Changelog

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.

We’ve introduced the Faraday Predictor Leaderboard—a live, filterable view into the most predictive traits across our platform. Updated weekly, it showcases which traits are driving outcomes across industries and use cases, offering insights into the real-world signals powering our models.

You can also sort by industry and use case to zero in on the traits that are most relevant to your team. Take a look for yourself.

Modeling strategies used in each Outcome are now available via the API via (GET /outcomes/:id/analysis) and in the dashboard. Each strategy includes details like name, description, and data providers—making it easier to audit models, trace predictions, and build smarter automations.

Note: Strategy metadata will only appear for newly generated Outcomes. Older Outcomes may not include this information unless reprocessed.

To learn more, review our full blog post on the subject, or visit the Outcome API documentation!

We weren't seeing enough usage of the Recipes builder to justify keeping it around. So, we have removed it from the Faraday dashboard so we can focus on building the features that matter most to you. You can still use all other interfaces or API to build the same resources you could with Recipes.

Now you can confidently run performance analyses on dynamic predictions—even when individuals have already attained the outcome.

When dynamic prediction is enabled, Faraday updates scores in real time based on the latest available data. Previously, if someone had already attained the predicted outcome at the time of processing, their score fields were left empty. This made it difficult to analyze how well the model performed after the fact.

With this update, those individuals now retain their original probability and percentile scores—captured as they were at the moment of attainment. That means cleaner, more complete reporting when comparing predicted vs. actual outcomes over time.

No changes are required to your pipeline setup or outputs; these updated values will be included automatically where applicable.

You can now get a quick snapshot of who’s in your deployment—no spreadsheet required.

Every Target now includes a row count by default. For deeper insights, you can opt into analysis by specifying traits (like age or income) and geographies (like ZIP code or state). Faraday will return a structured breakdown via API, plus a downloadable PDF if requested, which are perfect for internal reporting—or for sharing results with clients and partners.

To enable it, just include analysis_config when creating or updating a Target. More information can be retrieved from our "Retrieve a target's analysis" docs.

Faraday's Lookup API now supports payload fallbacks, a new strategy for resolving partial identity matches. When a full match isn't possible, we aggregate traits at the household, address, or ZIP code level to return meaningful results—along with an update to our match_type field so it now includes a new postcode_only value. This means more complete data, fewer gaps, and better predictions from every lookup.

Want to learn more? Here's a blog from the very engineer who merged the new feature!

When creating a CSV dataset, you can now upload multiple files simultaneously. Previously, you had to upload your first file and then navigate to the Data tab within your dataset to append new files.

Each CSV must have the same headers and data types, but now you can upload them all at once. This feature is particularly beneficial for large datasets that are divided across multiple files.

Additionally, you can upload multiple files at once in the Data tab of your dataset. Other overall user experience issues have also been addressed in this UI.