Digital twins
Digital twins of your customers.
Synthetic respondents grounded in real people and real interviews: what they say, why they say it, and what they do. Ask a thousand of them anything and read the answer in minutes.
Rents a two-bedroom with a partner and a four-year-old. Logistics coordinator for a regional grocer, eleven years in.
- Household
- Two adults, one child
- Income
- Under $40k
- Banks with
- A national bank since college, a credit union for savings
- Money habits
- Checks rates quarterly, pays everything on autopay
- Last switch
- Moved the checking account in March 2024
- Media
- YouTube for anything practical, no TV
- Phone
- iPhone, every app has notifications off
- Values
- Will pay for anything that saves a weekend
- Interviewed
- Twice, by voice, 24 minutes in total
- Says
- Why
- The rate is not the objection. The setup is.
- Did
- Opened a new account in March. Moved the deposit in April.
What it does
One twin, one real person.
Every twin starts from a real interview: what the person said, why they said it, and what they went on to do. It answers the way that person would, not the way an assistant would.
A thousand twins, a population.
Put the same question to all of them. Ask it again tomorrow. Ask the segment you could never recruit. The twins keep learning from every interview you run.
A distribution that reflects the population.
Assistant models give the agreeable answer forty times out of forty. Jean's twins answer the way the real people did, so you get the yes, the maybe and the stubborn no in the right proportions.
Measured against the real people.
Random guessing agrees with the real answers one time in five. The same people asked again agree with themselves nine times in ten. The twins sit close to the people.
Why teams use them
- 10x fasterAnswers in minutes, not weeks.
- 10x cheaperTen ideas tested for the price of one study.
- Qual + QuantWhat they would do, and why.
- Any segmentEven the ones you cannot recruit.
- Always onAsk again tomorrow. And the day after.