Every CMO feels it: growth only happens when you know exactly where your opportunity is. But consumer behavior keeps shifting. Acquisition costs keep rising. Your best regions last year aren’t guaranteed to be your best regions next year. And the pressure to deploy budget intelligently (across markets, personas, and channels) has never been higher.

Most teams do not have a reliable way to see where their next customers actually are.

Today, market clarity isn’t optional. It’s the foundation of any GTM plan and you need the right tools to solve this problem.

## Market Opportunity Analysis (MOA) is the tool you need to get that clarity

Faraday’s [Market Opportunity Analysis](https://faraday.ai/docs/abstractions/MOA) is a modern, data-driven way to understand your market at a level your CRM or BI team can’t show you. Now, you can finally see:

- Where you’re overpenetrated and hitting diminishing returns
- Where real growth potential remains across the U.S.
- Which personas offer disproportionate upside
- Which states, metros, and postcodes are quietly becoming your highest-value regions

It connects your customer data to the [Faraday Identity Graph](/features/faraday-identity-graph), our nationwide consumer dataset, which includes thousands of behavioral, demographic, financial, and predictive signals that never appear in your CRM. And because FIG represents the full U.S. population, it shows not just who you’ve already captured, but who you’re missing, and how large the opportunity truly is.

[![Three market opportunity analysis screenshots organized as header image](/images/blog/MOA-menu/moa-the-menu-blog-header-image.png)](/images/blog/MOA-menu/moa-the-menu-blog-header-image.png)

## Delivered in days, not months

Even better, Faraday can deliver completed MOA reports in days (assuming your data is structured). Modern marketing leaders know they do not have time for slow delivery or long analytical cycles. They need accurate intelligence, and they need it quickly. It’s as simple as this:

1. Upload your historical customer data. That is the only lift on your side.
2. Faraday enriches and analyzes the dataset to generate a MOA analysis unique to your customers
3. You receive a clear, executive-ready view of your total addressable market, penetration, and where your strongest opportunities live.

This speed to delivery makes MOA an ideal planning tool, giving leaders the clarity they need right when decisions are being made.

## What Faraday’s MOA can reveal

Decision makers need a clear picture of what they will actually receive. Faraday’s MOA delivers three core categories of insight that mirror the way CMOs structure their planning: Market Size, Market Penetration, and Market Opportunity.

### 1. Total Addressable Market (TAM)

Every MOA starts with a clear view of your total addressable market across the United States. This shows how big your opportunity truly is and how much room you have to grow, broken down by state, metro, and postcode.

[![MOA TAM analyis showing total addressable market](/images/blog/MOA-menu/moa-tam.png)](/images/blog/MOA-menu/moa-tam.png)

### 2. Penetration analysis

We map your existing customers against the wider population to show where you are saturated and where you still have headroom. This often surfaces high value regions where you have little or no presence.

[![MOA penetration analyis showing where current customers exist](/images/blog/MOA-menu/moa-penetration.png)](/images/blog/MOA-menu/moa-penetration.png)

### 3. Geographic opportunity (State, Metro, Postcode)

With TAM and penetration in place, the MOA highlights your strongest pockets of geographic opportunity at the state, metro, and postcode levels. This gives you a multi-layered view of where your next customers actually are.

[![MOA geo analyis showing where current customers exist and where lookalikes could be found](/images/blog/MOA-menu/moa-geotargeting.png)](/images/blog/MOA-menu/moa-geotargeting.png)

### 4. Persona opportunity

The MOA identifies which personas represent the largest opportunity for growth and how those personas vary across regions. This helps inform segmentation, messaging, and channel strategy.

[![MOA geo analyis showing where current customers exist and where lookalikes could be found](/images/blog/MOA-menu/moa-persona.png)](/images/blog/MOA-menu/moa-persona.png)

### 5. Actionable next steps

Faraday provides clear next steps based on the data, including where to allocate budget, which regions to prioritize, and which personas to focus on. These recommendations make the MOA immediately useful in planning.

### 6. Fully custom add-ons

For organizations with more complex questions, the MOA can include fully custom add-ons. These deepen the standard analysis and answer specific strategic questions through tailored cuts of the data. Examples include:

- Shopping behavior differences between retail and eCommerce customers
- Demographic patterns such as age or gender distributions
- Product-level performance broken out by persona
- Store-level penetration and catchment insights

## Three ways to receive your MOA

Faraday offers three MOA delivery formats, each designed to meet teams at different levels of analytical maturity and strategic need.

### Option 1: Just the data

This is the most flexible format and the one preferred by teams with strong in-house analytics. You receive a standardized CSV output containing everything needed for deeper analysis: TAM estimates, persona distributions, and geographic breakdowns at the state, metro, and postcode levels. There are no visuals and no recommendations attached.  
Just clean, structured data ready for your team to explore however they choose.

### Option 2: Essential insights

If you want a fast, executive-ready readout, this is the most streamlined option. Faraday distills the MOA into a set of core visualizations paired with up to four recommended next steps. It gives you a clear, concise narrative of where your strongest opportunities exist without requiring any internal analysis. Many teams use this version directly in planning decks and leadership reviews.

### Option 3: Fully custom

For organizations with more complex questions or multiple business lines, the fully custom MOA provides a deeper layer of insight. Faraday builds tailored cohorts and segmentations to answer your specific strategic questions, whether that involves product-level differences, demographic splits, store-level penetration, or other custom cuts. You also receive up to two rounds of refinement as new questions emerge.

## Closing

Understanding your market shouldn’t require guesswork. Faraday’s Market Opportunity Analysis gives you the data, clarity, and confidence to know exactly where your next customers are, and where your strongest growth potential lives.

If you want clarity before the new year kicks off, we’re here. Talk to us about running your MOA.

This post explains how Faraday’s Market Opportunity Analysis uses your customer data and the Faraday Identity Graph to reveal your true TAM, current penetration, and where to focus future growth.

Understand your market with Faraday’s Market Opportunity Analysis

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

---

For insurance and debt-resolution teams, the high-volume, parallel-dialing era is breaking under modern spam protections and compliance pressure. The old assumption that maximizing dials will eventually produce results is no longer reliable.

Parallel dialing, which made it easy to brute-force a list by firing multiple calls at once, is now a liability. Carrier spam systems are stricter, VoIP behavior is aggressively penalized, and consumers block unknown numbers faster than ever. The harder you dial, the faster your numbers get flagged as “Scam Likely.”

Because of this, many organizations are shifting toward [progressive dialing](https://www.convoso.com/blog/what-is-a-progressive-dialer/), a system where Agents work leads one at a time. This reduces abandoned calls and keeps compliance risk in check. That does not mean high-volume or parallel strategies never make sense. It does mean that, for most day-to-day calling, more intentional, progressive behavior is becoming the norm. But this shift introduces a new requirement: if each call is deliberate, every prospect needs a real chance of converting.

The thing is that progressive dialing doesn’t fix weak filtering; in fact, it makes those weaknesses impossible to ignore. The raw volume safety net is gone. Once volume goes away, the quality of your scoring becomes the deciding factor in whether your call center generates value or falls flat.

