Synthetic Personas — A Marketer’s Dream?

Marketing personas, created through market research and customer data, are an integral part of developing any marketing strategy. These semi-fictional personas help companies understand the behaviors, pain points, demographics, and goals of their ideal customers, ultimately allowing them to create more relevant campaigns. Today, generative AI is helping marketers take this age-old practice a step further with synthetic personas.

What are synthetic personas? What advantages do they offer? Can they be trusted? Eidosmedia will answer these questions and more.

Get to know synthetic personas

Imagine conducting a focus group without ever having to assemble real people. Well, with synthetic personas, that just might be possible. These data-driven user profiles simulate real people and may be used in research for everything from design to digital experience testing.

Synthetic personas are dynamically modeled by algorithms ... using massive, anonymized datasets.

Quick Creator reports, “Unlike traditional, static personas crafted from survey or interview snippets, synthetic personas are dynamically modeled by algorithms—often powered by Large Language Models (LLMs) and other forms of generative AI—using massive, anonymized datasets.” They are interactive and on-demand, are continuously updated, and are easily scalable. In many ways, they are vastly superior to traditional personas, created with the help of some data, marketers’ hunches, and a hint of institutional knowledge.

Building synthetic personas

These synthetic personas promise to make marketers’ lives easier and their work more efficient, but building them is a multi-step process. Getting off to the right start with accurate data is key. Quick Creator outlined the five-step process:

• Data Gathering & Cleansing: Collect broad, privacy-compliant behavioral, demographic, and synthetic data from analytics, CRM, surveys, and more.
• AI Modeling (Driven by LLMs): Feed this data into AI models, which synthesize it into nuanced persona “profiles” capable of simulating attitudes, preferences, and responses.
• Bias Detection/Mitigation: Use fairness tools to balance data and flag stereotype risks—e.g., auto-checking whether a persona’s traits skew unfairly.
• Human Oversight & Validation: Analysts test for realism and accuracy; outputs are caught early before being used in major research or product work.
• Deployment & Monitoring: Personas are embedded in workflows (such as generative blog platforms or SaaS onboarding simulations) and monitored for accuracy, drift, and bias.

Some of these steps hint at the potential pitfalls of synthetic personas, which are much the same as those of other AI applications. As always, the quality of your data is critical to success, but it’s also important to monitor your personas for bias. Perhaps most important is simply to ensure that the synthetic personas behave in ways that ring true.

Putting synthetic personas to work

Creating a synthetic persona is one thing; putting it to use is another. So, how are companies applying these tools in the field? Well, Forbes suggests that marketers can use them to replace the practice of conventional A/B testing: “Today’s marketers can run email subject lines past synthetic personas, asking them to A/B test copy for optimal engagement.”

Bain suggests another use for synthetic personas: “Simulate how customers might respond to new features, pricing, and bundles before committing to a full launch.” From testing UX design to training salespeople, the potential uses for synthetic personas are varied. Some companies are even using it to generate marketing personas, as Olena Zanichkovska, co-founder at The Gradient, writes, “For example, a B2B software company used an AI persona generator to identify four archetypes among its clients – such as the ‘Innovative IT Manager’ and the ‘Cost-Conscious CFO.’ Each AI-generated persona came with detailed motivations, pain points, and preferred channels, which the company then used to tailor its messaging and product development.”

Companies are putting this new technology to use to validate ideas early, ensuring they invest time and money into developing products and offerings that pass the synthetic persona sniff test. Thor Olof Philogène, CEO and co-founder of Stravito, told Digiday, “Think of it as an early warning system, not as a final validation.” This advice hints at the question that is always lurking in the background of new use cases for generative AI: Can the LLMs be trusted?

Can marketers trust synthetic personas?

By now, we all know that generative AI has its limitations. We often worry about AI’s hallucinations, but because it is often designed to please its users, it’s not always great at providing reality checks, becoming sycophantic over time. Many developers are adding checks to mitigate this risk. Still, marketers should resist becoming overly dependent on synthetic personas and add their own checks and balances to workflows.

In a world where marketers are often encouraged to look past their own gut instinct to rely on data, Digiday warns, “The bottom line is that marketers employing personas still need to use their own judgement. As she summed up: ‘Without humans, you’ll eventually just get generic slop.’” In other words, the advice for synthetic personas is the same as it is for nearly every use of generative AI: it’s just a piece of the puzzle and should be used in conjunction with human guidance and real-world experience.