**See our complete financial services guides:**

- [Predictive AI for banks and credit unions](/blog/banks-credit-unions-complete-context-guide)
- [Predictive AI for financial services (debt, mortgage, lending)](/blog/debt-mortgage-lending-complete-context-guide)
- [Predictive AI for insurance brands](/blog/insurance-complete-context-guide)

---

If you’re leading marketing at a financial services brand—credit unions, regional banks, fintech lenders, mortgage providers—you already know how critical data is to keeping your funnel moving. You’ve built strong programs to generate leads, pre-qualify applicants, and feed loan officers. But there’s always another level of efficiency and ROI to unlock when you have richer context on the households you’re targeting.

Think about prioritizing applications in the call center: better data means fewer unqualified prospects, more productive reps, and stronger portfolio growth. That’s where Faraday comes in.

## The Faraday data platform

To take your efforts further, you don’t just need more data—you need the right data, delivered in a way that’s immediately usable across your CRM, loan origination system, and campaigns.

That’s where [Faraday’s three-part data platform](/blog/what-data-do-you-need-for-marketing) comes in:

- **Identity completion**: [Fill in the blanks](/blog/what-data-do-you-need-for-marketing#layer-1-identity-completion-building-the-contact-foundation) with verified names, addresses, emails, and phone numbers so every record maps to a real household.
- **Consumer data**: [Enrich your customer records](/blog/what-data-do-you-need-for-marketing#layer-2-consumer-profile-enrichment-closing-the-context-gap) with the [Faraday Identity Graph (FIG)](https://faraday.ai/blog/whats-in-faraday-identity-graph), which includes over 1,400 responsibly sourced [data points](https://faraday.ai/catalog) covering 240M U.S. adults and households. These data points span demographics, lifestyle, property, and behavioral attributes that make segmentation, targeting, and prioritization easy—even before layering on custom predictions.
- **Custom predictive data points**: [AI-trained scores](https://faraday.ai/blog/predictive-lead-scoring-b2c) [built on your own records](/blog/what-data-do-you-need-for-marketing#layer-3-custom-predictive-data-anticipating-customer-next-action)—like Likelihood to Refinance, Default Risk Proxy, or ValueScore—so you can prioritize and personalize at scale.

## Essential data packages

One way we make the consumer data layer easier to use is by curating **industry essential data packages**: sets of 20 data points that we’ve determined to be most predictive for your industry. We identify these data points using our Faraday [predictor leaderboard](/leaderboard), which ranks data points by their predictive power in each particular industry vertical. For financial services, these packages are tuned to help you qualify applicants, segment portfolios, and drive smarter outreach.

- **How they’re chosen**: We analyze all 1,400+ data points in the FIG and surface the ones with the strongest predictive signals for your vertical. That way you get the attributes that matter most, without paying for data you don’t need.

- **How they’re delivered**: Essential data packages show up just like any other Faraday data point—directly appended to your records in your CRM, call center platform, or lead management system in [real time through our API or in scheduled batch](/blog/real-time-vs-batch-which-drives-roi). No extra setup required.

- **How they’re different**: Most data vendors hand you a pile of raw attributes. Faraday packages are curated, validated, and aligned to your outcomes. Our data is high-quality, responsibly sourced, and refreshed regularly—and we take on the work of figuring out what’s actually useful for you.

## The financial services essential data package

Here’s what’s included in the standard package for financial services brands:

| Data Trait                                                                                                                 | Description                                                                                                                                             |
| :------------------------------------------------------------------------------------------------------------------------- | :------------------------------------------------------------------------------------------------------------------------------------------------------ |
| [Is millennial](https://faraday.ai/catalog/v2/is_millennial)                                                               | Indicates whether a household contains an adult from the Millennial generation                                                                          |
| [Shopping styles](https://faraday.ai/catalog/v2/shopping_styles)                                                           | Segments households by preferred shopping behavior—online, in-store, luxury, deal-driven, and more—based on transactions, media habits, and survey data |
| [Household income](https://faraday.ai/catalog/v2/household_income)                                                         | Estimated annual household income, based on modeled narrow- and broad-band income ranges                                                                |
| [Gender](https://faraday.ai/catalog/v2/gender)                                                                             | Gender of the individual                                                                                                                                |
| [Dwelling type](https://faraday.ai/catalog/v2/dwelling_type)                                                               | Structural classification of the residence (single-family, duplex, townhouse, apartment, mobile home)                                                   |
| [Child presence in household indicator](https://faraday.ai/catalog/v2/child_presence_in_household_indicator)               | Indicates whether a child is present in the household                                                                                                   |
| [Family composition](https://faraday.ai/catalog/v2/family_composition)                                                     | Classifies the household by the marital status of adults and the presence or absence of children                                                        |
| [Miscellaneous online topic spending interest](https://faraday.ai/catalog/v2/miscellaneous_online_topic_spending_interest) | Flags households with reported online spending in topics not covered by other interest categories                                                       |
| [Age](https://faraday.ai/catalog/v2/age)                                                                                   | The age of the individual                                                                                                                               |
| [Is gen x](https://faraday.ai/catalog/v2/is_gen_x)                                                                         | Indicates whether a household contains an adult from Generation X (born 1965–1980)                                                                      |
| [Is baby boomer](https://faraday.ai/catalog/v2/is_baby_boomer)                                                             | Indicates whether a household contains an adult from the Baby Boomer generation (born 1946–1964)                                                        |
| [Home market value change direction](https://faraday.ai/catalog/v2/home_market_value_change_direction)                     | Indicates whether a household's estimated home market value has recently increased or decreased                                                         |
| [Individual marital status](https://faraday.ai/catalog/v2/individual_marital_status)                                       | Marital status                                                                                                                                          |
| [Purchased via internet](https://faraday.ai/catalog/v2/purchased_via_internet)                                             | Likely to make purchases via the internet channel                                                                                                       |
| [Credit card payment indicator](https://faraday.ai/catalog/v2/credit_card_payment_indicator)                               | Indicates whether the individual has made a purchase using a credit card                                                                                |
| [Household income change direction](https://faraday.ai/catalog/v2/household_income_change_direction)                       | Indicates the household income has had an increase or decrease                                                                                          |
| [Childrens spending quintile](https://faraday.ai/catalog/v2/childrens_spending_quintile)                                   | Ranks an individual into five tiers based on spending on children's products including apparel, toys, furniture, games, and educational materials       |
| [New adult to file indicator](https://faraday.ai/catalog/v2/new_adult_to_file_indicator)                                   | Indicates a new young adult has recently been added to the household record—an early signal of first-time credit or account activity                    |
| [Income tier derivation](https://faraday.ai/catalog/v2/income_tier_derivation)                                             | Indicates the type of information used to derive a household's income tier assignment                                                                   |
| [Low to median household income pct](https://faraday.ai/catalog/v2/low_to_median_household_income_pct)                     | Estimates the share of households in the surrounding postcode earning at or below 80% of the postcode median household income                           |

These data points give you immediate visibility into whether a lead or applicant is likely to be a fit for your business, before your loan officers ever pick up the phone.

## Why this matters

When you add these essentials to your leads and customer records, you:

- **Save money** by rejecting unqualified applicants earlier in the funnel.
- **Increase conversions** by prioritizing the right households for your products.
- **Boost efficiency** by making sure your reps and loan officers aren’t wasting time on poor fits or low-probability approvals.
- **Prove ROI** to the CFO with data-driven evidence that marketing spend is driving real portfolio growth.