## Fewer dials, higher stakes

[![Diagram showing the different flows of leads through parallel or progressive dialers](/images/blog/progressive-dialing-diagram.png)](/images/blog/progressive-dialing-diagram.png)

Parallel dialing allowed teams to get results even with imprecise filtering, as the sheer volume of attempts eventually surfaced the best leads anyway. Progressive dialing changes that dynamic. When agents call leads one at a time, each conversation carries more value. Time spent on a poor-fit record is time not spent on a viable customer and this shift exposes a structural weakness in most systems:

> The filtering logic in front of the dialer was built for a world where volume covers for imprecision.

In most organizations, existing filters rely on outdated assumptions, demographic shortcuts, and scoring models that don’t reflect actual conversion outcomes.

This filtration layer often fails because it uses:

- Guesswork filters, like “higher income is better” or “certain ZIPs respond more.”
- Manual rules added years ago that rarely get revisited.
- Lead scores that rely only on demographic data alone without factoring in conversion outcomes.
- A disconnect between real-time conversion data and day-to-day prioritization.

Under parallel dialing, this was “good enough” but with progressive dialing, that safety net disappears.

> If your system can’t surface your most viable prospects early, agents waste their best hours and expensive leads get burned without meaningful lift.

## What better filtration looks like

So you might find yourself asking: what filters do I actually need for a financial-services call center?

The answer isn’t a single score or one magic rule. It is a handful of signals that give your tools and teams the context they need to decide which prospects are worth prioritizing, which deserve immediate outreach, and which should take a different path entirely.

Better filtration in insurance and debt-resolution usually comes down to a few core areas:

- **Quality at the moment of purchase:** Teams need a way to understand which leads are likely to engage or advance, and which should be down-bid or skipped altogether. This decision happens before anyone makes a call, and it depends entirely on having meaningful context at the point of purchase.
- **Early engagement signals:** A significant amount of agent time is lost on people who are not in a position to meaningfully engage. Early behavioral or modeled signals, whether from form data or enrichment, give teams the context they need to focus agents on prospects who are more prepared for a productive conversation.
- **Likelihood to convert:** Once a lead is in your system, the order you work it in matters. Teams rely on predictive context to decide which records to prioritize during peak windows so the highest-value prospects get attention when it is most likely to count.
- **Matching prospects to the right path:** Not every lead should go straight to an agent. Context helps teams decide which prospects warrant immediate outreach, which should enter warm-up flows in email or SMS, and which should move into slower, lower-cost nurture until their behavior changes.  
   (The routing itself happens in the CRM, dialer, or marketing tools. Faraday provides the signals those systems rely on.)

When these decisions line up, the entire funnel tightens. Teams use their resources more effectively, agents spend more time in productive conversations, and performance improves without adding volume. The difference comes from having the right context in place to drive smarter decisions.

## Where Faraday fits in

For insurance and debt-resolution teams, Faraday regularly sits at the very top of the funnel. Many teams connect us through [LeadConduit](https://activeprospect.com/leadconduit/) where we score inbound leads in real time and help prioritize which records deserve immediate attention. Many also use our credit score proxy at this stage, since it gives an early signal that informs routing decisions and focuses agent time without requiring a hard pull. That alone keeps teams engaging the people who are most likely to respond or stay in the conversation.

Once a lead is accepted, that same score carries downstream into your CRM or dialer. Teams use this context to decide who should be worked first, who should enter warm-up through email or SMS, and who should move into slower, lower-cost nurture until their behavior changes. Faraday provides the intelligence that supports these decisions while the actual routing happens in the tools teams already use.

The impact isn’t just a cleaner scoring step, it’s the continuity it gives the entire funnel. From buying the lead to deciding who should be called next, the same prioritization signal flows through. Teams avoid overspending on records unlikely to engage, agents spend more of their day in meaningful conversations, and the call center starts to feel productive again instead of overloaded.

## The bottom line

Parallel dialing lets you get away with weak filters. Progressive dialing won’t.

As the industry standardizes around progressive behavior to stay compliant, filtration becomes the real competitive advantage for most day-to-day calling. There are still situations where higher-volume strategies are appropriate. The point is to match the dialing approach to the job at hand and support both with strong intelligence in front of the dialer.

If your 2026 plan involves dialing fewer people more thoughtfully, the key question isn't which dialer you use. It’s whether the intelligence in front of it is smart enough to make every call slot count.

As parallel dialing collapses under compliance pressure, teams win by pairing progressive dialing with smarter, outcome-driven filtration that prioritizes the right prospects at every step.

When every call is intentional, context matters more than ever: Progressive dialing makes smarter filters critical for your call center

Picture this: You’ve just landed a new CMO job at your dream home services brand. National distribution, huge budgets, and an even bigger mandate to drive real growth, fast.

Congratulations, **and good luck.**

Because here’s the truth: you don’t have six months to understand your new market. You probably don’t even have six weeks. Everyone from the CEO to the sales team wants to know how you’re going to grow, and they wanted to know yesterday. In fact, it’s probably why they decided to hire a new CMO in the first place.

That means you need to work efficiently from day one. Before you touch a campaign or sign a media contract, you need a clear, quantitative view of where your opportunity actually lies; not vaguely, not anecdotally. Quantitatively.

That’s where [Market Opportunity Analysis (MOA)](/docs/abstractions/MOA) comes in.

## Why Market Opportunity Analysis matters in your first week

The unfortunate reality is that a lot of CMOs walk into new roles and inherit fragmented data: CRM exports, agency reports, dashboards that don’t line up. It’s a recipe for confusion and risk: marketing and sales chasing different audiences, agencies optimizing for the wrong KPIs, leadership making calls on partial truths.

MOA cuts through that noise. It takes your existing customer base, maps it against the [total addressable market](https://corporatefinanceinstitute.com/resources/management/total-addressable-market-tam/), and reveals where you’re overperforming, underpenetrated, or completely invisible.

For a home services brand, that means understanding where your most profitable jobs come from, down to the ZIP code, address, or even individual. Then finding more just like them. You can uncover patterns in income, housing age, household size, or hundreds of other datapoints that predict demand.

The result? You can start targeting growth with precision, speed, and confidence.

## Real insights fast

Once you’ve seen how MOA reveals your market reality, the next question is why it matters: why it’s the first thing every new CMO should reach for. In a role where time and clarity are everything, MOA delivers both. It doesn’t just show you where the opportunities are; it gives you the confidence and direction to act on them immediately.

1. ### Speed to decision-making

MOA compresses the time between orientation and action. By combining your first-party data with Faraday’s pre-built national consumer dataset, you get a complete view of your market in days, not months.

For you, this means walking into early leadership meetings armed with hard numbers (penetration rates, opportunity values, and clear maps of where to grow next) earning trust and alignment from day one.

2. ### Richer context and clarity

Most CMOs start out surrounded by data, but without context. MOA changes that by layering your internal records over [Faraday’s Identity Graph (FIG)](/features/faraday-identity-graph), giving you a complete view of how your customers fit within the broader market.

From there, it reveals the lookalike consumers you haven’t reached yet and the patterns driving demand: where you’ve over-indexed, who you’re missing, and which factors actually matter. That clarity turns scattered metrics into meaningful insight.

