How do I create a persona?

 

Many marketing, sales, or recruiting initiatives fail not because of insufficient budgets or a lack of creativity. Often, the problem runs deeper: companies do not understand their target audiences well enough. While many organizations have general profiles of customers or applicants—based on factors such as age, gender, or industry—this information is rarely sufficient to understand the actual decisions people make.

Traditional target audience descriptions provide only a superficial view of potential customers. They do little to answer questions such as: Why does someone choose a particular product? What expectations does a job applicant have of an employer? Or what internal barriers prevent someone from making a purchase or applying for a job? Yet it is precisely these aspects that are crucial for successful communication.

data-driven personas come into play at this point. They translate complex target audience data into understandable profiles of typical users. This gives abstract data a concrete face. Personas help teams put themselves in their target audience’s shoes and consistently align decisions with their needs.

Unlike purely hypothetical target audience models, data-driven personas are based data-driven personas systematically collected data. They are derived from the analysis of real users—for example, through interviews, surveys, usage data, or market research.

This makes personas a key tool for companies that want to consistently align their strategy with the perspective of their customers or candidates.

What is a data-driven persona?

Definition and Delimitation: Persona, Target Audience, ICP

A persona is a data-driven, prototypical representation of a target audience. It combines numerous data points—such as demographic characteristics, interests, attitudes, and behaviors—into a clear profile that represents many real people.

Unlike a traditional target audience description, a persona does not simply describe a statistical group. Instead, it creates a concrete user profile with a name, background, goals, and typical behavior. This profile represents a larger group of people with similar characteristics.

In practice, various terms are often used interchangeably. These include target audience, segment, Ideal Customer Profile (ICP), and persona. However, these concepts describe different levels of target audience analysis. While a segment is typically defined statistically, a persona translates this data into a concrete user profile. This makes complex target audience data easier for teams to understand and more useful in their day-to-day work.

What Really Defines a Persona

A good persona goes far beyond demographic information. While details such as age, occupation, or place of residence can be part of a persona profile, the factors that influence actual decisions are far more important.

These include, for example, a person’s goals, the challenges they face in daily life, their motivations, or their expectations of a product or employer. Attitudes and decision-making processes are equally important. They provide insight into why people make certain decisions and what factors play a role in those decisions.

Personas therefore typically describe not only external characteristics but also internal perspectives: needs, desires, fears, and expectations. This information enables companies to better tailor their offerings, communications, and services to their target audiences.

Common Misconceptions

Despite their widespread use, personas are often misunderstood. A common misconception is that personas are merely marketing profiles. In fact, they are a strategic tool that can be used in many areas—such as marketing, sales, product development, or recruiting.

Another common misconception is to view personas as purely fictional characters. While personas do intentionally include narrative elements such as names or brief backstories, these serve only to make the profiles more tangible. They are always based on real data about actual users.

After all, it is often assumed that a single persona is sufficient. In most markets, however, there are several relevant user groups with different needs. That is why, in practice, companies usually work with multiple personas.

The Difference Between Assumption-Based Personas and Data-Driven Personas

Proto-personas as a starting point

Many persona projects begin with what are known as proto-personas. These are initial hypotheses about target audiences that are developed in workshops with internal stakeholders. Employees from sales, marketing, or customer service contribute their day-to-day experiences to this process.

These proto-personas serve as a valuable starting point because they consolidate existing knowledge within the company. However, they primarily reflect assumptions. Only through systematic data collection and analysis can these hypotheses be tested and refined.

Why Many Personas Fail

In practice, many personas are created exclusively in workshops. Teams draw on their experiences, collaboratively develop target audience profiles, and visualize them in the form of profiles. At first glance, such personas seem plausible.

The problem, however, is that they are often not based on empirical data. This creates the risk that stereotypes or the personal assumptions of individual employees will shape the perception of the target group. Such personas are often not used in everyday practice because they do not provide a solid basis for decision-making.

What data-driven personas Apart

data-driven personas fundamentally from such assumption-based personas. Their defining characteristic is a solid data foundation. These profiles are developed through the systematic analysis of user information—such as interviews, surveys, usage data, or market research.

The personas developed from this data reflect real patterns in user behavior. They help teams understand target audiences not just intuitively, but based on evidence. This makes them a reliable tool for strategic decision-making.

The Data Foundation: What Data Is Used for Personas

The most important component of any persona is the data it is based on. The more comprehensive and diverse the data set, the more accurately the persona reflects its target audience.

Generally speaking, there are two types of data: primary data and secondary data.