## Smarter data, real results

Industry essential data packages are how Faraday makes your records AI-ready, without heavy lifting from your team. Combined with verified identity completion and custom predictive data points, they give you the full modern data stack your financial services brand needs to compete.

Ready to see how these data points can transform your funnel? [Get started on Faraday Pro](/signup) for self-serve access, or [talk to a Context Consultant](/contact) if you're evaluating for a larger contract or want to talk through activation angles.

Faraday’s financial services essential data package delivers curated, high-impact data points that help banks, credit unions, and fintechs qualify applicants faster, prioritize the right households, and drive portfolio growth.

Just the essentials: get the data points financial services brands need to make better decisions

**See our complete guide:** [Predictive AI for home services brands](/blog/home-services-complete-context-guide).

---

If you're leading marketing at a home services brand—kitchen, bath, remodeling, roofing, solar—you already know how critical data is to keeping the funnel moving. You've built strong programs to generate leads, feed the call center, and keep installers busy. But there's always another level of efficiency and ROI to unlock when you have richer context on the households you're targeting.

Think about qualifying appointments in the call center: better data means fewer no-shows, more productive reps, and more installs. That's where Faraday comes in.

## The Faraday data platform

To take your efforts further, you don't just need _more_ data—you need the _right_ data, delivered in a way that's immediately usable across your CRM, call center, and campaigns. That's where Faraday's three-part data platform comes in:

- **Identity completion**: [Fill in the blanks](/blog/what-data-do-you-need-for-marketing#layer-1-identity-completion-building-the-contact-foundation) with verified names, addresses, emails, and phone numbers so every record maps to a real household.
- **Consumer data**: [Enrich your customer records](/blog/what-data-do-you-need-for-marketing#layer-2-consumer-profile-enrichment-closing-the-context-gap) with the [Faraday Identity Graph (FIG)](https://faraday.ai/blog/whats-in-faraday-identity-graph), which includes over 1,400 responsibly sourced [data points](https://faraday.ai/catalog) covering 240M U.S. adults and households. These data points span demographics, lifestyle, property, and behavioral attributes that make segmentation, targeting, and prioritization easy—even before layering on custom predictions.
- **Custom predictive data points**: [AI-trained scores](https://faraday.ai/blog/predictive-lead-scoring-b2c) [built on your own records](/blog/what-data-do-you-need-for-marketing#layer-3-custom-predictive-data-anticipating-customer-next-action)—like Likelihood-to-Purchase, Best-First-Product, or Credit Score Proxy—so you can prioritize and personalize at scale.

## Essential data packages

One way we make the consumer data layer easier to use is by curating industry essential data packages: sets of 20 data points that we've determined to be most predictive for your industry. We identify these data points using our Faraday predictor [leaderboard](/leaderboard), which ranks data points by their predictive power in each particular industry vertical. For home services, these packages are tuned to help qualify leads, score calls, and prioritize installs.

**How they're chosen:** We analyze all 1,400+ data points in the FIG and surface the ones with the strongest predictive signals for your vertical. That way you get the attributes that matter most, without paying for data you don't need.

**How they're delivered:** Essential data packages show up just like any other Faraday data point—directly appended to your records in your CRM, call center platform, or lead management system in real time through our api or in scheduled batch. No extra setup required.

**How they're different:** Most data vendors hand you a pile of raw attributes. Faraday packages are curated, validated, and aligned to your outcomes. Our data is high-quality, responsibly sourced, and refreshed regularly—and we take on the work of figuring out what's actually useful for you.

## The home services essential data package

Here's what's included in the standard package for home services brands:

| Data Trait                                                                                                                 | Description                                                                                                                                             |
| :------------------------------------------------------------------------------------------------------------------------- | :------------------------------------------------------------------------------------------------------------------------------------------------------ |
| [Shopping styles](https://faraday.ai/catalog/v2/shopping_styles)                                                           | Segments households by preferred shopping behavior—online, in-store, luxury, deal-driven, and more—based on transactions, media habits, and survey data |
| [Is baby boomer](https://faraday.ai/catalog/v2/is_baby_boomer)                                                             | Indicates whether an adult born between 1946–1964 (the Baby Boomer generation) is present in the household                                              |
| [Family composition](https://faraday.ai/catalog/v2/family_composition)                                                     | Classifies the household by the marital status of adults and the presence or absence of children                                                        |
| [Purchased via sweepstakes](https://faraday.ai/catalog/v2/purchased_via_sweepstakes)                                       | Likely to make purchases via the sweepstakes channel                                                                                                    |
| [Gender](https://faraday.ai/catalog/v2/gender)                                                                             | Gender of the individual                                                                                                                                |
| [Mail order buyer interest](https://faraday.ai/catalog/v2/mail_order_buyer_interest)                                       | Likely to make purchases via the mail order channel                                                                                                     |
| [Miscellaneous online topic spending interest](https://faraday.ai/catalog/v2/miscellaneous_online_topic_spending_interest) | Flags households with reported online spending in topics not covered by other interest categories                                                       |
| [Individual marital status](https://faraday.ai/catalog/v2/individual_marital_status)                                       | Marital status                                                                                                                                          |
| [Facebook interest](https://faraday.ai/catalog/v2/facebook_interest)                                                       | Flag for household interest in or active use of Facebook, derived from social media engagement signals                                                  |
| [Dwelling type](https://faraday.ai/catalog/v2/dwelling_type)                                                               | Structural classification of the residence (single-family, duplex, townhouse, apartment, mobile home)                                                   |
| [Low to median household income pct](https://faraday.ai/catalog/v2/low_to_median_household_income_pct)                     | Estimates the share of households in the surrounding postcode earning at or below 80% of the postcode median household income                           |
| [Household income](https://faraday.ai/catalog/v2/household_income)                                                         | Estimated annual household income, based on modeled narrow- and broad-band income ranges                                                                |
| [High dollar belief causes donor likelihood](https://faraday.ai/catalog/v2/high_dollar_belief_causes_donor_likelihood)     | Ranks likelihood to donate $500 or more annually to religious causes                                                                                    |
| [Income tier derivation](https://faraday.ai/catalog/v2/income_tier_derivation)                                             | Indicates the type of information used to derive a household's income tier assignment                                                                   |
| [Home year built](https://faraday.ai/catalog/v2/home_year_built)                                                           | Four-digit year the primary building on the property was originally constructed                                                                         |
| [Available home equity amount](https://faraday.ai/catalog/v2/available_home_equity_amount)                                 | Current market value of the home minus outstanding mortgage balance or liens                                                                            |
| [Prominent household profession](https://faraday.ai/catalog/v2/prominent_household_profession)                             | The most prominent profession associated with the household                                                                                             |
| [Child presence in household indicator](https://faraday.ai/catalog/v2/child_presence_in_household_indicator)               | Indicates whether a child is present in the household                                                                                                   |
| [Credit score proxy](https://faraday.ai/catalog/v2/credit_score_proxy)                                                     | Groups individuals into 12 ranks across 5 tiers (Best to Poor) that approximate credit score; rank 1 (~760) is best, rank 12 (~590) is lowest           |
| [Credit card payment indicator](https://faraday.ai/catalog/v2/credit_card_payment_indicator)                               | Indicates whether the individual has made a purchase using a credit card                                                                                |

These data points give you immediate visibility into whether a lead is likely to be a fit for your business, before your sales team ever picks up the phone.