3. ### Tactical direction and alignment

Every MOA ends with concrete recommendations you can act on immediately: which regions to prioritize, which audiences to target, and how to allocate resources for maximum ROI. It replaces intuition with quantifiable strategy and gets marketing, sales, and leadership aligned fast around the same growth plan.

The result is more than analysis: it’s acceleration. You’re not just understanding your new market; you’re steering it.

## How to use MOA to guide your next moves

Understanding your market is one thing but acting on it is where the real value shows up. MOA isn’t a static report; it’s a decision engine that turns market clarity into measurable outcomes. Here’s how to put it to work from your first week onward:

1. ### Prioritize geographies

Focus your paid media, outreach, and sales enablement in areas with high lookalike potential: regions where your best customers have close behavioral or demographic twins in the broader market but low current penetration.

Faraday’s Market Opportunity Analysis identifies these lookalike clusters at the entity level, aligning them to real households and properties through the Faraday Identity Graph. This precision makes it easy to prioritize spend where incremental investment drives exponential growth.

2. ### Align with sales

Bring MOA findings into your next territory or franchise planning session. When marketing and sales share the same data-backed view of opportunity, you can align resources quickly and avoid wasted effort.

3. ### Refine messaging

Use MOA insights to tailor creative and offers to the consumer profiles that dominate your highest-value areas, and to extend those learnings to lookalike audiences with similar behaviors and needs.

This approach replaces gut feeling with precision. It sets a precedent for analytical leadership from day one and gives your team a clear, evidence-based framework for growth.

## The takeaway

Your first few weeks as a CMO set the tone for everything that follows. Market Opportunity Analysis is your shortcut to situational awareness. It’s a fast, objective way to see the whole board before you make your first move.

When you walk into that first strategy meeting, you won’t just be the new CMO with fresh ideas. You’ll be the one who already knows where the growth is hiding, how much there is, and the exact steps to capture it. That turns day-one clarity into your first real win.

## FAQ

### What is Market Opportunity Analysis (MOA)?

MOA is Faraday’s framework for mapping your customer data against national consumer data to identify untapped market potential.

### How fast can a new CMO get insights from MOA?

Most analyses run in a few days, not months, helping new leaders make confident, data-backed decisions quickly.

### Why does MOA matter more than standard reporting or dashboards?

Dashboards tell you what’s already happened. MOA shows where to go next. By layering Faraday’s consumer data over your internal CRM data, CMOs gain a forward-looking view of opportunity that’s immediately actionable: where to invest, which segments to prioritize, and how much potential is still on the table.

### How does MOA connect to Faraday’s other predictive tools?

MOA is the foundation. Once you know where opportunity lies, Faraday layers predictive scores—like Likelihood-to-Convert or Credit Score Proxy—and persona insights onto those markets. From there, the Identity Graph maps that intelligence down to real households and individuals, turning high-level opportunity into precise, actionable targeting.

### How does MOA improve marketing and sales alignment?

Because MOA outputs are inherently shareable (like maps, penetration metrics, and opportunity rankings), it gives both teams a shared source of truth. Sales knows where to focus outreach, marketing knows where to invest, and leadership sees the projected return.

For new CMOs under pressure to grow, Market Opportunity Analysis cuts through data chaos to deliver a quantitative map of untapped markets. It provides a clear, actionable growth plan in days, not months, aligning marketing and sales from day one.

The CMO's shortcut to growth: how Market Opportunity Analysis delivers day-one wins

While there are 1,400+ demographic features available per American adult (from companies like Faraday), feeding all this raw data to an AI agent would overwhelm its "attention budget" - the limited cognitive capacity LLMs have to process context. Anybody who has used a code copilot like [Cursor](https://cursor.com) or [Claude Code](https://www.claude.com/product/claude-code) knows the feeling when its attention has wandered... after too much information, the LLM is distracted, not helpful, pissed off, stuck up a creek without a paddle. This is because of attention scarcity:

> LLMs have an “attention budget” that they draw on when parsing large volumes of context. Every new token introduced depletes this budget...

Anthropic's Applied AI team recently wrote a [short essay about the evolution from "prompt engineering" to "context engineering"](https://www.anthropic.com/engineering/effective-context-engineering-for-ai-agents). The upshot is that you must continuously manage the information available to the LLM, keeping it focused by discarding unnecessary tokens but keeping "high-signal" tokens. Advanced AI agents like Cursor and Claude Code are rapidly getting better at this. It's much harder to tire them out now, thanks to strategies like:

- Tools - using vector search and `grep` to find only relevant code, instead of reading the whole codebase.
- Sub-agents - "sub-agents [handling] focused tasks with clean context windows [returning] condensed, distilled summar[ies] of [their] work."
- Compaction (compression) - "the practice of taking a conversation nearing the context window limit, summarizing its contents, and reinitiating a new context window with the summary" - often with the output of the tools or sub-agents.

This is relevant to my industry, [demographic data](https://faraday.ai/features/faraday-identity-graph) and [customer behavior predictions](https://faraday.ai/features/customer-behavior-predictions), because the amount of data available about every American adult - at least 1,400 features are available from Faraday alone - would easily overwhelm most LLM attention budgets. If you want to [build AI agents that interact with American consumers](https://hightouch.com/demo-ai-agents) and take advantage of third party data, you need to keep the signal while discarding the rest.

Let's detail 3 ways to do context engineering with demographic data. The upshot is to use quantitative methods as tools - really, as deterministic sub-agents - to compress the surfeit of raw demographic features into high-signal tokens and maximize the attention budget left over for AI agents to do their job:

## Decision tree models

Decision tree models compress thousands of demographic features into a single propensity score by learning which combinations of features predict a specific outcome. They work by recursively splitting the population based on feature values—much like a flowchart—to find patterns that distinguish people who achieve an outcome from those who don't. **Instead of forcing an AI agent to reason about every demographic detail, the model distills this complexity into one number that captures everything relevant to the prediction.**

The input is historical examples: people who converted versus those who didn't, along with demographic and behavioral features describing them. The output is a percentile score (0-100) or probability for each person, indicating how likely they are to achieve the outcome. This compression is dramatic—from potentially 1,400 input features per person down to a single score that the agent can easily incorporate into its decision-making.

[![Decision trees efficiently compress thousands of demographic datapoints into high-signal tokens for LLMs](https://developers.google.com/static/machine-learning/decision-forests/images/DecisionTree.png)](https://developers.google.com/static/machine-learning/decision-forests/images/DecisionTree.png)
_Decision trees efficiently compress thousands of demographic datapoints into high-signal tokens for LLMs ([source: Google ML](https://developers.google.com/machine-learning/decision-forests/decision-trees))_

Decision trees give you numeric scores and lift that can go straight into your context:

[![Help the LLM understand how convinced the user is](/images/blog/finance-lift-table.png)](/images/blog/finance-lift-table.png)
_Help the LLM understand how convinced the user is ([source: Faraday blog](/Users/seamus/fdy/src/magic/src/content/blog_posts/how-to-optimize-ads-for-compliance-in-finance.md))_

Consider a credit union building an AI agent to help members with financial planning. Without propensity models, the agent would need to process 1,400 demographic fields per member every time it considers whether to recommend opening a savings account. With a decision tree model, the agent simply receives a score indicating each member's likelihood to open an account, preserving its attention budget for actual conversation and advice.

