What Traditional Research Methodology Gets Wrong

Most traditional quantitative methods still sort people by demographics. They capture things like age, gender, religion, income, and postcode. Those categories have run marketing and polling for generations, largely because they're what the census collects and passes down. Because that data is widely available, it's become the baseline for how we model behaviour. Their ubiquity creates the impression that these markers matter in predicting people, when in truth they're a by-product of an outdated methodology.

Imagine two people who share the same age, gender, income bracket, and postcode. How likely are they to buy nappies? On demographics alone you can't say, because you're missing the most important factor: whether they are a parent. Demographic data won't tell you that. Context and cultural background are key.

Build a synthetic audience on demographic labels alone and you get a stereotype in a clean interface. Convincing on the surface, wrong underneath.

How a Good Model Works

A good model works by taking customer data and using their responses to predict what a customer would say next. It goes deep on preferences and behaviour, capturing what people believe and value rather than the boxes they tick.

This is also what separates Electric Twin from other LLMs: the ability to find the outliers in your respondents, not just the average. A state-of-the-art LLM trains on all data it can find, so that it ends up asking everyone, with no way to anchor it to the specific person you actually care about.

Because ET builds personas from real data, we're able to segment out exactly what each respondent would say, helping you catch outliers and detractors at a 96% accuracy rate. Our model isn't designed to give the best-sounding answer; it's designed to extract the precise one.

The Impact

A synthetic audience model that goes beyond demographics lets you dig deeper into who your customers really are. It opens up what you can test: ideas too small for a full study, audiences too niche to reach any other way, and questions you can ask on repeat, so that decisions about people are made with evidence, instead of generalisations and stereotypes.

At Electric Twin, we want to make it possible to know your audience deeply, with a living audience platform that behaves like the real thing.