A multi-brand home services company brought in 1.8x to 3x more leads from direct mail by scoring its mailing lists with Faraday and sending mail only to the households most likely to become customers.

## At a glance

- **The challenge:** Rising digital and Google costs, along with slower home sales, drove an increase in the company’s cost per lead and cost to win each new customer.
- **The solution:** The company scored its HVAC mail lists with Faraday, combining its own customer records with the consumer data in the [Faraday Identity Graph](/blog/whats-in-faraday-identity-graph), and mailed only the households most likely to buy.
- **The result:** Scored mail brought in 1.8x to 3x more leads, and those leads converted at higher rates. The company has since expanded scoring to plumbing, generators and its other brands, and each new round of models has done better than the last.

## Why the company's cost per lead was climbing

The company's central marketing team runs customer acquisition for a family of HVAC, plumbing and electrical brands. In home services that work starts with a lead.

But at the start of 2026, those leads were getting more expensive. Slower home sales meant fewer new homeowners fixing the houses they'd just bought, and Google, one of the company's biggest paid channels, was delivering fewer leads for the same spend. Each new customer cost more to win as a result.

> _"We were dealing with a turbulent demand environment, especially within digital and Google channels, which drove up our cost of marketing, cost per lead, and cost of customer acquisition. We had to step back, optimize our channel mix, and find more targeted and surgical ways to reach our desired audience."_
>
> CMO, multi-brand home services company

For the marketing team, one of the first places to get more surgical was direct mail, a channel it already relied on alongside Google, lead aggregators and print. Unlike an ad auction, mail lets the team decide exactly which households it pays to reach. And because the cost of each piece is set by printing and postage rather than by bidding against other advertisers, better targeting translates directly into a lower cost per qualified lead. The question the team brought to Faraday was which households on its lists were worth mailing.

## How the company started scoring mail lists with Faraday

With the decision made to sharpen its direct mail, the company went looking for a partner and found Faraday through a referral. The decision was made to start with HVAC, one of its biggest service lines.

The company already knew who its customers were. What it didn’t know was which of the households on its lead lists looked most like its best customers. To find out, Faraday built a [custom prediction](/features/customer-behavior-predictions) trained on the company’s own customer records and enriched with the [Faraday Identity Graph](/blog/whats-in-faraday-identity-graph), which covers 1,400+ attributes on 240M U.S. adults and their households. That added the household and property details that customer records alone can’t show, giving the model a fuller picture of who actually becomes a customer.

Faraday then scored every household on the HVAC lists from most to least likely to become a customer, and the company mailed the households at the top. From there, the test came down to what showed up in its call centers (read more about Faraday’s approach to [lead suppression](/templates/lead-suppression)).

## Results: 1.8x to 3x more leads and higher conversion rates

The results were clear: scored mail delivered 1.8x to 3x more leads than the company would otherwise expect from direct mail. For a business where every install starts with a phone call, that’s a big deal, and the CMO was clear about how big: in a category as lead-intensive as home services, he said, leads are “everything.”

These leads weren’t just more likely to respond. They were also more likely to buy. Conversion rates rose for the households Faraday picked, and the CMO believes better targeting is part of why:

> “Targeting the right customer with the right message at the right time establishes a little more trust when the technician is in the home. And we see that carry through in conversion rates.”
>
> CMO, multi-brand home services company

## How the results held up across the company's other trades

With HVAC working, the company brought the same approach to its other trades, including plumbing and generators. Faraday produced a bespoke model for each trade, built around who buys that particular service, and the results held.

Even better, the models have improved with each round. The company's second generation of models outperformed the first, and the newest models are landing consistently closer to the 3x end of the range.

## Ready to make direct mail a revenue channel?

These results didn’t come from a single scored mailing. They came from an ongoing partnership. Faraday built a model for each service line, improved those models with every round of results, and worked with the company’s team on the next questions their mail program raised.

If direct mail is a meaningful part of your marketing, [talk to our team](/talk-to-sales) about how we can help you get the most out of every piece you send.

## Direct mail FAQ

### How can I improve my direct mail response rate?

The biggest factor is who gets the mail. Sending to households that look like your existing customers usually does more than changing the design or the offer. Predictive scoring ranks every household on a list by how likely it is to respond, so you can mail the top of the list and skip the rest.

### How do I reduce direct mail costs without losing results?

Mail fewer of the wrong households. Every piece costs the same to print and send, so cutting the households least likely to respond lowers your spend while keeping most of your responses. Set your cutoff where the expected return of a household no longer covers the cost of mailing it.

### What is predictive targeting for direct mail?

It’s a way to choose recipients using a model trained on your past customers. The model learns what your customers have in common, then scores a mail list so you can see which households look most like them. One multi-brand home services company used this approach to get 1.8x to 3x more leads from its direct mail.

### How do I measure whether a direct mail campaign worked?

Tie every response back to the household that received the mail, using unique offer codes, dedicated phone numbers or address matching. To see if your targeting made a difference, mail a small group of lower-ranked households in the same drop and compare their response to your top households.

### What data do I need to target direct mail better?

You need records of customers who’ve already done what you want, like booked a service or made a purchase. You also need the list you plan to mail. Adding household details like income, home ownership and life stage helps a model find patterns your customer records alone won’t show.

How a home services company got up to 3x more leads from direct mail

A multi-brand home services company scored its direct mail lists with Faraday and mailed only the households most likely to buy, bringing in 1.8x to 3x more leads.

How a multi-brand home services company got 3x more leads from direct mail

Today we're adding new mover data to the [Faraday Identity Graph](/blog/whats-in-faraday-identity-graph) (which already covers 1,400+ attributes on more than 240 million US adults and their households). It shows which households just moved into the areas a brand serves, and flags a brand's own customers who look likely to move soon, so brands can reach new neighbors while they're still choosing providers and keep their customer records current. To see the numbers for your area, get a ZIP report, free for anyone, on the [new mover data page](/features/new-movers).

This post covers why timing matters with new movers, how the data works, what brands use it for, and how to get started.

## Why timing matters with new movers

When a household moves, a lot of decisions open up at once: a new HVAC company, a new bank, new insurance. Those choices often stick for years, so the brands that show up early have an edge.

The hard part is timing. Most brands find out a household moved after the fact, once a competitor has already won them or the mail starts coming back. A list of new addresses helps, but on its own it treats every mover the same. A home services company doesn't need every household that moved into its ZIP codes. It needs the ones most likely to become customers.

About one in eight Americans moved in 2024 ([US Census Bureau](https://www.census.gov/data/tables/time-series/demo/geographic-mobility/state-to-state-migration.html)). In their first year after closing, buyers of new homes spend $21,306 on appliances, furnishings, and changes to the home, four times what owners who didn't move spend ([NAHB](https://eyeonhousing.org/2022/06/what-do-home-buyers-buy-after-moving-3/)). Buyers now expect to stay in a home for about 15 years ([NAR](https://www.nar.realtor/news/real-estate-news/nar-2025-profile-of-home-buyers-sellers-reveals-market-extremes)), so the providers they pick early can keep them for a long time.

## How Faraday's new mover data works

Faraday tracks three separate signals: a home listed for sale, a home under contract, and a household that finished a move. A listing or a contract means a move is coming. A finished move means the household has landed. Brands can act on whichever stage fits what they sell.