## Why this matters

When you add these essentials to your leads and customer records, you:

- **Save money** by rejecting unqualified leads earlier in the funnel.
- **Increase conversions** by prioritizing the right households.
- **Boost efficiency** Make sure your reps aren't wasting time on unqualified leads, low-fit or no show appointments.
- **Prove ROI** to the CFO with data-driven evidence that marketing spend is driving installs and revenue.

## Closing: smarter data, real results

Industry essential data packages are how Faraday makes your records AI-ready, without heavy lifting from your team. Combined with verified identity completion and custom predictive data points, they give you the full modern data stack your home services brand needs to compete.

Ready to see how these data points can transform your funnel? [Get started on Faraday Pro](/signup) for self-serve access, or [talk to a Context Consultant](/contact) if you're evaluating for a larger contract or want to talk through activation angles.

Faraday’s essential home-services data package gives brands 20 curated data points—like household income, property type, and credit score proxy—to qualify leads, cut waste, and boost installs.

Just the essentials: get the data points home services brands need to make better decisions

**See our complete guide:** [Predictive AI for e-commerce and retail brands](/blog/retail-ecommerce-complete-context-guide).

---

If you’re leading marketing at a retail or e-commerce brand—like apparel, furniture, or subscription boxes—you already know how critical data is to keeping the funnel moving. You’ve built strong programs to drive traffic, grow carts, and keep customers coming back. But there’s always another level of efficiency and ROI to unlock when you have richer context on the households you’re targeting.

Think about segmentation for campaigns: better data means fewer wasted impressions, more personalized offers, and higher conversion rates. That’s where Faraday comes in.

## The Faraday data platform

To take your efforts further, you don’t just need more data—you need the right data, delivered in a way that’s immediately usable across your CRM, ESP, and ad platforms. That’s where Faraday’s three-part data platform comes in:

- **Identity completion**: [Fill in the blanks](/blog/what-data-do-you-need-for-marketing#layer-1-identity-completion-building-the-contact-foundation) with verified names, addresses, emails, and phone numbers so every record maps to a real household.
- **Consumer data**: [Enrich your customer records](/blog/what-data-do-you-need-for-marketing#layer-2-consumer-profile-enrichment-closing-the-context-gap) with the [Faraday Identity Graph (FIG)](https://faraday.ai/blog/whats-in-faraday-identity-graph), which includes over 1,400 responsibly sourced [data points](https://faraday.ai/catalog) covering 240M U.S. adults and households. These data points span demographics, lifestyle, property, and behavioral attributes that make segmentation, targeting, and prioritization easy—even before layering on custom predictions.
- **Custom predictive data points**: [AI-trained scores](https://faraday.ai/blog/predictive-lead-scoring-b2c) [built on your own records](/blog/what-data-do-you-need-for-marketing#layer-3-custom-predictive-data-anticipating-customer-next-action)—like Likelihood-to-Purchase, Best-First-Product, or Credit Score Proxy—so you can prioritize and personalize at scale.

## Essential data packages

One way we make the consumer data layer easier to use is by curating industry essential data packages: sets of just the data points that we’ve determined to be most predictive for your industry. We identify these data points using our Faraday predictor [leaderboard](/leaderboard), which ranks data points by their predictive power in each particular industry vertical. For retail/e-commerce, these packages are tuned to help segment audiences, tailor offers, and boost conversions.

**How they’re chosen**: We analyze all 1,400+ data points in the FIG and surface the ones with the strongest predictive signals for your vertical. That way you get the attributes that matter most, without paying for data you don’t need.

**How they’re delivered**: Essential data packages show up just like any other Faraday data point—directly appended to your records in your CRM, ESP, or ad platform in real time through our API or in scheduled batch. No extra setup required.

**How they’re different**: Most data vendors hand you a pile of raw attributes. Faraday packages are curated, validated, and aligned to your outcomes. Our data is high-quality, responsibly sourced, and refreshed regularly—and we take on the work of figuring out what’s actually useful for you.

## The retail/e-commerce essential data package

Here’s what’s included in the standard package for retail & e-commerce brands:

| Data Trait                                                                                                                                       | Description                                                                                                                                             |
| :----------------------------------------------------------------------------------------------------------------------------------------------- | :------------------------------------------------------------------------------------------------------------------------------------------------------ |
| [Fashion accessories and beauty spending quintile](https://faraday.ai/catalog/v2/fashion_accessories_and_beauty_spending_quintile)               | Average dollars spent on the fashion accessories and beauty category, bucketed into 5 quintile groupings                                                |
| [Fashion accessories and beauty purchase recency](https://faraday.ai/catalog/v2/fashion_accessories_and_beauty_purchase_recency)                 | Recency of purchase in the fashion accessories and beauty category                                                                                      |
| [Gender](https://faraday.ai/catalog/v2/gender)                                                                                                   | Gender of the individual                                                                                                                                |
| [Online amount spent on purchases quintile](https://faraday.ai/catalog/v2/online_amount_spent_on_purchases_quintile)                             | Total dollars spent on online purchases within lifetime activity, bucketed into 5 quintile groupings                                                    |
| [Shopping styles](https://faraday.ai/catalog/v2/shopping_styles)                                                                                 | Segments households by preferred shopping behavior—online, in-store, luxury, deal-driven, and more—based on transactions, media habits, and survey data |
| [Miscellaneous online topic spending interest](https://faraday.ai/catalog/v2/miscellaneous_online_topic_spending_interest)                       | Flags households with reported online spending in topics not covered by other interest categories                                                       |
| [Purchased via internet](https://faraday.ai/catalog/v2/purchased_via_internet)                                                                   | Likely to make purchases via the online channel                                                                                                         |
| [Home market value](https://faraday.ai/catalog/v2/home_market_value)                                                                             | Modeled market value of the property in dollars                                                                                                         |
| [Length of residence](https://faraday.ai/catalog/v2/length_of_residence)                                                                         | The number of months the resident has lived at this location                                                                                            |
| [Retail spend share quintile](https://faraday.ai/catalog/v2/retail_spend_share_quintile)                                                         | Ratio of transactions within the retail channel compared to overall transactions, bucketed into 5 quintile groupings                                    |
| [Online purchase count quintile](https://faraday.ai/catalog/v2/online_purchase_count_quintile)                                                   | Total lifetime number of online purchases made, bucketed into 5 quintile groupings                                                                      |
| [Home and fashion trend spending quintile](https://faraday.ai/catalog/v2/home_and_fashion_trend_spending_quintile)                               | Average dollars spent on the latest trends in home decor and fashion, bucketed into 5 quintile groupings                                                |
| [Household education](https://faraday.ai/catalog/v2/household_education)                                                                         | Highest education level attained by any adult member of the household                                                                                   |
| [Child presence in household indicator](https://faraday.ai/catalog/v2/child_presence_in_household_indicator)                                     | Indicates whether a child is present in the household                                                                                                   |
| [Home and fashion trend purchase recency](https://faraday.ai/catalog/v2/home_and_fashion_trend_purchase_recency)                                 | Recency of purchase in the trendsetters category                                                                                                        |
| [Total amount spent on purchases quintile](https://faraday.ai/catalog/v2/total_amount_spent_on_purchases_quintile)                               | Total dollars spent on purchases within lifetime activity, bucketed into 5 quintile groupings                                                           |
| [Sports merchandise activewear spending quintile](https://faraday.ai/catalog/v2/sports_merchandise_activewear_spending_quintile)                 | Average dollars spent on the sports merchandise & activewear category, bucketed into 5 quintile groupings                                               |
| [Beauty and spa spending quintile](https://faraday.ai/catalog/v2/beauty_and_spa_spending_quintile)                                               | Average dollars spent on the beauty and spa category, bucketed into 5 quintile groupings                                                                |
| [Low to median household income pct](https://faraday.ai/catalog/v2/low_to_median_household_income_pct)                                           | Estimates the share of households in the surrounding postcode earning at or below 80% of the postcode median household income                           |
| [High price female apparel accessories spending quintile](https://faraday.ai/catalog/v2/high_price_female_apparel_accessories_spending_quintile) | Average dollars spent on the high ticket female apparel/accessories category, bucketed into 5 quintile groupings                                        |

These data points give you immediate visibility into whether a prospect is likely to be a fit for your brand and how best to reach them, before you spend on acquisition or campaigns.

