Brand clients are paying agencies for strategic advantage, not execution. But when every agency in the pitch is working from the same third-party datasets, the same audience tools, and the same campaign playbooks, differentiation collapses to price and account management. The agencies winning high-value accounts right now aren't the ones running the most campaigns — they're the ones delivering **[consumer context](/blog/consumer-context-data-ai)** their competitors can't access.

## Where agencies lose ground

Agencies trying to move from execution partner to strategic growth driver run into three recurring problems.

### Client audience assumptions go untested

Most agencies inherit their clients' beliefs about who their customers are. The client says "our buyers skew younger" or "our customers are price-sensitive," and the agency builds campaigns around those assumptions without validating them. When the campaigns underperform, it's hard to know whether the creative was wrong, the channel was wrong, or the audience was never who the client thought it was.

**The fix:** [Consumer data enrichment](/blog/get-started-with-consumer-data-enrichment) gives you ground truth. Demographic, financial, household, and lifestyle data points appended to existing CRM records reveal the gaps between assumption and reality — surfacing things about a client's customer base that the client didn't know, and giving your agency the foundation for campaigns that actually resonate.

### Segmentation doesn't go deep enough to drive strategy

Most agency segmentation is built on purchase behavior and basic demographics — what someone bought, when they bought it, maybe their age or ZIP code. That's enough to split an email list but not enough to inform channel strategy, partnership decisions, creative direction, or product recommendations. The segments describe transactions, not people.

**The fix:** [AI personas](/blog/what-are-ai-customer-personas) built on the [Faraday Identity Graph (FIG)](/blog/whats-in-faraday-identity-graph) group your client's customers by shared patterns in demographics, household economics, life stage, and lifestyle — revealing not just what they buy but who they are and why. Those personas sync directly into your client's Klaviyo, HubSpot, or other marketing tools, so the intelligence lives in their operating systems, not a slide deck.

### Insights don't compound across engagements

A one-off audience analysis that lives in a PowerPoint doesn't build durable value — for your client or your agency. The next campaign starts from zero, the intelligence doesn't carry forward, and the client has no structural reason to keep paying your agency instead of switching to whoever underbids you.

**The fix:** Build infrastructure, not decks. Faraday's personas and [propensity models](/blog/propensity-models-predict-customer-behavior) update as new data flows in, creating a persistent intelligence layer your client depends on. When the enrichment, the personas, and the scoring logic all live inside the client's marketing stack — and your agency is the team that built it — you stop being replaceable.

## Case study: Performance marketing agency drives 17% lift in repeat purchases

A performance marketing agency working with a growing consumer brand faced a familiar problem: repeat purchase rates had plateaued, and the team's assumptions about their audience had never been tested.

### The problem

The client believed their growth was being driven by younger online buyers. Campaigns were built around that assumption — creative, channel mix, messaging, and product recommendations all calibrated to a younger demographic. But repeat purchase rates kept stalling, and no one could explain why.

### The solution

The agency used Faraday's demographic enrichment to append consumer data points to their client's CRM records, then built AI personas to segment the customer base by who they actually were. The enriched data revealed that the brand's "younger" online buyers were actually five to ten years older than the client had assumed.

The agency synced persona assignments directly into the client's Klaviyo instance, then refined welcome flows, product recommendations, and campaign messaging to reflect the audience's real profile and motivations.

### The result

Within five months, the client saw a **17% lift in repeat purchases** compared to the same period the year before. Customer lifetime value improved alongside the repeat rate, and the marketing team gained confidence in their targeting for the first time.

For the agency, the engagement transformed the relationship — from execution partner running campaigns to strategic growth driver delivering data-backed understanding their competitors couldn't match.

## The data behind it

Every persona, propensity score, and enrichment project your agency delivers is powered by the [Faraday Identity Graph](/blog/whats-in-faraday-identity-graph) — a continuously maintained dataset covering 240M U.S. adults and their households, with [1,400+ verified consumer data points](https://faraday.ai/catalog) spanning demographics, financial signals, property data, life events, and lifestyle indicators. It's what transforms a client's CRM from a transaction log into a complete picture of who their customers actually are.