The mover data comes from a data partner that tracks listings, contracts, and completed moves across the US. Faraday matches it to its consumer profiles and refreshes it daily.

Because every mover is matched to a full Faraday profile, a brand can rank them by how likely they are to buy, using the same predictions it already builds in Faraday. Say a homeowner named Dana just bought a 1987 colonial three miles from a brand's nearest location, and she looks a lot like the customers who signed up for a maintenance plan last year. She's one of the new neighbors to welcome first.

## What brands use it for

Which use matters most depends on where in the customer's path a brand wants to reach them, more than what industry it's in.

**Reach people new to the area.** Faraday sends weekly lists of households that just moved into a brand's ZIP codes, ranked against the brand's own customers. That way the brand reaches the movers most likely to buy instead of all of them. Paired with [Nearest Location](/features/nearest-location), the brand also knows which of its locations is now closest to each new household, so it can welcome them to that one.

**Keep customers who are about to move.** When a customer's home is listed or goes under contract, it's an early sign they're leaving. That gives the brand a chance to keep them if they're staying in range, or to hand them off to the location nearest their new address.

**Stop mailing people who've left.** Brands can drop customers who moved out of range, so the mail budget goes to people who can still buy.

**Add moves to predictions.** Whether someone recently moved or is about to move becomes one more input to the [predictions](/features/customer-behavior-predictions) and [lead scores](/blog/predictive-lead-scoring-b2c) a brand already builds in Faraday.

## Getting started

Start with the [ZIP report](/features/new-movers), which is free for anyone. Paste in the ZIP codes you serve to see how many homes were listed, how many went under contract, and how many people moved in over the last 30, 90, and 365 days. The report shows counts only, with no names.

When you want the households behind those counts, Faraday can send you the movers landing in your ZIP codes. The data updates daily, and we send you a fresh list of movers every week. Each list is ranked by how likely each household is to buy from you, and it's delivered to your ads, CRM, or mail house. If you're already a Faraday customer, you can also add new mover data through the API, real-time lookups, or recurring deliveries into the tools you already use.

## Availability

New mover data is available now. Anyone can run the free ZIP report; Faraday customers can get ranked weekly lists or add it through the API. To get started, [talk to sales](/talk-to-sales) or try the free report on the [new mover data page](/features/new-movers).

See which households just moved into your ZIP codes and which customers are about to move, ranked by how likely each is to buy. Free ZIP report inside

Introducing new mover data: Reach households right after they move

_TL;DR: Visitor Context is a new Faraday capability that identifies anonymous visitors to your website and adds Faraday's consumer context to each one. That context comes from the [Faraday Identity Graph (FIG)](https://faraday.ai/blog/whats-in-faraday-identity-graph), which covers 1,400+ attributes on 240+ million US adults and their households, plus scores from your own Faraday [predictions](https://faraday.ai/blog/predictive-lead-scoring-b2c). Brands can see which visitors look like their best customers and reach them while they're still interested. Visitor Context is available now._

Every day, people visit your website, look around, and leave without filling out a form. Some of them are exactly the customers you want. Learning their names helps, but a name alone doesn't tell you which visitors are worth a follow-up, what to say to them, or where to send them.

That's the [context gap](https://faraday.ai/blog/consumer-context-data-ai), and Visitor Context is built to help close it.

**[Visitor Context](/features/visitor-context)** identifies the people who visit your site anonymously, then adds what Faraday knows about each one: who they are, whether they look like your best customers, and how likely they are to buy, so you can target and prioritize with data-backed confidence.

"Your website is full of people raising their hand. Knowing their name is only the start. With Faraday's context on top, brands can reach out to each of them in the right way with a personalized message," said Andy Rossmeissl, CEO and co-founder of Faraday.

## How Visitor Context compares to other website visitor identification pixels

Many brands we work with already have some form of pixel to de-anonymize their website traffic. What sets Visitor Context apart from these alternatives is the context Faraday adds to every visitor. A standard pixel can tell a roofing company that Brad visited its site and lives in its service area. Visitor Context can add that Brad owns his home, it was built in 1994, and he looks a lot like the people who bought a roof replacement from the company last year. Now the company knows Brad is worth a call, and which of the day's other visitors can wait.

## How it works

### 1. Add one tag

Create a Faraday Pixel dataset in Faraday and add the tag it gives you to your site, usually through your tag manager. There are no databases or credentials to connect.

### 2. Identify

A third-party identity partner matches your visitors to real people. Visitors who can't be identified stay anonymous and are never delivered.

### 3. Add context

Faraday adds its consumer data and your prediction scores to each identified visitor. Each one becomes a known contact you can score, segment, and suppress with the same tools you already use for leads and customers.

### 4. Take data-driven action

Send the visitors worth reaching to your mail vendor, CRM, or ad platform. For example:

- Mail a postcard to high-scoring visitors who browsed and left.
- Point each visitor to the location closest to them.
- Leave out existing customers and anyone who isn't a fit.
- Compare the visitors you reached with those you didn't, and see who bought.

## Who it's for

| Who                                         | How they use it                                                         |
| ------------------------------------------- | ----------------------------------------------------------------------- |
| Consumer and e-commerce brands              | Follow up with shoppers who browsed and left                            |
| Home services and multi-location businesses | Reach nearby visitors who look like their best customers                |
| Financial services and insurance            | Follow up with people who researched a product but didn't start a quote |
| Agencies and platforms serving many brands  | Offer visitor identification and scoring to their own clients           |

## Privacy and consent

Visitor Context is built to work with your consent tools. You install the tag behind your consent manager so it doesn't fire for visitors who decline, and it respects opt-outs, including Global Privacy Control. Only identified visitors are delivered to you. The tag doesn't capture what people type into forms, and it isn't permitted on pages that handle passwords, payments, or health information. Your visitor data is kept in your own Faraday account and isn't shared with other Faraday clients. Visitor Context is subject to our [terms for the pixel](/legal/pixelterms), which set out the notice, consent, and site requirements for brands that use it.

## Ready to see who's already visiting your site?

Whatever the industry, most of the people who visit a brand's website leave without saying who they are, and many of them are worth a follow-up. **Visitor Context turns the people already visiting your site into prospects you can prioritize.**

Visitor Context is available now for websites. [Reach out to sales](https://faraday.ai/talk-to-sales) to learn more, or if you're an existing Faraday customer, reach out to your account team.

## Frequently asked questions

### What is Visitor Context?

Visitor Context identifies anonymous visitors to your website and adds Faraday's consumer context and scores to each one, so you can see which visitors are worth reaching.

### How is it different from a standard visitor identification pixel?

Identifying a visitor is the first step. Visitor Context also tells you who that person is, whether they look like your best customers, and how likely they are to buy.

### How many visitors will it identify?

It varies by site and traffic. Browsers and ad blockers limit how many visitors any tool can identify.

### What do I get for each visitor?

For each identified visitor: contact details where available, when they visited, and Faraday's context and scores.

### Does it work in real time?

Identified visitors arrive in Faraday within a day, which works well for mail, email, and ad follow-up.

### Does it work in mobile apps?

Not today. Visitor Context works on websites.

