## What is customer churn?

Churn refers to the point at which a customer decides to stop using a product or service your company offers. It can happen for a variety of reasons — poor experience, irrelevant outreach, better offers elsewhere — but the bottom line is always the same: a churned customer brings no revenue to your business. No brand can eliminate churn entirely, but the right strategy can reduce it meaningfully and protect the customers worth keeping.

## Why churn prevention deserves your attention

The math on retention is hard to argue with. It costs [5–6x more](https://www.americanexpress.com/en-us/business/trends-and-insights/articles/retaining-customers-vs-acquiring-customers/) to acquire a new customer than to reactivate an existing one. You have a [60–70% chance](https://blog.hubspot.com/service/statistics-on-customer-retention) of selling to a current customer versus a 5–20% chance with a new one. And returning customers spend [67% more](https://www.business.com/articles/returning-customers-spend-67-more-than-new-customers-keep-your-customers-coming-back-with-a-recurring-revenue-sales-model/) on average than first-timers.

Despite this, [44% of businesses](https://www.semrush.com/blog/customer-retention-stats/) prioritize acquisition, while only 18% focus on retention. That's not a strategy problem — it's a data problem. Most brands don't have the context they need to know which customers are about to leave, which ones are worth fighting for, or what it would take to keep them.

That's exactly what machine learning — and the right consumer data — solves.

## What causes customer churn?

Understanding why customers churn is the first step toward preventing it. Common culprits include poor customer service, generic outreach that misses the mark, offers that don't match the customer's actual profile, or simply acquiring the wrong customers in the first place — discount shoppers who were never going to stick around past the promo window.

The challenge is that first-party data alone rarely tells the full story. You know how customers interact with your brand, but you don't know who they are — their financial situation, life stage, household makeup, or likelihood to respond to a given type of offer. That missing context is what makes churn hard to predict and even harder to prevent.

## Churn prevention strategies

Effective churn prevention starts with data and compounds from there.

### Mitigate churn higher in the funnel

Not all churn happens at the end of the journey — much of it is baked in at acquisition. Discount shoppers, casual one-time buyers, and customers acquired through generic targeting are far more likely to leave. Knowing what your best customers look like lets you adjust acquisition spend toward people who are more likely to stick. And if demographic analysis reveals that a specific customer profile churns consistently, you have two options: reposition your brand to serve them better, or stop targeting them altogether. Either way, the insight compounds — better acquisition targeting means lower churn before retention efforts even enter the picture.

### Use data to identify causes of churn

Segmenting customers into meaningful subgroups reveals patterns: what do churners have in common? What distinguishes your best customers from the rest? Machine learning accelerates this analysis dramatically — instead of manually combing through data, you get a predictive model that surfaces those patterns automatically. For a deeper look at how propensity modeling works in this context, see [our guide to propensity models for consumer brands](/blog/propensity-to-buy-machine-learning).

### Create personal and frictionless experiences

One bad experience — an irrelevant email (or too many emails), a missed touchpoint, an offer that doesn't fit — can be enough to tip a loyal customer toward leaving. In high-consideration categories like home services, operational friction is a major culprit: needing to set an appointment, going through a credit check, or simply not getting a call answered can be the moment a customer decides to look elsewhere.

## How machine learning enhances churn prevention

All of the strategies above depend on knowing your customers well enough to act intelligently. That's where most teams hit a wall: the data they have on their own customers isn't enough to drive real personalization, and manual analysis can't keep pace with the volume of decisions a modern retention program requires. Machine learning, paired with rich consumer data, changes the equation on all three fronts.

### Richer segmentation with the Faraday Identity Graph

Traditional segmentation is limited by what you know from your own data. Machine learning expands that picture by unifying your first-party data with the [Faraday Identity Graph](/features/faraday-identity-graph) (FIG) — over 1,500 consumer data points across 240M U.S. adults and their households, sourced from the best data vendors available. FIG data spans demographics, psychographics, property, financials, life events, and more.

