Changelog

A regular vendor data refresh has been released. This update adds over 1 million new individuals to the Faraday Identity Graph and includes updated vendor-level datasets.

No issues have been observed so far, but please flag anything unexpected in your checks.

Trait appends can now fall back to broader levels of granularity when an individual match isn’t available in the Faraday Identity Graph (FIG).

Previously, traits were only appended at the individual level. Now, if we can’t identify a person directly, Faraday automatically resolves to the household or even regional level to ensure the record still receives relevant context.

For example, if a trait like age can’t be resolved for an individual, it will be approximated using the average age of the household associated with that address.

This update extends the same multi-resolution logic used in our predictive scoring to trait appends, improving overall coverage and consistency.

We’ve updated persona naming to remove automatically generated names and avatars.

Previously, persona sets included names like “Glamorous Grandma Gloria” to make results feel more human. But these labels could unintentionally introduce bias—implying that everyone in a persona shares the same gender, age, or lifestyle.

From now on, new persona sets will use neutral labels such as “Persona 1,” “Persona 2,” and so on. This provides a clean, unbiased foundation for your own interpretation and naming conventions.

Impact

• No API changes: names will simply appear as “Persona 1,” etc.
• Existing sets unaffected: older persona sets will retain their names until rebuilt.
• Optional customization: you can still generate descriptive names (e.g., using LLMs) based on the summary text if desired.

This change is live now and will roll out gradually to all accounts. Learn more in the persona set documentation.

In datasets console, you can now set an incremental column (e.g. updated_at, order_updated_at) on databases and data warehouses. This dramatically improves refresh efficiency by only processing rows that have changed since the last update.

When paired with a primary key, the refresh process works like this:

1. Select rows from the source where incremental_column is newer than the last run.
2. Merge (upsert) those rows into the dataset using primary_key.

This makes dataset refreshes faster and more efficient, especially for large tables.

We’ve expanded Credit Score Proxy, our predictive proxy for household financial reliability.

Now, in addition to the original 12-rank format (A1–E3), you can now use a simplified 5-category version for easier segmentation and reporting. Both formats are available in the Faraday Identity Graph and can be appended to your datasets like any other data point.

Learn more in our traits (or data points) docs.

Breaking change: We have dropped support for uploading datasets as multi-file ZIP or TAR archives due to unavoidable security issues. Raw CSV uploads remain supported, as well as single-file compression formats like .gz and .bz2 for batching and storage efficiency.

See our API docs for further information.

In outcome creation and editing, we moved the attrition cohort selection to the Advanced section. We determined that this field was being overutilized in unexpected ways, so this change should help create more accurate outcomes.

See https://faraday.ai/docs/abstractions/outcomes for more information.

The Faraday Identity Graph just got a major upgrade:

• Expanded young adult coverage: Millions of new records for ages 18–34, improved age sourcing, and sharper demographic insights.

• Smarter match boosting: Now supports both batch and real-time workflows using a waterfall approach to resolve even sparse or messy records.

• Multi-identity return: Get multiple validated emails or addresses for a single person—perfect for fragmented systems and multichannel outreach.

These improvements are live and already powering your workflows. Learn more.

We’ve released Match Boost, a new add-on that increases enrichment coverage for sparse or single-identifier records and appends identity data to your deployments.

• Available via checkbox in the dashboard or identity_providers: [{"provider": "fig"}, {"provider":"match_boost"}] in the API
• Improves match rates, prediction coverage, and omnichannel reach
• Enrichment impact is visible directly in the dataset index view
• Identity data will automatically be included in Identified and Referenced deployments.

Read the full announcement.

You can now mark individual conditions as optional in both dataset output-to-stream settings and cohort filters—enabling OR logic in addition to the default AND.

This update gives you more flexibility when defining the criteria that matter for your audiences or events. For example, you can now say: “include anyone who either closed a deal or reached a specific deal stage,” instead of requiring both to be true.

We also updated the way logic is displayed in the UI: flat tag lists are now replaced with nested AND/OR trees, shown in popover previews for easier understanding at a glance.

Learn more by reading about conditional events in our Datasets documentation page.