Architecting Household-Level Identity Grouping for Related Contact Association

How-To-Articles
1

Architecting Household-Level Identity Grouping for Related Contact Association

What This Guide Covers

You will configure a deterministic identity resolution pipeline that aggregates disparate contact signals into a single household entity using Genesys Cloud CX Integration Studio and Custom Attributes. The end result is a unified customer view where calls, chats, and digital interactions from any family member or shared address are linked to a single household_id, enabling cross-member analytics and context-aware routing.

Prerequisites, Roles & Licensing

  • Licensing: Genesys Cloud CX 2 or higher with Integration Studio license.
  • Permissions:
    • Integrations > Integration > Edit
    • Customers > Customer > Edit
    • Telephony > Trunk > Read (for inbound number validation)
    • Analytics > Report > Create (for validation reporting)
  • External Dependencies:
    • A CRM or Customer Data Platform (CDP) that supports household-level data structures.
    • A deterministic matching rule set (e.g., shared address + last name, or explicit account linkage).

The Implementation Deep-Dive

1. Designing the Identity Resolution Logic

The core challenge in household grouping is distinguishing between coincidental data matches (e.g., two unrelated people with the same name living at the same address) and true familial relationships. We do not rely on probabilistic matching for this architecture because false positives create data pollution that is difficult to cleanse later. Instead, we implement a deterministic three-tier matching strategy within Integration Studio.

Tier 1: Explicit Linkage
If the upstream system provides a household_id or account_id that explicitly links records, we accept this as the source of truth. This is the highest confidence match.

Tier 2: Address + Name Composite
If no explicit ID exists, we match on a composite key of normalized_address + last_name. This captures families living at the same residence. We normalize addresses using a standard geocoding service or a pre-processing step to handle variations like “St.” vs “Street”.

Tier 3: Phone Number Clustering
We analyze shared phone numbers. If two distinct individuals share the same primary phone number, they are likely part of the same household or business unit. This is a weaker signal than address data but valuable for digital channels.

The Trap: The Circular Dependency in Identity Creation
A common architectural error is attempting to create the household record in the CRM during the inbound call flow. If the CRM API is slow or fails, the call drops or times out. Furthermore, if you create the household record on every interaction, you risk creating duplicate household records for the same family if the matching logic is not idempotent.

Architectural Reasoning:
We decouple the identity resolution from the real-time call flow. Identity resolution happens asynchronously via Integration Studio triggered by CRM updates or nightly batch jobs. The real-time Architect flow only performs a lookup against the resolved household_id. This ensures sub-100ms lookup times during the call and prevents telephony timeouts.

2. Configuring Integration Studio for Household Aggregation

We use Integration Studio to orchestrate the data synchronization. This process runs on a scheduled basis (e.g., every 15 minutes) to keep the household data fresh without impacting real-time performance.

Step 2.1: Define the Data Schema

In your CRM, ensure the Contact object has a lookup field to the Household object. In Genesys Cloud, we will use Custom Attributes on the Contact object to store the household_id.

Step 2.2: Build the Integration Flow

  1. Trigger: Set the trigger to Scheduled with a frequency of 15 minutes.
  2. Action: Query CRM Contacts: Use the CRM connector to retrieve all contacts updated in the last 15 minutes. Filter by last_modified_date > {{now() - 15 minutes}}.
  3. Action: Loop Through Contacts: For each contact, perform the following logic:
    • Check for Existing Household ID: If the contact already has a household_id, skip to the next contact. This is the idempotency check.
    • Execute Matching Logic:
      • Query the CRM for other contacts sharing the same address_id and last_name.
      • If matches are found, assign the household_id of the first matched record to the current contact.
      • If no matches are found, create a new Household record in the CRM and assign its ID to the current contact.
  4. Action: Update Genesys Cloud Contact: Use the Genesys Cloud connector to update the contact’s Custom Attributes.
    • Endpoint: PUT /api/v2/customobjects/instances/Contacts/{{contact_id}}
    • Payload:
      {
  "data": {
    "custom_attributes": {
      "household_id": "{{new_household_id}}"
    }
  }
}

The Trap: Batch Size Limits and API Throttling
If you query 10,000 contacts in a single batch, you will hit API rate limits in both the CRM and Genesys Cloud. Genesys Cloud imposes a limit of 100 requests per second for custom object updates.

Architectural Reasoning:
We implement pagination in the CRM query and use a Delay node in Integration Studio between batches of 500 records. This spreads the load over time and prevents throttling. We also add a Try-Catch block to handle transient API errors, logging failures to a separate error table for manual review rather than failing the entire integration.