The result: your customer Jane Doe stops being just "purchased a dog toy last month" and becomes a fully drawn profile — dog owner, parent, household income over $200k, prefers in-store shopping. That's the difference between generic outreach and an email that genuinely lands.

### Churn prediction at the individual level

What if you could know a customer was about to leave before they even knew they were considering it? Machine learning churn scores do exactly that — predicting, on an individual basis, how likely each customer is to churn. That means you can proactively reach out to at-risk customers with personalized retention offers, rather than reacting after they've already gone quiet.

### Churn modeling in reverse: predicting loyalty and return

Most churn models are built to surface risk — who's about to leave. But the same modeling framework can be flipped to answer a different question: who is likely to *stay*, or who is likely to *come back*?

This inverse approach is especially valuable for brands with long sales cycles or inconsistent buying patterns. A tire retailer, for example, can't expect monthly purchases — but they can predict which customers are likely to return when they're ready to buy again. An eyewear brand can identify who's a loyal repeat buyer versus a one-time purchaser, even if those visits are years apart.

For subscription businesses, likelihood-to-remain scores add a layer of nuance that pure churn risk can't capture: instead of only flagging who might cancel, you can identify who's deeply engaged — and let that shape how you invest in them. A high likelihood-to-remain score is a signal to reward loyalty, not just a reason to deprioritize outreach.

The mental model shift matters here. A high score is a good thing — it tells your marketing and sales teams exactly who to lean into.

### LTV prediction: knowing who's worth fighting for

Not every at-risk customer deserves the same retention investment. LTV prediction adds a second dimension to churn scores — so you can define cutoffs for how deep a discount or how much outreach effort is actually worth it. A customer with a high churn score and high predicted LTV is worth a personalized win-back campaign. One with a high churn score and low LTV might be fine to let go.

## How Faraday predicts and prevents churn

Here's how the pieces come together in practice.

### Connect your data and define your customer groups

Start by connecting your customer data to Faraday and defining two groups: your current active customers and your churned customers. These become the foundation for everything that follows.

### Build personas to understand who your customers really are

With FIG data layered in, Faraday generates personas — distinct customer segments defined not just by purchase behavior but by the full consumer profile behind each person. Age, lifestyle, shopping habits, financial indicators — but also how long someone has been in their home, what sports they follow, what causes they donate to, and hundreds of other signals that marketing teams don't typically think to include in a churn model. That's exactly what makes them powerful: the nuances that make personalization feel personal.

### Generate churn predictions

Define your predictive outcome: who is likely to move from your active customers group into your churned customers group? Faraday builds a custom model on your data, scores every customer, and surfaces the results alongside their persona. The score tells you who is likely to churn — the persona tells you how to reach them. Together, they turn a list of at-risk customers into a targeted, personalized retention campaign.

### Deploy predictions to your stack

Once your churn scores are live, push them wherever you need them — your CRM, email platform, data warehouse, or any downstream tool. Your retention campaigns now have the intelligence to target the right people with the right message at the right time. For example, one fitness brand plugged Faraday scores directly into [Klaviyo](https://www.klaviyo.com/) and bucketed their members into three tiers: green (very likely to remain), yellow (on the fence — intervene now), and red (very unlikely to remain). That simple structure gave their sales and marketing teams a clear, actionable playbook — discounts, messaging, and campaigns calibrated to exactly where each member stood.

### Scale prevention upstream: find better customers to begin with

The most durable churn strategy is acquiring customers who were never going to churn in the first place. Use the same modeling framework — but flip the attainment cohort to your best customers instead of your churned ones — and you get a lookalike acquisition model that finds more people like the ones who stick around. And the cycle compounds: the more you learn about who churns and who stays, the sharper your models get — which means better retention, better acquisition targeting, and a clearer picture of your best customers over time. Churn modeling isn't a one-time fix; it's a continuously improving asset.

