What data do you need for marketing — and what kind actually moves the needle?

Most brands have data. What they don't have is the right data — the kind that actually tells them who their customers are and what to do about it.

The typical marketing stack is full of first-party signals: clicks, purchases, email opens, form fills. That data tells you what happened. It doesn't tell you why it happened, or who was behind it. And without that context, every targeting decision, personalization effort, and predictive model is working with an incomplete picture. For a full explanation of why first party data isn’t enough, see our complete guide to customer context.

So what data do you actually need? At Faraday, we've found that effective consumer marketing requires three distinct layers of data — each one building on the last.

Data Layer	What It Solves	Key Components	Business Outcome
1. Identity Completion	Bridges reachability gaps	Verified names, emails, phones, physical addresses	Higher match rates, lower bounce rates, multichannel reach
2. Profile Enrichment	Fixes the "context gap"	Demographics, financial signals, life stage data (e.g., FIG)	Precise segmentation, solving the "cold start" problem
3. Custom Predictive Data	Removes guesswork	AI-generated propensity & churn scores trained on your data	Algorithmic prioritization for call centers, email platforms, and AI agents

Layer 1: Identity completion (building the contact foundation)

Before you can understand your customers, you need to be able to reach them. That starts with verified identity data — filling in the gaps in your existing records with name, email, phone number, and physical address.

This sounds basic, but it's where many brands lose ground before they even start. Incomplete records mean lower match rates, missed contacts, and smaller addressable audiences. A lead who submitted a web form with just an email is functionally unreachable through direct mail. A customer whose phone number is outdated can't be contacted by your call center. Identity completion solves this by appending verified contact information to your existing records, so your outreach can actually land.

This is the foundation. Everything else — enrichment, predictions, personalization — depends on knowing who you're talking to in the first place.

Layer 2: Consumer profile enrichment (closing the context gap)

Identity tells you how to reach someone. Consumer profile data tells you who they actually are.

This is where first-party data hits its limits. Your CRM knows what a customer bought and when. It doesn't know their household income, whether they own or rent, how many people live in their household, what life stage they're in, or what their broader lifestyle looks like. That missing context is the difference between a generic campaign and one that actually resonates.

Consumer profile enrichment fills that gap by matching your customer records against a comprehensive external dataset. At Faraday, that dataset is the Faraday Identity Graph (FIG) — 240M U.S. adults and their households, with over 1,400 verified consumer data points spanning demographics, financial signals, property data, life events, and lifestyle indicators.

The result: every person in your database transforms from a row of transaction history into a complete consumer profile. A customer who bought baby clothes becomes a first-time parent in a dual-income household with a recent mortgage — context that completely changes how you'd talk to them, what you'd offer, and when.

This is also what solves the cold start problem. Even if a customer just showed up for the first time and you have zero behavioral data on them, enrichment gives you enough signal to segment, target, and personalize from the very first interaction.

Layer 3: Custom predictive data (anticipating customer next action)

Identity gets you to the right person. Enrichment tells you who they are. Predictive data tells you what to do next.

Custom predictive data points are AI-generated scores trained on your specific historical outcomes — not generic, off-the-shelf models. They answer the questions that actually drive marketing decisions: How likely is this person to buy? Are they about to churn? What product should I recommend? What's their projected lifetime value?

These predictions are built from the combination of your first-party data and the full depth of FIG's consumer data points. That's what makes them dramatically more accurate than CRM-native scoring tools that can only work with whatever data happens to be in your system. A propensity model trained on 1,400+ data points finds patterns that no human analyst — and no first-party-only model — would ever surface.

The practical impact is that every customer and lead in your database gets a set of scores tailored to your business. Your call center knows who to call first. Your email platform knows which offer to send. Your AI agents know whether they're talking to a high-value prospect or a window shopper. The guessing stops and the math takes over.

Why you need all three

Each layer adds value on its own, but together they create a data foundation that's accurate, actionable, and ready for both human teams and AI agents to act on.

Consider how they compound. Identity completion ensures you can actually reach someone. Enrichment ensures you understand who they are when you do. Predictions ensure you know what to say, offer, or recommend — and in what order of priority.

Without identity, your campaigns don't land. Without enrichment, your targeting is blind. Without predictions, you're still making decisions based on instinct rather than evidence. Most brands are operating with one or two of these layers. The ones pulling ahead have all three.

How you access the right data

Enriched records and predictive scores don't sit in a report. They flow directly into the tools your team already uses — CRM, email platform, data warehouse, ad platforms — through a robust API with options for real-time lookup and recurring deployment.

For AI-native workflows, Faraday delivers consumer data via Model Context Protocol (MCP). This allows enterprise AI agents to access production-ready consumer grounding instantly, eliminating the need for complex engineering pipelines when teaching LLMs about your customers.

Ready to build your data foundation?

If you want to talk through what consumer data strategy could look like for your brand, talk to a Context Consultant. Or if you'd rather explore our data catalog and get started on your own, try it on buy.faraday.ai.

FAQ

What's the difference between first-party and third-party data? First-party data is what you collect directly through interactions with your brand — purchases, clicks, form submissions. Third-party data is collected externally and covers data points you can't observe through direct interactions, like household income, life stage, or lifestyle indicators. Both are essential: first-party data tells you what someone did, third-party data tells you who they are.

Does cookie deprecation affect third-party consumer data legality? No.Third-party cookies and third-party consumer data are completely different things. Cookies tracked browsing behavior across websites without meaningful consent. The consumer data Faraday uses is consented, offline data — sourced from permissioned vendors with full privacy compliance. Cookie deprecation doesn't affect it.

How much first-party data do I need before enrichment is useful? Not much. Even a basic customer file with names and emails is enough to match against FIG and start enriching. That's the whole point of solving the cold start problem — enrichment is most valuable when you have the least behavioral data to work with.

Can I use enriched data in my existing tools? Yes. Faraday delivers enriched records and predictive scores directly into your CRM, email platform, data warehouse, or ad platform through API, batch deployment, or real-time lookup. No migration required.

What is the difference between a data broker and a consumer context platform like Faraday? Traditional data brokers sell raw data files that require months of contracting, significant engineering to integrate, and in-house data science to make useful. Faraday delivers production-ready context — curated, normalized, and structured for immediate use — with usage-based pricing and no six-figure upfront licensing fees.