3. Implementing Real-Time Lookup in Architect

Once the household_id is populated in Genesys Cloud, we can use it to enrich the inbound interaction. This allows us to display a unified view of all household members in the Agent Desktop and route based on household-level SLAs.

Step 3.1: Enrich the Interaction

In the Architect flow, immediately after the Start node, add a Get Contact node. This retrieves the contact record associated with the inbound phone number or email.

Step 3.2: Extract the Household ID

Add an Expression node to extract the household_id from the contact’s custom attributes.

  • Expression: contact.custom_attributes.household_id
  • Set this to a flow-level variable: household_id.

Step 3.3: Query Related Contacts

Use the Get Contacts node to find all other contacts with the same household_id.

  • Filter: custom_attributes.household_id = {{household_id}}
  • This returns a list of all family members.

Step 3.4: Aggregate Interaction History

To provide a holistic view, we query the interaction history for all members of the household.

  • Use the Get Interactions node for each contact in the list.
  • Aggregate the results into a single list of interactions, sorted by timestamp descending.
  • Pass this aggregated list to the Agent Desktop via a Set Contact Attributes node.

The Trap: N+1 Query Problem
If a household has 10 members, making 10 separate API calls to get interactions will slow down the flow significantly. Under load, this can cause call setup times to exceed 5 seconds, leading to caller abandonment.

Architectural Reasoning:
We limit the household lookup to the most recent 5 interactions per member and cap the total number of members queried at 5. This ensures the lookup completes in under 200ms. For larger households, we rely on the Agent Desktop to load additional history on-demand via the CRM integration, not the real-time Architect flow.

4. Configuring Agent Desktop for Unified View

The Agent Desktop must present the household data in a way that is actionable. We do not want agents to scroll through a list of 20 family members. We want a summary view.

Step 4.1: Create a Custom Widget

Use the Genesys Cloud Developer Portal to create a custom widget that displays the household_id and a list of related contacts.

  • Fetch the household_id from the contact’s custom attributes.
  • Display a table with columns: Name, Phone, Last Interaction, Sentiment Score.

Step 4.2: Implement Context-Aware Routing

We can now route calls based on household-level attributes. For example, if any member of the household has had a negative sentiment score in the last 24 hours, route all inbound calls from that household to a specialized retention team.

  • In Architect, add a Condition node:
    • If any(household_members.sentiment_score < 0.5), route to Retention Queue.
    • Else, route to General Support Queue.

The Trap: Over-Routing to Specialized Teams
If too many households are flagged for retention, the specialized queue will become overwhelmed, leading to long wait times for genuine high-value customers.

Architectural Reasoning:
We implement a tiered routing strategy. Only households with multiple negative interactions or high-value lifetime spend are routed to the specialized team. This ensures the retention team can handle the volume effectively. We monitor the queue wait times in real-time and adjust the threshold dynamically using a Report node that feeds into a Condition node.

Validation, Edge Cases & Troubleshooting

Edge Case 1: The Divorced Household

The Failure Condition:
A contact updates their address to a new location, but their ex-spouse remains at the old address. The system continues to link them as the same household because the last_name and phone_number may still be shared or linked in the CRM.

The Root Cause:
The deterministic matching logic relies on static attributes that do not reflect life events. The CRM update for the address change is not synchronized with Genesys Cloud in real-time.

The Solution:
Implement a household_status flag in the CRM. When a contact updates their address, the CRM workflow checks for other contacts with the same last_name and phone_number. If found, it prompts the user to confirm if they are still part of the same household. If not, it breaks the link and creates a new household_id. This status is then synced to Genesys Cloud via Integration Studio.

Edge Case 2: The Shared Office

The Failure Condition:
Multiple unrelated employees share the same business address and phone number. The system incorrectly groups them into a single household, leading to privacy violations when agents view each other’s interaction history.

The Root Cause:
The matching logic does not distinguish between residential and business addresses.

The Solution:
Add an address_type field to the CRM. Only apply the Address + Name matching rule if address_type = 'Residential'. For business addresses, rely solely on explicit household_id linkage. This prevents accidental grouping of unrelated business contacts.

Edge Case 3: The API Timeout During Batch Sync

The Failure Condition:
Integration Studio fails to update 500 contacts due to a temporary CRM outage. The next batch run skips these contacts because they are marked as “processed” in the integration history.

The Root Cause:
The integration marks records as processed before confirming the update in Genesys Cloud. If the update fails, the record is not retried.

The Solution:
Implement a two-phase commit pattern. First, update the CRM status to “Syncing”. Then, update Genesys Cloud. If the Genesys update succeeds, update the CRM status to “Synced”. If it fails, update the CRM status to “Failed” and log the record to an error table. A separate integration job retries failed records every hour. This ensures data consistency and prevents data loss.

Official References