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JamLoop Attribution Methodology 

Overview

JamLoop Attribution Methodology: Overview

1. Why Attribution Matters in CTV

Attribution in Connected TV (CTV) advertising is both powerful and complex. Unlike clickable environments, CTV relies on household-level identity, not direct clicks, to connect ad exposures to conversions. This makes accurate measurement essential but also challenging.

JamLoop’s attribution framework is built to meet this challenge. It combines advanced household identity modeling, rigorous data validation, and continuous refinement to ensure that advertisers understand which exposures truly drive outcomes — without over- or under-crediting.

Our goal: deliver transparent, defensible, and continuously improving attribution that our clients can trust.

2. The Foundation: Household Identity

The Role of the Household

CTV advertising revolves around households, not individual devices. Ads are viewed on a connected TV in one setting but conversions often happen later: on a phone, laptop, or tablet linked to the same household.

The key challenge? People move between devices and networks (home Wi-Fi, cellular, office), making it easy to lose track of the true path to conversion.

IP Addresses and Their Limitations

An IP address often identifies a household — but not always. Shared Wi-Fi (offices, hotels), VPNs, or privacy tools like iCloud Private Relay can mask or mix signals. That’s why JamLoop goes beyond raw IP matching, using a layered identity approach to balance accuracy and scale.

3. The Household Graph

To connect devices and channels, JamLoop uses a household graph — a system that links devices, identifiers, and interactions to a common household ID.

We partner with identity providers to maintain this persistent identity framework, combining:

  • Deterministic signals (e.g., hashed emails, logins) for precision

  • Probabilistic signals (e.g., shared IP patterns, usage timing) for coverage

Dynamic and Privacy-Safe

The graph evolves as households change devices, networks, or behaviors. It’s rebuilt daily using a 90-day sliding window, ensuring up-to-date yet stable household relationships.

Each build is idempotent, meaning the same inputs always yield the same results, ensuring consistency across processing cycles.

4. JamLoop’s “Goldilocks” Approach

JamLoop’s philosophy is about balance; not attributing too much, not too little, but “just right.”

Checks and Balances

  • Device-to-Household Thresholds: Prevents over-attribution when too many devices appear under one household.

  • Outlier Detection: Filters improbable household behaviors or conversion spikes.

  • Selective Serving: Excludes unreliable IPs and signals from the start to reduce noise.

These safeguards maintain integrity and prevent inflation in performance reporting.

5. Measurement & Credit Assignment

Connecting Impressions and Conversions

JamLoop reconciles ad impressions with conversion events through a unified ID system spanning:

  • Household ID

  • IP (IPv4/6)

  • Session and cookie identifiers

  • Click-through and scan-through links (for banners or QR codes)

Attribution Models

  • Last-Touch Attribution (LTA): 100% credit to the most recent impression.

  • Multi-Touch Attribution (MTA): Distributes fractional credit across all exposures within the lookback window (default: even-weighted).

Example: 3 exposures before a purchase → each receives ⅓ of the credit.

Click & Scan Attribution

When users interact via click or QR code, JamLoop uses those signals as high-certainty matches, a “ground truth” that helps validate non-clickable CTV conversions and calibrate the system’s accuracy.

6. Data Hygiene and Processing

De-Noise Filters

We remove statistical outliers that could distort results:

  • IPs or households with unusually high device counts

  • Internal client IPs (to exclude HQ/test traffic)

  • Abnormal conversion clusters

Processing Cadence

Attribution runs daily (5–7 AM ET) for every advertiser, automatically refreshing data and maintaining continuity without manual triggers.

Tracking Setup

  • JamLoop Pixel: JavaScript-based (recommended) or image-tag fallback.

  • Custom Parameters: Capture revenue, product codes, or order IDs.

  • E-commerce Integration: Seamless setup for Shopify and similar platforms.

7. Reporting & Interpretation

Impression-Time Reporting

Conversions are credited to the time of the impression, not when the conversion occurred. This approach better reflects media impact and helps analyze patterns (e.g., daypart or day-of-week).

Attribution Windows

Configurable lookback periods (e.g., 7 days) define how long after an impression conversions can still receive credit.
Windows are calendar-based, simplifying campaign comparison and analysis.

Always-On Accuracy

Every day’s processing refreshes and backfills conversions within the active window — so minor daily adjustments are expected as new conversions come in.

8. Continuous Improvement

Attribution isn’t static, it’s a living system.
JamLoop continually monitors match-type distributions, household patterns, and benchmarks against click-based “truth sets” to identify drift and recalibrate models.

This closed-loop feedback system ensures ongoing accuracy and trust in reporting.

9. Privacy & Compliance

JamLoop’s attribution system follows privacy-by-design principles, using only pseudonymous signals such as hashed IPs and anonymized household IDs.

All data:

  • Is processed within JamLoop’s U.S.-based, secure infrastructure

  • Uses encryption and strict access controls

  • Complies with CCPA/CPRA, GDPR, and industry frameworks like the DAA

For full details, visit jamloop.com/privacy.

10. Transparency and Governance

Every update to our methodology undergoes:

  • Internal testing and regression checks

  • Pilot rollouts before production

  • Client communication on changes and their rationale

JamLoop ensures that attribution remains both technically sound and business-relevant, empowering advertisers to measure real impact with confidence.

In summary:
JamLoop’s Attribution Methodology blends identity science, statistical rigor, and practical transparency — giving marketers a clear, trustworthy picture of how their CTV, OLV, and Display media drive results.