It's 2025, and direct mail isn’t just alive—it’s thriving.

Despite the rise of digital channels, businesses continue to invest heavily in direct mail because it works. Consumers engage with physical mail in ways that digital ads simply can’t replicate. Seniors rank it as their second-favorite method of business communication, and even Gen Z—raised in the digital age—often find it a welcome reminder of the tangible world. In fact, over [one in ten](https://www.pymnts.com/credit-cards/2024/boomers-prefer-email-gen-zs-opt-for-apps-as-top-choice-for-reward-delivery/) Gen Zers say direct mail is their favorite way to hear about banking rewards, proving that a well-executed campaign can capture attention across demographics. So it’s no surprise, then, that companies are doubling down, with a [staggering 82% of businesses](https://go.lob.com/rs/900-QJF-050/images/2024-Lob-State-of-Direct-Mail-Marketing.pdf) having increased their direct mail budgets in 2024, a sharp jump from 58% in 2023\.

And the reason? Direct mail delivers. As our partner, [Lob](https://www.lob.com/state-of-direct-mail/2025-report)—one of the best direct mail providers in the business—[reports it,](https://www.lob.com/state-of-direct-mail/2025-report) nearly 4/5 of executives they surveyed last year said that direct mail was their best outreach channel. That’s how much value they see it driving for their brands. And as AI and automation tools continue to improve execution—now used by 56% of marketers, up from 40% in 2023—direct mail is becoming even more effective, making it an essential part of modern marketing strategies.

But here’s the catch: it’s not always easy to implement.

Firstly, it can come with a serious price tag. Marketers are finding themselves paying more and more each month to reach high-intent prospects via mail. The United States Postal Service (USPS) plans to raise stamp prices five times by 2027, with a significant increase expected in July 2025\. This follows a 5-cent hike in July 2024 and a series of price increases in recent years, [raising stamp costs by 36% since 2019\.](https://www.axios.com/2024/09/26/usps-stamp-price-increase-2025) Additionally, USPS has outlined plans for twice-yearly increases in 2026 and 2027, subject to approval. With rising costs adding up quickly, maximizing the efficiency of direct mail campaigns has never been more important. Secondly, even the best campaigns need to be smart to get good response rates. Even if your messaging is on point, your product is best-in-class, and your brand is well known, it’s very possible that many of those sends won’t get a glance if your campaign isn’t properly targeted.

So you’re likely starting to get the picture. That despite the cost going up, direct mail isn’t going anywhere.

But where does that leave the rest of us marketers? Those without unlimited budgets. Most of us need to carefully manage each and every dollar of marketing spend to ensure the greatest value for our brand, and that means direct mail is a fantastic but risky tool that needs to be used correctly.

## So what should we do about it?

One of the best solutions for managing the low response rate problem is comprehensive direct mail targeting. Highly targeted campaigns allow brands to send mailers to only the most promising prospects, improving conversion rates. However, this approach also has its challenges. First, fully customized direct mail campaigns can still be expensive, as they require detailed audience segmentation and personalized messaging. Second, they can be too complex for many brands, requiring significant time and resources to manage effectively. Third, they depend on high-quality data to ensure accuracy. Although Faraday customers don’t have to worry about many of these issues thanks to the [Faraday Identity Graph (FIG),](https://faraday.ai/features/faraday-identity-graph) which provides rich consumer insights on nearly every US adult and their households, for better targeting, [as well as our powerful AI models](https://faraday.ai/templates), which handle personalization and optimization, automatically, this tool is still effortful and overkill for some campaigns. Finally, postage for first class mail is three to four times the expense of bulk mailing options.

On the other end of the spectrum, the USPS offers a more budget-friendly option: [Every Door Direct Mail (EDDM)](https://www.usps.com/business/every-door-direct-mail.htm). EDDM allows marketers to send mailers to every address along a specific mail carrier’s route, without the need for custom prospect lists or addressing individual recipients. This can significantly reduce costs, sometimes as low as 20 cents per mailer, making it an attractive option for marketers looking to cut down on expenses.

However, the trade-off is clear: while the cost savings are substantial, EDDM lacks the targeting precision that custom direct mail offers. Since it sends to all households in a given area, marketers can’t ensure that their mailers are reaching high-intent or relevant prospects. This lack of segmentation means that some of the mailers will inevitably go to recipients who may have little to no interest in the product or service being advertised. As a result, brands risk wasting valuable marketing budgets on less targeted customers, potentially diluting the effectiveness of the campaign. Additionally, without the ability to target specific demographics or behaviors, the impact of the message might not be as strong or relevant, leading to lower conversion rates.

So, marketers often find themselves stuck between two good but limited options. Both can be powerful, but in some cases, neither can meet their exact needs. Do they want:

- High-cost, highly targeted direct mail campaigns, or
- Low-cost, more loosely targeted EDDM campaigns?

As is often the case, a middle ground option would often best serve the needs.

## Enter: AI-optimized EDDM

That’s exactly what Faraday has provided with our new high propensity AI-optimized EDDM service. With approximately 15 carrier routes in an average zip code, knowing which ones perform best could strike the perfect balance between cost and effectiveness. Instead of choosing between expensive, hyper-targeted direct mail and inefficient, low-cost EDDM, brands can optimize their spend by targeting the best routes available through EDDM, striking a balance between the other options.

Carrier route aggregation provides a new tool by which brands can identify the carrier routes most likely to contain high-converting prospects but this functionality isn’t entirely new. Instead it expands on other existing aggregations we offer, including those at the state and household level (among a range of other options). But this feature is uniquely designed to take advantage of the EDDM option provided by the USPS. For marketers looking to take advantage of this specific service is a more targeted and efficient way, carrier route aggregation can be the perfect answer. With it, Faraday empowers clients to:

- Identify high-propensity carrier routes
- Deploy cost-effective EDDM campaigns with improved targeting
- Reduce waste while maintaining strong performance

And it is important to note that we’re not suggesting that aggregating by carrier route level is some magic bullet that will solve all your direct mail problems. The reality is that knowing which targeting method is best suited to the situation in question is the most important question to answer for effective targeting.

Luckily for our clients, we have a team of expert, forward-deployed data scientists (as well as some pretty spiffy AI models) who can help you answer those questions or just solve the problem for you. At Faraday, you don’t just get predictions, you get targeted implementation strategies that mean those predictions can actually move your business forward, quickly.

## Client example

Let’s unpack how this is already helping one of our home service clients.

Before this new feature, this client was dropping roughly 10 million EDDM mailers a year relying on broad demographic filters like household income and age to choose carrier routes. After developing and deploying a [likely customer AI model](https://faraday.ai/templates/lead-prioritization) across their service territory, they discovered that the top 10% of routes identified by the model delivered more than **3x** the profit per route compared to their previous average.

In practice, the client was able to maintain the benefit of EDDM’s lower cost while still ensuring that mailers reach the most likely converters, resulting in significantly improved marketing and sales KPIs. The bottom line? Smarter targeting makes direct mail more efficient, cost-effective, and impactful—helping brands win more customers while optimizing their marketing spend.