The same FIG that powers enterprise deployments is what you're working with when you run enrichment or build personas through Faraday — which means the insights you deliver carry the same depth and production quality as anything a brand would build in-house, without the data licensing, engineering, or ongoing maintenance that would otherwise make it impossible to offer at agency scale.

## Ready to differentiate your agency?

Whether you're looking to win bigger accounts, retain the ones you have, or build a defensible data practice competitors can't replicate, there are two ways to get started.

**For discrete projects:** Use [Faraday Pro](/signup) to enrich a client file right now — append consumer data points in minutes, turn an audience analysis around in days, and bill the work as part of the engagement. No contract, no minimums, pay only for matches.

**For ongoing client infrastructure:** [Talk to a Context Consultant](https://faraday.ai/talk-to-sales) to build persona models, propensity scores, and recommenders that sync directly into your client's marketing stack and update as new data flows in.

Both models scale with your book of business without committing to licenses or capacity you don't need.

## FAQ

### Do we resell Faraday to clients, or use it on their behalf?

Either works. Some agencies use Faraday as an internal capability and deliver insights as part of their engagement. Others build Faraday-powered services into their offering and bill clients for the predictive intelligence layer. The right model depends on your business and your client relationships.

### Can we white-label Faraday's outputs?

Yes. The personas, predictions, and enriched data you deliver to clients can be presented under your agency's brand. Your clients see your work product — your analysis, your recommendations, your dashboards.

### How quickly can we deliver value to a client?

For enrichment-based projects, you can append consumer data points to a client file via [Faraday Pro](/signup) in minutes and turn an audience analysis around in days. For persona modeling or propensity scoring, expect a few weeks from data connection to deployed scores.

### What if our client doesn't have much first-party data?

Even a basic CRM file with names and email addresses can be matched against FIG to build full [consumer profiles](/blog/what-data-do-you-need-for-marketing) — giving your client and your agency the context to segment, target, and [personalize](/blog/personalization-tactics-that-drive-conversions) before they've accumulated deep behavioral data.

Learn how marketing agencies use Faraday to deliver proprietary predictive insights their competitors can't match — and how one agency drove a 17% lift in repeat purchases by discovering their client's audience was nothing like they assumed.

The complete guide to customer context for marketing agencies

## At a glance

- **The core challenge:** Advia Credit Union knew their members needed auto loans and other financial products — but couldn't identify which members to target, when, or with what offer, and couldn't risk fair lending violations in the process.
- **The approach:** Enriched historical member data with the Faraday Identity Graph, built a propensity model trained on past auto loan outcomes, and ran bias mitigation to ensure fair treatment across protected classes before deployment.
- **The results:** Application rate climbed from 1.19% to 5.18% in 90 days — generating $2.7M in new auto loans without hiring a data scientist or building proprietary modeling infrastructure.

—

[Advia Credit Union](https://www.adviacu.org/) had a wallet share problem. Their members were financially active, but they weren't always channeling that activity through Advia. The average American holds eight financial products — but only [19% of Americans](https://www2.deloitte.com/content/dam/Deloitte/us/Documents/financial-services/us-kickingitupanotch-092614.pdf) have three or more products with their primary financial institution. Advia knew their members needed auto loans, mortgage refinancing, and debt consolidation products. What they didn't know was which members to talk to, when, and with what offer — and how to make sure they captured that business before another institution did.

Adding to the challenge, Advia's lead data scientist had recently left the organization — making it clear that building this kind of targeting capability in-house wasn't a realistic near-term option. What they needed was a way to get smarter about their existing member base without rebuilding a data science team from scratch or running afoul of fair lending regulations.

## The wallet share problem

[Credit unions and banks](/blog/banks-credit-unions-complete-context-guide) face a structural challenge that most consumer brands don't: their best growth opportunity is sitting right inside their existing member base, but activating it requires navigating a uniquely complex data and compliance environment.

