ResearchSampling

Research Sampling

Balance respondent groups or detect demographic skew before running interviews, polls, or user studies.

Start with 500 free creditsDocumentation

Email gender · 1 credit per lookup · no monthly fee

Respondents

Skew detected

72% one group · rebalance before you run

The problem

Survey and study results are only as trustworthy as the sample behind them. If your respondent pool skews heavily toward one group and you never notice, every conclusion inherits that bias, and the report ships with a blind spot nobody caught until it mattered.

With Encrata

Estimate the likely gender distribution of your respondent list before you run the study, spot skew early, and rebalance the sample while you still can. The check is aggregate by design here: you are measuring the shape of the pool, not labelling individuals, so it supports fair sampling rather than undermining it.

How it works

1

Send the address

Call the gender endpoint with just an email address; the name in the local part is enough to work from.

2

We infer the verdict

Encrata reads the name embedded in the address and returns a likely gender and country with a confidence score.

3

Use it with care

Segment, personalise or enrich on the verdict, always keeping neutral fallbacks and letting people override. One credit per lookup.

One call, structured answer

Research Sampling runs on the email gender lookup at 1 credit per lookup. Same API key, same JSON shape as every other Encrata lookup.

  • A single male / female / unknown verdict with a 0-100 confidence score
  • Country signal included, so ambiguous names resolve by region
  • 1 credit per lookup, with free repeats for six months
  • unknown is returned honestly when a name is ambiguous, never a guess
Terminal
curl -X POST "https://app.encrata.com/api/email/gender" \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"email": "jane.doe@example.com"}'
response.json
{
  "email": "jane.doe@example.com",
  "gender": "female",
  "country": "US",
  "probability": 98,
  "credits": 1
}

Measure the pool, not the person

Estimate the likely gender distribution of your respondent pool before the study runs, compare it against the population you are trying to represent, and rebalance the sample while recruitment is still open. The check operates on the pool in aggregate, you are measuring its shape to correct for bias, not labelling any respondent, which is exactly the responsible use.

Skew caught before it ships

Catching skew early is the whole point: a sample that leans heavily one way quietly biases every downstream conclusion, and the fix is cheap before fieldwork and expensive after. Document the estimated distribution alongside your methodology, reviewers trust results more when the sampling frame's balance is shown rather than assumed.

More email gender use cases

View all
jane.doe@acme.com
+ Gender fieldFemale · 98%

Gender Enrichment

Add a probabilistic gender field to lead and customer profiles when users have not provided one

CRMEnrichment

Newsletter reach

58%
42%
Female 58%Male 42%

Audience Analytics

Understand campaign reach by inferred gender in aggregate across lists, subscribers, and event signups

MarketingAnalytics
Women's collection12,480
Men's collection9,020
2 segments built

Marketing Segmentation

Tailor content, product recommendations, or creative tests where gender relevance is legitimate

EcommerceMarketing
Start with 500 free credits
$curl https://encrata.com/api/agent/lookup -H "Authorization: Bearer YOUR_API_KEY" -d '{"e": "satya@microsoft.com"}'