## In conclusion

Direct mail isn’t going anywhere, but the way marketers use it needs to evolve. Rising USPS costs make efficiency more critical than ever, and blindly choosing between ultra-targeted campaigns or broad EDDM mailings isn’t always the smartest move. Instead, the key is flexibility—having the ability to adjust your strategy to get the best return on your marketing spend. Services like Faraday provide this flexibility and the expertise to utilize it effectively.

We’re not here to tell you that carrier route aggregation is the only answer—but we are saying that using the right tool for the job will make your direct mail campaigns more cost-effective and impactful. Sometimes, broad, untargeted EDDM campaigns make sense (like when you need to reach every homeowner on a street). Other times, hyper-targeted mailers are worth the investment. And now, with carrier route aggregation, you have a powerful middle-ground option to strike the perfect balance between reach and efficiency.

At Faraday, our goal is to equip you with the best possible tools for any situation. Whether you need pinpoint accuracy, cost-effective broad reach, or something in between, we’ve got you covered. So what’s stopping you? Hate making money? If not, let’s talk and see how Faraday can help you optimize your direct mail strategy today. [Sign up here](https://faraday.ai/signup) for a free trial (with a complimentary predictive model validation).

Faraday’s AI-optimized EDDM strikes the perfect balance between high-cost, well-targeted direct mail campaigns and low-cost, low precision EDDM by identifying the most profitable carrier routes, maximizing impact while minimizing waste.

Finding the sweet spot in direct mail: Cost-effective targeting with AI-optimized EDDM

See our complete guide: [How predictive lead scoring works — and why it outperforms traditional methods](/blog/predictive-lead-scoring-b2c)

The real estate industry is undergoing a major shift. In March 2024, the [National Association of Realtors (NAR)](https://www.nar.realtor/) reached a settlement that will change how real estate agent commissions are handled. [Under the new rules](https://www.bankrate.com/real-estate/real-estate-commission-changes/), sellers will no longer be required to offer compensation to buyers’ agents via the [MLS (Multiple Listing Service)](https://www.mls.com/). Instead, buyers and their agents must negotiate compensation directly, rather than relying on a commission included in the home’s sale price. While this change is intended to increase transparency, it also adds complexity for buyers, who must now determine how they will compensate their agent—potentially before viewing homes. This shift could lead to more negotiation hurdles and uncertainty, potentially slowing down transactions for both buyers and agents. This new landscape presents a significant challenge for real estate agents who have historically relied on the established commission structure.

In this evolving landscape, one thing has become clear: data-driven decision-making, particularly through the strategic use of lead scoring, is now essential for real estate agents to thrive. With commissions becoming more negotiable and unpredictable, agents need to focus on the highest-potential clients. Faraday’s custom predictive data points and [lead scoring models](https://faraday.ai/templates)—leveraging advanced AI to provide unparalleled accuracy in identifying and prioritizing prospects in this shifting market—help real estate professionals prioritize the right prospects, streamline their pipeline, and navigate the new commission structure with confidence.

### The impact of new commission rules on real estate agents

The most significant change in the new commission rules is the requirement for buyers' agents to have a written agreement with clients about fees before showing any properties. This means buyers will be more directly aware of how much they’re paying for representation, making it crucial for agents to clearly communicate their value.

At the same time, some predict a "price war" will erupt, with buyers' agents competing fiercely for clients in an attempt to offer lower fees. Others fear this change will create confusion and extend the home-buying process, especially for first-time buyers who are already navigating high prices and interest rates.

### How lead scoring helps

In this new environment, agents who can prioritize high-value leads will have a significant edge. Lead scoring can use data to assess which leads are most likely to convert, allowing agents to focus their time and resources on the clients who are most likely to result in a sale. Faraday’s predictive lead scoring models analyze a wide range of signals—from behavioral patterns to buying intent—to give agents a clear picture of which leads are worth pursuing.

With Faraday’s platform, agents can assess leads in real-time, adjusting their strategies quickly to align with changing market conditions. Instead of wasting time on leads that aren’t ready to engage, agents can focus on those who are most likely to convert, maximizing their chances of closing deals while keeping costs in check. Agents using predictive lead scoring have reported a significant increase in conversion rates by focusing their efforts on the most promising leads.

### Addressing the new challenges for buyers and sellers

The new commission structure also increases transparency, but it has its drawbacks. Buyers will be more aware of the costs of hiring an agent, and many may feel the pressure to negotiate commissions themselves. Sellers, meanwhile, could face more complicated negotiations if buyers demand concessions or if they struggle to cover commission fees upfront.

Lead scoring helps agents respond to these challenges by providing a data-driven way to approach both buyers and sellers. For buyers, lead scores can indicate their level of urgency and financial readiness, helping agents tailor their approach to address potential commission concerns proactively. For sellers, understanding buyer agent interest and engagement through lead scoring can inform strategies for offering competitive compensation. Agents can use lead scores to identify clients who are ready to negotiate or those who may need additional support. This allows agents to tailor their approach to meet each client's specific needs, whether that’s offering strategic advice on negotiations or focusing on clients who are already financially prepared.

### Stay ahead in a shifting market

As commission structures continue to evolve, and the world around us changes every day, the real estate industry faces both challenges and opportunities. Agents who embrace data-driven tools like lead scoring will be better equipped to adapt to these changes and thrive in a competitive market. By focusing on high-value leads, real estate professionals can ensure that they’re making the most out of every opportunity—even in uncertain times.

Faraday’s predictive lead scoring tools provide real estate agents with the insights they need to make smarter, more strategic decisions. In a world where commissions are more negotiable than ever, having a clear understanding of lead potential is key to staying ahead.

### Ready to transform your real estate business?

Learn more about how Faraday can help you navigate the new real estate landscape and stay ahead of the competition. [Request a demo](https://faraday.ai/talk-to-sales) to see Faraday in action and discover how our AI-powered lead scoring can revolutionize your business.

Faraday’s custom predictive data points help real estate agents adapt to new commission structures by identifying high-value prospects, streamlining their pipeline, and maximizing conversions in an increasingly competitive market.

Navigating new commission structures in real estate: How lead scoring can help agents adapt

**See our complete guide:** [How to get started with predictive AI in minutes](/blog/get-started-with-consumer-data-enrichment).

---

Before committing to any predictive modeling platform, you need to know if their services actually work for you. That’s where a [validation test](https://faraday.ai/blog/cross-validation-technical-blog) comes in. Here at Faraday, a validation test (such as a holdout or cross validation) lets you see how well our AI models can predict real outcomes—before you even sign up. By using your historical data, we can prove the accuracy of our models without ever seeing your future results. Essentially, if the model is predictive, that means we're a good fit for helping you decide who to send direct mail to, how to accept and organize leads with lead scoring, and more. It’s the best way to validate AI-driven predictions—before you invest, and without any risk.

## How does it work?

A holdout test works by splitting your historical data into two sets: **one we see** (pulled from your recent conversion data, this is known as the test set) and **one we don’t** (pulled from the **most** recent conversion data, this is known as the holdout). First, we use only the training set to build a predictive model. Once it is built, we generate predictions and compare them against the conversions that were ‘held out.’ If our model accurately predicts those hidden outcomes, you can be confident in its ability to drive real results.