First-party data tells you what a member has done with your institution. It doesn't tell you where they are in their financial life, what products they might need next, or how likely they are to act on a given offer. That missing context is what turns "grow wallet share" from a strategy into a guessing game — and what leaves most institutions relying on broad campaigns that erode member trust faster than they build revenue.

## Balancing growth with fair lending compliance

Financial services is one of the few industries where getting targeting wrong doesn't just waste budget — it creates legal exposure. The [Equal Credit Opportunity Act (ECOA)](https://www.consumerfinance.gov/compliance/compliance-resources/other-applicable-requirements/equal-credit-opportunity-act/) prohibits discrimination in credit transactions based on race, color, religion, national origin, sex, marital status, age, or receipt of public assistance. Any scoring model used for member targeting must be built and validated with these constraints in mind.

The risk isn't always intentional. Historical member data often reflects past patterns of underrepresentation, and a model trained on that data without bias mitigation can quietly perpetuate those patterns at scale. This is why many financial institutions either avoid data-driven targeting altogether or deploy it without the guardrails they need.

Faraday treats [bias mitigation](https://faraday.ai/blog/faraday-bias-mitigation-machine-learning) as a foundational step, not an afterthought. Before any model is trained, Faraday rebalances [historical](https://faraday.ai/blog/fig-v2-historical-data) data to correct for underrepresentation across demographic groups and [monitors the resulting scores for fairness across protected classes](https://faraday.ai/blog/faraday-bias-reporting). The result is a model that's both more accurate and more compliant — not a tradeoff between the two.

For Advia, this was as important as the performance numbers. A campaign that drives loan applications but creates fair lending exposure isn't a win.

## How Faraday built Advia's member scoring

Advia's auto loan campaign started with a data connection. They linked their historical member data — including records of which members had previously taken out auto loans — to the [Faraday Identity Graph (FIG)](/blog/whats-in-faraday-identity-graph): a continuously maintained dataset covering 240M U.S. adults across [1,400+ verified consumer data points](https://faraday.ai/catalog), spanning demographics, financial signals, property data, life events, and lifestyle indicators.

That enrichment step is what transforms a member record from a transaction history into a full [consumer profile](/blog/what-data-do-you-need-for-marketing). FIG adds the context that first-party data alone can't provide — household income, life-stage signals, financial behavior patterns, and more — giving the scoring model far more signal to work with.

With enriched data in place, Faraday built a [propensity model](/blog/propensity-models-predict-customer-behavior) trained on Advia's historical auto loan outcomes. The model learned the profile of members who had taken out auto loans in the past — and used that pattern to score every current member by their likelihood to apply for one now. Bias mitigation ran in parallel: Faraday identified demographic imbalances in the training data and rebalanced the model to ensure fair treatment across protected groups before any scores were deployed. Faraday validated model performance using holdout testing before deployment.

The output was a ranked list of members by auto loan propensity — with the top percentile identified as the highest-priority targets for Advia's campaign.

## The results

Advia used Faraday's scores to segment their member base and target the top percentile through [personalized](/blog/personalization-tactics-that-drive-conversions) email and direct mail campaigns (see what integrations are available for these types of campaigns on our [integrations page](https://faraday.ai/integrations)). The benchmark they were working to beat: a 1.19% auto loan application rate. And here’s what they got:

**30-day results:** Application rate reached 2.41% — more than double the baseline — generating **$1.1M in new auto loans**.

**90-day results:** The application rate continued climbing to 5.18% after 90 days, producing a total of **$2.7M in new auto loans**.

That's more than a 4x improvement in application rate, achieved without hiring a new data scientist, without building proprietary modeling infrastructure, and with a compliance posture Advia could stand behind.

## Expanding predictive scoring across the entire portfolio

The Advia story started with auto loans, but the same approach applies to any financial product a credit union or bank wants to grow. FIG enrichment, propensity scoring, and bias mitigation can be applied to mortgage refinancing, debt consolidation, credit cards, savings products, and more.