## Recommender systems

Recommender systems compress interaction history—purchases, clicks, views—into ranked predictions of which product or action comes next. These systems analyze the matrix of who-interacted-with-what across your entire customer base to find latent patterns: "people who bought A and B tend to buy C next." **Instead of an AI agent processing raw transaction logs and product catalogs to figure out what to recommend, it receives a pre-computed ranked list that captures all that complexity.**

The input is a transaction or interaction matrix (rows are people, columns are products or actions) plus any product metadata. The output is a ranked list of recommendations for each person—typically the top 5-10 options most likely to resonate. This compression is especially powerful because it captures collaborative filtering patterns (what similar people liked) without forcing the agent to analyze the entire interaction history every time.

[![Recommenders use matrix factorization to efficiently compress product affinity and transaction history into high-signal tokens for LLMs](https://developers.google.com/machine-learning/recommendation/images/Matrixfactor.svg)](https://developers.google.com/machine-learning/recommendation/images/Matrixfactor.svg)
_Recommenders use matrix factorization to efficiently compress product affinity and transaction history into high-signal tokens for LLMs ([source: Google ML](https://developers.google.com/machine-learning/recommendation/collaborative/matrix))_

Imagine an e-commerce site building an AI shopping assistant. Instead of the agent analyzing every past purchase across thousands of products to determine what to suggest next, it receives "most likely next 5 products: [Product A, Product B, Product C, Product D, Product E]" and can focus its attention on how to present these recommendations conversationally based on the current chat context.

## Cluster models

Cluster models group similar people together and identify the defining characteristics of each group, compressing individual-level variation into persona-level patterns. Rather than treating each consumer as a unique snowflake across thousands of attributes, clustering finds natural segments where people share meaningful similarities. **An AI agent can then work with a handful of personas instead of reasoning about every individual's complete demographic profile—a massive reduction in cognitive load.**

[![Don't tell your LLM all the dots... tell it the cluster](https://developers.google.com/static/machine-learning/clustering/images/clustering_example.png)](https://developers.google.com/static/machine-learning/clustering/images/clustering_example.png)
_Don't tell your LLM all the dots... tell it the cluster ([source: Google ML](https://developers.google.com/machine-learning/clustering/overview))_

The input is your customer population with demographic and behavioral features. The output is typically 3-7 personas, each with defining characteristics and a label for every individual indicating which persona they belong to. For example, a model might identify distinct groups like "Budget-Conscious Families" and "Premium Singles," automatically determining which datapoints (income, household size, urban vs. rural) meaningfully distinguish these personas.

Consider a subscription service building an AI agent for customer support. Instead of the agent processing each customer's unique demographic and usage profile from scratch, it receives "this customer belongs to the Budget-Conscious Families persona" and can immediately apply relevant context about typical concerns, preferences, and communication styles for that segment. The agent's attention is freed up to focus on the specific support issue rather than figuring out who this customer is.

## Faraday implementation notes

For readers interested in how these compression techniques are implemented in practice, here's how Faraday handles each approach:

**Decision tree models**: Faraday [Outcomes](https://faraday.ai/docs/abstractions/outcomes) handles this compression using multiple algorithms including [random forests](https://cloud.google.com/bigquery/docs/reference/standard-sql/bigqueryml-syntax-create-random-forest) and gradient boosted trees. The system trains separate models for different customer lifecycle stages (how long someone has been in the sales funnel), handles both vendor-enriched demographic data and customer-only data, and applies fairness adjustments to ensure protected demographic groups aren't disadvantaged by the predictions.

**Recommender systems**: Faraday [Recommenders](https://faraday.ai/docs/abstractions/recommenders) uses a two-stage approach: [matrix factorization](https://cloud.google.com/bigquery/docs/reference/standard-sql/bigqueryml-syntax-create-matrix-factorization) finds collaborative filtering patterns, then propensity models (similar to Outcomes) refine those recommendations using demographic data. The system handles cold-start scenarios (new customers with no purchase history) by falling back to demographic-based predictions, and adapts to different data availability levels—working whether you have rich behavioral data or just basic customer information.

**Cluster models**: Faraday [Persona Sets](https://faraday.ai/docs/abstractions/persona_sets) implements this using [k-means++](https://cloud.google.com/bigquery/docs/reference/standard-sql/bigqueryml-syntax-create-kmeans) clustering. The system automatically selects the optimal number of groups (balancing too few vs. too many personas), removes outliers before clustering to prevent extreme cases from distorting the personas, and identifies which specific datapoints distinguish each persona—importantly, not forcing every datapoint to matter when some simply don't differentiate between groups. It also builds lifetime value predictions for each persona, helping prioritize which segments deserve more attention.

## Conclusion

When you use AI copilots like Cursor or Claude Code, you can feel them efficiently quantifying and summarizing your code base. They use tools like vector search and `grep` to pull out the high signal tokens and leave themselves lots of attention budget to write code. This is similar to what's necessary with AI agents that want to use third party data to interact with consumers. You have to selectively compress information about the consumers; decision trees, recommenders, and clusters are a simple quantitative way to do this.

Just as code copilots like Claude Code use tools like vector search to pick out relevant code (saving attention for writing code), consumer-facing AI agents should use predictive models to pick relevant demographic signals (saving attention for conversation and decision-making).

Context engineering and demographic data

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

---

## Who this guide is for

This post is written for marketing and operations leaders in the home-services industry—the people responsible for filling calendars, scheduling appointments, and keeping call centers busy at roofing, solar, and plumbing companies. Whether you’re running campaigns in [ServiceTitan](https://www.servicetitan.com/), [Housecall Pro](https://www.housecallpro.com/), [Salesforce](https://www.salesforce.com/) or [HubSpot](http://hubspot.com), your challenge isn’t collecting more leads. It’s figuring out which ones will actually book jobs.

If you’re a CMO, VP of Marketing, or Marketing Ops leads in home services, this piece will show how a predictive data layer can plug into your existing [CRM](https://www.salesforce.com/crm/what-is-crm/) to help your team focus on high-intent prospects—and stop wasting time on leads who were never going to convert.

## The problem: Your CRM knows what happened before but not what happens next

Your CRM is great at logging calls, tracking appointments, and storing customer info. But it’s not helping you anticipate what will happen next, who is likely to convert, or how much they might buy. Your team is still guessing who to call first. Campaigns are firing blind. Reps are wasting hours chasing low-quality leads.

The problem isn’t your CRM—it’s what it’s missing: predictive context. You’ve got historical data, but not behavioral or financial signals that show who’s ready to book. That’s where adding data AND predictions comes in.

## The solution: The predictive data layer: what it is and why it matters

A predictive data layer adds real-world behavioral and financial signals drawn from verified consumer data to each contact in your CRM. In addition to first party data you collect from web form fills and past interactions, you get third party data points and scores based on how likely each lead is to book right now or in the next 30, 60, or 90 days.

Faraday’s predictive data layer integrates directly into your CRM—no replacement required—and automatically adds key datapoints that reveal intent, readiness, and fit. The result: your reps spend more time on leads that close, not leads that clog up the queue.