Cross-validation works similarly, but instead of holding out a single set for testing, it splits the data multiple times into different training and validation sets. Each subset takes a turn as the validation set while the rest are used for training, ensuring a more comprehensive assessment of model performance.

If you’d like a more detailed description of how this process works, take a look at this [blog](https://faraday.ai/blog/holdout-testing-with-faraday) from our COO, Robin Spencer, or for more detailed setup instructions, see this [blog](https://faraday.ai/blog/holdout-testing-evaluate-prediction-accuracy) from our CEO, Andy Rossmeissl.

Validation testing gives you a real-world view of the model’s accuracy, providing the reassurance that our predictions will stand the test of time—by making sure the AI isn't just memorizing past data, but identifying trends that matter going forward. But for the test to work, we need the right data—uploaded securely. Here’s what you need to know to get results as quickly as possible.

## What data do you need?

To set up a validation test, you must provide **names and dates** for both **leads and conversions**.

### Leads

We need to know when someone became a lead. Use the [Leads template](https://faraday.ai/data_solicitation/leads_template.xlsx) to provide this information, making sure to include the date for each lead. A leads file should ideally include the following fields:

- **First name**
- **Last name**
- **Address** (Street, City, State, Zip)
- **Optional:** Email, Email Hash, Phone
- **Date** (e.g., the date a lead was added to your system or when they received a mailer)

These personally identifiable details (PII) allow us to match leads to our demographic dataset. Without PII, we won’t be able to validate predictions, and the validation test cannot be run.

🚧 **Note on date formatting:** You can format the date in any standard way (e.g., YYYYMMDD, MM/DD/YYYY) and our system will automatically handle it.

### Conversion event data (transactions)

We also need the dates of individual transactions—along with optional details like product type and amount. Use the [Orders/Purchases/Transactions template](https://faraday.ai/data_solicitation/orders_purchases_transactions_template.xlsx) to submit this data. The conversion file should have the same structure as the leads file (first name, last name, address, etc.), but instead of lead dates, it should contain conversion dates.

### Conversions (customer-level data)

Customer-level conversion data (e.g., just the first or last transaction date) **is not ideal** because we need each individual transaction date. Although you can provide customer-level data using the [Customers template](https://faraday.ai/data_solicitation/customers_template.xlsx), we typically do not use it for a validation test.

## Uploading your data securely

To keep your data safe, always upload it through the **Faraday dashboard** in the Datasets section. You can also connect your platform to your existing data warehouse, but as this integration is more involved, this step would likely come after your successful validation test has been completed and you’ve decided to sign up with Faraday\!

🚧 **Do not email us any data.** The Faraday dashboard is the only secure place to upload files.

### Steps to upload:

1. Go to [app.faraday.ai](https://app.faraday.ai) and sign in.

[![Sign in page](/images/blog/are-you-ready-holdout/sign-up.png)](/images/blog/are-you-ready-holdout/sign-up.png)

2. If you see a test mode warning, disable it before uploading your data.

[![Make sure to leave test mode](/images/blog/are-you-ready-holdout/leave-test-mode.png)](/images/blog/are-you-ready-holdout/leave-test-mode.png)

3. Navigate to the **Datasets** section.

[![Navigate to the Datasets section](/images/blog/are-you-ready-holdout/datasets.png)](/images/blog/are-you-ready-holdout/datasets.png)

4. Click **New Dataset** and select **CSV**.

[![Select new dataset](/images/blog/are-you-ready-holdout/new-dataset.png)](/images/blog/are-you-ready-holdout/new-dataset.png)

5. Upload your **leads.csv** file and your **conversions.csv** file.

[![Choose CSV upload](/images/blog/are-you-ready-holdout/upload-csv.png)](/images/blog/are-you-ready-holdout/upload-csv.png)

## Want a little more help?

If you're still unsure how to set up your validation test, we've got you covered. [Watch this video walkthrough](https://www.loom.com/share/ab3750fdb50d47c2b6a0fc05e96e10dd?sid=aefe17c6-ba5f-458f-986f-73db3cf43877) with Faraday CTO Seamus to see the process step by step.

And if you still have questions, reach out to us at **support@faraday.ai**—we're happy to help\!

And that’s it\! Once uploaded, we’ll take care of the rest. Ready to get started? Upload your data today and see how Faraday’s AI models can drive real results for your business.

Validate Faraday’s predictive accuracy risk-free with a holdout test—just upload your data, and we’ll prove our models work before you commit.

Are you ready for your pre-purchase holdout validation?

Score aggregation is a method we use to enhance the accuracy and stability of predictions when individual-level data is incomplete. Instead of relying solely on aggregated data points—an approach that works great when appending data but can introduce volatility in model outputs when scores are produced with these aggregated inputs—we take the next step and aggregate scores themselves. This ensures that models can still generate high-quality predictions, even when full individual identity resolution isn’t possible.

By leveraging aggregated scores, we can create smoother, more consistent predictions across varying levels of enrichment. This builds on our existing strategy of using aggregated data points for data appends but tailors it specifically for improving modeling outcomes.

## A simple example

Sounds a little complicated? It’s actually easier than you think\! Let’s break this down with a simple scenario that unpacks two hypothetical individuals:

#### John Doe – Fully identified

John lives at 456 Oak Street, and we have all the necessary PII to match him confidently in our system. Because of this, we can generate a highly individualized score based on his specific data points and history no problem. This is how our identity resolution process ideally works.

#### Jane Doe – Partially identified

Jane, on the other hand, lives at 123 Main Street, and we don’t have enough personal information to fully match her. However, we do know a lot about her household. Previously, we would have used aggregated data points from the household (or street, postal code, depending on how far out we’d need to go to find accurate information) to generate her score, but this could result in fluctuations.

Now, instead of scoring her based on those aggregated data points directly, we leverage an **aggregated score**—we average the **scores** of people in similar groups (in this case her household) rather than creating an average person based on data points and then scoring that, itself. This provides a more stable and accurate prediction, reducing potential variability and improving reliability.

[![Score aggregation diagram](/images/blog/score-aggregation.png)](/images/blog/score-aggregation.png)

## So how does it affect you?

The key impact of score aggregation is that it makes every model better. However, you might also notice changes to individual scores, and here’s why:

- **More reliable predictions:** Instead of introducing potential spikes from scoring based on aggregated data points, we now rely on aggregated scores, which reflect a more stable signal.
- **More inclusive modeling:** Even when we can’t fully identify an individual, we can still provide a meaningful prediction using data from their household or surrounding population.
- **Fewer gaps in data:** If a person’s identity is incomplete, we don’t discard the opportunity to score them—instead, we use the best available aggregated score to fill in the blanks.

### Key considerations

When implementing score aggregation, there are a few key considerations to keep in mind. First, using aggregated scores helps prevent sudden shifts in predictions that might occur when only aggregated data points are used. This approach provides a more stable and reliable signal. Second, our methods adapt based on the available data: when full PII (Personally Identifiable Information) is available, we prioritize direct individual scoring. However, when only partial data is present, aggregated scores serve as a reliable fallback. Lastly, if only household-level data is available, rather than estimating an individual’s score based on household data points, we derive a score from the averaged predictions of others in similar households, ensuring that the prediction remains accurate and meaningful despite incomplete individual information.