Each product becomes its own scored outcome: who among your current members is most likely to need this right now? Layered together, those scores give your team a clear view of which offer is most relevant for each member at each stage of their financial life — and the confidence to act on it without compliance risk.

For a broader look at how banks and credit unions use customer context across their member base, see our [guide to predictive AI for banks and credit unions](/blog/banks-credit-unions-complete-context-guide).

## Ready to grow wallet share?

If you want to talk through how customer context could work for your institution, [talk to a Context Consultant](https://faraday.ai/talk-to-sales). Or if you'd rather get started on your own, [try it on Faraday Pro](/signup).

Learn how Advia Credit Union used Faraday’s consumer data to get the context they needed on their member base, which allowed them to navigate fair lending compliance and generate $2.7M in new auto loans in 90 days.

How Advia Credit Union used Faraday’s data and AI to generate $2.7M in new auto loans in 90 days

## At a glance

- **The core challenge:** An established auto dealer marketing platform was building AI-powered audience features but had no visibility into who car buyers actually were — only what happened inside dealerships.
- **The integration:** Embedded Faraday's API across 1,000+ dealer accounts, provisioning a dedicated prediction environment for each dealer with custom propensity models trained on their own transaction history.
- **The business impact:** Audience generation now runs programmatically across every connected account with automated monthly retraining — no data science team or manual intervention required.

—

Every [platform](/blog/powered-by-faraday-complete-guide-embedded-predictive-ai) today knows it needs better data and smarter AI tools. But the question is how to get it.

For [newer companies](/blog/real-time-automotive-lead-enrichment-faraday), the answer is often baked in from day one — consumer intelligence built into the architecture before the first customer signed on. But for established platforms — ones that have been delivering real value for years, with hundreds or thousands of customers already depending on them — the path looks different. You can't rebuild from scratch. You need to add intelligence to what already works.

That tension is at the center of what Faraday calls the [context gap](/blog/consumer-context-data-ai): brands know how their customers behave inside their product, but they don't know who those customers actually are. Their financial picture. Their life stage. Whether they're likely to be in-market right now. That kind of consumer context doesn't live in a SaaS platform's database. It doesn't come from a CRM feed. And building the infrastructure to generate it — the data sourcing, the modeling, the ongoing maintenance — is simply off the table for most established teams.

That's exactly where a leading automotive software and marketing technology company found itself. Years of real customers, real trust, and real product depth — a system that worked. But as they built out their AI-powered audience features, they kept running into the same wall: their product knew everything about what happened inside a dealership, and almost nothing about the real people interested in buying cars — beyond their clicks and views.

## Why do established SaaS platforms face a "Consumer context gap"?

The instinct for many platforms at this point is to go [get the data themselves](/blog/powered-by-faraday-complete-guide-embedded-predictive-ai). But sourcing production-ready consumer data isn't a weekend project. It means negotiating with data vendors, normalizing and unifying records across sources, building the modeling infrastructure to turn raw data points into useful predictions, and then maintaining all of it on an ongoing basis as data ages and customers change. Most established platforms don't have a data science team sitting idle waiting for that project. And even if they did, it would take years to reach the quality and coverage that purpose-built consumer data infrastructure can deliver from day one.

Building it in-house wasn't the answer. Embedding it was.

## The solution: Faraday as the embedded context layer

Rather than rebuilding from scratch, the platform embedded Faraday directly into its product architecture via [API](https://faraday.ai/docs/reference).

Faraday's role was to supply what the platform was missing: real consumer context. That means access to [the](https://faraday.ai/catalog) [Faraday Identity Graph (FIG)](/blog/whats-in-faraday-identity-graph), which contains [1,400+ data points](https://faraday.ai/catalog) on 240M+ U.S. adults — wealth indicators, life stage signals, behavioral data points, geographic context. But [consumer profiles](/blog/what-data-do-you-need-for-marketing) alone aren't enough to build smart audiences. What dealers actually need to know isn't just who their customers are — it's who among the thousands of consumers in their market is most likely to be ready to buy right now. So on top of that consumer context layer, Faraday builds custom [predictive models](/blog/propensity-models-predict-customer-behavior) trained on each dealership's own transaction history — turning static profiles into forward-looking propensity scores that power every audience the platform generates.

