CRMResolution

Data Deduplication

Use gender as a weak supporting signal when matching records, spotting anomalies, or enriching gaps.

Start with 500 free creditsDocumentation

Email gender · 1 credit per lookup · no monthly fee

j.doe@work.comFemale · 97%
jane.d@gmail.comFemale · 95%
Likely same person · gender supports match

The problem

The same person shows up in your database three times: a work address, a personal address, a typo'd address. Matching them is hard when the only shared field is a name that half your records spell differently, and merges you are unsure about are risky to run.

With Encrata

Use inferred gender and country as supporting signals alongside name and other fields to raise or lower confidence in a match, so two records that resolve to the same likely gender and country reinforce a merge you were already leaning toward. It is a tiebreaker in the dedup pipeline, never the sole basis for combining records.

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

Data Deduplication 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
}

A tiebreaker, never the basis

Add inferred gender and country as supporting features in your match-scoring logic, alongside name similarity and shared attributes, so two records that resolve to the same likely gender and country nudge the confidence up on a merge you were already considering. It is a tiebreaker that helps resolve the ambiguous cases, never the sole reason to combine two records.

Merges you can trust

Surface the merge as a suggestion with the evidence attached and let a human confirm, false merges are expensive to unwind, and the inference is probabilistic. Used as one signal among several, it measurably improves match precision on records that share little more than a differently-spelled name, without introducing merges you cannot defend later.

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"}'