### In conclusion

By adopting score aggregation, we ensure that models remain robust and useful even when some data points are incomplete. This helps businesses make better decisions with confidence, regardless of data availability. If you'd like to learn more about how this—and the numerous other services and [AI use cases](https://faraday.ai/templates) that Faraday provides—could benefit your business, [reach out](https://faraday.ai/talk-to-sales)\!

Score aggregation improves prediction accuracy and stability by averaging scores from similar groups rather than estimating individual scores from aggregated data points.

Noticed a change in your score reporting? Here’s how score aggregation is improving your results

When you’re ready to use Faraday to build [AI models that deliver custom predictive data points](https://faraday.ai/features/customer-behavior-predictions), the first step is getting your data into our system. There are two primary methods to make that happen—using a data warehouse or uploading individual `.csv` files. Your best option depends on your team’s setup and resources. This guide covers how to ingest your data and best practices to make it usable for modeling as quickly as possible.

## Part 1: Locating your data

### Data warehouse vs. file upload

**Data warehouse:**  
If your data resides in a warehouse (e.g., [Snowflake](https://www.snowflake.com/en/) or [BigQuery](https://cloud.google.com/bigquery?hl=en)), integration is straightforward. Your team can provision a small, dedicated space within the data warehouse for Faraday, providing secure, efficient, and timely transfers without the hassle of repeated manual uploads. Our [connections](https://faraday.ai/docs/how-to/connections) console provides the necessary credential details for integration.

**File uploads:**  
If your data is managed as individual files, you can upload them directly via our datasets console. Although this method is simple, it is time-consuming to keep data fresh over time because of the level of effort required each time.

- `note:` Faraday is looking for `.csv` (comma-separated values) files - they can be exported from databases, various business platforms and spreadsheets alike.

  **Automation considerations:**  
  For brands using cloud storage (e.g., [Amazon S3](https://aws.amazon.com/s3/) or [Google Cloud Storage](https://cloud.google.com/storage?hl=en)), setting up automated data transfers is simple and efficient. These services can be connected to Faraday with minimal effort and can be nearly as effective as connecting directly to a data warehouse.

  An SFTP transfer option is equally suitable, but may require slightly more knowledge and access to resources within your organization.

---

## `.csv` file formatting requirements

To ensure smooth processing, follow these guidelines when preparing your CSV files:

- **Format:**
  - **_Files:_** Most of these files are comma-separated and UTF-8 encoded, ideally with a header row listing the column names. If headers are missing, you can add them in the dataset configuration's 'Advanced options'.
  - **_Data warehouse_:** Sticking as closely to simple and common types of fields such as decimals, integers, boolean (true/false) and text (string/varchar) are most effective. Tables or views are both acceptable.

- **Size:**
  - **_Files:_** Individual files can be up to 5GB in size, and generally should be fewer than 50 columns. Consider splitting larger files into smaller parts and-or removing any fields not related to _behavior_, _time_, _quality_ or _identity_.
  - **_Data warehouse_:** Generally, there is no size limit for data warehouse tables, but large tables should be handled with care because the larger the dataset, the longer the processing time.

- **Structure:**
  - **_Files:_** If providing updates to data, ensure consistent structure across all files in a dataset (cloud bucket/SFTP: "_prefix_") - column names, orders and the data types they are set to (if applicable).
  - **_Data warehouse_:** If using a table, ensure it remains up to date through a regular refresh process. Flattened tables (not nested) are preferred in most cases. Plan on establishing a set of fields to include and avoid changing them over time (such as adding or removing fields).

- **Delimiter consistency:**
  - **_Files:_** Prefer commas as standard delimiters, but this setting is customizable at the dataset level (tabs, semicolons). If necessary, ensure all columns are quoted.

- **Additional flexibility:**
  - **_Files:_** We accept zip-compressed files such as .gz or .bz2 if you are batching and saving on storage. Further information can be found in our [API docs](/docs/reference/createupload).

---

## Part 2: Data essentials for accurate predictions

Once your data is uploaded, it must include the necessary elements for precise predictions. Faraday supports various data types, but certain formats yield better results.

### Types of data

- **Event stream data:**  
  This type of data is preferred for its detailed insights into customer behavior, where each row represents an event (e.g., account creation or purchase) - with a _timestamp_. This granularity is key for understanding customer journeys. (See our [Datasets documentation](https://faraday.ai/docs/abstractions/datasets) for more details.)

  Flavors:
  - email engagement data
  - website engagement data
  - app usage data
  - order data
  - sales funnel data
  - customer journey mapping
  - direct mail response tracking

- **Customer-level (summary) data:**  
  This type of data captures one row per customer with summary information such as: purchase history metrics, customer tier, original source or lifetime value. With a joining key (e.g., customer_id), it works best when combined with event stream data.
  - `note:` Summary type data may hold valuable insights on your customers' [first-party traits](https://faraday.ai/blog/first-party-features). It _can_ also include event timestamps as additional columns when relevant and is frequently seen in CRM exports.

### Essential fields

To ensure accurate modeling, include these key fields:

- **Personally identifiable information (PII):**  
  First name, last name, address (street, city, state, postal code), email, and phone number. At least name + address or email are required at a minimum. More PII enables enhanced data enrichment through the [Faraday Identity Graph](/docs/abstractions/traits), and can unlock a world of value.

- **Event timestamps:**  
  Accurate timestamps are critical for tracking when events occur. "YYYY-MM-DD" is the preferred format, but other common selections are available via the app and API.
  - `note:` Ensure all time-based fields follow the _same_ format, whichever option you choose.

  - **Event properties:**
    Other values which are captured at the time of the event that may exist in your records. (Cost of goods, method of payment, unique products or services, result of event)

    In these cases, the additional data should answer the question: `How much value was generated for your business by this event?`

- **Unique identifiers:**  
  Each set of data should have a _primary key_ (e.g., an order number) and a _reference key_ to link said event to an individual. These data points deliver maximum flexibility to both you and Faraday through the process of updates.

### Identity resolution and matching

After your first-party data is connected, Faraday’s identity resolution software links individuals to additional third-party attributes with over 1,400 available data points and counting. Providing as much quality PII as possible improves matching accuracy and minimizes reliance on aggregated data points.

At a minimum, you must provide an email or a combination of first and last name with street address and postal code.

---

## Note on data hygiene

Organizing and labeling your data correctly is vital — poor quality input yields poor quality output every time. Clean, well-curated data leads to smoother processing and more accurate insights.

**Best practices include:**

- Maintaining consistent formatting: no type mixing across fields, names and addresses follow a similar structure, categories have fixed sets of standard values, and avoiding filler values for missingness (e.g., "None", "N/A")
- Verifying that phone numbers are a full 10 digits, accurate emails are provided, and names come with _full_ physical addresses
- Deduplication
- Ensuring you do not have large gaps in field coverage or in time between groups of events
- Not providing more than you intend to use (aside from future use cases)

Investing time in even minimal data hygiene upfront saves time and reduces the likelihood of missing a problem that was lying in wait.