## Real results: how a subscription box brand cut churn and boosted revenue

A leading subscription box brand came to Faraday with a familiar problem. They were manually assigning monthly shipments using hand-written rules — logic informed by customer preferences, but still rigid, prone to human bias, and impossible to scale across a growing catalog.

They replaced those manual rules with Faraday's AI-powered recommendation engine, which uses a two-sided matching approach: scoring both customer affinity (how likely a specific person is to engage with a product) and product performance (how well that product has performed with similar audiences). The result is smarter pairings optimized for both personalization and retention — at scale.

The results from a rigorous A/B test:

- **\~5% lift** in revenue per customer  
- **\~3% reduction** in churn rate  
- **5x+ monthly ROI** generated with Faraday  
- **Four consecutive months** of algorithmic recommendations outperforming their manual rules

And because the models improve as more data flows through them, these aren't ceiling numbers — they're a starting point. [Read the full case study.](/blog/monthly-subscription-box-uses-two-sided-recommendations)

## Frequently asked questions

**How much first-party data do I need to build a reliable churn model?**

It varies by industry and subscription type, but in most cases a few months of customer and churn event history is enough to get started. FIG data fills in the gaps on the consumer side — so even if your first-party data is thin, the model has strong signals to work with. Faraday will tell you upfront if your data isn't sufficient.

**What's the difference between churn prediction and LTV prediction — do I need both?**

They answer different questions. Churn prediction tells you who's likely to leave. LTV prediction tells you who's worth keeping. Used together, they let you prioritize retention spend where it actually pays off — high churn risk, high LTV — rather than treating every at-risk customer the same way.

**Can churn prediction work for non-subscription businesses?**

Yes. The subscription context is common, but the same approach applies anywhere customers can stop buying — retail, home services, financial services, SaaS. If you have repeat purchase behavior and a meaningful gap between your best and worst customers, churn prediction adds value.

**How does this connect to acquisition?**

Directly. The same models that identify your highest-LTV, lowest-churn customers can be flipped into lookalike acquisition models — helping you find more people in the market who match that profile before they ever become a customer. Retention and acquisition intelligence, same platform.

**Can Faraday predict who's likely to stay, not just who's likely to leave?**

Yes — and for many businesses, this framing is more actionable. Likelihood-to-remain scores flip the model: instead of surfacing risk, they identify your most loyal customers so you can reward and invest in them accordingly. For businesses with long sales cycles or inconsistent buying patterns — think automotive, eyewear, or home services — likelihood-to-return scores serve a similar purpose, flagging who's likely to come back even if purchases are infrequent. Both approaches run on the same modeling framework as churn prediction.

## Ready to reduce churn?

If you want to talk through how predictive churn modeling could work for your business, [talk to a Context Consultant](https://faraday.ai/contact). Or if you'd rather get started on your own, [try it on buy.faraday.ai](https://buy.faraday.ai).

Learn how consumer brands use machine learning and the Faraday Identity Graph to predict churn, identify loyal customers, and build more profitable retention and acquisition strategies.

Customer churn prediction: a complete guide for consumer brands

Predictive lead scoring uses machine learning to rank your leads by their likelihood to convert — so your team knows who to call, when to reach out, and how to engage before a single conversation starts. It's the single highest-leverage place most B2C teams can add customer context to their sales motion. This guide covers how it works, how the best teams use it, and what it takes to build a system that moves the needle.

## What is lead scoring?

Lead scoring ranks leads by their likelihood to convert so sales and marketing teams can focus on the right ones. But not all lead scoring is created equal — there are two fundamentally different ways to do it, and the choice shapes everything downstream.

**Traditional lead scoring** models often rely on a point-based system where humans determine the lead scoring criteria—typically a combination of basic demographic information and online behaviors such as pageviews, email engagement, or form submissions—and assign points to leads when they meet it.