## What smarter CRMs deliver

Companies that add predictive enrichment to their see immediate results:

- [Fewer wasted calls as reps focus on high-intent prospects](/stories/call-center). If you could sort your call list, why wouldn't you?
- Higher close rates across campaigns. By avoiding bad leads, or leaving them for last, you get better results sooner.
- Cleaner funnels and more predictable revenue forecasting. You can calculate an "estimated value" (EV) for every lead, even if it just appeared in your pipeline.

## Faraday has the datapoints that change everything

Most CRMs weren’t designed to predict—they were designed to record. Faraday fills that gap by adding a predictive data layer that plugs directly into your existing tools. You don’t need a data science team, a new platform, or an overhaul. Just a connection to the [**Faraday Identity Graph**](/features/faraday-identity-graph), which powers over 1,400 verified consumer datapoints—everything from financial readiness to life-stage signals.

While our platform is chocked full of data that drives value, some are particularly useful for home services brands like yours. Here are the high-impact datapoints Faraday adds to your CRM:

**1️⃣ Credit score proxy:** Predicts a household’s financial readiness without a hard credit pull. Perfect for screening prospects before offering financing options.

**2️⃣ Likelihood-to-Convert:** Scores leads based on real behavioral patterns, showing who’s statistically most likely to book a job this week.

**3️⃣ Dwelling type:** Distinguishes renters from homeowners—critical for knowing whether to push service contracts or new installs.

**4️⃣ Household income & equity:** Adds key context for premium upsells, like full HVAC replacements or solar upgrades.

### So in short, with Faraday’s data, you can:

✅ Predict who can pay  
✅ Predict who will book  
✅ Predict what to offer next

## How to add predictive data to your existing CRM

Adding the datapoints you need to your CRM with Faraday is easy and fast. In a few careful steps, you can turn your existing customer database into a predictive engine that shows you who’s ready to book, who needs nurturing, and who’s not worth chasing—without touching your current workflows.

1. **Connect your records.** Faraday integrates directly with platforms like ServiceTitan, HubSpot, or your internal CRM through a secure API. Our [developer-friendly API](/docs/reference) and [detailed documentation](/docs) are a good starting point- and we'll give you a hand getting it set up. You can also give it a try using simple CSV uploads and downloads, to get comfortable with the data.
2. **Enrich with Faraday’s Identity Graph.** We fill in the missing details—household, property, demographic, and behavioral data that CRMs alone can’t provide.
3. **Deploy predictive datapoints.** Append fields like Credit Score Proxy or Likelihood-to-Convert so your team can instantly see which leads matter most.
4. **Automate routing and prioritization.** Use these datapoints to filter and prioritize your call queues or lead-routing automations—whether that’s directly inside your CRM or through integrations like [ActiveProspect’](https://activeprospect.com/)s [LeadConduit](https://activeprospect.com/leadconduit/). This ensures your highest-value prospects are called first and passed to the right reps in real time, reducing manual triage and wasted dials.

> Implementation should take a couple weeks—after that, your CRM doesn’t just record what happened—it starts predicting what’s about to happen next.

## FAQ

1. **Do I need to replace my current CRM (like ServiceTitan or HubSpot) to use Faraday?** No. This is the most common misconception. Faraday is a predictive data layer, not a new CRM. It plugs into your existing system and enriches the customer records you already have. Your team keeps working in the tool they know, but with new, predictive data at their fingertips.

2. **What’s the difference between Faraday’s predictive scoring and my CRM's built-in lead scoring?** Traditional lead scoring is manual and behavior-based (e.g., "opened email" \= \+5 points). It only knows what a lead does with you. Faraday’s predictive scoring is AI-driven and market-based. It compares your leads to a massive, verified consumer dataset to find patterns. It answers a different question: not "Is this lead engaged with my marketing?" but "Does this lead look like someone who will actually book a job?"

3. **Do I need a data science team or data engineers to implement this?** It makes it easier, but not necessarily. Faraday has integrated with hundreds of marketing and operations teams and we can provide more or less help depending on your situation because we connect via pre-built integrations and APIs, you don't need data scientists to build or maintain models. Your ops team can map the new data fields (like Likelihood-to-Convert) into your CRM, and your marketing team can start using them in automated workflows immediately.

4. **How does a call center rep actually use a "Likelihood-to-Convert" score?** Faraday appends this score directly to each lead record in your CRM or routing system.

- **Qualification:** The system can filter out bad leads before they ever make it to your call center, if you want.
- **Prioritization:** Reps can sort call queues by highest Likelihood-to-Convert, so they start with leads most likely to book—no guesswork or manual filtering.
- **Routing:** Through integrations like **LeadConduit**, high-scoring leads can be automatically routed to top reps or live-transfer partners, while lower scores move to nurture sequences. The result: reps spend time where it counts—on the leads that actually book.

5. **Is Credit Score Proxy compliant?** The Credit Score Proxy is a modeled attribute, not actual credit bureau data. It is privacy-safe, does not require a "hard" or "soft" credit pull, and does not use or access any data from credit bureaus, making it fully compliant for marketing and prospecting use cases. More information can be found [here](/blog/prioritize-with-credit-score-proxy).

## Putting it all together

So what should you walk away with? Your CRM isn’t broken—it’s just incomplete. By layering in Faraday’s powerful and varied datapoints, you give your team the visibility they’ve always needed: who’s likely to book, who’s financially ready, and who’s worth a follow-up later.

The result? Smarter routing, cleaner funnels, and a marketing team that can finally prove its impact in booked jobs—not dashboards.

If you’re ready to make your CRM predictive, reach out or take a look at our [API docs](/docs/reference).

Faraday’s predictive data layer turns your home services CRM into a tool that shows which leads are most likely to book, helping your team focus on high-intent prospects and close more jobs efficiently.

Your home-services CRM needs predictive data to predict who will actually book

If you’re using predictive models, the number one question you’ll eventually ask is: _Are these predictions actually working?_

At Faraday, we love that question. We’re not here to spin our wheels—we want to generate tangible value for each and every client. That ethos is built right into our sales process: before any client signs on the dotted line, we offer to run a custom [holdout](/blog/holdout-testing-evaluate-prediction-accuracy) analysis on their data to prove our models can accurately predict how their customers behave. That’s **transparency** and **trust** from the very beginning.

But after those lines are signed and once those models are live, it can sometimes become harder for clients to get that same level of visibility. Sure we have a variety of other [explainability and transparency](/features/ai-safety-and-responsible-ai) features but here at Faraday, we aren’t really ones for leaving well-enough alone.

Trust in AI isn’t built on promises, it’s built on proof. So that’s why we’ve released **Efficacy Reporting**, a new feature in the Faraday dashboard that gives you yet another layer of insight into how your models are operating.

## What it does

Just like a holdout test, Efficacy Reporting measures the correlation between Faraday’s predictions and what actually happens. Reports are autogenerated for pipelines that have an [Outcome](/docs/abstractions/outcomes) in the payload (which must have eligibility explicitly configured).

> 📌 Note: Efficacy Reports require a live, regularly updated dataset connection. One-off uploads won’t generate meaningful reports, since we need to verify outcomes over time.

