Persona knowledge: the history of buyer personas

The pioneer: Alan Cooper

Personas originally came from software development and usability research. Back in the 1980s, software developer Alan Cooper had already taken a critical look at the fact that his industry was more concerned with what could be programmed than how and whether future users would interact with it. Many programs were user-unfriendly and led to a lot of frustration among users - even though the desired features were actually there.

So Cooper began interviewing software users to find out what their pain points were in relation to the software. He then wanted to write software without pain points and offer his customers user-friendly products. However, the first result of his interviews was not new software, but in 1985 "Kathy" was born, the first persona. Named after one of Cooper's interview partners, Kathy was the synthesis of all the interviews, a fictitious software user whose needs Cooper was then able to align his software with.

This was followed in 1995 by "Cooper Chuck", "Cynthia" and "Rob" for the business intelligence company Sagent Technologies. These three personas were based on in-depth user interviews and were grouped by Cooper on the basis of their tasks, goals and skills. He therefore called them "targeted personas". With their introduction, the user-friendliness and quality of the products made great leaps forward. The targeted personas were so successful that the company used them to define a new product segment.

In his 1998 book "The Inmates are Running the Asylum" about problems that non-engineers have with using software, Cooper first introduced a distinction between buyer personas and user personas. He recommended that developers adapt their designs to the user personas in order to develop easy-to-use software.

The Marketer: Angus Jenkinson

Under the name "Customer Prints", marketing expert and former university professor Angus Jenkinson developed the concept of seeing customer segments as a coherent identity almost in parallel with Cooper in the early 1990s. He presented his development as "descriptions of archetypes of a day in the life" of fictional characters that represented the customers. He placed particular emphasis on grouping customers with similar attitudes and behaviors to gain insights into their likely buying behavior that went far beyond mere segmentation to include values, desires, hopes and frustrations - just like in real life.

The innovator: Clay Christensen

With "customer prints" and "user/buyer personas", the marketing world could have been in such good order. Unfortunately, however, many companies were still struggling and the hoped-for success often failed to materialize: It was difficult for companies to find out what data, behaviors and characteristics of the personas were relevant to them, and therefore what products their customers were likely to want.
Clay Christensen argued in his 2003 book "The Innovator's Solution", based on an idea by Tony Ulwick from 1991: people want to do a job and buy a product to do it. Marketers therefore need to know which jobs people want to have done in their everyday lives in order to offer the right products for them. This was the birth of the "jobs to be done" theory. Many companies had failed because they had focused too much on WHO their customers were instead of asking WHAT BENEFITS THEM, what "job" their customers want to have done.

The practitioners: Steve Mulder and Ziv Yaar

In the early 2000s, many companies were already aware of personas, but many marketers could neither create them correctly nor introduce or apply them in the company. In 2006, the two UX/Usalibity experts Steve Mulder and Ziv Yaar wrote "The User is Always Right", an easy-to-understand guide on what personas can be used for, how to create them and, perhaps most importantly, how to communicate them to others. They focused on three approaches:

Qualitative personas: This "traditional approach" includes user interviews, field studies and usability tests, as with Alan Cooper , in order to collect data that is used to divide users into groups that share common characteristics (e.g. goals, motivation and attitudes). Such a group is then condensed into a persona by adding more details about goals, behaviors and attitudes. Although qualitative personas can improve customer understanding, they have the disadvantage of not using quantitative data, which reduces their credibility with stakeholders with quantitative requirements.

Quantitative personas are based on big data. To create them, you analyze the behavior of real customers and then use statistical methods to identify groups or "clusters" of similar customers. These clusters form the basis for the quantitative personas. The advantage of this is that these personas are based on objective data and are therefore accurate and reliable. However, creating them requires access to large amounts of data and expertise in handling data.

Mixed personas combine the best of both worlds: qualitative personas that are verified or falsified by quantitative research. This verification provides the quantitative evidence needed to successfully present the personas to various stakeholders. They also have a lower probability of error than the non-combined methods. Disadvantage of this method: It requires a lot of expertise, access to quantitative data and time.

The new world: data-based and automatically generated personas

The term "Data Driven Personas" was first introduced in 2006 by Karen Lindsay Williams in her doctoral thesis on personas in the design process at the Georgia Institute of Technology.

data-driven personas have evolved in response to the rise of social media, online data and user analytics platforms: Large amounts of data are now available easily and for free via social media, APIs and online analytics platforms. This makes it easier for companies to analyze customer behavior and preferences to create data-driven personas. In addition, there are major technological advances in analytics, programming and artificial intelligence that make it easier to collect and process data: You can now create data-driven personas using persona generators that derive data from analytics tools such as Google Analytics and social media platforms (YouTube, Twitter, Instagram or Facebook). The first automatic persona generation (APG) was developed in 2016-2017 by a research group led by Soon Gyo-Jung and Joni Salminen. APG processes millions of user interactions with thousands of online digital products on various social media platforms such as Facebook and YouTube, identifies both different user segments and then creates persona descriptions by automatically adding relevant characteristics such as names, photos and personal attributes.

Algorithms now define and create personas based on real statistical data and facilitate the segmentation of a diverse number of buyers.

More and more companies are therefore using customer data to make well-founded decisions about their customers.

This is all the more important because customers today are more volatile creatures than ever before: Whereas marketing was a relatively straightforward endeavor deep into the 1990s, with no marketer needing to track customer preferences and their customer journey across multiple platforms and devices, today's customers are constantly exposed to new content, social media and products, not to mention inundated with stimuli. So if you want to sell something today, you need to create relevant and engaging content that captures the attention of people with a very short attention span. The data-based buyer persona has proven to be the best option to date for finding out what customers find relevant.

Unlike Gyo-Jung et al. and others, which are based data-driven personas are based on behavior-based data, we at the Persona Institute base our data-driven personas on specially collected scientific data, representative studies and statistics from publicly accessible sources such as statistical offices, residents' registration offices or the Federal Employment Agency and associated institutes as well as market media studies.