While these point-based lead scoring models can help marketing and sales teams prioritize their efforts, they come with significant drawbacks. Lead scoring criteria are often arbitrarily decided, resulting in lead scores that may not accurately reflect a lead's true potential. Additionally, because they rely heavily on first-party engagement data, lead scores often end up being a lagging indicator of a lead's conversion likelihood.

**Predictive lead scoring** uses machine learning trained on your actual conversion outcomes. The model learns which combinations of attributes — first-party behavior plus third-party consumer context — predict that a lead will convert. With the right dataset, it can score a lead's likelihood to buy before they ever visit your site or open an email.

![Image of Faraday model report features table](/images/blog/product/feature-importance.png)

Note: the image above illustrates important features surfaced in [Faraday model reports](/blog/faraday-model-reports).

| Feature | Traditional lead scoring | Predictive lead scoring |
| --- | --- | --- |
| **Method** | Rule-based (manual point systems) | Machine learning trained on past outcomes |
| **Data used** | Basic demographics, behaviors, form fills | Hundreds of behavioral, demographic, and contextual signals |
| **Scalability** | Manual tuning required per campaign | Fully automated, adaptable, and always learning |
| **Accuracy** | Assumptions-based, subjective | Outcome-driven, empirically validated |
| **Maintenance** | Static, often neglected | Continuously improved via retraining |
| **Transparency** | Clear but simplistic | Advanced but explainable (e.g., model reports) |

Predictive lead scoring is one application of a broader practice called [propensity modeling](/features/customer-behavior-predictions) — predicting any future customer behavior, not just conversion. The same techniques power churn prediction, LTV modeling, and next-best-offer recommendations.

## What you need for great B2C predictive lead scoring

The best predictive lead scoring doesn't rely on a single source of data. It combines two things: the real-world context of who your leads actually are — their financial situation, lifestyle, and household — and the actual engagements they've had with your brand. You bring one half. Faraday brings the other.

### What you bring

- **Your lead records** (identity data) — names, addresses, phone numbers, and emails — the starting point for connecting your data to real-world consumer profiles.
- **Your conversion history** (transactional data) — who became a customer, when they converted, and what they paid — defines the cohorts the model learns from: customers, leads, stale leads. Without clearly labeled outcomes, the model has nothing to learn from.
- **Your engagement history** (engagement data) — pageviews, email subscriptions, free trial signups — captures how your leads have interacted with your brand and how those behaviors relate to conversion.

### What Faraday brings

Before any modeling happens, Faraday completes your identity data — filling in the gaps in your CRM records with missing addresses, phone numbers, and emails. This alone maximizes your match rates and expands your addressable audience.

But that's only where it starts. Once your records are complete, we combine your first-party data with our third-party consumer records — 1,500+ attributes across demographic, financial, property, and lifestyle dimensions, drawn from the **[Faraday Identity Graph (FIG)](/features/faraday-identity-graph)** covering 240M U.S. adults — to build models trained on your real customers and outcomes.

The result is [custom predictive data](/blog/three-types-of-data-that-power-smarter-decisions): bespoke data points generated specifically for your business. Lead scores are one example. Others include next-best-offer predictions, churn likelihood, and lifetime value estimates.

## How B2C teams use predictive lead scoring

Predictive lead scoring is an optimizer for your sales motion at every stage — giving reps the context to know how, when, and where to engage every lead. Scores deploy in real time into your CRM, ad platforms, and routing tools, so the intelligence shows up where your team already works. Suppression and rejection are part of the toolkit, but they're the floor, not the ceiling.

Here's where the highest-leverage use cases sit, in order:

- **Lead prioritization:** This is the use case that produces the biggest gains. Predictive scores let you tier your leads and route the strongest ones to your best reps, with the most aggressive follow-up cadence. Mid-tier leads move into the right workflow. Lower-fit records shift into nurture instead of taking up agent time. American Standard worked with Faraday and outboundIQ to do exactly this — moving lead scoring from a top-of-funnel blocker to a full-funnel routing optimizer. Their [contact rate jumped from 5.56% to 20%, and high-score leads converted at 3x the rate of others](/blog/how-we-changed-american-standards-mind). [Momentum Solar took the same approach](/stories/momentum-solar) and improved call center conversions by 33%. These patterns play out across consumer verticals, but they're especially powerful in [home services and other industries](/solutions/lead-management) where contact center economics make every dial count.