---

## What’s next?

Getting your data into Faraday is just the beginning. By following these best practices, your data will be structured and ready for predictive modeling—whether you’re predicting lead conversion or conducting market opportunity analyses. If you have trouble checking all the boxes above, don't worry - Faraday will help find a solution for your needs.

Let us know if you have additional questions as you prepare your data. You can connect with support at support@faraday.ai - happy modeling!

To get started with Faraday, proper data ingestion is essential. This article offers a technical overview of the processes and best practices for preparing and ingesting your data into Faraday's system, ensuring fast and accurate predictions.

Preparing your data for seamless ingestion - a technical guide

When brands partner with [Faraday](https://faraday.ai), the goal is clear: deploy predictive models as quickly and effectively as possible to [drive business ROI](https://faraday.ai/stories/financial_services_client). But before any predictions can happen, the foundation has to be set, and that means getting the right data into the platform.

For marketers without a dedicated data scientist on their team, this step can feel like a hurdle. What data does Faraday need? In what format? How can you ensure a smooth process that avoids issues with matching and model performance?

These are big questions, and answering them correctly could be the difference between actionable predictions in a week or two versus a much slower and less efficient process.

## Start with the big picture

Before diving into spreadsheets and data exports, take a step back. The first question we ask any new client is: **_What problem are you trying to solve?_** Your goal determines the data you’ll need. For example:

- Are you looking to improve conversion rates? You’ll need historical data on past conversions.
- Want to identify high-value customers? Transactional data will be key.
- Need to optimize outreach? Engagement metrics and response data should be included.

Knowing your objective up front ensures that the data you provide aligns with your business needs.

So what data do we need? The best answer is probably to give us access to *everything—*or at least, as much as is feasible for your company. While some data points are necessary to make the right predictions, all of them can help us to build a better and more holistic understanding of your existing customer base and the best potential customers you could reach within the US population.

![Meme about how much we love data](/images/blog/welovedata-meme.png)

## So more IS better, but formatting matters

_So we’ll just give Faraday all our data as is and let them figure it out?_

**So, the answer is both yes and no.** While more data is generally better, it’s not just about volume—it’s about data quality. Brands without an established system to ensure [data hygiene](https://www.indeed.com/career-advice/career-development/data-hygiene) may encounter issues like malformed addresses (e.g., missing state or zip code), missing customer details (e.g. no address at all), or improperly formatted columns (e.g., first and last names in one field). While Faraday’s team can spot these issues early on, working with clean, well-structured data from the start will drastically speed up the process, leading to faster, more accurate predictive models.

If you're facing challenges with data hygiene, don’t hesitate to reach out. In addition to our standard data cleanup, we offer more robust professional services upon request, with a variety of packages designed to help you wrangle your datasets. Even if your data isn’t perfect, we’ll help you identify and fix any issues, setting you up for quicker results.

## So what data should I include?

At a minimum, the following fields should be included to ensure proper matching and predictive accuracy:

- **Customer identifiers:** Name, email, phone, and address (as complete as possible)
- **Leads:** Name and the date that someone became a lead
- **Conversions (transaction-level preferred):** Dates of individual transactions, along with product details and amounts (if applicable)
- **Engagement metrics:** Website visits, email opens, and interactions

#### Customer-Level vs. Transaction-Level Data

While you can provide customer-level conversion data (e.g., first or last transaction date), we usually do not need it and cannot use it for a holdout test. Instead, transaction-level data—with a date for each transaction—is preferred, as it enables more accurate predictive modeling.

And if you're unsure where to start, focus on conversion rates. Defining how your brand measures conversion—whether through completed purchases or booked appointments—helps us create more accurate predictive models and simplifies the entire process.

## Where does your data live?

Once you’ve identified the right data, the next step is understanding where it lives. Is it spread across multiple systems like a CRM, e-commerce platform, and customer support database? Do you have easy access to it and permissions to share it externally, or will you need internal approvals? For enterprise brands, pulling data may require specific requests within your organization. Understanding these logistics early on can prevent delays.

In the best case, your data lives in an organized data warehouse like [Snowflake](https://www.snowflake.com/en/), [Bigquery](https://cloud.google.com/bigquery?hl=en), or [Redshift](https://aws.amazon.com/redshift/). This makes ingress easy, as Faraday is a fully composable platform that can integrate with existing data warehouses, functionally sitting on top of them as a decisioning layer, and then automatically routing predictions to channels like [meta ads](https://aws.amazon.com/redshift/), [Shopify](https://www.shopify.com/), or many others. To learn more about our available integrations, take a look at [our integrations landing page](https://faraday.ai/integrations).

![Faraday adds a decisioning layer on top of your data warehouse](/images/blog/data-warehouse-first-strategy.png)

If your data isn’t stored in a warehouse, you can upload individual CSV files through our dashboard, but if you do make sure to pay attention to your data hygiene\!

## Data hygiene, what’s that?

Data hygiene is the practice of keeping your data clean, structured, and accurate to ensure the best possible outcomes when using predictive modeling and AI tools like Faraday. Poorly formatted, incomplete, or inconsistent data can lead to mismatches, errors, and weaker insights. Clean data doesn’t just make processing smoother—it directly impacts the quality of predictions and business decisions, **and this is the single most important thing to pay attention to when importing your data into Faraday\!**

The first step to good data hygiene is ensuring consistency in formatting. Data points such as names, addresses, and dates should follow a structured format, ideally with first and last names in separate columns and dates consistently standardized in a format like YYYY-MM-DD (I[SO 8601](https://en.wikipedia.org/wiki/ISO_8601)). Additionally, emails and phone numbers should be complete (no 7 digit phone numbers or filler values like “noemail@none.com”) to enable seamless enrichment with third-party attributes in the Faraday Identity Graph. Clean PII helps ensure the best possible enrichment rates for model performance.  
Properly labeled and complete fields prevent confusion and make it easier to match records correctly whereas missing or incomplete records can significantly weaken predictive accuracy.

Despite the fact that data gaps can be somewhat mitigated by aggregated scoring, better results are always seen when providing a sound dataset from the get-go. If certain data points are unavailable, we can still make predictions, but providing as much information as possible—especially time-bound event data—will dramatically improve our ability to analyze your data quickly and accurately.

So in conclusion, taking the time to check, clean and structure your data before ingesting it into Faraday will save time, prevent headaches, and lead to stronger, more reliable predictions. If you have questions about how to prepare your data, don’t hesitate to reach out to your Strategic Account Manager or Faraday support.

And speaking of our supportive team…

## Leveraging forward-deployed data science (FDS) for a faster start

Getting your data into Faraday doesn’t have to be a challenge—we offer multiple options to help you onboard seamlessly. Enterprise clients can access Forward-Deployed Data Science (FDS) for hands-on guidance, while all clients benefit from professional services, dedicated support, and self-serve resources to streamline data ingestion.

Our team helps eliminate bottlenecks by structuring and preparing data, optimizing predictive models, and providing strategic guidance. Whether you need help standardizing fields, refining lead suppression strategies, or aligning predictions with business goals, we ensure your models are built on a strong foundation.