Primary data

Primary data is collected specifically for each persona project. It comes directly from the people who will later be represented by the persona. Typical methods include interviews, focus groups, surveys, and user testing.

Such methods provide particularly deep insights into motivations, attitudes, and decision-making processes. For example, interviews can reveal the problems users face in their daily lives or the expectations they have of a product.

Secondary data

In addition to primary data, secondary data also plays an important role. This refers to data that already exists and can be used for persona analysis.

Typical examples include CRM data, web analytics, social media analytics, and industry studies. This data often provides valuable insights into user behavior—such as what content they consume, what products they buy, or what communication channels they use.

The combination of primary and secondary data provides the most comprehensive picture possible of the target audience.

The Process: How to Create a data-driven persona

The creation of data-driven personas follows a structured process. This process also forms the basis of DIN SPEC 33462, which is the first standard to describe a standardized approach to developing data-driven personas.

The goal of this structure is to make the development of personas systematic, transparent, and reproducible.

Step 1 – Defining the Goal

The first step is to clearly define the objectives of the persona project. Companies should first determine which questions the personas are intended to answer.

Possible goals might include, for example, making marketing campaigns more targeted, optimizing sales processes, or improving the candidate experience in recruiting. The more clearly these goals are defined, the more effectively data can be collected.

Step 2 – Data Collection and Analysis

The next step involves identifying and analyzing relevant data sources. This process takes into account both existing data and new information gathered from interviews or surveys.

Combining different data sources makes it possible to develop a comprehensive picture of the target audience. At the same time, different perspectives can be compared and contrasted.

Step 3 – Data Analysis and Segmentation

Data collection is followed by analysis of the information. The goal is to identify patterns in user behavior. This involves examining the similarities and differences between specific groups.

Based on this, target audience segments are identified—that is, groups of users with similar characteristics or needs. These segments form the basis for subsequent persona modeling.

Step 4 – Persona Modeling

A persona profile is then developed for each relevant segment. This profile describes the typical characteristics of the respective user group. These include, for example, goals, needs, challenges, and decision-making processes.

The persona serves as a clear model of the target audience. It helps teams visualize specific users and take their perspective into account when making decisions.

Step 5 – Creating a Persona Profile

The most important information about a persona is often summarized in what is known as a "sedcard." This is a concise summary of the persona's profile.

A persona profile typically includes information about the persona’s background, goals, challenges, and behavior. It serves as a practical guide for teams in their day-to-day work.

Step 6 – Validating the Persona

The final step involves validating the personas. This involves verifying whether the profiles developed actually reflect the reality of the target audience.

This review can be conducted, for example, through additional interviews or by gathering feedback from various departments. Personas should also be updated regularly, as markets and user behavior change over time.

Personas as a system rather than individual profiles

In many organizations, personas are initially viewed as individual profiles. In practice, however, it often turns out that multiple personas interact with one another.

For example, in a B2B context, various decision-making roles may be involved in a purchasing process. Each of these roles can be represented by its own persona. Together, these personas form what is known as a persona ecosystem.

Such a system enables companies to better understand complex decision-making processes and tailor their communication accordingly.

How data-driven personas are used data-driven personas

The greatest benefit of personas comes not from creating them, but from using them in day-to-day work. Personas help teams consistently view decisions from the perspective of the target audience.

In marketing, personas can help with tasks such as developing content strategies or campaigns. They provide insights into which topics are relevant to specific target audiences and which communication channels are preferred.

Personas can also provide valuable guidance in sales. They help you understand typical customer objections and tailor your sales pitch accordingly.

In recruiting, personas are often used as so-called candidate personas. They help companies better understand potential applicants and tailor their recruiting efforts more effectively.

Common Mistakes in Creating Personas

Although personas are a powerful tool, mistakes are often made when creating them in practice. One of the most common mistakes is working with too little data. If personas are based solely on a few interviews or internal assumptions, a distorted picture of the target audience can quickly emerge.

Another problem can be inappropriate segmentation. When different user groups are grouped together into a single persona, the model loses its validity.

After all, personas are often created but then not actively used. Without being integrated into processes and decision-making structures, they remain a theoretical concept.

data-driven personas a strategic tool

data-driven personas much more than just a marketing tool. They are a tool for embedding customer- and candidate-centricity within the organization.

By translating complex target audience data into clear user profiles, they create a common foundation for decision-making in marketing, sales, product development, and recruiting.

When used correctly, personas provide a deeper understanding of target audiences and help companies consistently tailor their offerings and communications to the needs of their users.

In this way, they make an important contribution to more relevant communication, more efficient processes, and more successful long-term strategies.