Each report breaks down performance by decile—the top 10% of predicted scorers, the next 10%, and so on—so you can quickly see how outcomes line up against model expectations. A strong model will show the classic “lift” shape: higher attainment in the top deciles, tapering off toward the bottom.

[![Model efficacy report in Faraday dashboard](/images/blog/efficacy-reports-in-dashboard.png)](/images/blog/efficacy-reports-in-dashboard.png)

## Where to find it

Efficacy reports live in the [Pipeline tab](https://app.faraday.ai/pipelines) of the Dashboard for any qualifying pipeline. As they’re auto-generated, there’s no extra setup required, and they’re tied directly to the models they evaluate, so you’re always looking at performance in context.

## Who it’s for and how to use it

Efficacy reporting is designed for the teams and leaders who rely on predictive insights to make decisions.

- Marketing and growth teams can use it to build confidence that their campaigns are aligned with reality.
- Data teams can use it to validate that models are holding up over time without pulling ad‑hoc reports.
- Executives can use it to get clear, ongoing evidence that predictive AI is delivering ROI.

In practice, efficacy reports let you validate Faraday models over time without extra manual work, spot potential performance issues early, and build confidence with internal stakeholders that predictions are worth scaling.

## What’s next for transparency at Faraday?

Efficacy reporting is just one step toward making the performance of your predictions as visible and explainable as possible. We’ll keep expanding this framework so you can always see, in concrete terms, how Faraday is driving real outcomes.

Because at the end of the day, predictive AI should be more than a [black box](/blog/the-ai-blackbox-is-a-liability) — it should be something you can measure, trust, and build on.

Efficacy reporting lets you see how well your Faraday models are performing in the real world by comparing predicted outcomes to actual results.

What's new: Efficacy reporting makes modeling even more transparent

If you’ve spent five minutes in the identity resolution world, you’ve probably heard someone throw out a number like _“we match 99% of records, guaranteed\!”_

Sounds great, right? Until you make campaign projections for your CMO that … don’t entirely pan out.

The thing is, flashy numbers might close deals, but do they drive value? Only if they’re accurate.

So sure, Faraday can hit 99% matching in certain conditions (we’ll get to that later). But does that mean it’s the number you should plan your campaign around? Well, that depends: on what identifiers you’re looking for, and what you put in to find them.

[![Jack Black magic number gif - school of rock](/images/blog/magic-number.gif)](/images/blog/magic-number.gif)

## Match rates aren’t one-size-fits-all, and they’re not a magic number

We’ve written before about why [match rates matter](/blog/identity-matching-makes-or-breaks-predictions) (because they’re the foundation of enrichment and predictions) and why [transparency and accuracy matter even more](/blog/is-your-id-match-rate-real) (because vanity numbers break downstream models). But here’s the thing: when it comes to match rates, there’s no universal “magic number.” Anyone selling a consistent 90%+ is probably doing one of three things:

- Counting duplicates (two bad emails for the price of one\!)
- Leaning on easy identifiers like household address and presenting that rate as always true
- Or—a personal favorite—straight-up inflating numbers.

In reality, match rates depend almost entirely on **what identifiers you hand us**. Think of it like cooking: bring fresh ingredients, and you’ll get a great meal. Show up with a half-empty pantry and a can of expired beans, and we can only do so much.

Moreover, it’s not just about whether something matches—it’s about _at what level_ they match. Faraday can resolve identities at the person or household level, and in some cases at a broader level like ZIP code or metro area. Which one matters depends entirely on your use case. Sending direct mail? Household-level matches are usually perfect. Calling or emailing individuals? Then person-level accuracy is what you need.

## So what can you actually expect?

With all of that context in mind, here’s what you can actually expect with Faraday:

- **Email-only datasets** → **\~60%+** individual match rate. Not bad, considering how many emails are throwaways, duplicates, or churned.
- **Add phone numbers** → things stabilize. Old numbers still get recycled, but you’re closer to **\~65%+**.
- **Bring addresses into the mix** → now we’re cooking. Household match rates can climb into the **80–99%**+ range depending on data quality, with **70–80%** at the individual level.

**And here’s an important point**: yes, on a clean dataset with addresses, we can technically match **99% of residences**. But do we advertise a 99% match rate?

No. Because that would be misleading. Of course we can recognize residence level matches on that dataset—we literally have the addresses—but what we’re actually proud of in that example is that we can match **\~80%** of _individuals_ on the same dataset. Is that less flashy? Sure. But is it accurate, actionable, and potentially value driving? Absolutely.

And that means you won’t be backtracking later when projects built on inflated numbers don’t pan out.

## Why this matters for CMOs

If you’re a CMO you already know where this is headed. Inflated numbers might look good in a pitch deck, but they don’t hold up when you’re presenting results to the board. Every padded “match” that isn’t real will:

- Waste spend in acquisition
- Poison your enrichment
- Undermine predictive models you rely on

That’s why Faraday takes the harder road: we dedupe, we verify, and we only count matches that are actually usable in production. For you, that means you can confidently plan campaigns and allocate budget knowing the foundation is solid—no backtracking when the numbers don’t pan out.

## So what’s the punchline?

At the end of the day, match rate isn’t about chasing the biggest number—it’s about trusting the one that will actually power your campaigns. Faraday’s approach gives you the clarity and reliability you need to move forward with confidence.

Stop planning campaigns on vanity numbers. Let's see what _actual_ match rates look like with your data.

Faraday delivers real, verifiable match rates—not inflated vanity numbers—so CMOs can confidently plan campaigns on accurate foundations that actually drive value.

Where's my 99% match rate!? Well, right here, but it’s complicated…

**See our complete guide:** [Predictive AI for banks and credit unions](/blog/banks-credit-unions-complete-context-guide).

---

Credit unions exist to serve their members and strengthen their communities. That mission-driven focus is what sets them apart—but it also means they don’t always have access to the same marketing tools, resources, or budgets as megabanks and fintechs. The challenge today is clear: how can credit unions compete against larger institutions armed with endless resources and cutting-edge technology?

If you’re leading marketing or member experience at a credit union, you know this pressure firsthand. You’re competing with organizations that have bigger budgets, flashier tech, and armies of data scientists. Meanwhile, your team is lean, your resources are stretched, and the board wants clear proof that every marketing dollar is driving measurable growth.

The issue is not that you don’t have data — you’ve got mountains of member information. The challenge is turning it into something actionable. And too often, your campaigns default to generic blasts: everyone gets the same credit card offer, the same auto loan promo, the same mortgage refi pitch. Members tune out, conversions lag, and you’re left struggling to show ROI.

The truth is simple: **without predictive product recommendations, you’re leaving millions on the table every quarter.**

## The problem: your members don’t take your offers

If you’ve gotten this far into the blog, you probably already know some of this pain:

- **Spray-and-pray campaigns don’t resonate.** When every member gets the same offer, most of them see it as noise.
- **Lean teams can’t build predictive models in-house.** You don’t have the bandwidth to mine your data and run experiments the way megabanks do.
- **Boards expect measurable ROI.** It’s hard to prove marketing’s value when campaigns feel like guesswork.