### How does Visitor Context handle privacy?

It's built to work with your consent tools: installed behind your consent manager, the tag doesn't fire for visitors who decline, and it respects opt-outs, including Global Privacy Control. Only identified visitors are delivered, and your visitor data stays in your own Faraday account. Use of Visitor Context is subject to our [terms for the pixel](/legal/pixelterms).

### How do I get started?

[Reach out to sales](https://faraday.ai/talk-to-sales). Existing customers can contact their account team.

Faraday's Visitor Context identifies anonymous website visitors and adds Faraday's consumer context and scores to each one, so brands know which visitors are worth reaching.

Introducing Visitor Context: Know who's visiting your website, and which visitors are worth reaching

The end of the year is coming, and with it, planning season. Budgets get set, channels get picked, and targets get handed down. Every one of those decisions rests on an assumption about who your customers are and where you'll find more of them.

So before you lock anything in, ask yourself: can you really plan if you don't _truly_ know your audience?

Most teams know a lot about how people interact with their brand. They know who clicked, who visited, and who bought. What they often don't know is who those people are: their life stage, their household, their finances, and what sets them apart from the people who didn't buy. That's the [context gap](https://faraday.ai/blog/consumer-context-data-ai), and it matters most when the stakes are highest.

When teams run into that gap at planning time, the usual options aren't complete. Go with gut feel. Settle an internal debate about which segment matters most. Or bring in a consulting firm, which can cost six figures and often takes longer than you'd hoped.

An Insights Report from Faraday is a different option: answers built on real data, delivered in less than one week, not after weeks or even months of lead time.

## What an Insights Report is

It brings together two things Faraday already does: [persona analysis](https://faraday.ai/blog/what-are-ai-customer-personas) and [market opportunity analysis](https://faraday.ai/blog/unlocking-growth-with-market-opportunity-analysis). Persona analysis tells you who your best customers are by grouping them into distinct, data-driven profiles. Market opportunity analysis tells you how many more people like them are out there, and where. Together, they answer the question behind most planning: who should we be going after, and how big is the opportunity?

We start with the two or three questions that matter most to you. Common ones are "Who are our best customers?", "Why do some leads convert and others don't?", and "What's our penetration by market?" Then we match your customer records to the [Faraday Identity Graph](https://faraday.ai/blog/whats-in-faraday-identity-graph), which covers 1,400+ data points on 240+ million US consumers and their households. Your records show you who bought. Faraday's data shows you all the details behind that purchase: who those people _actually_ are.

The report connects and compares the two, using the same data and back-end systems that power the rest of the Faraday platform (not an LLM guessing from a prompt and limited context). The result is one report that shows who your best customers are, what sets them apart, and how many more people like them you can still reach.

## Using an Insights Report to plan

For most marketers, planning boils down to a handful of critical decisions: who to target, how to reach them, what to say, and where to put the budget.

An Insights Report gives you context for each one:

### Who your best customers are

Most customer bases aren't a single audience. When we group a client's best customers into personas, they often split into several distinct profiles that need different creative and different media. Knowing that before you plan means you can build for each group instead of averaging across them.

### Who converts, and who doesn't

Comparing people who bought with people who engaged but didn't is often where the most useful findings come from. Sometimes the two groups look nearly identical on the surface, and the difference only shows up in one or two specific traits. Those traits are what you can target, score, and act on.

### How much opportunity is left

Once you know who your best customers are, you can see how many more people like them exist and where they live. That tells you whether a market deserves more spend, or whether you've already reached most of it.

### Which channels and messages fit

Personas carry more than demographics. They show how people tend to shop, whether they respond to mail, and what they care about. That gives you a starting point for channel mix and messaging before you spend a dollar testing it.

## It's not just for end-of-year planning

And while planning season is a natural time for this kind of analysis, it isn't the only one.

Take a new CMO a few weeks into the job. They've inherited a strategy, a budget, and a set of segments someone else built. They need to know whether those segments hold up, and they need to know before their first big decision. This analysis gives them a data-driven read on the customer base in their first 90 days, so they can keep what works and change what doesn't.

The same goes for any moment when a big question comes up: entering a new market, launching a product line, rethinking how you buy leads, or settling a debate about who your best customers are. If the answer depends on knowing who your customers are, an Insights Report is built for it.

## Another way to get these insights

If you'd rather explore the data yourself, [Ask Faraday](/features/ask-faraday) lets you ask questions about your customers in plain language, in whatever LLM you already use. But it isn't the same as "asking ChatGPT." Ask Faraday's answers are backed by Faraday's data, not a hallucination or a guess. It's the same data our enterprise predictive models are built on, piped directly into your LLM. With it, you can see how your customers compare with your markets or with the US as a whole, without building the analysis yourself.

## No two Insights Reports look alike

Every report starts from your questions, so every report looks different. Here are a few examples, with details left out to keep our clients anonymous.

A financial services company wanted to understand why some leads enrolled while others engaged and then walked away. We analyzed their full funnel, from people who never responded to long-standing clients, and grouped each stage into personas. Their best clients turned out to be three distinct profiles, each reached through a different channel. The clearest difference between people who enrolled and people who didn't came down to a single financial trait, a signal they can build directly into how they score leads. They got the report along with the underlying data, so their own team could keep working with it.

![Sample customer analysis report for a financial services company, showing records analyzed, match rate, outcome cohorts, and personas](/images/blog/insights-reports-planning/financial-services-customer-analysis.png)

_Sample report with illustrative data._

An online apparel brand had launched a new product line and wanted to know who was buying it. We compared those buyers with the brand's other customers and looked at where the line fit in the broader market. The new buyers earned about the same as everyone else. What set them apart was their life stage, where they lived, and how they found the brand. That pointed to a clear plan: target on life stage rather than income, concentrate spend in the metro areas where buyers clustered, and hold the price.

![Sample customer insights report for an apparel brand, comparing new-collection buyers with other customers by life stage](/images/blog/insights-reports-planning/apparel-new-collection-buyers.png)

_Sample report with illustrative data._

A home improvement company wanted to turn its personas into campaigns. Their report became a persona starter kit. For each persona, it covered what the data shows, how to personalize, a core message, sample subject lines, the tone to aim for and avoid, and three tests to run first. It reads more like a campaign brief than an analysis.

![Sample persona starter kit for a home improvement company, showing four personas with key stats and a core message](/images/blog/insights-reports-planning/home-improvement-persona-starter-kit.png)

_Sample report with illustrative data._

Yours will look different, because your questions are different.

## Get started

Planning season is already here. [Talk to a context consultant](https://faraday.ai/talk-to-sales) about an Insights Report, and go into next year knowing who your customers actually are.

## FAQ

### What data do I need to provide?

Your customer records, at minimum. The more of your funnel you can share, such as who converted, who engaged but didn't, and who never responded, the more questions the report can answer. Faraday matches those records to the Faraday Identity Graph and adds the context.

### How long does it take?

It depends on your questions and how quickly your data is ready. Once your data is in, the analysis can come together in days, not months.

### Is an Insights Report the same as Faraday Enterprise?

No. It's a focused engagement built around a few specific questions. From there, teams typically move into an Enterprise plan to put what they learned to work. Others find the report shows them exactly which data points they need, and they go straight to Faraday Pro.

An Insights Report combines Faraday's persona analysis and market opportunity analysis into one report, built around the questions you need answered before your next big decision.

