Identity Decay

Assigns a dynamic weight to identifiers based on recency. A hashed email key verified via login today has a confidence of 1.0, while a device fingerprint last seen 30 days ago may decay to 0.2. This prevents a profile from being anchored to outdated touchpoints.

• Uses logarithmic or exponential decay functions
• Fresh deterministic events reset the decay curve
• Prevents session stitching errors caused by shared devices

The mathematical core that governs how linkage confidence erodes. Common models include exponential decay (rapid initial drop) and linear decay (steady reduction). The half-life—the time for confidence to drop by 50%—is tuned per identifier type.

• Cookies: Short half-life (hours/days)
• Hashed emails: Long half-life (months/years)
• IP addresses: Ultra-short half-life (minutes/hours)

Specific events that reset an identifier's confidence to maximum. A deterministic match, such as a user logging in with a passkey or completing a purchase with a known email, instantly revives a decaying profile.

• Deterministic matching events override decay
• Probabilistic matching with high scores can slow decay
• Prevents redundant cold start problem mitigation for returning users

When an identifier's confidence falls below a defined threshold, it is quarantined or purged from the active identity graph. This automates compliance with data minimization principles and reduces storage costs.

• Removes ghost profiles from deprecated third-party cookies
• Supports differential privacy by forgetting inactive users
• Reduces noise in customer lifetime value forecasting models

Decay is not isolated to a single device. When a user's mobile ID decays but their desktop ID is refreshed, the canonical ID must reconcile these states. The graph propagates the highest-confidence signal across all linked nodes.

• Prevents fragmentation of the golden record
• Uses graph neural networks for complex propagation
• Ensures consistent cross-device attribution windows

Decay models operationalize the principle of storage limitation found in GDPR and CCPA. By automatically degrading device fingerprints and household IP matches, the system ensures data is not retained longer than necessary for its original purpose.

• Aligns with Global Privacy Control (GPC) signals
• Reduces risk surface in data clean rooms
• Automates lifecycle management without manual deletion scripts

A centralized data structure that links all known identifiers—email addresses, device IDs, and usernames—to a single unified customer profile. The identity graph serves as the backbone of cross-device personalization, maintaining the relationships between identifiers and the canonical ID. When identity decay reduces linkage confidence for a specific identifier, the graph must either refresh the signal or gracefully degrade the connection without corrupting the master profile.

A statistical approach to identity resolution that uses non-personal signals like IP address, browser type, and behavioral patterns to infer device ownership. Unlike deterministic matching, probabilistic methods assign a confidence score rather than a definitive link. Identity decay directly impacts these scores—as signals age without revalidation, the confidence threshold drops, potentially triggering re-authentication or removal from the identity graph.

The process of algorithmically connecting multiple discrete web or app sessions into a single, continuous behavioral journey. Identity decay complicates session stitching when stale identifiers create false breaks in the journey or incorrectly merge distinct users. Effective decay models apply temporal weighting to prioritize recent, high-confidence touchpoints over aged, ambiguous signals.

The definitive, best-version-of-the-truth customer profile created by applying survivorship rules to conflicting attributes from multiple source systems. Identity decay functions as a critical input to golden record maintenance—when an identifier's confidence score falls below a configurable threshold, the survivorship logic must decide whether to retain associated attributes or purge them to prevent profile pollution.

A secure, neutral environment where multiple parties can combine and analyze first-party data sets without exposing raw, user-level data. Identity decay models are essential in clean room contexts to ensure that stale identifiers from partner datasets do not produce spurious matches. Temporal validation rules prevent aged, low-confidence linkages from skewing attribution analysis or audience segmentation.

The single, golden identifier assigned to a customer after deduplication and entity resolution. Identity decay governs the lifecycle of the canonical ID itself—if all linked identifiers expire or degrade below minimum confidence, the canonical ID may be archived or anonymized. This ensures that ghost profiles from churned users do not consume resources or introduce noise into downstream personalization models.