Relational Mapping

What it means

A standard skip trace returns contact info for one person. A relational map returns the people around that person. This matters because the most valuable real-estate leads — inherited property, distressed owners, off-market sellers — are also the people most likely to have changed numbers, moved, or stopped answering unknown calls.

A good relational map identifies each connection's relationship type (spouse, sibling, parent, adult child, business associate) so you know which family member to call first when the primary line goes dead.

How relational mapping actually works

The underlying identity graph doesn't just store a name and a phone number — it stores edges between people: shared addresses over time, shared last names at the same property, and other public-record connections that indicate a real household or family relationship. A relational trace walks those edges outward from the subject and returns whoever shows up as reliably connected, ranked by how strong and recent that connection is.

The output isn't a flat list of "people who might know this person" — it's a small set of named relationships (spouse, sibling, parent, adult child), each carrying its own contact info where available. That relationship label is what makes the data usable: a caller who knows they're dialing the subject's spouse can open the conversation honestly, instead of cold-calling a stranger's number with no context at all.

When relational mapping matters most

• Inherited property. The owner of record may be deceased; the person who can actually discuss a sale is an heir who may not live at the property address at all.
• Long-vacant or absentee properties. A disconnected number is common on distressed lists; a spouse or adult child frequently still has a working line.
• Low-connect-rate batches. When a batch trace comes back with a high match rate but a low answer rate, running just the non-connects through Deep Search is often cheaper than re-driving or re-mailing the same properties.

It matters least on fresh, high-quality lists where the primary contact info is already current — relational fan-out is a recovery tool for dead ends, not a first resort.

How Ava Data handles relational mapping

Ava Data's Deep Search returns the relational graph alongside the primary subject's contact info in a single call. Each related person includes their relationship type, their known addresses, and — when available — their own current phone and email so you can dial through the network without running a second lookup.

In the dashboard, a Deep Search result shows the subject at the top and their family/associate cards underneath, each with a one-click dial. Through the API, the same data comes back as a structured relatedPeople array your CRM or dialer can parse directly — useful for queuing "call the spouse" as an automatic fallback task when the primary number doesn't connect.

Deep Search costs 10 credits (20¢) per match and is intentionally a single-search tool, not a bulk endpoint, because relational fan-out at scale produces diminishing returns — and a real cost — on records that just need a primary phone. The pattern most teams use: run Standard Search (1–2 credits) across the full list first, then send only the non-connects through Deep Search.

For developers: example API call

If you'd rather click than code, the Ava Data dashboard returns the same data without a single line of JSON. The snippet below is for teams wiring Ava Data into a CRM, dialer, or AI pipeline.

{
  "firstName": "John",
  "lastName": "Smith",
  "address": "123 Main St",
  "city": "Austin",
  "state": "TX"
}

{
  "success": true,
  "data": {
    "matchFound": true,
    "creditsCharged": 10,
    "subject": {
      "phones": [{ "number": "5125551234", "type": "mobile" }],
      "emails": [{ "address": "john@example.com" }]
    },
    "relatedPeople": [
      {
        "firstName": "Jane",
        "lastName": "Smith",
        "relationship": "Spouse",
        "phones": [{ "number": "5125559876", "type": "mobile" }]
      },
      {
        "firstName": "Mark",
        "lastName": "Smith",
        "relationship": "Sibling",
        "addresses": [
          { "street": "456 Oak Ave", "city": "Austin", "state": "TX" }
        ]
      }
    ]
  }
}

Related terms

• Skip Tracing — Skip tracing is the process of locating a person's current contact information — phone numbers, email addresses, and known associates — by stitching together public records, utility data, and proprietary identity graphs.
• People Search API (Person Data API) — A people search API — often called a person data API — programmatically returns identity and contact information for an individual given a name and a few disambiguating fields: address, city, state, age, or DOB.
• Absentee Owner — An absentee owner is the legal owner of a property whose mailing address differs from the property address — typically a landlord, an inherited-property holder, or an out-of-state investor — and a top target for off-market acquisitions.

Frequently asked questions

What is relational mapping in skip tracing?

It's the practice of returning contact information for the people connected to a skip-trace subject — spouse, siblings, parents, adult children, known associates — alongside the subject's own data, so there's a fallback path when the primary number is dead.

How is a relational map different from a regular skip trace?

A standard skip trace (Standard Search) returns only the subject's own phone and email. A relational map is part of Deep Search — it additionally returns named relationships with their own contact info where available.

When should I use relational mapping instead of a standard search?

When the subject's own number doesn't connect, or on lead types prone to dead primary numbers — inherited property, where the owner of record may be deceased, long-vacant absentee properties, or the non-connects left over from a batch trace you've already run.

How much does relational mapping cost?

Ava Data's Deep Search, which includes the relational graph, costs 10 credits (20¢) per match — more than a Standard Search (1–2 credits) because it resolves and returns multiple related identities, not just one.