Who are your customers, really? Find out with a Faraday Insights Report

Today we're launching Ask Faraday. It lets the AI assistant your team already uses answer questions about American consumers and about your own customers, using data from the [Faraday Identity Graph](/blog/whats-in-faraday-identity-graph), which covers more than 240 million US adults and their households. [See Ask Faraday in action and request access](/features/ask-faraday).

This post covers the story behind the launch: why we built it, how it keeps personal information out of reach, and a set of prompts you can try once you're connected.

## Why we built it

The questions marketers have about their customers are often simple ones. How do my customers compare to the people who live near them? Do the neighborhoods where I sell well have something in common? And they usually come up at decision time, while a campaign is being planned, a budget is being set, or a new market is on the table.

Getting a solid answer has usually meant going the long way around: commissioning a research study, waiting for a data team to pull a custom report, or piecing together public statistics that don't quite match your own customers. An AI assistant on its own will reply right away, but without data about real people behind it, it's working from general knowledge. Either way, the answer tends to arrive too late to help, or isn't solid enough to act on.

We built [Ask Faraday](/features/ask-faraday) so that the people who need an answer can get it when they need it. Your AI assistant asks the question directly and brings back an answer drawn from real data on US households, in the same conversation.

## How privacy works

Letting an AI assistant ask questions about people directly raises a fair question of its own: what stops it from asking about one person? We designed Ask Faraday so that the answer doesn't depend on the assistant behaving well. The protections sit in the data and the database, where no prompt can reach them.

- **Small groups are refused.** Any result that describes fewer than 50 people is refused. If you narrow a question until it describes only a handful of households, you get no answer rather than one that could point to someone.
- **The minimum is set on the data, not in the conversation.** The 50-person minimum is set on the data itself in Google BigQuery, the database where it's stored. Asking the question a different way can't lower it. It holds even if someone tricks the assistant into ignoring its instructions, and even if our own code has a bug.
- **Personal details were never there to begin with.** Names, emails, and addresses aren't in the data the assistant can query at all. They aren't filtered out of answers afterward. They were never there.
- **It's not a new idea.** BigQuery's data clean rooms, which companies use to work with shared data without exposing individual records, rely on the same mechanism.

One more design choice is worth explaining. Behind the scenes, the assistant asks its questions in SQL, the standard language for querying databases, which AI assistants already know well. We could have invented a Faraday-specific query language instead, but that would only have given the assistant something new to learn. Safety comes from what the data contains and what the database will return, not from how the question is written.

"Every SaaS company is bolting a chat box onto a pile of data and getting back confident answers nobody can check," said Andy Rossmeissl, CEO and co-founder of Faraday. "We let the assistant write real queries against real data, and we let the database refuse the ones it shouldn't answer."

## Use it alongside your other tools

Because Ask Faraday runs inside your own AI assistant, it isn't a separate app you have to switch to. It works alongside the other tools you've connected to that assistant, such as your CRM, so a single request can draw on more than one of them.

If you've connected HubSpot, for example, try a prompt like this:

> "Pull my closed-won and closed-lost deals from HubSpot, then use Faraday to compare who those people are against the US market."

The assistant passes the work between the two tools, so you don't have to move a list from one to the other yourself.

## Getting started

If you'd like to try it yourself, here's what a first session looks like, from connecting your assistant to turning answers into next steps.

### Connect your assistant

Setup takes about five minutes. In Claude or ChatGPT, add Faraday as a custom connector, then sign in with your Faraday account. To check that everything is working, ask:

- "Give me an overview of my Faraday account."

### Ask about the market

Questions about the US market don't need any data from you. You can start asking right away:

- "How many homeowners aged 35–54 live in Colorado, and what's their household income range?"
- "Which counties in the Southeast have the most households with young children?"
- "What share of households in Phoenix are renters vs. owners?"

### Compare against your own customers

The most useful answers often come from comparing the market with the people who already buy from you. To do that, add a customer or lead file to Faraday and ask the assistant to create a group from it. Then you can ask questions like:

- "How are my customers different from the general US population?"
- "How do repeat buyers differ from one-time buyers?"
- "Which of my markets have fewer customers than you'd expect, given how many similar households live there?"

### Turn answers into next steps

Once you know more about who your customers are, the same conversation can help you decide what to do about it:

- "Based on my best customers, which channels or creators would reach more people like them?"
- "Write a one-page summary of who to target and why."

### Tips for better answers

A few habits make a noticeable difference:

- **Be specific.** Name the place, the age range, or the kind of household you care about.
- **Ask for comparisons.** "How do my customers compare to…" usually tells you more than a single number on its own.
- **Ask follow-ups.** Questions like "break that down by region" build on the previous answer, so you don't have to start over.
- **Widen the question if it's turned down.** If a question is rejected for being too narrow, that's the 50-person minimum at work. Widen the area or loosen a filter and try again.

## Availability and pricing

Ask Faraday is available as a $500/month add-on for Faraday Enterprise customers. To add it to your account, contact your account team. If you're not on Enterprise yet, you can [talk to our sales team](https://faraday.ai/talk-to-sales).

To see what a conversation with Ask Faraday looks like, or to request access, visit the [Ask Faraday page](/features/ask-faraday).

Why we built Ask Faraday, how it keeps personal details private, and prompts to try in Claude or ChatGPT, from market sizing to customer comparisons.

Introducing Ask Faraday: Your favorite AI assistant, now with data-backed answers about your customers and market

We've added five new data points to the [Faraday Identity Graph](/blog/whats-in-faraday-identity-graph): the names of up to two owners on a property's title and up to three lenders on its mortgage. They sit alongside the mortgage data we already had, including rate, [origination date](https://faraday.ai/catalog/v2/mortgage_1_origination_year_month), [loan amount](https://faraday.ai/catalog/v2/mortgage_1_amount), and how likely the household is to refinance.

## What's new

| Data point                                                                              | What it tells you                          |
| --------------------------------------------------------------------------------------- | ------------------------------------------ |
| [Mortgage lender 1 name](https://faraday.ai/catalog/v2/property_mortgage_lender_1_name) | The lender on the home's mortgage          |
| [Mortgage lender 2 name](https://faraday.ai/catalog/v2/property_mortgage_lender_2_name) | A second lender on record for the property |
| [Mortgage lender 3 name](https://faraday.ai/catalog/v2/property_mortgage_lender_3_name) | A third lender on record for the property  |
| [Property owner 1 name](https://faraday.ai/catalog/v2/property_owner_1_name)            | The first person on the property's title   |
| [Property owner 2 name](https://faraday.ai/catalog/v2/property_owner_2_name)            | A second person on the property's title    |

## Who it's for

**Mortgage lenders and the platforms that serve them.** You can now see whose borrower a homeowner is, not just whether they're a good refinance candidate. Go after a competitor's borrowers who are paying above today's rates, keep your own customers out of acquisition campaigns, and flag homes with more than one lender on record for home equity or consolidation offers.

**Anyone whose offer depends on homeownership.** Owner names let you confirm you're reaching the person on title, not just whoever lives at the address.

## Try it on your own list

Upload a list of homeowners or addresses to [Faraday Pro](https://pro.faraday.ai) and get these fields back for each record. Signing up takes a few minutes and there's no call to book.