## Why this matters

When you add these essentials to your records, you:

- **Save money** by excluding low-fit audiences before they eat up ad budget.
- **Increase conversions** by personalizing campaigns to the right households.
- **Boost efficiency** by matching offers to the channels households actually prefer.
- **Prove ROI** to the CFO with data-driven evidence that marketing spend is driving sales and lifetime value.

## Closing: smarter data, real results

Industry essential data packages are how Faraday makes your records AI-ready, without heavy lifting from your team. Combined with verified identity completion and custom predictive data points, they give you the full modern data stack your retail or e-commerce brand needs to compete.

Ready to see how these data points can transform your funnel? [Get started on Faraday Pro](/signup) for self-serve access, or [talk to a Context Consultant](/contact) if you're evaluating for a larger contract or want to talk through activation angles.

Faraday’s essential retail/e-commerce data package gives brands the curated data points—like household income, online vs. offline purchase ratios, and channel preferences—to qualify customers, cut waste, and boost conversions.

Just the essentials: get the data points retail & e-commerce brands need to make better decisions

A holdout evaluation is the industry-standard method data scientists use to validate predictive models: train on historical data, set aside a portion the model has never seen, then check whether the highest-scoring leads actually converted at higher rates. If they did, the model works.

If you're evaluating predictive data vendors, this is the test worth asking for. With tight budgets and increasing pressure to show ROI, you can't afford to bet on unproven tools or spend months figuring out if a vendor will live up to the promise. You need a way to separate legitimate predictive tools from the noise.

That's what our holdout evaluation does. It's not a methodology we invented — it's an industry standard used to validate [custom predictive data](https://faraday.ai/blog/predictive-lead-scoring-b2c), including [propensity-to-buy scores](https://faraday.ai/blog/propensity-to-buy-machine-learning) used across lead buying, direct mail, and [personalization](/blog/personalization-tactics-that-drive-conversions).

Below we've outlined how it works and what to look for when evaluating whether it's delivering real value.

## What is a holdout evaluation?

A holdout evaluation is a method for validating predictive models by comparing their predictions to real-world outcomes. We **set aside a portion of recent conversion data**—data the model has never seen before—and use it as an independent benchmark, against which predictions can be compared.

This "holdout set" acts as a real-world proving ground. It is untouched until after the predictive model has been trained.

Meanwhile, the rest of your historical data is combined with rich consumer data from the [Faraday Identity Graph (FIG)](/blog/whats-in-faraday-identity-graph) ([which includes over 1,400 data points on 240M+ U.S. adults and their households](https://faraday.ai/features/faraday-identity-graph)). We use this combined data to train the new predictive model.

Training a predictive model simply means finding patterns in historical conversions. In other words, what can we learn from your past customers to predict future outcomes? Once trained, the model generates predictions—scoring leads based on their likelihood to convert and only after these predictions are made do we compare them to the **real conversion rates** in the holdout set.

If the highest-scoring leads actually convert at higher rates than lower-scoring ones, the model is validated as accurate and reliable.

Holdout evaluations ensure predictive models aren't just theoretical—they work in the real world, using real data, to deliver real results.

## How holdout validation works at Faraday

We've streamlined this validation process to be transparent and straightforward:

1. ![customer conversion data](/images/blog/holdout_testing_diag/holdout_testing_diag-01.png)

   #### Step 1: Set aside a holdout group

   We start with your recent [lead conversion data](https://faraday.ai/blog/first-party-vs-third-party-data-marketing). Before any processing occurs, we reserve a portion of your most recent leads as a "holdout set"—data that the model will never see during training. This ensures we have an independent benchmark for evaluating predictions.

2. ![merged with FIG](/images/blog/holdout_testing_diag/holdout_testing_diag-02.png)

   #### Step 2: Enrich with consumer data

   With the holdout set reserved, we then combine remaining data with Faraday's [built-in consumer data](https://faraday.ai/features/faraday-identity-graph) sourced from the Faraday Identity Graph (FIG). This expanded dataset provides a deeper view into key predictive signals, like property and demographic information, life events, buying habits, and more.

3. ![model is trained and predictions begin](/images/blog/holdout_testing_diag/holdout_testing_diag-03.png)

   #### Step 3: Train the model and score leads

   Using this enriched dataset (excluding the holdout set), we train a predictive model to identify patterns in conversion behavior—learning from consumer signals, demographic data, and behavioral patterns.

   Once the model is trained, it scores each lead based on its likelihood to convert, generating a ranked list of opportunities—from high-value leads to those less likely to convert.

4. ![model is validated](/images/blog/holdout_testing_diag/holdout_testing_diag-04.png)

   #### Step 4: Compare predictions to real outcomes

   Finally, we put the model to the test: we compare its predictions against the real outcomes in that untouched holdout set. If the highest-scoring leads don't convert at significantly higher rates, we go back to the drawing board.

## How to know if a holdout validation was successful

A holdout validation succeeds when predicted scores align with actual conversion rates across segments. Look for these three signs:

1. **Clear correlation between predicted probability and actual conversion rates**  
   The model's predicted likelihood to convert should directly align with the actual conversion rates. If the predictions are accurate, high-probability leads should convert at a noticeably higher rate than lower-probability ones.

2. **High-scoring leads convert at significantly higher rates**  
   The top-scoring leads should consistently perform better than those ranked lower. This demonstrates that the model is identifying truly valuable leads.

3. **Consistent performance across different customer segments**  
   The model should show reliable results across various segments—whether you're targeting different demographics, regions, or other customer profiles. If the model works well across a broad set of data, it proves its reliability and scalability.

When a model meets these criteria, it's ready to generate custom predictive data points—whether that means optimizing [lead buying](https://faraday.ai/docs/how-to/use-cases/leads), refining your [direct mail strategy](https://faraday.ai/templates/lead-suppression), or enhancing your email [personalization](https://faraday.ai/templates/personalization) approach.

## Questions you should ask us

We want you to push us on what matters most for your business. Here are key questions to evaluate if we're the right fit:

1. **How does this model's performance compare to the lift you are seeing with similar businesses in our industry?**

   While we can't share client specifics, we can provide insight into broader trends and expected performance benchmarks.

   → **Why this matters:** Understanding industry benchmarks helps you gauge whether custom predictive data is truly a competitive advantage for your business.

2. **How much historical data do we need for reliable predictions?**

   The answer varies by business model and conversion volume. We'll help determine whether you have enough data—or how to supplement it if needed.