Critically, this wasn't a one-size-fits-all enrichment layer. The platform operates at the individual dealer level, and Faraday mirrors that structure. Using Faraday's multi-account API architecture, the platform provisions a dedicated prediction environment for each dealer — its own data connections, its own cohorts, its own trained outcomes. Audiences are built on models that understand what a buyer looks like for that dealer, in that market — not a generic consumer profile applied uniformly across accounts. And because franchise businesses can only sell to customers they can actually reach, each dealer's audiences are geofenced to their specific area, so targeting never spills outside the radius they can serve.

When an account's data updates — new transactions, new leads, new service visits — Faraday retrains. The consumer context layer stays current automatically, with no manual intervention required.

## What changed

Before Faraday, the platform's AI audiences were built from the inside out: start with what the dealer knows, and try to find patterns. After Faraday, audiences are built from both directions — the dealer's first-party signals enriched and extended by real-world consumer context that no CRM can generate on its own.

For the platform, the operational effects were meaningful:

- Audience generation, which previously required manual rules-based configuration, now runs programmatically across every connected account
- Monthly data refreshes trigger automated model retraining — no human intervention required to keep predictions current
- Every audience the platform generates now reflects real consumer behavior — not just dealership CRM activity.

None of this required the platform to hire data scientists or build modeling infrastructure. Faraday handled that complexity behind the API.

### Key results

- Audience generation automated across 1,000+ dealer accounts
- Monthly model retraining runs with no human intervention
- Every audience now reflects real consumer behavior, not just CRM activity

## The bigger pattern: why established platforms are embedding consumer intelligence

This case reflects something happening across B2B software right now. Established platforms are under real pressure to make their AI claims real — not just in the product copy, but in the actual outputs. And the honest answer is that most AI features, left to first-party data alone, produce mediocre results. The data simply isn't there.

What Faraday offers platforms in this position isn't a dataset to license. It's a consumer intelligence infrastructure they can embed without rebuilding their product. The AI story stays theirs. The context that makes it credible comes from Faraday.

For a platform serving hundreds of accounts, each with their own customers and markets, that's the difference between AI as a feature name and AI as a real capability.

## Ready to optimize your platform?

If you want to talk through how customer context and embedded AI could work for your platform, [talk to a Context Consultant](https://faraday.ai/talk-to-sales) or explore our [developer documentation](https://faraday.ai/docs) to see how easily we could integrate into your existing stack. Or if you'd rather get started on your own, [try it out yourself on Faraday Pro](/signup).

Learn how a leading auto dealer marketing platform closed the consumer context gap in its AI product — embedding Faraday to power individualized predictions for every account it serves, without building a data science team.

How an established marketing platform delivered AI-powered audiences across 1,000+ accounts

## At a glance

- **The core challenge:** A real-time automotive sales intelligence platform needed consumer context for its AI co-pilot — without the six-figure costs and months of engineering required by traditional data brokers.
- **The tech stack integration:** Embedded Faraday's Lookup API directly into the co-pilot's CRM lead intake flow.
- **The business impact:** Achieved instant access to the Faraday Identity Graph (1,400+ attributes across 240M+ U.S. adults), dropping deployment time from months to days while maintaining strict privacy compliance.

—

Every [platform](/blog/powered-by-faraday-complete-guide-embedded-predictive-ai) today knows it needs better data. The question is how to get it — and how fast.

That's the position one automotive AI startup found themselves in. Their product is a real-time AI co-pilot for automotive sales teams. When a lead comes in — someone who filled out a form on a dealer's website, or clicked through from a listing — the co-pilot builds a psychological profile and generates a live call script on the rep's screen before they dial. The rep knows how to open the conversation, which questions to ask, how to handle objections, and what the person on the other end of the line is likely to respond to.

The product works because it knows who that lead actually is. And getting that consumer intelligence quickly enough to ship a working product was the problem the team needed to solve.