If you want to go further, like scoring a list for refinance likelihood or building an audience around a specific lender's borrowers, [our team can set that up for you](/talk-to-sales).

Five new data points in the Faraday Identity Graph show who's on a property's title and who holds its mortgage, so lenders can see whose borrower a homeowner is and anyone can confirm they're reaching the owner.

See who holds the mortgage: new lender and owner data in Faraday

Every team that comes to Faraday wants the same thing: [context](/blog/consumer-context-data-ai) on the people behind their customer records. Who they are, what their household looks like, what they're likely to do next. How teams want to get there varies a lot. Some know exactly which data points they need and want them today. Others have a business problem and want help figuring out what data would solve it.

That's why Faraday has two products, [Faraday Pro](/signup) and [Faraday Enterprise](/talk-to-sales). They're two ways into the same platform, built on the same [Faraday Identity Graph](/blog/whats-in-faraday-identity-graph), with the same [1,400+ data points](/catalog) on 240 million U.S. consumers and their households. Neither is the beginner version of the other. The real difference is where the thinking happens: whether you bring the judgment yourself, or want Faraday working through it with you.

## What's the same

Whichever product you use, you get the same underlying data, the same identity resolution, and the same bar for [security and data protection](/security). Pro isn't a "lite" tier with a trimmed-down dataset. What changes is the way in, not what's behind it.

## Faraday Pro: the data and the tools, self-serve

Faraday Pro is for teams that know what they need and want to get it now. You sign up at [pro.faraday.ai](https://pro.faraday.ai), create a project, and pick the data points you want. If you're not sure what a field is called, you can describe what you're looking for, and the suggested attribute search surfaces the right attributes.

Then you bring your records whichever way fits. You can upload a CSV and get it back with your attributes appended, look up a person in real time through the [API](/docs/pro/projects#real-time-lookup-by-api), or connect Pro to your data warehouse, database, or cloud bucket for ongoing [enrichment](/blog/get-started-with-consumer-data-enrichment). Pro also works through our [MCP server](/docs/pro/projects#real-time-lookup-by-mcp), so it's addressable from agentic workflows.

[Pricing](/pricing) is pay-as-you-go. You're billed per matched attribute, so you pay for the data we find, not the rows you submit. Credits are \$0.05 each, with volume discounts available. No contracts, no minimums, and no sales call required.

**Pro is a good fit if:**

- You know what data points you are looking to append to a list of customers or leads.
- You'd rather pick attributes and hit an API than sit through a procurement process.
- You have a team that can put the data to work once you have it.

## Faraday Enterprise: the data plus the expertise

Faraday Enterprise is a managed account. You get the same platform, plus a dedicated context consultant who learns your business and helps you turn consumer data into revenue. That includes activation guidance, modeling strategy, and ongoing expert support.

Enterprise is also built for more complex use cases, like models and audiences built from your full event history, such as purchases, signups, and churn over time. It also supports sub-accounts, so agencies, partners, and companies with several brands can run a separate, isolated Faraday account for each client or brand, all managed from one parent account. It's an annual engagement, it's sales-led, and it lives at [app.faraday.ai](https://app.faraday.ai).

**Enterprise is a good fit if:**

- You have a business problem, like [churn](/blog/churn-prevention-machine-learning), [lead quality](/blog/predictive-lead-scoring-b2c), or [finance declines](/blog/finance-decline-guide), and want help deciding what data and models will solve it.
- You want predictions built from your full event history, not just a customer list.
- You manage data for several clients, brands, or business units and need each one kept separate.
- You want a partner, not just a data source.

### Starting with an Insights Report

If you don't know which Pro data points will drive the most value for your brand, but Enterprise sounds like too big a commitment, you can always start with an Insights Report. It's our way of helping you understand your best customers, your total addressable market, and which data might give you the most leverage to win more customers.

Every business is different, so every report is scoped to you. We start with the two or three questions that matter most to you. Common ones are "Who are our current customer segments?" and "What's our penetration by market?" Then we build the report from Faraday's data.

From there, teams typically move into an Enterprise plan to put what they learned to work. Others find the report shows them exactly which data points they need, and they go straight to Pro.

## How to decide

The simplest way to think about it: do you want the data, or the data plus the expertise? If you have a straightforward data need and know what you're going to do with it, start with Pro. If you want more guidance, aren't sure where to start, or have a more complex problem to solve, Enterprise might be the better fit, and an Insights Report is an easy first step. Here's a breakdown by use case:

| If you want to...                                                                               | Faraday Pro | Faraday Enterprise |
| :---------------------------------------------------------------------------------------------- | :---------: | :----------------: |
| Add demographics, financial signals, life stage, or homeownership to a customer or lead list    |      ✓      |         ✓          |
| Look up a person in real time from an app, form, or AI agent                                    |      ✓      |         ✓          |
| Keep records enriched through a connection to your warehouse, database, or cloud bucket         |      ✓      |         ✓          |
| Build a propensity or persona model from a list of your best customers                          |      ✓      |         ✓          |
| Test the data on a few rows before committing                                                   |      ✓      |         ✗          |
| Pay as you go with a credit card, no contract                                                   |      ✓      |         ✗          |
| Build models and audiences from your full event history, like purchases, signups, and churn     |      ✗      |         ✓          |
| Run a separate account for each client, brand, or business unit under one parent account        |      ✗      |         ✓          |
| Work with a dedicated context consultant on which data and models will solve a business problem |      ✗      |         ✓          |
| Understand your customer segments or penetration by market with an Insights Report              |      ✗      |         ✓          |

If most of your answers land in the Pro column, you can [start today](/signup) and be enriching records in minutes. If they land in the Enterprise column, [talk to us](/talk-to-sales). And if you're an Enterprise client curious about Pro, your account team can help you find the right setup.

## Two ways to act on the same context

We talk a lot at Faraday about what AI tools are actually for. The best ones don't replace human judgment. They help people act on it. Data or a model can tell you which customers are likely to churn, but someone still has to decide what to offer them, when, and whether it's worth the cost. The value comes from the decision, and the data's job is to make that decision better.

That's the real difference between Pro and Enterprise. It isn't about which one is more powerful, since the data underneath is the same. It's about where the thinking happens.

With Pro, you bring the judgment. You already know the question, like "which of these leads can afford a \$20,000 remodel?" You need the context to answer it, and Pro gets out of your way so you can act.

With Enterprise, Faraday brings the data and works alongside you on what to do with it. Maybe you know finance declines are eating into revenue, but not which signals predict them or what to do about it. A context consultant works through that with you: defining the problem, choosing the data, building the model, and helping you [measure whether it worked](/blog/holdout-testing-with-faraday).

Either way, the goal is the same. It's people making a better call with better context. Pro and Enterprise are two ways to get there, depending on how much of the path you've already mapped out.

## Get started

[Create a free account on Faraday Pro](/signup) and start with the data you need, or [talk to a context consultant](/talk-to-sales) about Enterprise (or an Insights Report).

## FAQ

### Is the data in Pro the same as in Enterprise?

Yes. Both run on the Faraday Identity Graph, and the data is identical. The difference is the delivery model: self-serve and pay-per-match, or a managed annual engagement.