   → **Why this matters:** Predictive models work best when trained on strong data. Knowing the threshold for reliability ensures you're making data-driven decisions with confidence.

3. **What happens if the model doesn't perform as expected?**

   We have a structured iteration process to refine models when necessary, ensuring they reach meaningful predictive power.

   → **Why this matters:** No model is perfect on the first run. Understanding the process for continuous improvement ensures long-term success.

4. **How will this integrate with our existing marketing stack?**

   We'll provide technical specs, discuss implementation timelines, and outline the resources needed to get up and running.

   → **Why this matters:** Seamless integration means faster time-to-value and less friction in your workflow.

## Why holdout evaluations work

Holdout evaluations work because they test a model against data it has never seen — making it impossible to game the results. Any predictive data vendor can show you strong in-sample performance. Holdout testing is the check that confirms those patterns hold in the real world.

At the end of the day, three things matter to us:

1. **Confidence before commitment:**  
   You see proof before you commit

2. **Higher marketing efficiency:**
   Your marketing dollars go to the leads most likely to convert

3. **Stronger sales performance:**
   Your sales team focuses on opportunities worth their time

If the validation doesn't show meaningful lift, we'll tell you. If it does, we'll quantify exactly what that means for your business.

Interested in generating value you can trust? [Talk to a Context Consultant](/contact).

## Frequently asked questions

**What's the difference between a holdout evaluation and an A/B test?**

A holdout evaluation validates that a predictive model works—it confirms that high-scoring leads actually convert at higher rates before you commit to anything. An A/B test measures the impact of a decision you've already made (like sending a campaign to scored vs. unscored leads). Faraday runs the holdout evaluation first so you have confidence in the model itself, not just the downstream campaign.

**Do I need a large dataset for holdout testing to be meaningful?**

Not necessarily. The threshold varies by industry and conversion volume. In many cases, a few months of lead and conversion history is enough to build a reliable model. We'll tell you upfront if your data isn't sufficient—and if it's borderline, we can discuss ways to supplement it with data from the [Faraday Identity Graph](https://faraday.ai/features/faraday-identity-graph).

**What happens to my data during the validation process?**

Your first-party data is used solely to train and validate the predictive model—it is never shared with other clients or used to enrich Faraday's broader data assets. The holdout set is kept completely separate from training and is only used as an independent benchmark after predictions have been made.

Faraday uses holdout testing to validate custom predictive data before you commit, ensuring it delivers real, measurable results.

Predictive data generates tangible value, and we can prove it

Updated: May 2026

In [our last post](https://faraday.ai/blog/b2c-customer-data-stop-guessing-start-executing), we made the case that B2C brands are leaving value on the table because the data they have about their customers isn't complete enough to act on. Brands know what customers bought. They have a name, probably an email address and physical address. What they’re missing is the broader context — who the customers actually are, what their financial situation looks like, where they are in life — and that's exactly what's missing when campaigns underperform and outreach falls flat.

The first step to closing that gap is data enrichment: appending verified, high-quality external data points to your existing customer records so you're working with a complete picture instead of a partial one. That's what Faraday can do for your brand, and this post covers what data is available, how the enrichment process works, and how to get started.

## What data is available for enrichment through Faraday

Faraday's data enrichment is powered by the [Faraday Identity Graph (FIG)](https://faraday.ai/catalog) — a comprehensive, continuously maintained dataset covering **240M US consumers and their household, with over 1,400 verified, pre-cleaned, and feature-engineered data points.** Every record is tied to a verified consumer identity, which means the data you enrich actually belongs to the person you're trying to reach.

FIG’s data points cover a range of categories including:

- **Demographics** — age, gender, household income, education level, marital status
- **Property and housing** — homeownership status, home value, roof type, garage spaces, length of residence
- **Financial signals** — Credit Score Proxy, spending percentiles, investment behavior, debt indicators
- **Life events** — recent move, new homeowner, new parent, marriage, retirement
- **Lifestyle and interests** — hobbies, brand affinities, vehicle ownership, charitable giving patterns
- **Contact and identity** — verified name, address, email, phone, household composition

We recently completed a major refresh of the entire FIG catalog as part of FIG v2, updating data points across every category. You can browse everything that's available at [faraday.ai/catalog](https://faraday.ai/catalog) or purchase data directly at [Faraday Pro](/signup).

## How data enrichment actually works

A data enrichment is the process of matching your existing customer records to FIG and appending the data points you select. The process is straightforward:

1. **Select your data points.** Choose the specific attributes that address your business question — whether that's segmenting your audience, scoring leads, expanding into direct mail, or understanding your ICP. You can select individual fields or use one of Faraday's pre-built essentials packages, curated by vertical for home services, retail and ecommerce, and financial services.

2. **Match against FIG.** Faraday resolves your customer records to verified identities in FIG using a combination of name, email, phone, and address fields. This is deterministic matching — not probabilistic — so you can trust the appended data actually maps to the right person.

3. **Receive enriched records.** Your data comes back in a clean, structured format ready to load into your CRM, CDP, data warehouse, ad platform, or wherever you need it.

[![Data-appends diagram](/images/blog/data-appends-diagram.png)](/images/blog/data-appends-diagram.png)

## How brands use data enrichment

Data enrichment can unlock your entire marketing strategy — [better segmentation](https://faraday.ai/blog/smarter-segmentation-for-banks), [smarter messaging](https://faraday.ai/blog/how-dtc-brands-build-living-personas-faraday-heatseeker), [new channels](https://faraday.ai/blog/match-boost-improves-enrichment) you couldn't reach before. But here are the three ways we see brands put it to work first:

**Fill contact gaps to expand your reach.** A brand that's built its entire customer relationship through email has email addresses — but no physical addresses, no phone numbers. If they want to run direct mail or a call center program, enrichment is what makes that possible. You get the missing contact fields enriched to records you already own.

**Understand your customers beyond the transaction.** Your purchase history tells you what people bought. It doesn't tell you who they are — their financial situation, life stage, household context, or what else they might be in the market for. Enrichment fills that picture in so your segmentation and messaging are based on something real.

**Research your market before you move into it.** If you're evaluating a new geography, a new channel, or a new customer segment, enrichment lets you analyze the landscape before you commit — understanding what the population looks like and where the opportunity actually is.

## Why not just find the data yourself?

Some companies attempt to enhance their marketing efforts by purchasing third-party data directly from various vendors themselves, rather than using packaged and processed services like Faraday’s data enrichment. However, this approach often presents significant challenges:

**Contracting complexity**: Sourcing offline data can involve lengthy negotiations and large upfront commitments.

**Security risks**: Handling sensitive data requires strict compliance frameworks, such as SOC-2 certification. Unlike many vendors, all Faraday data enrichment is privacy-compliant, ethically sourced, and adhere to high standards of data protection, ensuring your customers' personal information remains secure while providing actionable insights.

**Regulatory challenges**: Companies storing offline data must navigate consumer privacy laws, including opt-out and deletion requests. Faraday manages these complexities for you, so you can focus on what matters—leveraging new insights to drive revenue.

**Data science overhead**: Raw offline data often requires significant processing, feature selection, and bias mitigation before it can be effectively used. This can result in costly time and resource investments as companies try to integrate third-party data into their models.

Faraday handles all of this before the data reaches you. The trade-off isn't 'Faraday or DIY' — it's spend your team's time building and maintaining data infrastructure, or spend it running better campaigns.