- **Product recommendations:** If your predictive system supports product-specific modeling, [you can predict which products or services a lead is most likely to purchase](https://faraday.ai/templates/next-best-offer). Reps and marketing systems use those scores to make informed recommendations across every touchpoint.

- **Custom incentives and discounts:** Instead of blanket promotions, predict the likelihood of leads converting to high-LTV customers and [offer targeted incentives only where they'll move the needle](https://faraday.ai/templates/adaptive-discounting). This protects margin on customers who would have bought anyway and concentrates spend where it matters.

- **Lead suppression:** [Rank your leads, set a suppression threshold, and stop spending on the bottom tier](https://faraday.ai/templates/lead-suppression) — whether that's direct mail, paid follow-up, or sales effort. Useful, but a much smaller lever than prioritization.

- **Lead rejection:** If you buy leads from generators or aggregators, you can use predictive scoring to reject leads below a minimum conversion probability before they ever hit your CRM. Saves money, but it's the narrowest application of the technology.

Reject and suppress when it makes sense. Prioritize everywhere else.

## Why Faraday

Most data vendors give you a snapshot of who someone appears to be today. FIG includes longitudinal data spanning years of each individual's history — something no other source in the market provides. A lead who looks identical in a snapshot today may have a very different trajectory, and trajectory is what actually predicts conversion. That's what sets Faraday apart.

It's also production-ready out of the box. The data is normalized to meet modern data science requirements, so there are no expensive licensing negotiations and no raw data to clean and restructure before you can use it.

The data is also responsibly sourced — no third-party cookies, no social scraping.

## Faraday vs. CRM-native scoring

Your CRM only knows what's in your CRM. Klaviyo, HubSpot, and other platforms with built-in predictive scoring are limited to your first-party data — how leads have behaved in your system — because that's all they can see.

Faraday trains on the combination of your first-party data and 1,500+ FIG consumer attributes, identifying patterns about who your leads actually are in the real world: their financial situation, lifestyle, household, and trajectory over time. That's the difference between scoring based on what someone did on your website and scoring based on who they actually are.

## FAQ

#### Isn't predictive AI just a black box?

Not at Faraday. Every model includes explainability breakouts that document the data used, what the model was trained to predict, and how well it performed. Feature importance scores show which attributes are driving conversion likelihood, and segment insights break down what's working in human-readable terms. For a deeper technical dive, see [Seamus Abshere's post on why first-party data and RFM isn't enough](/blog/why-first-party-data-and-rfm-is-not-enough).

#### What if I don't have enough first-party data?

Most brands have more than they think. And when first-party data is genuinely sparse, FIG fills in the gaps with [verified third-party context](/blog/identity-matching-makes-or-breaks-predictions). Models can learn from behavioral and contextual signals well beyond what's in your CRM. Predictive lead scoring doesn't require perfection — it needs patterns.

#### What are the biggest challenges of running predictive lead scoring well?

Two things. First, data quality: the accuracy of your predictions depends on the completeness and consistency of the data you feed the model — particularly identity data, which determines how well your records match against third-party consumer profiles. Second, retraining: customer behavior shifts over time, and models need to be refreshed regularly to stay accurate. A good platform handles both of these for you.

#### How long does it take to get predictive lead scoring up and running?

For most B2C teams using Faraday, a working predictive lead scoring model can be deployed within days, not months. The longer pole is usually data preparation on the client side — getting clean, well-labeled historical lead and customer data into a usable format. Once that's in place, model building, validation, and deployment happen quickly.