### Can I get predictions on Faraday Pro?

Yes. In Pro, you can build [propensity](/blog/propensity-models-predict-customer-behavior) and [persona](/blog/what-are-ai-customer-personas) models by uploading a list of the customers you want to predict more of. Enterprise builds models from your full event history, like purchases, signups, and churn over time, with a context consultant helping you decide what to predict and how to act on it.

### Can I switch from one to the other?

Yes. Plenty of Pro customers will never need Enterprise, but if your needs change, we can help you move. Enterprise clients can also talk to their account team about adding Pro.

### Is Pro only for small teams?

No. Pro is about how you want to work, not how big you are. Some large teams know exactly what they need and just want access to the data.

### What if I'm interested in Enterprise but not ready to commit to an annual engagement?

Start with an Insights Report. We'll scope your top two or three questions with you and build the report from Faraday's data. Teams typically move into an Enterprise plan from there, and some find the report tells them exactly which data points to pull in Pro.

Faraday Pro and Faraday Enterprise run on the same data. The difference is how you want to work with it. Here's how to choose.

Faraday Pro vs Faraday Enterprise: which one is right for you?

We all know that effective marketing requires personalization. Faraday enriches leads with contextual data so you can personalize every interaction and drive conversion.

Years ago, one of our earliest clients wanted to confirm this premise for themselves. Instead of sending one generic email to their entire list, they created unique, hyper-tailored content variants specifically crafted for the distinct vocabularies and motivations of their unique Faraday Personas. The results were undeniable: a 30% lift in conversion.

Yet, despite the clear upside, they didn't stick with the new system very long. Why? Because in order to actually use Personas to personalize, you have to understand what makes each Persona special and then produce personalized materials unique to their needs and interests. Doing both of these things manually is incredibly hard and tedious.

This is the paradox of personalization: the more you tailor your messaging, the better your results — but the more manual work you have to do. Our clients want ROI through a lift in engagement, leading to better LTV and/or CAC, but the friction of execution often gets in the way.

The [Faraday MCP server](https://faraday.ai/docs/features/mcp) closes that gap.

All you have to do is connect your agent once, and it can read your Persona sets, cohorts, and models directly from your account — then write the copy, build the audience, and ship it to your destination. The analysis and the execution happen in the same conversation, in the agent you already use.

## Before you start

Connecting takes about a minute. In Claude, ChatGPT, Cursor, or any other MCP-compatible client, add the server at `mcp.faraday.ai/mcp` and sign in with your existing Faraday login. Authorization runs through OAuth, so your agent never handles an API key. Ask it to run `whoami` and it will confirm the account it's working in and list every tool it has.

Ready? Here is your tactical playbook.

## Campaign matchmaking

Sometimes you know exactly what you want to sell, but you aren't sure who wants to buy it. When you're running a promotion for a specific product line, a new category, or a holiday sale, you need to know which group of customers will actually care.

Instead of relying on a hunch, ask your agent to interpret the Persona data for you. It queries the live distributions in your account and matches your product's value proposition against the group that mathematically shows the highest affinity for it.

> "Pull up the Persona set on my customer cohort and summarize each Persona's salient traits. We're launching a premium product line in November at a \$2,000+ price point. Which Persona is most receptive, and what in the data tells you that?"

## Offer brainstorming

On the flip side, you might have a segment that's underperforming. Maybe a Persona is smaller than you want it to be, or your recent campaigns just haven't pushed their buttons.

Your agent can cross-reference that Persona's traits against the rest of your base and suggest offers built on what actually motivates them, rather than what you'd guess motivates them.

> "Persona 3 converts at half the rate of the others. Compare their trait distributions against my highest-converting Persona and tell me what's different. Then propose three promotional offers tailored to what you found, and explain the reasoning behind each."

## Producing personalized materials at scale

Writing one great email is hard. Writing four distinct variations of that same email is agonizing. Usually the friction outweighs the lift.

Once your targeting is decided, hand your agent your baseline materials and let it adapt them per Persona — subject lines, email and SMS copy, landing page variants — with the trait data already in front of it.

> "Here are our brand guidelines and the baseline email for the November launch. Write one variant per Persona in my customer Persona set. For each, give me a subject line, preview text, and body copy, and note which trait drove the angle you chose."

## Running synthetic panels

Brands have run focus groups forever to test messaging, but hiring an agency to build a representative panel is expensive and slow. Your agent can build one from your real Persona distributions instead.

> "Create a synthetic panel of 10 panelists, proportionately allocated across the Personas in my customer Persona set and consistent with each Persona's actual trait distributions. Invent a name, demographic details, and backstory for each. Then let me interview them individually or as a group."

Because the allocation comes from your account rather than the model's assumptions, the panel reflects your customer base and not a generic one.

## Sales training and roleplay

Marketing isn't the only team that benefits from understanding Personas. New reps — and veteran reps with ingrained biases — need to pivot their pitch based on who's across the table.

> "I'm a new sales rep for \[your brand\] — look us up so you know what we do. Pick one of the Personas in my customer Persona set and invent a representative customer who belongs to it, with a name, demographic details, and backstory grounded in that Persona's traits. Act as that person so I can ask about your perspective, your motivations, and what would stop you from buying."

## Closing the loop

Here's what changes when the analysis happens against your live account: the agent doesn't have to hand the work back to you at the end.

Once you've picked the Persona and written the copy, the same conversation can build the audience and deploy it — then come back in a month and tell you whether it worked.

> "Build a target of everyone in Persona 2 with a churn score above the 80th percentile, and deploy it to our Meta connection."

> "It's been 30 days since we activated the November audience. Pull the current numbers and tell me how the Persona 2 segment performed against the rest of the campaign."

## Bridge the gap between insight and action

There isn't one perfect analysis that illuminates every way to use your Personas. The value of your customer data shows up when it starts driving your daily decisions and your content.

Running that work through the MCP server removes the friction of personalization at both ends: the understanding and the execution. You no longer have to choose between a hyper-targeted campaign and your own productivity.

Already on Faraday? Connect your agent at `mcp.faraday.ai/mcp` and open a Persona set. Full details are in [the MCP docs](https://faraday.ai/docs/features/mcp).

New to Faraday? [Talk to a Context Consultant](https://faraday.ai/contact) to get some Personas set up.

A tactical playbook of prompts for using Faraday's MCP server to match campaigns to Personas, generate targeted copy, run synthetic focus groups, and roleplay sales scenarios — grounded in your live account data.

Turn your Personas into a marketing engine with the Faraday MCP server

Scoring solar leads means ranking every homeowner by how likely they are to book an appointment and install, using what you know about the home and household, not just the form they filled out.

Most solar companies don't have a lead shortage. They have a sorting problem. Leads come in all day from vendors, paid social and search. Reps call them in the order they arrived, and the homeowners most likely to install end up waiting behind people who were never going to buy. When Momentum Solar started [scoring its leads](/blog/solar-call-center-lead-conversion-boost), its call center needed 33% fewer calls to set the same number of appointments.

This guide covers why most solar leads never turn into installs, the signals that separate the good ones from the rest, and how to put scores to work in your call queue and your lead buying.

## Why most solar leads never turn into installs

Most solar leads don't install because the homeowner can't, won't qualify, or was never that serious. Nothing on the lead form tells you which.