## How to get started

Getting started with Faraday’s data enrichment is simple. Once you’ve identified the data points you need, we’ll work with you to integrate them into your existing systems. Whether you’re looking for a one-time data boost or ongoing data enrichment, our flexible pricing and easy integration make it simple to scale.

If you're not sure where to start, browse what's available at our new data [catalog](faraday.ai/catalog) — or talk to a [context consultant](/talk-to-sales) who can help you identify which data points are most likely to move the needle for your specific business before you commit to anything.

And if you just want the data today, you can find it yourself in our new self service [marketplace](/signup).

Faraday's data enrichment appends verified third-party data points from FIG to your customer records, filling contact gaps, deepening customer understanding, and powering smarter targeting and expansion decisions.

How data enrichment actually works with Faraday, and what data you can find

**See our complete guide:** [What is the Faraday Identity Graph?](/blog/whats-in-faraday-identity-graph).

---

If you use Faraday — for predictions, appends, personas, or anything in between — you've been using the [Faraday Identity Graph (FIG)](/catalog) whether you knew it or
not. FIG is the foundation everything runs on: a continuously refreshed collection of 1,400+ attributes on 240 million US consumers, curated and normalized from leading data vendors. Every lead score, every data append, every audience we help you build draws from it.

Consumer data is the foundation of modern marketing — and what businesses need from that foundation has changed. Models are getting more sophisticated and need richer, time-aware inputs to keep getting better. Marketers want to act on signals the moment they emerge, not weeks later. Data science teams want documentation and metadata they can actually build with. And operations teams want predictable release cadences that fit their change management processes.

FIG v2 is built for where consumer data needs to go next. It's a complete rebuild of our consumer data system that introduces historical data, an expanded and redesigned attribute catalog, continuous data refreshes, and release pinning — so the foundation underneath your marketing keeps pace with what your business is asking of it.

Here's what's new:

## 1. Hundreds of new attributes, with richer signal across the board

We expanded our underlying [attribute set](/catalog) significantly — adding hundreds of new data points to capture consumer signals we couldn't surface before. We also harmonized unwieldy groups of related attributes into cleaner replacements with more expressive, consistent names.

**What this means for you:** richer raw material for every prediction, append, and audience you build. More signal in the data means models can pick up on patterns that were previously invisible, which translates directly to better prospect targeting and higher marketing ROI.

## 2. A redesigned catalog you can actually navigate

We rebuilt our [data catalog](/catalog) from scratch around the expanded attribute set — retiring data points that weren't pulling their weight and reshaping what remains around the attributes that consistently drive better prediction quality. Every attribute now comes with detailed documentation: descriptions, visual data distributions, and technical metadata designed for both marketers and data scientists.

**What this means for you:** less time hunting for the right attribute, faster model iteration for your data science team, and a catalog that both marketers and analysts can actually use without a data science degree.

## 3. Historical data

FIG v2 carries the [full history of values for every attribute](/blog/fig-v2-historical-data), each one timestamped and annotated with precision and source metadata. Rather than knowing someone's income is \$85,000 today, you now know it was \$62,000 five years ago, \$74,000 three years ago, and \$85,000 today.

This is possible because Faraday has been a careful steward of consumer data for over a decade. We've been collecting, normalizing, and preserving these observations the whole time. FIG v2 is the first time we've been able to surface all of that accumulated history in a structured, ML-ready format — every attribute is now a timeline of events rather than a single point in time.

**What this means for you:** dramatically better predictive models. Models trained on current-state data are actually learning what people look like _after_ they convert — not before. Take a home remodeling brand trying to predict who'll buy new windows: after someone buys, there's a contractor transaction, possibly a HELOC, elevated home-improvement spend. Those signals weren't there when they were still a prospect. If your training data captures people as they look _after_ converting, the model goes hunting for prospects who already bought.

With FIG v2, we can press rewind and train on what a person looked like just before they became a lead — so models learn what a genuine pre-purchase prospect looks like and get dramatically better at finding more of them.

For data science teams, every historical observation comes with the metadata you need to use it directly: data types, statistical types, directionality guidance, coverage breakdowns. If your team builds internal models and wants rich consumer context that goes back in time, this is the dataset to do it with.

## 4. Fresher data, delivered continuously

Our new infrastructure [ingests fresh data from vendors as soon as we receive it](/blog/fig-v2-freshness-release-pinning) — no more waiting for scheduled monolithic releases.

**What this means for you:** less stale targeting and fewer wasted impressions. This matters most for time-sensitive signals like recent movers, life events, and income changes — the kinds of triggers where a few weeks of lag can mean the difference between reaching someone at the right moment and missing them entirely.

## 5. Release pinning

We've heard from many of you that you love getting the most current data automatically. We've also heard from teams with strict change management requirements who need more control over when their data updates. FIG v2 supports both. You can now [pin your account to a specific FIG release](/blog/fig-v2-freshness-release-pinning), and new updates won't apply until you move your pin date forward.

**What this means for you:** stability when you need it, freshness when you want it. Teams with regulated workflows, locked-in model versions, or careful release calendars get to upgrade on their own schedule — without sacrificing access to the latest data when they're ready.

### **What this means for your account**

- **FIG v2 is live today for all new accounts**
- **Existing accounts will be migrated over the coming months** — no account will be moved without coordination with your team
- **Your current account keeps running as-is** throughout the process — there's no disruption until you're ready
- **Questions about your migration or timeline?** Reach out to your account manager

## Ready for even better customer data?

FIG v2 is a fundamental shift in what consumer data can do inside Faraday — better models, fresher data, hundreds of new attributes, a catalog you can actually navigate, and a release cadence that respects your operations.

We're working through migrations as quickly as we can and will be in touch when it's your turn. In the meantime, if you want to go deeper on what's changed and why, reach out to your account management team or [read the full FIG v2 series](/blog/series/figv2).

FIG v2 is a complete rebuild of Faraday's consumer data system, introducing historical data, redesigned attributes, fresher updates, and new tools to make customer data easier to discover, deploy, and operationalize.

Now live: FIG v2 - the biggest upgrade to our customer data system, ever

If you've been following our FIG v2 series, you've already read about the [overhauled data catalog](/blog/fig-v2-data-catalog) and the [historical data upgrade](/blog/fig-v2-historical-data) that makes your models dramatically more accurate. Today I want to close out the series with something that might sound like a backend plumbing detail—but has real consequences for anyone running production pipelines on Faraday data: how we handle data freshness, and the new release pinning controls that come with it.

## How most identity graphs handle updates (and why that's a problem)

To understand why this matters, it helps to know how data updates typically work in the identity data industry.

Most identity graphs operate on a monolithic release model. Providers work with many data vendors, each of whom delivers updated data snapshots on their own schedule. The standard approach is to either wait for all vendors to deliver their latest data, or simply release on a fixed schedule—monthly, quarterly—regardless of when the underlying data actually arrived. Either way, the result is the same: a gap between when data is collected in the real world and when it's available to you.

The obvious problem: every day you spend waiting is another day the data sits there getting older. The release cycle itself introduces latency that has nothing to do with how fresh the underlying data actually was. **FIG v2 eliminates that bottleneck entirely.**

## Rolling releases: freshness without the wait

Now with FIG v2, instead of waiting for a full vendor sweep before publishing an update, we ingest each vendor's data the moment it arrives. Each new observation is processed, normalized, and added to the appropriate attribute's history vector—timestamped, sourced, and immediately available—without waiting for anything else.

