Daniel Victorino
Understanding the customer has always been the north star of any marketing strategy. But the world has changed, and trying to predict audience behavior with tools from the past is like driving while looking only in the rearview mirror. With advances in artificial intelligence and large-scale data analysis, it is now possible to go beyond perceptions.
Today, a new path opens: traditional personas or the new wave of Synthetic Personas powered by artificial intelligence.
What traditional personas are and where they work
Traditional personas are detailed portraits of ideal customers created through research, interviews, and real-data analysis. Think of “Maria, 35, lives in São Paulo, mother of two, uses social media to research children’s products.” They are semi-fictional profiles based on concrete information.
They help align teams, give a human face to data, and ensure everyone speaks the same language about the audience. For a long time, they worked well to guide campaigns, develop products, and define brand voice.
Limitations of traditional personas
As useful as they are, traditional personas have weaknesses in the digital era:
Slow and expensive updates: keeping a persona current requires repeating research and interviews, a slow and costly process. Markets and behaviors change quickly, so the persona may become outdated before a campaign or product launches.
Dependence on declared data: people often say one thing in research and do another. Classic personas rely heavily on what customers report, which can create bias and fail to reflect real behavior.
Limited scale: creating and managing many personas for different niches is manual, heavy, and much more expensive.
What Synthetic Personas are and how they work in practice
Synthetic Personas do not come from direct interviews alone. They are born from real qualitative and quantitative research data, processed by machine-learning algorithms after rigorous clustering. These models analyze large volumes of behavioral information, such as habits, motivations, and barriers.
In practice, these personas work like digital mirrors of the target audience. When a user interacts with them to test a campaign, layout, or product, it is possible to simulate how hundreds of virtual “Marias” would react. They are not real people, but they reproduce responses and behaviors based on data.
Synthetic Personas outperform in agility and prediction
At Galaxies, we have seen how Synthetic Personas accelerate brands’ ability to test hypotheses before the market. With that in mind, we compare the approaches based on the points that make the difference:
Agility: while a traditional persona takes weeks to create and change, Synthetic Personas can be generated and adjusted in minutes, adapting almost in real time to new trends.
Scalability: need many different personas for different markets? With AI, this is much faster and saves time in defining and adjusting personas.
Privacy and security: Synthetic Personas can be trained with anonymized or artificially generated data, reducing personal-data risks and compliance concerns.
Predictability and actionable intelligence: with Synthetic Personas, it is possible to understand in advance how real consumers would behave when exposed to different stimuli, allowing teams to simulate scenarios, test strategies, and anticipate outcomes.
A new chapter for customer understanding
The evolution of Synthetic Personas shows that customer understanding is becoming faster, more precise, and more predictive. Synthetic Personas do not cancel the value of traditional personas, but they offer a more powerful tool adapted to market speed.
Want to test how Synthetic Personas can anticipate strategic decisions? Talk to our team.
Galaxies