The usual reasons look like this:

- They don't own the home. Renters and many condo owners can't put panels on the roof, no matter how interested they are.
- The roof doesn't work. It's too old, too shaded, or the wrong material, and the site visit is where everyone finds out.
- They won't qualify for financing. Many homeowners finance solar, and a lead who gets declined for the loan has already cost you a rep's time and a site visit.
- The lead is old. Aged leads are cheap for a reason.
- They were just curious. Ads that promise big savings bring in plenty of clicks from people who aren't ready to buy.

Lead source matters, but it doesn't settle the question. Homeowner leads from any source vary a lot in how ready and able they are to buy, and a rep looking at a name, a phone number and a ZIP code can’t tell them apart. The leads aren't really the problem. The problem is treating every one of them the same.

## The signals that predict a solar install

Three kinds of signals predict whether a solar lead will install: whether the home and household are a fit, whether the homeowner can finance the system, and how the lead behaved and where it came from. The first two don't show up on a lead form, but they can be attached the moment the lead arrives. The third you already have, from your forms and vendors.

### Home and household fit

This is who the homeowner is and what the house is like. Useful signals include [homeownership status](/catalog/v2/home_ownership_status), [home year built](/catalog/v2/home_year_built), [length of residence](/catalog/v2/length_of_residence), [home market value](/catalog/v2/home_market_value) and [solar roofing interest](/catalog/v2/solar_roofing_interest_likelihood). At the neighborhood level, [solar installation qualified percent](/catalog/v2/solar_installation_qualified_percent) shows what share of roofs in a ZIP code are suitable for panels. A homeowner who has lived in the house for eight years is a very different lead from a renter who moved in last spring, even if they filled out the same form.

### Financing readiness

A homeowner who won't qualify for the loan costs you more than a homeowner who never answers, because they get all the way to the site visit first. A signal like [credit score proxy](/catalog/v2/credit_score_proxy) helps you decide who to call first. It doesn't decide who gets approved, since the lender still does that. Bath remodeler [Freedom Bath](/stories/freedom-bath) used Faraday Pro to screen out leads likely to fail credit checks before buying them, and went a full month with zero credit declines. Solar companies can use the same approach on financed leads. For more on that problem, see our guide to [finance declines](/blog/finance-decline-guide).

### Behavior and source

This is what the lead did and where it came from. Behavior includes how much of the form they filled out, whether they uploaded a utility bill, and how quickly they responded to the first call or text. Source is whether the lead was exclusive, shared, aged, bought through a [ping-post](/blog/so-what-is-a-ping-post-anyway) auction, or generated by your own ads. Exclusive solar leads usually cost more, so it's worth knowing whether they actually install at a higher rate for you.

The point isn't to hand-pick which of these signals matter most. A predictive model learns that from your own past leads and installs, so the weights come from what actually happened, not from a sales manager's best guess.

## How to score solar leads: rules-based vs. predictive

There are two ways to score solar leads. Rules-based scoring adds up points a manager assigns by hand. Predictive scoring uses a model trained on your own past leads and installs to rank every new lead by how likely it is to install.

A rules-based system might give a lead 20 points for owning the home, 10 for being in your service area and 10 for a complete form. It's easy to set up, but the points are a guess, and they never change. Every homeowner in your service area who fills out the form completely gets the same score, so the call queue still runs in the order leads arrived.

A predictive model scores each lead the moment it arrives, using everything known about the home and household, and keeps learning as you find out which leads actually installed.

Here's what that looks like with two leads that come in from the same vendor on the same afternoon. Both are in the same ZIP code, both say they own the home, and both pay about $200 a month for electricity. A rules-based system gives them the same 40 points. A predictive model sees that the first homeowner has lived in a 2005 house for nine years, with high solar roofing interest and strong financing signals, and that the second doesn't show up as the owner of the address and moved in last year. It scores the first lead 82 and the second 18. The first goes to your best closer within minutes, and the second goes into a text follow-up instead of a rep's dial list. These numbers are illustrative, but the pattern matches what Momentum Solar saw at scale.

For the full explanation of how these models work, see our guide to [predictive lead scoring](/blog/predictive-lead-scoring-b2c).

## Putting scores to work: routing, speed to lead and lead buying

A score only helps if it changes what happens next. For solar companies, it pays off in four places: the call queue, how fast you call, which leads you buy, and where your marketing budget goes.

### Tiered call queue

Split leads into tiers and route each tier differently. Top-tier leads go to your best agents first, middle-tier leads get your standard cadence, and bottom-tier leads go into nurture instead of taking up rep time. This kind of lead routing is exactly how Momentum Solar used its scores.

### Speed to lead by tier

Speed to lead matters most where the odds are highest. Call your top-tier leads within minutes, especially shared leads that other installers are also calling. Bottom-tier leads can wait for a slower cadence without costing you much.

### Buy smarter

Score leads as they arrive from vendors, and [reject low-fit leads](/templates/lead-rejection) before they reach your CRM, or push back on price. Over time, compare vendors on cost per install, not cost per lead. A cheap lead that never answers costs more than an expensive one that books.

### Stop spending on homeowners who can't install

The same scores work for marketing. Suppress low-fit homeowners from retargeting and direct mail, so your budget goes to people who can actually put panels on the roof.

## What Momentum Solar learned

Momentum Solar, one of the fastest-growing solar installers in the country, relies on its call center to turn purchased leads into on-site assessments. Its old lead scoring was rules-based, built on a handful of data points: lead source, demographics, homeownership and location. Once a lead had a score, it never changed.

Momentum switched to a Faraday model trained on its own customers and Faraday's consumer data. Scores went into the CRM automatically and updated as new customers came in and lead behavior changed. Leads were split into green, yellow and red tiers and given the same call cadence, so the test was fair.

Green leads converted to appointments at a 191% higher rate than the baseline, and yellow leads at a 51% higher rate. Red leads converted 4% below the baseline, confirming where the call center shouldn't spend its time. Overall, the call center needed 33% fewer calls to hit its appointment goal. Read [how Momentum Solar cut calls per appointment by 33%](/blog/solar-call-center-lead-conversion-boost).

## Where Faraday fits

Faraday scores solar leads by combining your own lead and install history with 1,400+ consumer and property data points on 240 million U.S. adults, then sends the scores back into your CRM and lead intake. We're not a lead vendor. We help you get more out of the ones you already buy.

## More solar leads won't fix a sorting problem

Most solar companies can buy all the leads they want. The hard part is knowing which ones to call first. Score every lead as it arrives, route the best ones fast, and stop spending time and money on homeowners who can't install. For more on how this works across [home services](/blog/home-services-complete-context-guide), read our guide to [predictive lead scoring](/blog/predictive-lead-scoring-b2c), or [talk to our team](/talk-to-sales) about scoring your own leads.

## Solar leads FAQ

### How much do solar leads cost?

Prices vary widely by lead type, region and vendor, and exclusive leads cost more than shared or aged ones. The number that matters more is cost per install. Track installs by vendor and source, not just the price per lead.

### Are exclusive solar leads worth it?

Sometimes. With exclusive leads, you're not racing other installers to the phone, but they cost more and still vary in quality. Score them like every other lead and compare installs per dollar by source.

### How fast should you call a solar lead?

As fast as possible for your best leads. Speed matters most for top-tier leads, especially shared ones other installers are also calling. Lower-tier leads can go into a slower cadence without losing much.

