Low-effort pilots to see synthetic value fast

The biggest misconception about synthetic experiments is that they aren’t real. People imagine made-up data, detached from what customers actually do. In truth, synthetic experiments are built from the same behavioral data we already use for prediction or regression.

The difference is who responds. Instead of a mathematical model, a large language model steps in and imitates an audience. It predicts what someone like your customer would do based on what’s already known. It’s still grounded in reality—just faster, safer, and more flexible.

Synthetic experimentation isn’t a replacement for live testing. It’s rehearsal. A space to explore without risk.

Here are three easy ways to get started before Q1.

1. Audience Pre-Testing

Take what you plan to launch next quarter and test it on a synthetic audience. Feed in your past engagement data and ask the model to react to variations in creative, tone, or offer. The results will tell you where attention will likely fall flat and where it might spark.

2. Fatigue Simulation

Most brands over-message. Synthetic audiences let you model how fatigue builds. You can simulate what happens after the third or tenth exposure and decide when to pull back before your real audience tunes out.

3. Creative Response Modeling

Gather your best and worst messages from the past year. Run them through a synthetic audience and read the qualitative “reactions.” What kinds of personas respond? What emotional tone creates pull or avoidance? It’s a focus group that never gets tired.

You don’t need a lot of infrastructure to start. A small set of historical data, access to a capable model, and an open mind are enough. The key is knowing both the limits of your data and the limits of the model.

Synthetic experiments are a sandbox. They let you try, fail, and learn before anything touches a customer. This isn’t the end of experimentation—it’s the beginning of faster, safer, and more creative testing.