## Ready to put context behind every lead?

If you're a B2C team that wants to see predictive lead scoring in your own data, there are two ways to start:

- **Self-serve:** [Get started on buy.faraday.ai](https://buy.faraday.ai) — upload your records and get scored leads flowing into your stack in minutes.
- **Enterprise:** [Talk to a Context Consultant](/contact) — we'll walk through your funnel, your data, and what's possible.

A practical guide to how predictive lead scoring works, what it takes to do it well in B2C, and how the best teams use it to optimize their entire sales motion.

How predictive lead scoring works — and why it outperforms traditional methods

"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](http://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

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 datapoints 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)](http://faraday.ai/catalog) — a comprehensive, continuously maintained dataset covering **240M US consumers and their household, with over 1,500 verified, pre-cleaned, and feature-engineered datapoints.** 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 datapoints 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 datapoints across every category. You can browse everything that's available at [faraday.ai/catalog](https://faraday.ai/catalog) or purchase data directly at [buy.faraday.ai](https://buy.faraday.ai).

## How data enrichment actually works

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

1. **Select your datapoints.** 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 datapoints 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](http://buy.faraday.ai).

Faraday's data enrichment appends verified third-party datapoints 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.

Deep dive: FIG v2 data freshness and release pinning

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

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

## What goes wrong when the data is wrong

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

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

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

## What the right data actually looks like

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

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

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

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

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

## What this actually changes

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

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

## Ready to get the right data?

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

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

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

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

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

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

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

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

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

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

## What Faraday does

Faraday enriches incoming identities against the [Faraday Identity Graph](/catalog) — 1,500+ consumer attributes across 240 million U.S. adults. You send us PII. We return a rich consumer profile, appended to your customer record, ready to pass directly to your agent or model.

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

## What this means for what you're building

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

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

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

## The bottom line

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

Faraday fixes that with a single API call — enriching every incoming identity with 1,500+ consumer attributes before your agent says a word. No data sourcing, no infrastructure build, no long procurement cycle. Just context, from day one.

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

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

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

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

## What "historical data" actually means

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

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

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

## Why this matters most for modeling

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

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

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

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

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

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

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

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

## What's coming next: change detection

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

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

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

## Try it this month

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

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

Deep dive: FIG v2 historical data and what it means for your models

[![diagram showing how marketing stacks have changed in 2026](/images/blog/bowties-diamonds/bowties-diamonds1.png)](/images/blog/bowties-diamonds/bowties-diamonds1.png)

Something very interesting has happened with the overall topology of a cutting-edge brand’s martech stack. Over the past year or so the pattern I’m seeing among leading brands (including many of our own clients) is a shift from “bowtie”-style stacks, like you see at the left, to “diamonds” like on the right. Here’s how and why this revolution is underway.

## The 2010s martech stack: Hub-and-spoke data warehouse models

Throughout the 2010s it was fashionable (and useful) to imagine any technology-forward organization’s stack as a “hub and spoke” with the data warehouse in the middle. Every component would pull the data it needed from the warehouse and, in return, supply the warehouse with data that other components might need.

In practice this was never quite so easy. Not every software product a brand needed to actually engage its customers natively talked to the warehouse. Sometimes this was for straightforward reasons—it’s hard to build and maintain countless native connectors to every “source of truth” across every vertical—and other times the end product saw the warehouse as a threat: they wanted to own the workflow _and_ the data.

[![diagram showing hub and spoke style marketing stack](/images/blog/bowties-diamonds/bowties-diamonds4.png)](/images/blog/bowties-diamonds/bowties-diamonds4.png)

## The early 2020s: ETL tools and the bowtie marketing stack

In the end, the market spoke, and robust Extract Transform and Load (ETL) tools like [Fivetran](https://www.fivetran.com/) became the crucial connector to feed data into the warehouse. Complementary Reverse ETL tools like Hightouch served the natural inverse role: delivering data from the warehouse back to the engagement tools marketers actually use.