### Can you score a solar lead before anyone talks to them?

Yes. Home, household and financing signals can be attached to a lead the moment it arrives, so it can be scored and routed before the first call.

### What is a good solar lead to appointment rate?

It depends heavily on lead source and how you count an appointment, so outside numbers are hard to compare. The more useful number is your own rate by source and score tier, measured over the same period.

Solar leads: How to score them and know who to call first

Buying solar leads is easy. Turning them into installs is not. Learn how to score solar leads, route the best ones fast, and stop paying for the rest.

Solar leads: How to score them and know which homeowners to call first

Direct mail is about as old school as marketing gets, and in a world of AI slop, that's exactly what makes it stand out. Where web ads are relatively cheap, flexible, and easily cancelled or updated if something goes wrong, mail is expensive, takes weeks to produce, and worst of all, a bad decision can't be undone.

These aspects of direct mail get filed away as the channel's weaknesses, and often become reasons to test it later, or not at all. But that expense has a side effect worth paying attention to: it forces intentionality. Because it costs more, we treat it differently; we’re less likely to thoughtlessly hand it off to AI. And I think that teaches us something about how we're engaging with all our LLM tools right now: we're not just handing off our thinking, we're handing off our intention, and too often we stop caring about what actually goes out the door.

## Why direct mail's cost forces better targeting

_When sending costs nearly nothing, there's no reason to think hard before you launch._

We'd never say it out loud, but let's be honest, this thinking has crept into a lot of outreach over the last few months. It's the same spray-and-pray we all know and roll our eyes at, but now on steroids thanks to AI. And for digital it _almost_ seems justifiable: if an email campaign misses, the financial penalty is relatively negligible, so the rational move is volume. Fire everything and see what sticks, right?

Mail won't let you do that. When each piece costs real money to send — orders of magnitude more than a digital impression — you feel actual pressure to answer the basic questions: who you're sending to, whether the list is right, what you're actually saying, whether the timing makes sense. You know, the questions we all know we're supposed to answer before we write a single word of copy. The economics aren't limitations, they're a forcing function to do better work, and it's why mail campaigns tend to be thought through in a way that email campaigns often aren't.

## What this looked like for us

Last month we launched a [self-serve portal](http://pro.faraday.ai) where you can directly pull consumer data from the [Faraday Identity Graph (FIG)](https://faraday.ai/blog/whats-in-faraday-identity-graph), and we wanted to announce it with a postcard, something we hadn't tried before. What I didn't expect was how different the creative process would be from our standard digital ad rollouts.

Our first version was built mostly with AI (let's be honest, who isn't doing that today). AI helped pick who we'd send it to, wrote most of the copy, and drafted the design. We had the whole thing mocked up in a day. If this had been an email, I'd probably have glanced at it for two minutes, and, noting that the cost to send would be negligible, said "sure, let's try it." But something at the back of my head kept reminding me that every one of these postcards was going to cost us real money, so I actually looked closer.

![The original AI-assisted Faraday postcard: the Faraday logo with the tagline "we have data for that" and faraday.ai on a pink background](/images/blog/why-direct-mail-cost-prevents-ai-slop/original-postcard.png)

This is the original, it wasn't bad. It met every requirement I’d given. But it was generic and frankly it wasn't something I'd respond to myself. We'd made _something_, _fast_. But with a little more intention, we could make _something_ _great_. So we went back to the drawing board on all of it: the list, the message, and even what we wanted the piece to feel like. And when we weren’t sure what the best path was, we didn’t hand it off to Claude, we went to the actual experts in our corner, our fantastic direct mail provider Lob, who walked us through the details that convert a campaign from acceptable to exceptional (like knowing that 6x9 postcards tend to get a better response than 4x6, who knew\!?).

![The final Faraday postcard: a "greetings from Vermont" banner over a photo of a red covered bridge surrounded by green hills](/images/blog/why-direct-mail-cost-prevents-ai-slop/vermont-postcard.jpg)

Round 2 landed on something highly personal and friendly: a casual, direct note from our Head of Sales paired with a classic VT postcard aesthetic (covered bridge and all). It was designed to feel like what makes postcards fun in the first place: a warm note from someone who was thinking of you. In this case, your old friend Dave (who happened to have an easy yes to save you some money).

![Happy customer posing with their recently recieved Faraday postcard](/images/blog/why-direct-mail-cost-prevents-ai-slop/happy-customer.jpg)

(That’s one happy customer)

## What the research actually shows

But it’s not just that we marketers get a warm, fuzzy feeling when we send a nice, intentional direct mail. The data shows this actually impacts the bottom line.

I was on [a panel with Lob, the USPS and SimpliSafe](https://www.lob.com/videos/state-of-direct-mail-2026-consumer-insights) earlier this month, digging into their [2026 State of Direct Mail research](https://www.lob.com/state-of-direct-mail/consumer-insights/2026-report), and the numbers make the case better than I can. [Nine in ten people](https://www.lob.com/state-of-direct-mail/consumer-insights/2026-report#personalization-and-conversion) say relevant, personalized mail is what moves them to act. [Two-thirds](https://www.lob.com/state-of-direct-mail/consumer-insights/2026-report#ai) now say AI has made them more skeptical of digital ads, while most are comfortable with AI in their mail — as long as it's making things more relevant rather than just making more of them. And [the households under the most financial pressure](https://www.lob.com/state-of-direct-mail/consumer-insights/2026-report#economic-pressure), the ones you'd assume have tuned marketing out entirely, are the most likely to act on mail and the most willing to try a brand they've never bought from who connected with them through direct mail.

People aren't reacting to the technology. They're reacting to whether anyone was paying attention.

## The trap is avoidable

Look at how we're all working right now. How often do you find yourself just conversing with Claude, passing prompts back and forth only to realize you could have written it yourself in 20 minutes? We've got more tools than we have problems to solve.

It's not a new take that we're outsourcing our thinking to AI — but I'd argue it's not just our thinking we're giving up. It's our intention.

For years marketers have been optimizing for attention — impressions, clicks, time on page, all the ways we measure how much of someone's day we managed to capture — but today we need to reorient. Suddenly, as we're all managing teams of autonomous (and somewhat competent) agents, we don't just need to optimize for the attention of the lead, but for our own intention. **When we're able to run 100 campaigns at once, the most important item to budget becomes focus.** This is what I call the intention economy, and it's just as critical to understand as the attention economy.

And this is where mail is instructive, because it's a channel where the option to stop paying attention never felt like an option at all. You can generate a hundred versions of a mailer in a minute; you still have to pay to put each one in a mailbox. **The bill arrives whether an AI wrote the copy or you did, but the leads might not.**

## Where that leaves the rest of your marketing

The useful thing about a constraint is that you can adopt it on purpose. Sure, postage might be the specific thing that makes you ask the right questions on your direct mail campaign — but those questions don't become less true in channels where nobody's forcing you to think about them. **You don't need a stamp to stop spraying and praying; you just need to hold your digital channels to the exact same standard.**

We need to take the reins of our campaigns and stop letting the tools, or some false sense of “efficiency” dictate our standards. The tools can scale our output, but the intention still has to come from us.

Why direct mail resists AI slop: its cost forces marketers to think before they send. I explain the intention economy and how to apply it to every channel.

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