For enterprise clients, FDS embeds our experts directly into your workflow, continuously refining models and adapting strategies to market shifts. No matter your level of support, we’re here to help you get started quickly and drive measurable impact from day one.

If you’d like to learn more about this available resource, take a look at our [recent blog post](https://faraday.ai/blog/FDS-changes-the-game) detailing our decision to forward deploy our data science team.

## Final checklist: Setting your team up for success

To make the handoff between your business and technical teams easier, follow this checklist:

✅ Clearly define your business objective

✅ Identify where relevant data lives

✅ Ensure core customer identifiers are included

✅ Provide date fields for key events

✅ Clarify how conversion rates are measured

✅ Work with FDS to streamline the process

By following these steps, you’ll avoid common pitfalls, speed up deployment, and get to actionable predictions faster—maximizing the impact of AI-driven insights on your business.

## In conclusion

Getting your data into Faraday doesn’t have to be complex. With the right preparation and support, you can streamline the process, move quickly from data ingestion to predictive modeling, and unlock actionable insights faster.

If you’re ready to learn more or have any questions about preparing your data for Faraday, don't hesitate to reach out. Or if you're feeling prepared to go at it alone, get started for free today, and [sign up here](https://login.faraday.ai/u/signup?state=hKFo2SBTU2FQa0dIbmg4QzBfV0kwTDczbXdiVHc5Wm1mOVB5TqFur3VuaXZlcnNhbC1sb2dpbqN0aWTZIHk0S3JUSGlUMExSSFVPejduNDdBQnpFeHBjZVVjN00xo2NpZNkgMDF6SWFodVNaV203SmRVMFNtWmVNUHMzZ2ZHcklqWTI)\!

To deploy predictive models effectively with Faraday, clients need to start with clean, well-structured data that aligns with their business objectives, ensuring smooth ingestion and accurate predictions. This blog serves as a high-level introduction to this process.

Preparing your data for seamless ingestion - a marketer's overview

**See our complete guide:** [Predictive AI for agencies](/blog/faraday-for-agencies).

---

At Faraday, we’re always looking for ways to make our tools more accessible and valuable for our users. And with our latest update, you can now download [Faraday](https://faraday.ai) [Personas](https://faraday.ai/docs/abstractions/persona_sets) as CSV files directly from the dashboard, bringing even more power to your analysis and reporting processes.

## What changed

There's now a "Download CSV" button on the Persona analysis page and under the Persona set ‘…’ menus. The CSV largely matches the layout of the Persona set analysis page. Although it’s less sortable than our internal Persona report, it’s easier to visually parse and can be used for a much broader range of applications after export.

You’ve always been able to do this in [API](https://faraday.ai/docs/reference/getpersonasets) but this new feature adds this ability to the dashboard—which makes this valuable workflow possible for non-technical marketers to use as well.

![/Graphic representation of FIG categories](/images/blog/downloadcsv.png)

## How this features helps brands and agencies

This update makes it easier than ever for both brands and agencies to leverage Faraday’s in-depth Persona analysis.

**For brands**: this means easier integration of Persona data into your internal reports, or an easier way to communicate the changes in your customers to your executive team.

**For agencies**: this means Persona data can now be integrated into customer reports, BI tools, dashboards for clients, and ultimately can have a bigger impact on the downstream activations and creative choices.

Previously, users had to manually extract Persona information and input it into reports to achieve these ends. This time-consuming process often meant only the most crucial insights were included—but as anyone who uses our Personas knows that they’re most valuable because of the depth of their analysis, so you really want to have access to everything\! Now, with just a click of the "Download CSV" button, you get a clean, visually accessible file that mirrors the layout of our Persona analysis page.

## How EmpowerFi™ is using this new feature to drive results

_This all sounds great in abstract but has anyone actually generated results with this feature?_

Well as it happens, yes\! [EmpowerFi](https://empowerfi.org/)™, a data-driven marketing firm (and one of our favorite partners) is already seeing the tangible benefits of Faraday’s new CSV download feature through their Faraday integration, [IntelliFi](https://empowerfi.org/solutions/intellifi/)™. Erin Lutz, the VP of Operations at EmpowerFi™, shared how this simple update has saved her team a significant amount of time and money.

As an agency that works with multiple clients, including credit unions and banks, Erin historically had to manually extract and input Persona data into reports. This process was time-consuming, often limiting the scope of data they could include. But with the new CSV download feature, Erin can now effortlessly download, customize, and export the information—saving valuable time while delivering more comprehensive insights, directly from Faraday’s dashboard.

This update has not only streamlined Erin’s reporting process but also allowed her to take much greater advantage of the robust Faraday Personas in a range of use cases. As a result, EmpowerFi™ can now offer clients a deeper, data-driven understanding of their target audiences, while advertising Faraday’s powerful Persona tool to their networks.

This feature has made Faraday’s Personas even more accessible, helping EmpowerFi™ continue to deliver exceptional value to their clients.

## Ready to get started\!?

Sounds pretty great, right? We think so too\! If you’re ready to get started today, reach out to me, [Dave Small](https://www.linkedin.com/in/davidtoddsmall/) and I’ll get you set up today\!

Faraday’s new CSV download feature makes it easier for customers to use Persona data outside of our platform—saving time, enhancing analysis, and making insights more accessible for non-technical marketers.

Faraday’s CSV download feature for Personas unlocks new possibilities for agencies like EmpowerFi™

When Faraday evaluates a customer list or lead file, our first job is to recognize as many individuals as possible. And we’re very good at it. [The Faraday Identity Graph (FIG)](/features/faraday-identity-graph) consistently delivers excellent match performance, confidently identifying individuals across channels and returning up to thousands of high‑quality appended data points per person. In fact, as we outline in our [recent blog on the subject](/blog/wheres-my-99-match-rate), in most consumer‑facing use cases, we surpass industry matching benchmarks, powering precise scoring, personalization, and segmentation.

But no identity resolution system (whether it’s Faraday, Experian, TransUnion, or anyone else) matches **every** individual with perfect 100% certainty. People use nicknames. They enter secondary homes instead of primary residences. Vendor files sometimes lack specific attributes. And occasionally the provided information simply isn’t complete enough to resolve a person at the individual level.

Historically, unmatched individuals came back with no appended context at all, a common limitation across identity resolution systems.

Aggregated traits change that.

## What aggregated traits are

Aggregated traits provide best‑available context for individuals we cannot match at the individual level. Rather than returning nothing, Faraday looks at the surrounding population (using a structured waterfall) to infer reasonable values.

This is not data imputation for known people. We do **not** overwrite or fill missing attributes for individuals who match successfully in the Identity Graph. We only generate aggregated values for individuals we cannot match.

Aggregated traits exist to answer a simple question:

> When a client sends us a person we can’t individually recognize, what’s the most useful information we _can_ provide?

## Identity matching at Faraday

Before getting into the specifics of aggregation, it helps to zoom out briefly. [Faraday’s matching pipeline](/blog/identity-resolution) evaluates every record using multiple levels of identity evidence: person‑level identifiers, address recognition, and household context. When we confidently match an individual, we return precise appended attributes directly from FIG.

