SoContact

Enterprise

Enterprise-grade B2B data, European-grade compliance

SSO, custom DPA, high-volume API and dedicated support for demanding organizations.

50 free credits · No credit card

SPOST /v1/enrich
$ curl -X POST api.socontact.com/v1/enrich \
  -d '{"linkedin_url":"in/camille-moreau"}'
{
  "email": "camille.moreau@acme.fr",
  "email_status": "verified",
  "mobile": "+33612••••18",
  "company": { "name": "Acme SAS", "siren": "812345678" },
  "gdpr": { "source": "registry", "eu_hosted": true }
}
At a glance

Security

SAML SSO, roles, audit log.

Compliance

Custom DPA, reviewed by your DPO.

Account manager

Onboarding and quarterly reviews.

The platform

From target to meeting, in a single platform

Describe your ideal customer in plain language: AI builds the list and verifies every contact.Learn more →
Sapp.socontact.com/search
Contact search
Your search

VP Sales · SaaS · France · 50-200 employees

  1. Target
  2. Contacts
  3. Contact details
Results 03Demo data
  • CMCamille MoreauVP Sales · Acme SAS
    Business emailcamille.moreau@acme.fr
    Direct mobile+33 6 12 •• •• 18
  • TLThomas LaurentHead of Sales · Nimbus
    Business emailt.laurent@nimbus.io
    Direct mobile+33 6 47 •• •• 02
  • SBSofia BianchiCRO · Lumen Data
    Business emailsofia@lumendata.eu
    Direct mobile+33 7 81 •• •• 55
Contact details revealed3 emails/3 mobiles

Your next customers. Within reach.

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Find my prospects

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Practical guide

B2B data for enterprise teams

Understand the need

Enterprise teams assess data services against commercial, technical and governance requirements. Segment relevance must be examined alongside contracts, security and processing responsibilities.

Practical example

An international organization wants consistent data across subsidiaries. It prepares a limited pilot and involves sales, IT, procurement and data-protection stakeholders in defining acceptance criteria.

Method and checkpoints

Document systems, fields, access and transfer constraints. Test no-match scenarios, failures and deletion requests. Evaluation should cover the data lifecycle, not only acquisition. To apply this approach, select a working segment and assign a follow-up owner. Build a small sample representing your accounts, then describe each account’s need, intended stakeholder and expected outcome. Teams should agree on what counts as a useful contact before counting retrieved data. A record without context can appear complete while remaining irrelevant to the project. Preserve observations from people actually using the lists; their feedback often explains differences that volume metrics cannot reveal. Keep the test conditions consistent so results from different segments can be compared fairly.

Limits and decision criteria

Require contractual confirmation for critical capabilities: authentication, audit, support, availability and volume. A marketing demonstration does not establish these guarantees. Validate major deployment terms before committing. Next, hold a shared review: which data helped prepare conversations, which was missing, and which matches required manual checks? Compare these observations with time spent on the current process. Do not automatically attribute commercial outcomes to one tool: message, offer, timing and follow-up matter too. Establish rules for objections and outdated records. Examples are educational scenarios, not customer results. Capabilities, prices and guarantees mentioned in a presentation should be confirmed before operational deployment. Document decisions and exceptions so the next team member can understand why a contact was included or excluded.

Prepare the next step

The next choice depends on channel and context: find emails, examine mobiles or enrich existing records. Consult the corresponding section rather than collecting every available field. This discipline supports coordination, reduces unnecessary information and makes each step easier to justify to the team. Revisit the process when your segment or commercial objective changes. For “B2B data for enterprise teams”, also ask which observations would confirm or challenge your initial hypothesis. This connects preparation with a verifiable decision and prevents generalizing results beyond their context. Review findings with the people involved before changing your process, and distinguish measured improvements from assumptions that still require evidence. Keep the evaluation transparent, specific and reproducible for every stakeholder involved. A documented review makes comparisons easier and preserves assumptions when requirements change. Use relevant observations rather than generic promises to support the decision.

Frequently asked questions

Understand the need — What should be understood?

Enterprise teams assess data services against commercial, technical and governance requirements. Segment relevance must be examined alongside contracts, security and processing responsibilities. Document systems, fields, access and transfer constraints. Test no-match scenarios, failures and deletion requests. Evaluation should cover the data lifecycle, not only acquisition.

Practical example — What should be understood?

An international organization wants consistent data across subsidiaries. It prepares a limited pilot and involves sales, IT, procurement and data-protection stakeholders in defining acceptance criteria.

Method and checkpoints — What should be understood?

Document systems, fields, access and transfer constraints. Test no-match scenarios, failures and deletion requests. Evaluation should cover the data lifecycle, not only acquisition. To apply this approach, select a working segment and assign a follow-up owner. Build a small sample representing your accounts, then describe each account’s need, intended stakeholder and expected outcome.

Limits and decision criteria — What should be understood?

Require contractual confirmation for critical capabilities: authentication, audit, support, availability and volume. A marketing demonstration does not establish these guarantees. Validate major deployment terms before committing. Next, hold a shared review: which data helped prepare conversations, which was missing, and which matches required manual checks?

Prepare the next step — What should be understood?

The next choice depends on channel and context: find emails, examine mobiles or enrich existing records. Consult the corresponding section rather than collecting every available field. This discipline supports coordination, reduces unnecessary information and makes each step easier to justify to the team.

Understand the need — What should be checked?

Enterprise teams assess data services against commercial, technical and governance requirements. Segment relevance must be examined alongside contracts, security and processing responsibilities. Document systems, fields, access and transfer constraints. Test no-match scenarios, failures and deletion requests. Evaluation should cover the data lifecycle, not only acquisition.

Practical example — What should be checked?

An international organization wants consistent data across subsidiaries. It prepares a limited pilot and involves sales, IT, procurement and data-protection stakeholders in defining acceptance criteria.