## The challenge: scaling real-time consumer Data enrichment for AI co-pilots

The platform's value proposition depends entirely on the quality of the [consumer profile](/blog/what-data-do-you-need-for-marketing) it can build in the seconds between a rep receiving a lead who submitted info on a dealership's site and dialing them back. The team had access to credit bureau data from [Equifax](https://www.equifax.com/) — useful for financial signals — but it was missing the consumer preferences, life events, lifestyle indicators, and psychographic signals that make a profile genuinely actionable.

The traditional path to filling that gap runs through data brokers. But for a startup, that path comes with real costs: [six-figure upfront licensing fees, months of engineering work to build ETL infrastructure, identity resolution pipelines, and ongoing privacy compliance overhead](/blog/powered-by-faraday-complete-guide-embedded-predictive-ai). And at the end of it, a static dataset with no [real-time API](https://faraday.ai/docs/features/lookup-api) — which meant limited use for a product that needs to enrich a lead in near-real-time, while a rep is about to make a call.

For a company focused on signing dealers and validating the product, that tradeoff didn't make sense.

### Faraday vs. traditional data brokers

| Feature / metric            | Traditional data Brokers              | Faraday lookup API integration           |
| :-------------------------- | :------------------------------------ | :--------------------------------------- |
| **Upfront costs**           | Six-figure licensing fees             | Scalable API pricing                     |
| **Implementation timeline** | Months of engineering (ETL/pipelines) | Live in days                             |
| **Data delivery**           | Static datasets (No real-time API)    | Near-instantaneous, real-time enrichment |
| **Compliance overhead**     | Manual privacy tracking               | Built-in compliance infrastructure       |

## The solution: Embedding Faraday’s Lookup API for instant identity resolution

The team embedded Faraday directly into the co-pilot's lead intake flow via the [Lookup API](https://faraday.ai/docs/features/lookup-api).

When a lead arrives in a dealer's CRM, the co-pilot fires a real-time call to Faraday. Within seconds, it gets back a consumer profile drawn from the [Faraday Identity Graph (FIG)](/blog/whats-in-faraday-identity-graph) — [1,400+ data points on 240M+ U.S. adults, covering demographics](https://faraday.ai/catalog), financial signals, lifestyle indicators, life events, and household context. That profile feeds directly into the co-pilot's AI, which uses it to generate the psychological profile and call script the rep sees on screen.

Privacy compliance, identity resolution, and data infrastructure came included. The engineering team focused on building the product, not on the data plumbing underneath it.

## What changed: real consumer context on every lead, in real time

Before Faraday, the co-pilot’s profiles were built primarily from credit bureau signals and whatever the CRM had on the lead. Useful, but incomplete — missing the consumer dimensions that actually shape how someone responds in a conversation.

With Faraday, every lead that comes through a connected dealership arrives enriched with a full consumer profile before anyone picks up the phone. The rep sees not just financial signals but lifestyle context, life stage indicators, and psychographic patterns — enough context to shape how a rep opens the conversation and handles objections.

For the platform, the operational results were what mattered at this stage:

- Lead enrichment that previously would have required months of infrastructure work was live in days
- A real-time pipeline that works within the seconds-level latency the co-pilot requires
- Built-in compliance infrastructure that would have taken significant engineering time to replicate

## The bigger pattern: consumer context as infrastructure, not a dataset

This startup's story is a version of a problem that shows up across the software industry. Whether you're building from scratch or retrofitting an established product, the moment you try to make your AI or your agents genuinely intelligent about the people they're engaging, you hit the same wall: the consumer context you need doesn't exist in your system, and building the infrastructure to generate it is a distraction from building the product itself.

Faraday’s consumer intelligence infrastructure acts as a plug-and-play data layer that handles identity resolution, data normalization, real-time API delivery, and privacy compliance out of the box.

For this team, that meant going from concept to production in weeks instead of months. For the dealers using the platform, it means every rep on every call has a complete picture of who they're talking to before the first word is spoken.