Aggregation only comes into play when that match isn’t possible.

## How it works: the aggregation waterfall

When Faraday cannot match an individual, we progressively widen the lens of context. Each step uses only populations that are both (a) in the client‑provided list and (b) known to Faraday.

**1. Cohabitant level**  
If Faraday can identify other individuals at the same address _who share the same last name_, we aggregate their traits. This is often highly accurate for household‑level attributes (e.g., property characteristics) and reasonably accurate for some person‑level attributes.

**2. Address level**  
If no cohabitants exist, we look for any other recognizable individuals at the same address. We then aggregate their known traits.

**3. Postcode level**  
If the address is new to us, we widen the scope to the postcode. We aggregate traits from recognizable individuals within that postcode _who also appear in the client‑provided population_.

**4. Scope‑level**  
If no address or postcode matches exist, we return the aggregated value across the entire client‑provided scope population.

A few important notes:

- Aggregation only occurs when we **cannot match the person**.
- We do **not** fill in missing attributes for matched individuals (e.g., if a known person’s age is NULL, we return NULL).
- We aggregate across **all data types** (numerical, categorical, date‑based, etc.) each using appropriate rules.
- For "everybody" scopes built entirely from Faraday data, no aggregation is needed because every individual in that scope is already known in the Identity Graph.

## Why aggregated traits matter

Aggregated traits make unmatched individuals actionable. Most clients still need to engage the people they can’t match, and without aggregation, those records would come back with no appended context at all, removing them from scoring, segmentation, routing, and personalization.

Instead of returning a blank row, aggregated traits provide structured, business‑relevant context based on the surrounding population _you_ provided.

This mirrors how sales and marketing teams make decisions every day. When two leads look similar, but one lives in a higher‑value ZIP code or a certain property type, that context shapes how outreach is prioritized. Aggregated traits allow your automated systems to apply that same practical, common‑sense reasoning, even when a perfect individual match isn’t possible.

## How this might show up in your industry

### Aggregated traits for home services

Call centers and installers deal with messy lead files every day: typos, short forms, secondary addresses. Aggregated traits help teams spot homeowners in high‑value neighborhoods, recognize likely property types, and prioritize leads that fit install eligibility, even when the exact match isn’t possible.

### Aggregated traits for retail & e‑commerce

Retailers often need to market to prospects who aren’t yet full customers. Aggregated traits help illuminate neighborhood affluence, household makeup, or lifestyle clues that make direct mail, audience building, and onsite personalization more effective, especially for lightly identified shoppers.

### Aggregated traits for retail & e‑commerce credit unions & financial services

Membership and loan pipelines frequently include incomplete or inconsistent records. Aggregated traits help maintain a baseline understanding of risk, intent, and opportunity across the entire applicant list, supporting eligibility checks, prequalification workflows, and smarter outreach.

Across all verticals, the principle is the same: when the alternative is returning _nothing_, providing the best available context leads to better targeting, sharper prioritization, and more confident downstream decision‑making.

## Transparency and control

Aggregated traits are currently enabled by default for client‑provided scope populations. If your team prefers not to use aggregated traits (or wants to discuss configuration options) your Faraday team can work with you directly.

## To wrap things up

Aggregated traits provide useful, structured context when we cannot match an individual, all without modifying or imputing data for people we do know.

This feature ensures broader feature completeness, supports downstream scoring and modeling, and helps teams work with the full lead population they care about. If you'd like to learn more, reach out to your account executive!

Aggregated traits add best-available context for people we can’t match, using nearby population data so unmatched leads stay usable without altering known individuals.

What's new: Aggregated traits provide consumer context even when individuals aren’t recognized

In today’s fast-paced, data-driven world, many businesses struggle to harness the full potential of predictive analytics. From disjointed data systems to resource constraints, the complexities of implementing machine learning in-house can be overwhelming. At Faraday, we’ve developed a solution to this challenge: forward-deployed data science. Making our experts directly accessible to your team, we make advanced analytics actionable, driving measurable business growth and transforming how you make decisions.

This approach has been so impactful that even industry giants like [OpenAI](https://openai.com/) have since adopted similar models, recognizing the value of integrating specialized expertise directly into client operations. Here's how this innovative model is changing the game for our clients.

## The power of embedded expertise

Forward-deployed data science means our experts work closely with clients—integrating directly into their existing workflows, providing hands-on support, and ensuring that predictive models are not just built, but actively refined and applied to achieve business goals. Rather than a one-size-fits-all solution, this approach allows us to tailor insights and strategies that align with each client’s unique needs, systems, and data environments.

By embedding data science expertise directly into our clients’ operations, we eliminate bottlenecks, accelerate implementation, and ensure that predictive models are continuously optimized. This means businesses don’t need to hire and train an in-house team or struggle with the complexity of data preparation, model iteration, and deployment on their own—just to get started. Instead, they gain a direct pipeline to experts who can streamline the entire process.

[![Image of Faraday data science team](/images/blog/FDS-team.png)](/images/blog/FDS-team.png)
_Some of Faraday’s wonderful data science team_

## What forward-deployed data science enables

By taking a hands-on approach, our data science team enables businesses to:

- **Ensure data readiness:** Clean, structured, and well-prepared data is the foundation of any successful predictive model. Our team assists in transforming disparate data sources into a cohesive, usable format—eliminating inconsistencies, standardizing fields, and enriching datasets where needed.
- **Optimize predictive models:** Many organizations have valuable data but lack the resources to develop, test, and refine predictive models. Our experts work iteratively to build, optimize, and validate models, ensuring that they accurately reflect real-world business scenarios and drive meaningful outcomes.
- **Drive smarter marketing & outreach:** Whether through digital channels or direct mail, predictive analytics can significantly improve targeting and campaign efficiency. Our data scientists help businesses identify the most valuable audience segments, test and optimize strategies, and measure ROI with precision.
- **Streamline data science without internal constraints:** Companies without a dedicated data science team—or those with limited capacity—can now access cutting-edge predictive modeling without the burden of managing the complexities themselves. Our team handles the heavy lifting, allowing businesses to focus on execution and strategy.

## Real clients, real wins

This sounds great in the abstract, but how does it look in practice?

Well, for one of our clients, a national financial services brand looking to optimize its direct mail program through lead suppression, the wins have already been rolling in thanks to our forward-deployed data science program. Let’s unpack how:

#### **Refining lead suppression strategies**

At Faraday, good enough is never good enough. That’s why, even after a strong initial ROI showing, we’ve continued to work closely with this client. Our ongoing partnership means updating models weekly with fresh data to provide the most accurate and actionable lead cutoff recommendations. This constant refinement ensures we only suppress the least-likely-to-convert individuals, saving money on poor-fit leads while retaining valuable opportunities that might otherwise fall below a less precise cutoff.

#### **Turning predictions into strategy**

Our partnership is about more than just predictions—it’s about using them to drive real value. From day one, we’ve collaborated to ensure that this client understands how to strategically choose the best models for their needs and the right data to fulfill their business objectives.