This all results in missed opportunities to deepen relationships and grow wallet share. Every generic email is a missed chance to connect with the right member, at the right time, with the right product. That means money is left on the table and members don’t get the services and products they actually need and want.

## The solution: predictive datapoints to show customer intent

While this is a complicated problem, Faraday makes the answer simple.

By combining your first-party member data with the [Faraday Identity Graph](/features/faraday-identity-graph), which features 1,400+ consumer datapoints on 240M U.S. adults and their households, we can generate custom predictive signals like: likelihood-to-open-auto-loan, likelihood-to-refinance-mortgage, or likelihood-to-apply-for-credit-card.

Most importantly, we can generate a predictive datapoint [that ranks which of your products each member is most likely to choose](/templates/next-best-offer). That way, instead of blasting every member with the same offer, you’ll know exactly which product resonates. Imagine seeing: Jan is 90% likely to open an auto loan, but only 40% likely to refinance her mortgage. You’d probably send her the offer for the auto loan over the mortgage refi — and as a result, she’d be much more likely to convert.

Our data solves various core challenges:

- **More relevance, less noise.** No one likes ads that don’t resonate, this way members see offers that actually matter to them.
- **Smarter use of lean teams.** You don’t need an in-house data science department to compete with megabanks, Faraday adds a predictive data layer into your existing tech stack.
- **Clearer ROI.** Campaigns become more efficient and easier to measure, making it simple to show the board real growth impact.
- **Transparent and compliant.** Every model is also fully transparent and auditable, so you can stay aligned with fair lending requirements.

Once the recommenders are built, delivery is seamless. Predictive datapoints flow directly into your CRM or marketing automation platform via API — no heavy IT project required. And because every model is fully transparent, you can align campaigns with clarity and demonstrate to the board how marketing spend translates into measurable member growth.

## How this looks in practice

Take [Advia Credit Union as an example](/blog/advia-auto-loan-growth). Like many credit unions, Advia wanted to deepen relationships with existing members and grow wallet share, but generic campaigns weren’t moving the needle. After linking their first-party member data with the Faraday Identity Graph, they used predictive product recommendations to identify which members were most likely to open an auto loan. That meant they could target offers only to the people who were truly interested — no wasted touches, no guesswork.

The impact was immediate. Within 30 days, Advia had already generated \$1.1 million in new auto loans. After 90 days, the campaign had delivered \$2.7 million in auto loans with an application rate more than four times higher than their baseline. And because every model was transparent and bias-checked, Advia was able to stay aligned with fair lending requirements while still giving members the personalized offers they actually wanted.

## Ready to stop guessing and start predicting?

If you’re tired of generic campaigns that don’t move the needle \- and tired of the board breathing down your neck for ROI \- predictive datapoints from Faraday can make growth measurable, compliant, and easy.

Faraday is built for teams exactly like yours. Ready to see how predictive data can unlock your next $2.7M? [Reach out.](/contact)

In a competitive market, credit unions need to make sure to maximize the value of every account holder. Without predictive product recommendations, they’re leaving millions on the table every quarter.

Tired of credit union members not taking your offers? Fix it with predictive data

When marketers start looking into lead scoring and predictive data, they often hear about "real-time" delivery and get pretty excited. And we get it. It has that instant-gratification appeal: the idea that you can get a prediction in the blink of an eye and make a decision on the spot. But here’s the catch—real-time isn’t always the smartest path. In fact, for most businesses and use cases, **batch predictions actually deliver richer, more useful results.**

At Faraday, we offer both methods—batch and real-time—because they serve different needs. But understanding when to lean on one versus the other can save you money, improve campaign efficiency, and make sure you’re not chasing speed at the expense of insight.

So when is "real-time" actually what you're looking for? Let's find out 👇

## Why batch predictions are the default

After that setup, you might be not surprised to learn that real-time actually isn’t the default way Faraday delivers data. 😱

That’s because batch processing is the real workhorse behind most predictive data and modeling. Think of batch like a doctor running a full health checkup versus just taking a pulse. Sure, the pulse might tell you something critical in a hurry — like Charlie isn’t dead — but it doesn’t actually reveal much else about what’s going on. For the doctor, both the pulse and the full checkup are useful, but they don’t serve the same purpose and can’t replace each other.

Like the full checkup, Batch doesn’t have the immediacy of the pulse check. But what it gives you instead is significantly more nuance and detail.

At Faraday, batch is our default delivery method because most of the time, you need the nuance and the deep context more than the immediacy. And beyond just getting more info about an individual, batch allows you to score an entire audience (your CRM, acquisition list, or customer database) all at once.

Batch is our default because it gives you the _complete picture_ instead of just a quick, decisive signal. Here’s what that really means in practice:

- Score entire populations you already know (like your CRM or customer list)
- Incorporate first-party and third-party attributes into your predictions
- Add multiple datapoints (including predictive datapoints) at once, rather than one-off signals
- Deliver results in flexible formats—CSV, Snowflake, S3—that plug into your systems
- Run on a custom schedule (daily, weekly, monthly) so the data is always refreshed at a rate that matches your use case and needs
- Enrich identity data more deeply, returning additional emails, phones, and addresses

Together this means that you’re not just scoring people, you’re understanding them in context (and scoring them even more). Want to prioritize segments of your CRM contacts? Assign reps to the right accounts? Qualify appointments with [Credit Score Proxy](/blog/prioritize-with-credit-score-proxy)? Batch makes all of that possible, while giving you flexibility and scale.

> **TLDR**: Batch gives you depth, flexibility, and a much more complete picture of your customers and prospects. That’s why it’s the default choice for most of our clients.

## When real-time makes sense

So yeah, batch is pretty great.

But there are moments when speed really does matter. Imagine a call center that has seconds to decide whether to accept or reject an incoming lead instantly in a [ping/post](/blog/so-what-is-a-ping-post-anyway) environment. Or a sales rep standing on someone’s porch with a tablet, trying to decide whether to make the pitch. In those scenarios, waiting for batch processing isn’t an option, or necessary. You don’t need the deep context, like the pulse, you just need a clear signal in a hurry.

That’s where [Faraday’s Lookup API](https://faraday.ai/docs/features/lookup-api) comes in. By pulling from our 3rd party data and pre-scored national populations, we can give you an accurate prediction on the fly. It’s perfect for situations like:

- [Bidding in ping/post systems](https://faraday.ai/blog/what-happens-when-predictive-AI-meets-real-time-lead-filtering) like with [ActiveProspect's](https://activeprospect.com/) [LeadConduit](https://activeprospect.com/leadconduit/)
- [Scoring addresses during door-to-door sales](https://faraday.ai/blog/salesrabbit) with tools like [SalesRabbit](https://salesrabbit.com/)
- Assessing likelihood to sign up on a landing page in real time

**The key tradeoffs:**

- Data must be sent via POST request to a specific API endpoint
- Egress path is narrow: you hit us, and you get the datapoint back — no [Snowflake](https://www.snowflake.com/en/), [S3](https://aws.amazon.com/s3/), or warehouse delivery
- Requires careful configuration of scope populations in advance
- You can only get one datapoint at once — giving you a less complete picture of your target

> **TLDR**: Use the Lookup API when you need an instant signal about someone you just met — like a new lead, form fill, or sales visit. It’s fast, simple, and scoped — but limited to national data only.

