Over the years, I’ve written a lot about customer understanding. Even wrote a book on it. In those writings, I’ve said that there are three ways to achieve customer understanding: listen (feedback, data), characterize (personas), and empathize (journey maps).

You’ll find a lot of articles on my site about listen and empathize, but there are fewer articles on characterize. That’s not necessarily intentional. I could certainly write more, but it’s an area that I often find has already been addressed by my clients, as both marketing and UX tend to develop personas for their purposes. Customer experience professionals can often use those are starting points, or they may put in the work to build their own. Either way, personas are an important understanding tool, as they tell us who the customer is: beyond demographics and psychographics, we also uncover needs, pain points, problems to solve, preferences, expectations, and more.

Let’s talk a bit about personas, and then I want to introduce you to a new concept that is starting to take hold: synthetic customers.

What Are Personas?

Personas are fictional characters that are created to represent ideal prospects or actual customers based on needs, pain points, problems to be solved, jobs to be done, preferences, expectations, goals, and more. Personas are research based, created by starting with customer interviews and then fine tuning with validation surveys. I also advocate for using some of the bread crumbs of data that customers leave behind as they interact and transact with the brand to make these personas more robust and lifelike. They have a name, age, occupation, and image to humanize them and to bring them to life.

Do not allow personas to be developed in your organization with internal thinking (because you think you know who your customers), i.e., gathering a group of stakeholders to talk about who they think your customers are. It perpetuates inside-out thinking. It’s not accurate. And it’s lazy. You have to talk to customers. After all, it’s called customer understanding, not business understanding or thinking.

When it comes to designing the customer experience, personas take us closer to the individual customer’s desires than anything can, short of customizing or personalizing for the individual. Experiences are designed at a macro level for each persona, making the job easier then for frontline folks to tweak and fine tune the experience at the individual level as they interact with the customer.

What Is a Synthetic Customer?

In this day and age of all things data and AI, using synthetic customers in your design efforts seem like the logical evolution or next step. A synthetic customer is a data-driven representation of a customer. It’s a simulation of customers’ behaviors, preferences, and decision making processes created using AI and machine learning algorithms. The data that drives its development includes demographics, purchase history, online behavior, and social media activity. Basically, it’s created using the bread crumbs of data that customers leave behind as they interact and transact with your brand.

Synthetic customers can be programmed to behave like real customers and can respond to stimuli, make decisions, and engage in simulated interactions. They are used to predict customer behaviors and responses to marketing campaigns, product features, customer service interactions, and more in a virtual environment before brands invest in resources to roll out the experience you’re testing. Simply said, they provide businesses with more accurate and realistic data for market research and product development, aka the customer experience.

What’s the Difference?

The differences between the two seem obvious, after reading those descriptions: synthetic customers are data-driven and predictive, while personas are more human-centric and empathetic. One comes from large datasets, while the other comes from research and observation. One simulates real customer behavior, while one is used to understand and empathize with the customer.

Synthetic customers are ideal for testing new marketing campaigns, product features, or customer service initiatives; personalizing customer experiences; and predicting customer churn. Personas, on the other hand, are great for developing new products and services, identifying pain points and unmet needs, and creating targeted campaigns.

You could create a dozen (or dozens) of personas, but ideally you’ll focus on three to five key personas. You could create numerous synthetic customers, too, but the ideal number will depend on their specific purposes and resource constraints (budget, skills, computational capabilities).

Start small. Focus on key personas and/or a small sample of synthetic customers, analyze their effectiveness, and slowly scale up, as needed. For both, you must monitor and refine continuously to ensure accuracy and relevance.

Both have their advantages and disadvantages. Let’s start with personas.

Pros and Cons: Personas

Some of the advantages of personas include that they:

  • Are relatable: Given that they’ve got a name and a face, they’re more relatable to humans than synthetic customers, making them easier to use for marketing and communication purposes.
  • Provide understanding: Personas help employees throughout the company understand who customers are and empathize with them and their needs. (Assuming you socialize your personas!)
  • Drive success: As a result of that understanding, success in product development (solving problems for customers) is likely.
  • Are easy to understand: They are transparent and easy to understand, which can help to build trust with stakeholders.

Some of the disadvantages include:

  • Time to create: It can take weeks or months to do the research and develop personas.
  • Accuracy: Personas might not be as accurate as synthetic customers, as they are fictional characterizations (yes, albeit rooted in customer research). Also, they are static and need to be updated on a regular basis to keep up with customers and their expectations, which are ever-evolving.
  • Expense: They can be expensive to develop, update, and maintain.
  • Less dynamic: They can’t adapt to changes in the market or customer behavior in (near) real-time, interact with stimuli, or make decisions.
Pros and Cons: Synthetic Customers

Some of the advantages of synthetic customers include:

  • Scalability: Synthetic customers can be created quickly and easily, allowing businesses to test and iterate on their ideas much faster than they could with traditional research methods.
  • Cost-effectiveness: Creating synthetic customers is significantly cheaper than recruiting and conducting research with real people, since you’re using existing data in your databases.
  • Control: You have complete control over the behavior and characteristics of synthetic customers, which can help to create more accurate and reliable results. (They can be programmed to behave like real customers, taking into account preferences, needs, motivations.)
  • Speed: Develop them and get insights in (near or) real-time, allowing you to make data-driven decisions and adapt your strategies on the fly.

Some of the disadvantages:

  • Lack of real-world experience: Synthetic customers may not be able to fully replicate the complexities and nuances of human behavior.
  • Bias: The data used to create synthetic customers – as well as the AI algorithms – can be biased, which can lead to inaccurate results.
  • Data quality: Building synthetic customers means you are relying on the quality and completeness of your data to be spot on 100%. That’s not always the case.
  • Limited emotional understanding: Synthetic customers may struggle to understand and simulate human emotions (an important component of the customer experience), thereby impacting the accuracy of their predictions and insights.
Which One Should You Use?

Ideally, the best approach is to use both across the organization – in conjunction with other research methods – to gain a comprehensive understanding and to make informed decisions. Remember that they are both used for different purposes, but across the entire journey, they complement each other. That just sounds like good sense – and good cents.

Personas are tried and true, while synthetic customers are a new and evolving technology that may take a little time yet to fine tune. I’m curious if your company is using either one or both? If so, how are they developed? And how are they being used?

Want to learn more? Check out this article on

By the way, as always, the understanding work that you do for customers applies to employee experience design, too.

Some people call this artificial intelligence, but the reality is this technology will enhance us. So instead of artificial intelligence, I think we’ll augment our intelligence. ~ Ginni Rometty, Former CEO of IBM


Annette Franz is an internationally recognized customer experience thought leader, coach, speaker, and author. In 2019, she published her first book, Customer Understanding: Three Ways to Put the “Customer” in Customer Experience (and at the Heart of Your Business); it’s available on Amazon in both paperback and Kindle formats. In 2022, she published her second book, Built to Win: Designing a Customer-Centric Culture That Drives Value for Your Business [Advantage|ForbesBooks], which is available to purchase on Amazon, Books A Million!, Target, Barnes & Noble, and thousands of other outlets around the world! Sign up for our newsletter for updates, insights, and other great content that you can use to up your EX and CX game.

Image created by/courtesy of Adobe Firefly.