It’s easy to see how central this ETL/RETL role became in the stack’s topology. So much so, in fact, that the “mental model” would often place it, rather than the warehouse itself, in the middle. That’s how we got the bowties of the early 2020s.

[![diagram showing bowtie style marketing stack](/images/blog/bowties-diamonds/bowties-diamonds2.png)](/images/blog/bowties-diamonds/bowties-diamonds2.png)

## 2026 martech stacks: Middleware and the diamond data model

The centrality of the ETL/RETL position did not escape its occupants’ notice. In many cases it was the _only_ tool with visibility on 100% of all of the organization’s data. The incumbent ETL/RETL vendors started to spread their wings, offering value-added services within this prime location—see Hightouch’s decisioning product and its latest agentic offering as examples. Other vendors like [OfferFit](https://www.braze.com/resources/articles/what-is-offerfit) (now a B[raze](https://www.braze.com/) product) sprung up in this “middleware” zone, leveraging data from the warehouse to orchestrate workflows within the engagement platforms.

Faraday, of course, thrives here as well, adding context to an organization’s data on its way to the engagement tools. Products like [cube](https://cube.dev/) are starting to imagine the middleware zone itself as multi-layered, offering normalized semantics that make its neighbors’ jobs easier.

The “source of truth,” meanwhile, at the bottom of the diagram, has crystallized in my mind to a single point. As Scott Brinker so eloquently [put it](https://newsletter.chiefmartec.com/p/the-saas-moats-are-crumbling-but-the-opportunity-is-bigger), once you “strip away the features, the interfaces, the proprietary data lock-in, the ecosystem lip service,” these systems essentially resolve to the same thing: the organization as represented by _data_.

[![diagram showing modern marketing stack](/images/blog/bowties-diamonds/bowties-diamonds3.png)](/images/blog/bowties-diamonds/bowties-diamonds3.png)

## The future of martech: AI agents and constellation structures

As the agentic paradigm deepens its hold on our industry, this diagram—already a far from perfect reflection of reality—will inevitably shift again. Where it goes is anyone’s guess, but I do believe it will be far less linear. Certainly agents will orchestrate everything, but I imagine their purview will extend beyond unidirectional customer engagement. A “constellation” structure is likely to emerge, with agents gathering data from every interaction and disseminating learnings throughout the stack.

The result may look eerily familiar — a modern recreation of the hub-and-spoke structure from a decade ago. Can’t wait to see where we go from there.

Martech has evolved from warehouse-centric “hub-and-spoke” systems to ETL-driven “bowtie” stacks and now to middleware-powered “diamond” models, with the next shift likely toward AI agent–orchestrated, more fluid “constellation” architectures.

Bowties to diamonds: 2026’s new marketing stack

**Update, May 2026:** This feature has been deprecated. The ability to interact with your Faraday personas and predictions is now available in a more powerful form through our [MCP server](https://faraday.ai/docs/features/mcp), which is currently in beta and gives AI agents real-time access to your Faraday data and predictions directly. We recommend exploring the MCP as the successor to this feature.

---

In 2026, we all know that effective marketing requires personalization.

But this wasn’t always accepted as fact. Years ago, one of our earliest clients—[a major home goods brand](https://www.bollandbranch.com/)—wanted to confirm the premise for itself. So they ran a rigorous experiment to find out just how much value Faraday’s predictive [Personas](/docs/abstractions/persona_sets) could generate. They committed to a strategy that most marketing teams of that era only dreamed of: instead of sending one generic email to their entire list, they created unique content variants for every single campaign. They had their team write a generic control version, as well as three hyper-tailored versions specifically crafted for the distinct vocabularies and motivations of their unique Personas.

The results were undeniable: [A 30% lift in conversion](/stories/boll-and-branch).

By every revenue metric, the experiment was a massive success. Yet, despite the clear upside, they didn’t stick with the new system very long.

