How Electric Twin Builds AI That Knows Your Customers

Electric Twin builds synthetic audiences from your real customer data, so you can ask questions and get research-grade answers in seconds instead of weeks. The model is validated against real survey responses with LSE, scoring up to 96% accuracy, and it's already in use at The Times, Wingstop, Sage, and Virgin.

Ask a general AI model what your customers think, and you get a generic average. Electric Twin grounds the model in your own customer data instead, so the answers are about your audience, not the internet's.

In this Spotlight, Tom Burke, Head of Market Development, and Diana Panizo, Lead Insight Analyst, both from Electric Twin, join us to talk through how the platform builds and validates a synthetic audience, and what it changes for teams who'd otherwise wait weeks for research.

What you'll hear:

• How Electric Twin grounds a general AI model in a company's real customer data
• How The Times went from rationing research to running it on demand, with the same team
• Where synthetic audiences work well, and where Electric Twin says use real people instead

Who should attend:

CIO, Product, marketing or insights leaders evaluating whether an AI tool's answers about your customers can be trusted.

What you'll leave with:

• A way to judge whether an AI vendor's data is grounded in reality or just a generic model with a new interface
• A real example of research turnaround dropping from weeks to seconds, and what changed operationally to make that possible (let me know if this is possible)
• A clear line on where synthetic audiences are useful today, and where you still need real people