## Ready to optimize your platform?

If you want to talk through how customer context could work for your platform, [talk to a Context Consultant](https://faraday.ai/talk-to-sales) or explore our [developer documentation](https://faraday.ai/docs) to see how the integration works. Or if you'd rather get started on your own, try it out yourself on [Faraday Pro](/signup).

Learn how a real-time automotive sales intelligence platform embedded Faraday's consumer data into its AI co-pilot — giving automotive BDC reps a real-time psychological profile and call script for every incoming lead, without building data infrastructure from scratch.

How a real-time automotive sales platform embedded consumer intelligence — without building data infrastructure

"If one record is wrong, how can I trust the rest?"

We sometimes hear this from clients, and we get why. You open a freshly enriched customer file, spot something you know is wrong — your own name at an address you left five years ago, a coworker tagged with an income that's clearly outdated — and suddenly the whole file feels suspect.

But that instinct — to judge the whole file by one bad record — is exactly the **wrong way to evaluate consumer data**. And it leads to worse decisions, not better ones.

## Spot-checking is the wrong test for data quality

First things first: spot checking for 100% accuracy is not a valid way to review data quality in a customer file.

Whether you have spotted an incorrect record in a 100k person audience for direct mail or you've identified an incorrect attribute from your customer file, the key is to go back to first principles: your ultimate goal is likely to drive an increase in conversion rates or reach more people. One bad row isn't going to spoil the bunch.No dataset at scale is 100% accurate, however, our mission is to have the best data set on the market. We rely on many upstream vendors and getting feedback on their data quality only helps us get better at our mission.

## What to do when you spot a bad record

When you find one bad record, here’s what to do:

- First, consider your use for the data. If the file is 95% accurate instead of 100% accurate, can you still achieve your goal?
- Second, report it to us via [faraday.ai/data-corrections](https://faraday.ai/data-corrections). When a client flags something, the issue almost always originates with one of our upstream vendors — but because it ended up in your file, it's our job to act on it. Our team reviews every submission, escalates with the upstream vendor when needed, and applies corrections back into the pipeline. It's not a quality guarantee — it's a correction loop, and it's how the dataset stays trustworthy over time.

Our number one goal is to provide data that drives meaningful lift on your campaigns, and ultimately delivers significant ROI for your company. (Side note: we strive to be assessed by the ROI we deliver, check out some of our [case studies](/stories) to see just how much lift we generate).

The reality is that chasing 100% accuracy at this scale would mean slower files, longer QA cycles, higher prices, and less freshness — all in service of a number that doesn't change the campaign outcome. So the right question isn't "is every record correct?" — it's "does this file help me reach more of my best customers than a random list would?"

## How to actually evaluate consumer data

If you want to know whether your file is doing its job:

- **Run a holdout test.** Pick a random subset of your enriched audience and a comparable subset without enrichment. Run the same campaign to both. The lift between them is your real answer.
- **Look at downstream metrics.** Response rate, conversion rate, CPA, ROAS. These are the only metrics that reflect whether the file is working.
- **Evaluate match rates in context.** Match rates depend on what identifiers you bring to the table (check out our [thoughts on this](https://faraday.ai/blog/wheres-my-99-match-rate)). Compare against realistic benchmarks, not vanity numbers.

None of these require inspecting individual rows. All of them tell you something a spot-check cannot: whether the file is actually delivering value.

## The bottom line

It’s just like life, don’t let one data point throw you off your game\! Stay focused on measuring what matters: response rate, CPA, lift versus a random list. If the numbers clear your benchmarks, the file did its job. If they don't, that's the signal worth investigating — not the one weird row that caught your eye.

When you do find a record that's off, [flag it](/data-corrections). We want to know. And if your campaign results aren't where they should be, [talk to us](/talk-to-sales). That's the conversation we want to be having.

When you spot a bad record in a 150,000-person file, your instinct is to question the whole dataset. The math says otherwise. Here's how to actually evaluate data quality.

Found a wrong record in your data? Here's what it actually means

**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

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

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.