Because, after all, our models (and other predictive models for that matter) are extremely powerful, but their true value is only realized when you know how to leverage them effectively. At Faraday, you get both the models and the expertise. And through our mentorship, clients like this financial services provider are empowered to align predictive insights with their unique goals, ultimately maximizing their ROI.

## The impact on businesses

The move to forward-deploy our data science team is already proving to be a major advantage for our clients. Businesses that once struggled with fragmented data, inefficient modeling processes, and underperforming marketing initiatives now have a seamless way to integrate predictive insights into their decision-making. With ongoing expert support, they can adapt models to shifting market conditions, validate results with confidence, and unlock new growth opportunities.

At Faraday, we believe that data science should be accessible, effective, and deeply embedded in how businesses operate. By bringing our expertise directly to our clients, we’re ensuring that predictive analytics isn’t just a tool—but a true competitive advantage.

Ready to explore how forward-deployed data science can transform your business? [Let’s talk](https://faraday.ai/talk-to-sales).

Forward-deployed data science at Faraday embeds expert support directly into client operations, ensuring predictions are actionable, continuously optimized, and drive measurable business growth.

How forward-deployed data science drives real business outcomes for Faraday’s clients

**See our complete guide:** [How to get started with predictive AI in minutes](/blog/get-started-with-consumer-data-enrichment).

---

A lot of our blogs end with a similar CTA: “Want to get started for free? [Sign up today](https://login.faraday.ai/u/signup?state=hKFo2SBTU2FQa0dIbmg4QzBfV0kwTDczbXdiVHc5Wm1mOVB5TqFur3VuaXZlcnNhbC1sb2dpbqN0aWTZIHk0S3JUSGlUMExSSFVPejduNDdBQnpFeHBjZVVjN00xo2NpZNkgMDF6SWFodVNaV203SmRVMFNtWmVNUHMzZ2ZHcklqWTI)\!”

And indeed, if you're looking to get started with Faraday, the first step is to create an account.

But how exactly does that process work? Whether you're new to predictive AI or just eager to see what Faraday has to offer, this guide will walk you through the steps so you can quickly gain access to our powerful platform.

### Step 1: Visit faraday.ai

To begin, navigate to [Faraday’s website](https://faraday.ai). This is the official platform where you can sign up and explore our predictive AI solutions.

### Step 2: Click on "Sign up for free button"

Once you're on the homepage, look for the purple, "[sign up for free button](https://login.faraday.ai/u/signup?state=hKFo2SBTU2FQa0dIbmg4QzBfV0kwTDczbXdiVHc5Wm1mOVB5TqFur3VuaXZlcnNhbC1sb2dpbqN0aWTZIHk0S3JUSGlUMExSSFVPejduNDdBQnpFeHBjZVVjN00xo2NpZNkgMDF6SWFodVNaV203SmRVMFNtWmVNUHMzZ2ZHcklqWTI)" button in the top-right corner. Click on it to begin the registration process.

[![Sign up button location](/images/blog/signingup-with-fdy-blog/sign-up-button.png)](/images/blog/signingup-with-fdy-blog/sign-up-button.png)

### Step 3: Enter your information

You'll be prompted to enter some basic details, including:

- Your full name
- Work email address
- Company name
- Password (ensure it meets security requirements, we do love our security)

[![Dashboard view](/images/blog/signingup-with-fdy-blog/integrations.png)](/images/blog/signingup-with-fdy-blog/integrations.png)

### Step 4: Verify your email

After submitting your details, you'll receive a verification email. Open your inbox and click the confirmation link to activate your account. If you don’t see the email, check your spam folder or request another verification email.

### Step 5: Log in & get started

Once your email is verified, return to [Faraday’s login page](https://faraday.ai/login), enter your credentials, and start exploring the platform.

At this point you’ll find yourself on our Dashboard (although all this information can also be accessed through our API).

[![Integrations landing page](/images/blog/signingup-with-fdy-blog/dashboard.png)](/images/blog/signingup-with-fdy-blog/dashboard.png)

### Step 6: Add your data

Now that you're in the Faraday app, the next step is to add your customer data. A great starting point is uploading transaction data, which allows you to generate customer-focused predictions. The easiest way to do this is by using a CSV file exported from your data infrastructure. Ideally, this file would include first name, last name, address, and phone number to facilitate matching but give us what you have and we’ll work from there.

Simply upload your file, map the relevant fields, and let Faraday begin building your transaction event stream in the background.
Most users eventually connect directly to their data warehouses or cloud storage for automated data ingestion—learn more about that in our [documentation](https://faraday.ai/docs/how-to/streams/transaction). Once your data is in, you're ready to start making predictions and uncovering valuable insights\!

### Step 7: Explore use cases and integrations

Now that you have access to Faraday, take some time to explore how you can apply our powerful predictive models to your business case. Browse our pre-built [templates](https://faraday.ai/templates) to see how Faraday can help you make smarter decisions with AI-driven insights.

Additionally, check out our wide range of [integrations](https://faraday.ai/integrations) that seamlessly connect with your existing tech stack. Whether you want to add a decisioning layer to your data warehouse (like [Snowflake](https://faraday.ai/integrations/predictions-with-snowflake)) or send predictions directly to your sales and advertising platforms like [Shopify](https://faraday.ai/integrations/predictions-with-shopify) or [Google Ads](https://faraday.ai/integrations/predictions-with-google-ads), Faraday makes it easy to integrate predictive AI into your workflow.

## More things to do

### Meet the Faraday team

But our technology isn’t our only core differentiator, another one is our people. Our data science team is forward-deployed, ensuring that you get hands-on expertise to tailor AI solutions to your needs. And our sales team? They’re truly the best in the business. Exploring the platform on your own is great, but if you really want the lowdown on Faraday, reach out to talk to Dave, Nate, Charles, or any of our other incredible team members who are ready to help.

### Learn about the Faraday Identity Graph

At the heart of our predictive AI is the [Faraday Identity Graph](https://faraday.ai/docs/abstractions/traits)—a robust dataset containing over [1,400 rich attributes and insights on nearly every U.S. consumer and household](https://faraday.ai/features/faraday-identity-graph). This consented third-party dataset includes the demographic, behavioral, and geographic information you need to push your models to the next level. Whether you're looking to enhance your predictions or simply enrich your data with appends, the Faraday Identity Graph is here to help.

### Troubleshooting & FAQs

**Q: I didn’t receive a verification email. What should I do?** A: Check your spam folder or click on "Resend Verification Email" from the sign-up page.

**Q: Can I use a personal email to sign up?** A: Faraday is designed for businesses, so we recommend using a work email for a smoother experience.

**Q: Where can I get help if I run into issues?** A: Visit our [support page](https://faraday.ai/contact) or contact our team at support@faraday.ai.

### Get started today\!

Creating a Faraday account is quick and easy. Follow these steps to get started, or if you'd rather talk to someone, say hi to [Nate](https://www.linkedin.com/in/nathan-novotny-3484227a/)!

This guide walks you through the simple steps to sign up for Faraday, add your data, explore AI-driven insights, and integrate predictive models into your workflow.

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