The result is rolling releases. Every time even a single vendor delivers updated data, that data flows into FIG v2 right away. There's no more monolithic "release event" to wait for. The gap between when data is collected in the real world and when it's available to you in Faraday shrinks dramatically.

For most attributes, this is just a nice quality-of-life improvement—fresher data, more often. But for certain attributes, it's much more significant. Pre-mover signals, for example, are almost entirely time-sensitive. A pre-mover flag that's six weeks stale is considerably less valuable than one that arrived yesterday. With rolling releases, those high-velocity attributes stay sharp.

## Release pinning: you control when freshness flows in

Fresher data is almost always better — but there's a legitimate exception. If you're running a mission-critical pipeline and you've carefully validated the data distributions it depends on, constant rolling updates can feel like a liability. Think an automated risk scoring model at an insurance company, or a lead prioritization pipeline that's deeply integrated into a sales team's daily workflow — cases where an unexpected shift in data distributions could quietly change outputs in ways that are hard to detect and costly to diagnose. If you're in one of those situations (or something similar), you don't want things shifting under your feet without warning.

That's what release pinning is for.

In FIG v2, you can pin any individual pipeline to a specific date. What that means concretely: any new consumer data incorporated into FIG _after_ that date is simply ignored for that pipeline. The pipeline keeps running—against exactly the data it was validated against—until you decide you're ready to move it forward.

The workflow this enables is deliberate and controlled. Say you're running a critical pipeline and a new round of vendor data has just been ingested into FIG. Rather than letting it flow in automatically, you:

1. **Make a copy of the pipeline.** The copy runs against the newer data.
2. **Do your validation.** Check distributions, spot-check outputs, confirm that everything downstream still behaves as expected.
3. **When you're satisfied, advance the pin date on your production pipeline.** The new data flows in. The copy gets retired.

You can do this on whatever cadence makes sense for your team—monthly, quarterly, or whenever a new vendor refresh comes in that you want to evaluate. You get the benefit of fresher data without ever being surprised by it.

For teams that don't need this level of control, the default behavior is simple: data stays fresh automatically, with no manual steps required.

## Freshness and stability, on your terms

The two ideas here—rolling releases and release pinning—are designed to work together. Rolling releases make FIG v2 the freshest consumer data platform we've ever built. Release pinning makes sure that freshness doesn't come at the cost of predictability for the teams who need it most.

Together, they represent something we think is genuinely new in the market: a consumer data system that treats data freshness not as a fixed property you accept or reject wholesale, but as a dial you can tune per-pipeline based on your actual operational needs.

If you want to learn more or start testing these capabilities in your account, reach out to your account management team. And if you missed the earlier posts in this series, you can catch up on the [data catalog deep dive](/blog/fig-v2-data-catalog), the [historical data deep dive](/blog/fig-v2-historical-data), or the [freshness and release pinning deep dive](/blog/fig-v2-freshness-release-pinning) on our blog.

And if you're new to the platform, reach out to [talk to a context consultant](/contact) to see what FIG v2 can do for your data.

FIG v2 features continuous rolling releases for immediate data freshness, while introducing release pinning to give teams precise control over when new data enters their mission-critical pipelines.

Deep dive: FIG v2 data freshness and release pinning

Most marketing teams aren't starting from zero. You have CRM records, campaign data, transaction history — probably more than you can act on. But having data and having the _right_ data are two different things. And the gap between them is where budget gets wasted, campaigns underperform, and your team ends up doing work that better data would have made unnecessary.

The right question isn't "how much data do I have?" It's "do I have the data that lets my team stop compensating and start executing?"

## What goes wrong when the data is wrong

The same patterns show up for consumer brands across industry verticals — sometimes as a campaign that outright fails, more often as the quiet drain of time your team spends manually compensating for gaps the data should have filled. Here’s how that might look for you:

- A brand builds their entire acquisition strategy around a customer profile that turns out not to reflect their most valuable buyers — and spends years optimizing for the wrong person.
- A paid media campaign misfires because the targeting is built on assumption-based profiles instead of actual purchase signals — the right message reaches the wrong people, and someone spends two weeks in spreadsheets trying to figure out why the numbers look off.
- An inbound sales team wastes hours on leads that were never going to convert, because there's no signal to distinguish high-value prospects from low-value ones until a rep is already on the phone.
- An outbound sales team can't reach half their customer list because the records are missing valid emails, phone numbers, or addresses — and somebody has to go fix that before anything else can happen.

In every case, the brand had data. They just didn't have the data that would have prevented the wrong decision.

## What the right data actually looks like

The right data isn't a single thing. It's three layers, each doing distinct work:

**Verified identity data.** Clean, foundational fields — name, phone, email, address. These make sure you can actually reach your customers, eliminate duplicates, and build a complete view of each household. Everything downstream depends on this being right.

**Customer profile data points.** Curated context like household income, homeownership, Credit Score Proxy, and industry-specific essentials packages (see our offerings for [home services](/blog/homeservices-essential-data), [retail/ecommerce](/blog/retail-ecom-essential-data), and [financial services](/blog/finservices-essential-data)). Use these to segment audiences, tailor messaging, and prioritize outreach based on signals that have actually been shown to drive value in your vertical.

**Custom predictive signals.** Predictive scores trained on your specific outcomes and customers — [likelihood to convert](/templates/lead-prioritization), [best product recommendation](/templates/next-best-offer), [persona assignment](/docs/abstractions/persona_sets). Because they're built on your data, not generic benchmarks, they reflect your business and get sharper over time. Use them to tell your team exactly where to focus: which leads to call first, which product to surface, which customers to retain.

The key is knowing which layer solves which problem. You don't throw all of it at your CRM at once and see what sticks. An informed data strategy means identifying which specific categories of data — and which specific data points within those categories — actually drive value for your business right now. When you get that right, here's what becomes possible.

## What this actually changes

Once you have the right data foundation, the same problems that were draining time and budget become solvable:

- **Know your real ICP.** Instead of building an acquisition strategy around an assumed customer profile, you have the data to understand who actually buys from you and where to find more of them.
- **Personalize at scale.** Instead of generic messaging that treats every customer the same, you have the context to tailor offers, timing, and creative to the specific people most likely to respond.
- **Score and prioritize leads.** Instead of burning hours on leads that were never going to convert, you have predictive signals that tell your team exactly where to focus their time.

## Ready to get the right data?

Most brands we talk to are a few targeted data points away from campaigns that actually work. Explore what's available at [Faraday Pro](/signup) — or [talk to a context consultant](/talk-to-sales) if you want help figuring out exactly which ones move the needle for your business.

Faraday gives B2C brands the verified identity data, customer profile attributes, and predictive signals they need to stop patching data gaps and run campaigns that actually work.

Get the B2C data your brand needs to stop patching gaps and start executing

**See our complete guide:** [Powered by Faraday — embedding predictive AI in your platform](/blog/powered-by-faraday-complete-guide-embedded-predictive-ai).

---

Even the most powerful model in the world can’t drive real value if it doesn’t know who it’s talking to. As our CEO, [Andy Rossmeisl](https://www.linkedin.com/in/rossmeissl), [put it](/blog/ai-needs-consumer-data-to-give-context): “Without the specific identity and history of the customer it’s talking to the world's most advanced AI can do little more than offer a polite, generic greeting."

