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Regardless of whether your target audience is very broad or not, you need to identify specific user groups in that audience group for use in product user experience research and design.

When the product serves a large number of different users, stakeholders sometimes point out that the product design must be aimed at "everyone". This approach may be more inclusive than focusing only on certain categories of people, and it is particularly attractive to companies that have a strong need to attract more customers and users.

However, avoiding any definition of the target audience will actually result in reduced availability for most people. Because, if you don't know who the user is, you are likely to get biased research results and wrong decisions.

In fact, you don't always need precise user feature categories, but you do need to know who will use the product design and what they will try to do with it.

Get to know your users with the idea of preparing for a trip

Before you travel, you will at least consider the following information:

How long are you going to stay?

What kind of activities are you going to do?

What's the weather like in your destination?

Imagine that if you don't know whether to go on a weekend excursion, a luxury resort or a six-week expedition to Antarctica, you won't be able to start preparing your luggage.

Designing products without knowing your users is like traveling with something regardless of your destination-both can end in an unpleasant way, which may be avoided with a little foresight.

The exact same principle applies to product design. Ensuring that our users have a good experience doesn't necessarily mean that we need to define everything about them, just some definitions of the trip they are about to start, such as what activities they will undertake and what content and features do they need to make these activities successful and enjoyable?

Test a specific user, not any user

In the research, there is often the problem of "unable to define the user". If you plan to conduct a study, but there are no restrictions on who can participate, it is easy to cause all participants to be very similar to each other. This is not a problem if your system is designed to serve only one type of user, but if your audience is a mass market user, you may miss out on a lot of useful advice and opportunities.

Imagine that you are designing a website for an urban transportation system used by local commuters and tourists. If you plan to have sex with any 10 people, you may end up with nine commuters and one tourist. Or worse: a convenient sample of 10 office workers (if you recruit users who live near your office. ) you may know a lot about office workers but little about your other important audiences (such as tourists).

We know that it takes only five users to learn a lot from usability testing. However, if your audience includes a range of people with fundamentally different tasks, needs, and expectations, then five users will not be enough. Instead, you will need 4-5 test participants from each major type of user (if there is still some overlap between the tasks accomplished by each group, then there will usually be fewer users in each group). Before you can find these people, you must know how to distinguish between different types of users.

An effective strategy to describe the "pan-audience"

A simple way to describe a large group of people is to turn to demographics, such as age, sex, occupation and income. When recruiting participants for user research, it is definitely a good thing to have some demographic diversity. But these facts usually don't describe the differences between your important target users, which actually affect the way they interact with your product designer.

For example, travelers on the urban transportation system can be divided into groups aged 16-24, 25-55, and over 55 by age. Because people at different stages of life tend to have different schedules, this diversity may find some differences. But we may still miss the views of people who have just visited or recently moved to the area but are not yet familiar with the city.

Products, operators, designers and researchers all need to understand the way people behave. Therefore, the best way to organize information for them is to group people according to their actual behavior, not just demographic categories.

For a large audience, trying to describe the full range of user behavior seems difficult to control. For example, different people may use the transportation system to travel between hundreds of different locations for many reasons, such as job interviews, parties, appointments, schools, doctor appointments, jobs, etc. The trick is to organize all these different usage scenarios based on which parts of them may actually cause people to interact differently with the system.

If your product is designed to serve a large audience, frequency may be a good starting point for identifying meaningful differences between different types of users.

The frequency of use usually distinguishes different groups.

Usually, the behavior of high-frequency users is different from that of low-frequency users. But if you only think about the behavior of "everyone", these important differences are easy to overlook. By dividing your audience into high and low frequency groups, you can start to detect important patterns and trends.

Imagine that you are using an application with a large number of users. Some people may use the application more than others. The change in usage frequency is both the cause and the impact of the important differences between user types:

High-frequency use will lead to familiarity with the interface and rapid completion of repetitive tasks.

High-frequency use is the result of requirements and goals (people use it more because tasks or content are important to them).

And vice versa-infrequent use means that the product is unfamiliar and less important to the user. Of course, there are exceptions: some usage scenarios are rare but very important (such as buying a house), and some interfaces remain a mystery even to people who use them every day. But for most product designs, grouping people according to how often people perform tasks or interact with the system will reveal key information about their needs and challenges.

Even if you need to serve high-frequency and low-frequency users, it is still important to distinguish them so that you can understand and support the behavior clusters associated with each group.

If you want to increase the overall size of your audience, you must define differentiated users as different groups, because these are your future prospects. Ideally, you should have some specific information about which new markets you want to expand to, in addition, even groups defined as "people who are not currently using our products" will help design and research by forcibly considering users who are not familiar with the design.

In addition to frequency, there are many other ways to subdivide a large audience into more specific groups:

→ Target priority: which is more important for certain types of users?

Ex.: if they are in a high-risk group for influenza complications, people may be more motivated to look for information about getting a flu vaccine.

→ domain knowledge: do some people know more about the industry or topic than others?

Ex.: current homeowners will know more about mortgages than first-time buyers.

Possibility of using →: are some people more likely to become future users than others?

Ex.: people who own cars are unlikely to become regular passengers of public transport.

Classification can improve the user experience

When you identify the important characteristics of your audience, you can apply them in the following ways:

Data-driven: analyze the data and divide the user group finely. For example, you may find that 10% of users visit every day, 70% of users visit every week, and 20% have fewer visits.

Simple distinction: even if you don't have quantitative data, you can choose a distinguishing point based on anecdotal user comments-for example, people who visit at least once a week are frequent users, and people who visit less than once a week are infrequent users. Your group may not exactly match the real distribution, but you'll still be much better if you just raise your hand and treat all users as if they were all.

More important than finding exact distinctions between groups is to make sure they are part of your research and design plan. Depending on the depth of the differences between groups and their impact on usage and business, you may think that these groups are unique enough that they need to be available separately for different tasks. Or, through less important distinctions, you can simply ensure the composition of people when recruiting participants for research, so that you can observe differences in their expected content and behavior. Share crowd categories with your partner team and demonstrate them in brainstorming and meetings (for example, by creating roles to represent each category).

While the above may seem difficult to control at first glance, identifying meaningful segments from a large audience is not necessarily very complex. Even if you make a little effort in this area, you can greatly improve your research and design results. If you are interested in user clustering scenarios and tools, you can learn about this: tag system, user clustering, user portrait "playful" interpretation.

Source: https://www.nngroup.com

Original author: KathrynWhitenton

Compilation: policy editor

Some deletions have been made during compilation.

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