## How theses look in the ✨real world✨

Both delivery methods generate real value for Faraday customers—but in very different ways.

Take real-time first: when a regional home-renovation company saw conversion rates slipping by \~21% during peak call‑center traffic, Faraday’s instant lead prioritization turned things around. By feeding a custom “likelihood‑to‑convert” datapoint straight into their CRM, reps focused on high‑quality leads right as they arrived. The impact? [A 13% lift in conversion during high‑traffic periods and a 22x monthly ROI](/stories/call-center).

Now contrast that with batch: [Bee’s Wrap](/stories/bees-wrap), a fast‑growing DTC brand, used Faraday to connect its [Shopify](https://www.shopify.com/) order data to the Faraday Identity Graph and run persona modeling at scale. The batch analysis surfaced detailed personas and geographic propensities that became the backbone of their Target retail pitch.

The outcome? Placement in 550+ stores and an initial six‑figure wholesale order.

Together, these examples show the complementary strengths: batch delivers the depth and context needed for strategic moves, while real‑time provides the immediacy required to win in the moment.

## So which is better?

Well here’s the thing, batch and real-time aren’t competitors — they’re complements. Two specialized tools to keep in your belt for different situations. Batch is about depth, flexibility, and context. Real-time is about speed and decisive action. Used together, they give you both the strategic insights to plan campaigns and the tactical edge to win in the moment.

At Faraday, we deliver both. Most of our clients rely on batch as their foundation, completing identities, enriching consumer data, and providing rich predictive datapoints for entire audiences on a schedule that keeps campaigns humming. Then, when a moment calls for immediacy, our Lookup API steps in to provide the instant signal. The combination ensures you’re never forced to choose between nuance and speed; you get both, working in concert.

Want to learn more about how these can work for you? [Come say hi](/talk-to-sales).

Faraday offers both batch and real-time predictions, helping brands balance the depth and context of large-scale scoring with the speed and decisiveness of instant lead insights to maximize ROI.

Real-time predictions vs. batch data enrichment: what you need to know

If you’ve ever sat through a pitch about data or identity resolution, you’ve probably heard at least one big shiny number: _“We can match 80% of your records\!”_

But here’s the catch: an 80% match rate is meaningless if those matches aren’t real people you can actually contact or use for modeling. False positives don’t just pad a report — they waste budget, erode trust with your customers, and poison downstream predictions and outreach. Worst of all, they mean you can’t plan effectively.

At Faraday, we’ve spent over 13 years refining a simple philosophy: every match should be a record you can confidently act on. And that philosophy isn’t just talk — it’s baked into how our system works day in and day out.

## Faraday delivers matches you can trust

Faraday’s identity resolution system is built to prioritize confidence over vanity stats that sound good on paper but fall apart in real campaigns. This results in a system designed not just to maximize match rates, but to ensure that when we say _“this is the person,”_ you can act with confidence.

Here are some steps we take to ensure this outcome:

**Deduplication and householding**  
We don’t just stitch data together — we actively dedupe records and recognize when multiple identifiers belong to the same household. That means one clean, accurate profile instead of duplicates inflating your numbers.

**Handling life changes**  
People move, change names, swap emails. Most systems struggle to keep up. But the [Faraday Identity Graph](https://faraday.ai/features/faraday-identity-graph) (FIG) is built on longitudinal data, so we can track those shifts and keep matches accurate over time.

**Built-in scale and enrichment**  
Every match taps into our FIG, which contains 1,400+ datapoints on 240M U.S. adults and their households. That means when we confirm a match, you’re not just identifying someone — you’re enriching them with trustworthy, actionable context for campaigns and predictions.

**Multiple paths to identity**  
Matches aren’t only about exact email or address. We account for nicknames, partial addresses, and alternative identifiers — always with strict rules to avoid false positives. And thanks to our newest update, we’ve added yet another path to identity. 👇

## Our latest update

As one of our incredible Data Engineers, Tom Caruso put it in our internal Slack announcement for this update:

_“This new update is cool because it uses the kind of context you can only get from dealing with national populations for years. The key insight we observed is that, beyond the traditional means by which matches are generated — like name \+ address or email \+ phone — many people in the U.S. have names that are actually unique within their geographic area. For instance, while there might be a dozen John Smiths in Los Angeles, there’s only one Tom Caruso in ZIP code 05405, and only one Ben Rose in all of Vermont.”_

By compiling these unique name \+ location pairs from the Faraday Identity Graph, we added a new matching source that kicks in when those other identifiers aren’t available. That means we can now match records confidently even when key fields like email, phone, or full address are missing.

This particularly benefits clients working with partial contact data — like name \+ email \+ state only — or with sparsely populated address fields, which are usually Faraday’s strongest component in identity resolution. By recognizing these unique combinations, Faraday can salvage records that would otherwise be lost, adding millions of verified people to campaigns. And because we only apply this method when a name-location pair is truly unique, the update strengthens our core promise: higher match rates without sacrificing confidence.

This is the critical distinction. Other vendors inflate match rates by accepting ambiguity — counting multiple possible matches as wins. Faraday’s approach adds coverage only when we’re certain it’s the right person.

**TLDR:** Faraday delivers identity matching you can trust. No noise, no padded numbers — just more real people you can actually market to.

## The proof in the numbers

Early testing of this update had already delivered major gains in match rates for multiple clients.

One DTC optics brand immediately saw an additional \+1.5 million matches on an existing dataset and another client, a performance marketing company, gained \+1 million immediately.

Every single match of these millions translates into fewer wasted dollars, more reliable models, and campaigns they could actually plan against with confidence.

For you, that’s the difference between “numbers on a slide” and ROI you can feel in the field and in the boardroom.

## Why marketing leaders need better matches

Identity resolution isn’t just about hitting a high match rate — it’s about whether you can trust those matches to guide your spend and strategy. That’s what sets Faraday apart.

- **Confidence at the core:** Every match is one you can confidently act on. We don’t inflate numbers with guesses or maybes.
- **Proven over time:** With 13+ years of experience and longitudinal data, we capture life changes and long-term patterns that competitors miss.
- **Built-in scale:** Our Identity Graph spans 240M U.S. adults and households, enriched with 1,400+ datapoints and continuously updated.
- **Innovation in motion:** Updates like unique name \+ location matching show that even after more than a decade, we’re still pushing for incremental lifts that flow straight to ROI.
- **Better outcomes downstream:** Trustworthy matches mean stronger predictions, more efficient campaigns, and marketing dollars that work harder.

## We’re always improving

This update is just the latest example of how Faraday treats identity resolution as a living system, not a one-and-done feature. Because of our longevity and ongoing innovation, our clients can trust their matches to be accurate today — and better tomorrow.

Identity resolution isn’t glamorous. But every percentage point of improvement falls directly to the ROI bottom line. And that’s what makes it worth obsessing over.

Stop guessing and start planning with confidence. [Get in touch with our team](/talk-to-sales).

Faraday delivers identity matches you can actually trust—clean, accurate, and enriched with real consumer context—so every record drives smarter predictions, campaigns, and ROI.