That's not a model problem. It's a context problem.

## The real issue isn't your model — it's what your model knows

Most AI agents start every conversation blind. Without history or clear signal about who's actually on the other end. So they default to generic responses. Not because they're poorly built, but because they have nothing to work with.

The instinct is to throw more data at the problem. More attributes. More integrations. More inputs. But raw data doesn't tell your agent anything useful about this specific person, right now. You can have a thousand attributes about someone and still not know whether they're price-sensitive, in-market, or a high-value prospect worth a personalized offer.

[As Andy puts it](/blog/ai-needs-consumer-data-to-give-context): "Context is the guiding light that turns a cold interaction into a warm conversion. It is the vital distinction between having a list of names and possessing the clarity to know exactly who a prospect is, and how to best engage them."

That shift — from dumping data at your model and hoping it figures things out, to feeding it curated signals — is what separates agents that plateau from agents that keep getting smarter.

## What Faraday does

Faraday enriches incoming identities against the [Faraday Identity Graph](/catalog) — 1,400+ consumer attributes across 240 million U.S. adults. You send us PII. We return a rich consumer profile, appended to your customer record, ready to pass directly to your agent or model.

For you, it’s as simple as a clean API call that turns an anonymous identity into someone your agent actually knows.

## What this means for what you're building

**Your agent personalizes from message one.** No more [cold-start](/stories/dtc-retailer) problems. The moment an identity hits your system, it can be enriched with the context your agent needs to skip the generic greeting and get to the relevant conversation. This is exactly what [Hazel](/stories/hazel) did with our data — their agentic marketing platform went from knowing nothing about a user to having real customer context on every interaction, with a working prototype in a single day.

**Your agent acts on signal, not noise.** Curated traits — household composition, lifestyle indicators, behavioral factors — slot directly into your agent's reasoning. Platforms like [Heatseeker](/blog/how-dtc-brands-build-living-personas-faraday-heatseeker) work with us to turn their customers' flat transactional records into rich consumer profiles overnight to power smarter personas, better targeting, and a shared language across the org.

**Your roadmap moves at API speed.** What would take a multi-year build internally — sourcing data, hosting it, modeling against it, maintaining it — becomes a few API calls. There's no infrastructure overhead, no six-figure procurement cycle, and no contract to renegotiate as you grow. Brands like [Hazel](/stories/hazel) and [SalesRabbit](/blog/salesrabbit) collapsed timelines from months or years down to days — and scaled on the same infrastructure they prototyped with.

## The bottom line

Your agent isn't underperforming because it's poorly built. It's underperforming because it doesn't know who it's talking to.

Faraday fixes that with a single API call — enriching every incoming identity with 1,400+ consumer attributes before your agent says a word. No data sourcing, no infrastructure build, no long procurement cycle. Just context, from day one.

If you’re ready to accelerate your roadmap and get the context your agent needs today, [start here](/signup) — or [talk to a context consultant](https://faraday.ai/talk-to-sales) about what enrichment looks like for your use case.

Faraday's API gives AI agents the customer context they need to personalize from day one — no data sourcing, no infrastructure build, no long procurement cycle.

Stop sending your AI agent into conversations blind: get customer context via the Faraday API

If you've been following our FIG v2 series, you already know about the [overhauled data catalog](https://faraday.ai/blog/fig-v2-data-catalog) and the broader [announcement of what's coming](https://faraday.ai/blog/now-live-faraday-identity-graph-version2). Today I want to go deep on what I think is the most technically significant upgrade in the entire release: historical data.

## What "historical data" actually means

The [Faraday Identity Graph](https://faraday.ai/catalog) has always given you an up-to-date snapshot of every US adult—what we know about them _right now_. FIG v2 changes that fundamentally. Instead of a single current value for each attribute, we have the full history of values going back many years, each one timestamped and annotated with precision and source metadata.

Think of it this way: rather than knowing that someone's income is \$85,000, you now know that their income was \$62,000 five years ago, \$74,000 three years ago, and \$85,000 today. Each of those data points has a timestamp, a confidence level, and information about where it came from. For you, this means every data attribute isn’t just a point-in-time number, but instead a timeline of historical events.

This is possible because Faraday has been a careful steward of consumer data for over a decade. We've been collecting, normalizing, and preserving these observations the whole time. FIG v2 is the first time we've been able to surface all of that accumulated history in a structured, ML-ready format.

## Why this matters most for modeling

The biggest immediate payoff from historical data is better predictive models. To understand why, it helps to think through how model training actually works.

Let's start with a propensity-to-buy model (i.e., a lead score model). When you build that model, you feed the algorithm historical examples: leads that converted and leads that didn't. The algorithm finds patterns—the characteristics that consistently differentiated the converters from the non-converters—and encodes those patterns into a model. Later, when new leads come in, the model applies those patterns to score them based on which is most likely to buy.

Here's the problem that a model without historical data has: if it’s trained without historical data then it is actually training on what the person looks like _after_ they purchased, NOT the actual time of purchase. Take a home remodeling brand trying to predict which prospects are likely to purchase new windows. To train that model, you feed it historical examples: people who bought windows and people who didn't. But here's the catch—a person looks very different in the data _after_ buying windows than they did _before_. After the purchase, there's a contractor transaction on their record, a home equity line of credit may have appeared, and their home improvement spend has spiked. Those signals weren't there when they were still a prospect. If your training data captures people as they look _after_ converting rather than just before, the model learns the wrong patterns entirely—and the prospects it finds will be people who match a post-purchase profile, not a pre-purchase one.

Until now, this was a problem we could only partially solve. If a brand had leads going back three years, we could append current FIG data to those leads, but "current" data for a lead from three years ago is not the same as that person's data _at the time they became a lead_. A lot has changed in three years.

With FIG v2, we can press rewind and assemble a training dataset based on what they looked like just _before_ they submitted the lead form and what they looked like after they purchased. The model learns what a genuine pre-purchase prospect actually looks like—and gets dramatically better at finding more of them.

## A first-of-its-kind resource for data science teams

Better internal modeling is the most immediate benefit, but FIG v2's historical dataset also opens up something that hasn't really existed before commercially: a comprehensive, historical consumer dataset that's fully ML-ready out of the box.

Every historical observation comes with the kind of metadata that data scientists actually need—data types, statistical types, directionality guidance, coverage breakdowns, and more. If your team is building your own internal models and wants to incorporate rich consumer context that goes back in time, this is the dataset to do it with.

## What's coming next: change detection

We're also excited about something that historical makes possible further down the road: monitoring when a person's profile changes in a meaningful way.

The most valuable signal isn't always the value of an attribute—it's the fact that the attribute just changed. Someone who _just became_ a new homeowner, or _just had_ their first child, or _just crossed_ an income threshold is a fundamentally different prospect than someone who has been in that state for years. Life events create moments of opportunity, and right now, most brands have no reliable way to know when those moments arrive.

Having a full historical vector for every attribute makes this kind of change detection possible. We'll be sharing more about how we're building that capability in Q2.

## Try it this month

FIG v2 is in beta now and releasing this month. If you want to get your hands on the historical data and see how it affects your model performance, reach out to your account management team. And keep an eye out—we have more deep dives coming before the full release.

FIG v2 adds historical timelines to consumer data, enabling far more accurate predictive models by training on pre-purchase profiles instead of post-purchase ones.

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