SELLEST OSAST
Apply the principle of inclusion to determine when education, salary, or demographics add value to a specific project. You will learn to filter optional data against client goals and research types to avoid bias and maintain relevance.
Learning Objective: By the end of this lesson, learners will be able to evaluate optional persona elements against specific project constraints to determine their inclusion.
Transcript The Principle of InclusionOptional content in a persona is not added by default. It is included only when it varies from product to product, client to client, or project to project. This principle of inclusion prevents you from cluttering a profile with data that doesn’t serve the current goal.
The decision to include specific data points depends directly on the type of user research performed. If your interviews didn’t capture salary data, you don’t have it, so you don’t add it. Similarly, inclusion depends on the type of product, application, or service being developed. A financial app requires different details than a social media platform.
Finally, constraints such as time and resources act as the final determinants for adding optional data. You must weigh the value of extra detail against the effort required to gather and maintain it. This ensures every element you include earns its place in the profile.
Key Points:
Optional content is not added by default; it is included only when it varies from product to product, client to client, or project to project
The decision to include specific data points depends on the type of user research performed
Inclusion depends on the type of product, application, or service being developed
Constraints such as time and resources are final determinants for adding optional data
You already build the core of every persona before adding anything else. That core is the least common denominator, the six pieces that make a user relatable: a photo, a name, age, location, occupation, and a biography. This baseline ensures the persona functions as a reference point for the team, grounding empathy in concrete details before any optional data is considered.
Once that foundation is set, you evaluate optional elements against specific project constraints. These include Education Level, Salary or Salary Range, Demographics, Quotes or Testimonials, and Priority Level. Each serves a distinct purpose, such as Education Level, which provides insight into habits, purchasing behaviors, and brand perceptions. A person with a high school diploma may have substantially different brand perceptions than one with a master’s degree, so including this data changes how the audience interprets the persona.
Similarly, Quotes or Testimonials provide explicit insights into how the persona thinks and feels, creating a direct connection for the reader. This isn’t just about filling space; it’s about ensuring the persona resonates with the specific stakeholders involved. If the project doesn’t rely on these nuances, the optional elements remain excluded, keeping the profile focused and relevant to the work at hand.
Key Points:
The baseline 'least common denominator' consists of six standard pieces: Photo, Name, Age, Location, Occupation, and Biography
Optional elements include Education Level, Salary or Salary Range, Demographics, Quotes or Testimonials, and Priority Level
Education Level provides insight into habits, purchasing behaviors, and brand perceptions
Quotes or Testimonials provide explicit insights into how the persona thinks and feels, creating a connection for the reader
Ground this in a specific scenario: you are building a persona for a marketing team targeting college-aged students. Demographics, which provide basic statistical metadata, become critical here because they offer additional insights into behaviors, needs, and expectations that a general biography might miss. In this context, the data is more important in marketing-focused scenarios, so you lean into it.
But there is a strict constraint. You must use this demographic data in a way that avoids invoking bias in the personas. If you list age and income without context, you risk defining the user by a stereotype rather than a behavior. The goal is to apply the principle of inclusion to decide whether to add salary or demographics based on actual evidence, not assumptions.
Age itself is a useful tool. It provides additional context on generational behaviors and expectations, helping you understand how a user might perceive a specific brand or interface. You are not just listing a number; you are mapping how that number shapes their digital habits. This turns a static fact into a dynamic insight about their needs.
When you create multiple personas, you also assign a Priority Level. This identifies who the primary, secondary, or tertiary users are, ensuring the team focuses on the right audience first. By evaluating these elements against your project constraints, you ensure the persona serves the specific client goal.
This careful evaluation prevents you from adding data that dilutes the core message, setting the stage for the specific scenario-based decisions that follow.
Key Points:
Demographics offer additional insights into behaviors, needs, and expectations, but are more important in marketing-focused scenarios
Demographic data must be used in a way that avoids invoking bias in the personas
Age is useful for providing additional context on generational behaviors and expectations
Priority Level identifies who the primary, secondary, or tertiary users are when creating multiple personas
So, when you're deciding what to add to your baseline, think about the specific project context.
If your work involves purchasing behavior analysis, include Education Level. A person with a high school diploma often has different brand perceptions than one with a master's degree, so that distinction matters here.
Now, ask yourself if the audience is marketing-focused. If so, Demographics can refine your behavioral insights, but you must check for bias to ensure the persona remains representative.
Consider whether your primary goal is empathy and connection. In that case, pull direct Quotes or Testimonials from your user research to show how the persona thinks and feels.
What about Salary or Salary Range? If resources are constrained, omit it unless it directly impacts the specific client goal.
You have the baseline, you have the optional elements. Now, weigh each one against your specific project needs to decide what actually adds value.
Key Points:
If the project involves purchasing behavior analysis, include Education Level to differentiate high school diploma from master’s degree perceptions
If the audience is marketing-focused, include Demographics to refine behavioral insights, but check for bias
If the goal is empathy and connection, include Quotes or Testimonials from user research
If resources are constrained, omit Salary or Salary Range unless it directly impacts the specific client goal
The most common error is adding optional data by default, treating education or salary as standard rather than varying them based on the specific product or client. This ignores the core principle that optional content is included only when it adds value to the unique project context.
Another critical mistake is skipping the baseline. Before adding any optional layers, the persona must contain the six standard components: photo, name, age, location, occupation, and biography. Without this foundation, the profile lacks the relational depth needed to draw stakeholders in.
You also risk including demographics without checking if they invoke bias in the persona profile. Statistical metadata can skew perceptions if applied carelessly, so you must verify that the data supports empathy rather than stereotyping.
Finally, don't overlook time and resource constraints. Adding complex elements like salary ranges or detailed behavioral analysis requires research depth you may not have. If the budget is tight, stick to the essentials and let the baseline do the heavy lifting.
When you sit down to build your next persona, start with those six pieces and only add what your specific client actually needs.
Key Points:
Adding optional data by default rather than varying it based on the specific product or client
Including demographics without checking if they invoke bias in the persona profile
Failing to use the baseline six components (Photo, Name, Age, Location, Occupation, Biography) before adding optional layers
Ignoring the constraint of time and resources when deciding to include complex optional elements like salary ranges