Why would a business walk away from a 30% lift? The answer is simple: because the manual labor required to understand what makes each Persona special, and then write, edit, and stage unique versions of every single email, was simply too hard and tedious to sustain. The friction of the process outweighed the lift in revenue.

This is the paradox of personalization: The more you tailor your messaging, the better your results—but the more work you have to do.

## Talk to your Personas

We built a new chat interface to solve that tedium.

Faraday has always been great at helping you understand _who_ your customers are. We have a range of tools—from [AI-generated summaries to salience trait detection](/blog/new-release-trait-salience)—that help you investigate exactly how they differ. But at the end of the day, you still have to figure out how to apply those insights and actually _talk_ to them.

Now, you actually can. Our new chat feature provides a familiar AI interface, but we preload the chatbot with all the in-depth details about your Personas. You don't have to teach the AI who your customers are. It already knows!

However, the agent does _not_ natively know anything about your brand. This is by design—it allows you to give the chat whatever context, guidelines, or quarterly goals you want so you get highly relevant outputs. You just chat!

[![Chat with personas feature in Faraday's dashboard](/images/blog/persona-chat/persona-chat-unfilled.png)](/images/blog/persona-chat/persona-chat-unfilled.png)

## Power up your marketing

This feature allows you to move from passive analysis to active execution immediately. Here is a sneak peek at a few ways you can start using it today:

- **Match Personas to campaigns:** Describe a specific product launch or holiday promotion and ask the chat which Persona in your set is most likely to respond.
- **Brainstorm tailored offers:** Identify an underperforming segment and ask the AI to suggest specific promotions or incentives that align with that Persona’s unique motivations.
- **Generate personalized copy at scale:** Paste your baseline messaging into the chat and instantly receive variants for subject lines, email body copy, and landing pages tailored to each Persona.
- **Build synthetic focus groups:** Save time and budget by asking the AI to generate a panel of virtual customers that proportionately mirrors the distributions of your actual Persona set.
- **Enable Sales roleplay:** Improve outreach by asking the agent to "act" as a specific Persona so your sales team can practice overcoming objections and perfecting their pitch.

_(Note: The chat does not currently retain memory or history between sessions, so you will want to provide your brand context in each new conversation.)_

## How to talk to your Personas

This feature is included at no additional charge for clients on our usage-based pricing (UBP) plans.

You can find the new chat feature directly within your existing Persona sets:

1. Go to a Persona set on the Dashboard.
2. Click the **Chat** tab at the top of the page, or simply add `/chat` to the end of the URL.
3. Start talking to the interface as you would with any other LLM.

[![Chat with personas feature in Faraday's dashboard](/images/blog/persona-chat/persona-chat-filled.png)](/images/blog/persona-chat/persona-chat-filled.png)

## A note on privacy and safety

We know that using AI with customer data requires trust. The chat agent **does not have access to PII (Personally Identifiable Information)**. The model operates on the aggregate patterns and statistical definitions of your Persona groups, not the private data of individuals.

## Ready to start?

The days of choosing between personalization and productivity are over. By talking directly to your Personas, you can finally operationalize the deep insights you already have, turning static data into dynamic content in seconds.

_Keep an eye out for our next post, where we will dive deep into step-by-step playbooks and actual prompts for getting the most value out of these new use cases!_

Faraday’s newest feature lets you talk to your Personas using an AI agent—preloaded with your unique Persona definitions—to instantly generate targeted marketing copy, subject lines, and campaign strategies without manual tedium.

Product

Customer churn prediction: a complete guide for consumer brands

How predictive lead scoring works — and why it outperforms traditional methods

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

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

ChangelogSee all

Persona Flow deprecated

Pipeline Analysis tab added

Talk to your Personas deprecated — succeeded by MCP

Dataset ingestion insights now available

Deep dive: FIG v2 data freshness and release pinning

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

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

Deep dive: FIG v2 historical data and what it means for your models

Bowties to diamonds: 2026’s new marketing stack

What's new: Talk to Personas