Are synthetic customers the future of data-driven marketing?

For years, we were led to believe that real was better. Real leather. Real grass. Real designer goods.

But as vegan leather, lab-grown diamonds and fake meat gain traction as ethical alternatives, it begs the question – is the future synthetic?

For marketers, the answer may soon be clear.

As traditional data sources become harder to access, ‘synthetic customers’ are emerging as an innovative, privacy-friendly way for marketers to understand their audiences.

These data-driven profiles offer a scalable, ethical way to study consumer behaviour, optimise campaigns and adapt to a cookie-free world.

Keen to know more?

In this article, we define synthetic customers, explain how they’re generated – and explore why they may be the key to future-proofing your marketing strategy.

So, what exactly are synthetic customers?

Synthetic customers are artificial customer profiles designed to simulate the behaviours and preferences of your target audience.

Built using advanced AI to replicate the statistical qualities of real-world data, they can be used to predict customer behaviour, test campaigns and refine marketing strategies. Think of them as digital doppelgängers: they’re not real customers, but they’re uncannily like them.

And therein lies the value for marketers.

Synthetic customers replicate the study-worthy behaviours of real customers, providing rich insights without needing the personal information of real people. This means no data infringement, privacy violations or any of the usual ethical concerns associated with handling real data.

Could synthetic customers prove the golden ticket for privacy-conscious marketers navigating a world without third-party cookies?

Privacy-compliant, endlessly scalable

The short answer is yes! Synthetic data could be the answer for privacy-focused marketers... if used responsibly.

Synthetic customers are designed to behave just like their real-world counterparts, giving marketers a rich sandbox for experimentation.

Want to test how a new product might resonate with different segments? Or predict how changes in pricing or messaging might impact sales? Synthetic data provides the answers without the risks.

In other words, they offer a lifeline in a world where data collection is under scrutiny. Let’s break down the benefits a little more:

• Privacy-first data innovation: With Australia’s privacy reforms reshaping the marketing landscape, compliance is non-negotiable. Synthetic data provides a privacy-safe way to understand customers without overstepping legal or ethical boundaries.

• Testing without limits: Ever wished you could run your campaign through every possible scenario before launch? Synthetic data allows you to simulate customer interactions at scale and test, tweak and optimise your strategies with zero risk.

• Improved scalability: One of the biggest challenges of working with real-world data is obtaining a statistically meaningful sample size. Synthetic customers can overcome this by creating large datasets that mirror the diversity of your actual audience. This means better segmentation, sharper insights and campaigns that are far more likely to hit the mark.

• A cost-effective solution: Collecting, storing and analysing real-world data can be expensive and time-consuming. Synthetic data reduces these costs while still delivering actionable insights.

• Faster insights, smarter decisions: Synthetic data is generated and analysed in real time, meaning marketers can quickly adapt to changing trends and refine strategies on the fly.

The question of ethics

Though synthetic customers offer a privacy-friendly way to gather insights, concerns over bias and inaccuracies remain.

Being artificial, these data-driven profiles can’t replicate the full scope of human emotion. This means the insights they provide – while valuable – may lack the nuance and emotional depth you get when engaging with real customers.

The quality of your input data will play a crucial role in determining the reliability of your synthetic data.

If the real customer data you start with is skewed towards certain demographics, preferences or behaviours, the synthetic customers it creates may amplify those biases. This can lead to campaigns that exclude or misrepresent segments of your audience.

Regular audits, diverse input datasets and testing are all essential to ensure your synthetic customers represent the real-world audience you’re trying to reach. Transparency is key, too. Communicate openly about how you’re using synthetic data and ensure you’re using it to enhance, not manipulate, customer relationships.

The best approach is to blend AI with real-world insights to achieve safer, smarter and more scalable marketing strategies.

When used responsibly, synthetic customers can help marketers innovate in a way that benefits both business and the consumer.

The process behind the profiles

With the benefits and ethical considerations nailed down, let’s take a closer look at how synthetic customers are actually made.

The process uses real-world data as a foundation – things like customer demographics, purchase patterns and browsing behaviours.

Using this data as a blueprint, advanced AI and machine learning algorithms create entirely artificial datasets that retain the statistical qualities and behavioural patterns of the originals.

Here’s a breakdown of the process:

Step 1: Gathering real-world data

The process starts with anonymised, aggregated data to prevent exposure of personal information. This data acts as the framework for building synthetic profiles.

For example, a dataset might reveal that 40% of customers prefer online shopping on weekends or that a specific age group is likely to click on fitness-related ads. (This is when it’s crucial to ensure your anonymised dataset is free from biases, as skewed or incomplete data can compromise the reliability of the synthetic profiles.)

Step 2: Generating artificial data

Next, the AI replicates the patterns identified in the real data.

Through techniques like generative adversarial networks (GANs) or statistical modelling, the system creates entirely new data points.

These artificial data points look and behave like real customer data but are free of any identifiable information.

Step 3: Testing and validation

Once the synthetic data is generated, it must undergo testing to ensure accuracy and reliability.

Marketers can validate the data by running it through simulations, comparing its performance to actual datasets and ensuring it delivers meaningful insights.

You can also perform bias audits and leverage tools like cross-validation or statistical analysis to identify potential biases or anomalies.

How to put synthetic customers to work (and drive results)

Now for the practical part: How can you start using synthetic customers to benefit your campaigns? Why not try…

• Product development insights: Use synthetic customers to test potential new products or features and gauge how different segments might respond before launching.

• Campaign optimisation: Test your messaging on synthetic customers to identify which creative elements resonate most.