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 traits—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 traits 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 traits 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 traits 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 traits 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 traits 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 traits, 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 traits 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 traits, 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 agents](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 traits.

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 Agents](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 .zip if you are batching and saving on storage.

---

## 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](https://faraday.ai/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 datapoints 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 1,500 available traits and counting. Providing as much quality PII as possible improves matching accuracy and minimizes reliance on aggregated traits.

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](http://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. Datapoints 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

You know your customers—but do you know how to quantify them? Have you ever considered that something as seemingly random as **"roof type"** or **"interest in boating"** might influence whether someone wants to buy your product? The reality is, we’re all made up of a range of traits, and we don’t always know which ones will most impact purchasing behaviors.

That’s why the [Faraday Identity Graph, a gigantic set of 1,500+ customer attributes](https://faraday.ai/features/faraday-identity-graph) is so valuable. It captures both the obvious and the unexpected, giving businesses an edge in understanding what truly drives conversions. Still not convinced? Let’s take a look at some of the weirdest, most surprising traits that can help businesses make smarter decisions.

*Now, please note that these are hypothetical examples—after all, we wouldn’t want to disclose any of our current customers’ “secret sauce.” However, these vignettes illustrate the unexpected ways our FIG has informed value-driving outcomes.*

## Vignettes: expected problems with unexpected solutions

### Predicting insurance customers who need premium coverage

* **Challenge:** An insurance provider wants to upsell premium policies to the right customers but relying solely on income data hasn't been cutting it.

* **Unexpected solution:** "Spending habits percentile" — Individuals who consistently spend at higher levels tend to value protection for their assets and financial well-being. By identifying high-spending individuals, the insurer can target them with personalized offers for premium life, home, and auto insurance policies, ensuring they have the right coverage for their lifestyle.

### Finding the best locations for a new coffee chain

* **Challenge:** A growing coffee brand needs to find locations with high morning foot traffic.

* **Unexpected solution:** "Propensity to own a dog" — Dog owners tend to follow a routine and take their pets for walks in the morning, making them prime candidates for coffee shop visits. By targeting neighborhoods with a high concentration of dog owners, the coffee brand can identify areas with a ready customer base for morning foot traffic.

### Predicting high-end jewelry buyers

* **Challenge:** A trendy jeweler is aiming to branch into new markets. They know some indicators like income level and location are relevant, but what else might help?

* **Unexpected solution:** "Garage parking spaces" — People who have garage parking are often homeowners with higher incomes, typically living in more affluent neighborhoods. This demographic is more likely to invest in luxury goods, including high-end jewelry. By targeting individuals with garage parking, jewelry brands can identify customers who are more inclined to make premium jewelry purchases, such as fine watches, diamond rings, or luxury accessories.

### Finding the right audience for debt consolidation services

* **Challenge:** A debt consolidation provider wants to reach consumers who are likely to need their services but may not be actively searching for help.

* **Unexpected solution:** "Catalog Showroom credit card usage" — People who frequently use catalog showroom credit cards may have higher levels of revolving debt, often at steep interest rates. By identifying these consumers, the provider can target individuals who are likely to benefit from consolidating their debt into a lower-interest option, offering relief before they fall into deeper financial strain.

### Identifying high-value clients for a financial services firm

* **Challenge:** A financial services company wants to identify potential high-value clients for premium investment products.

* **Unexpected solution:** "Vehicle category \- luxury cars" — People who own luxury cars tend to have higher disposable income and an interest in premium goods and services. By identifying this segment, the firm can target prospects who are more likely to invest in high-end financial products, such as wealth management services, private banking, or exclusive investment opportunities.

## Why AI sees what humans miss

Does all this sound a little far-fetched? Well, we don’t mean to offend you, but that might be because you’re a lowly human. The whole reason predictive AI agents are so valuable is their ability to recognize patterns we might never think to look for.

We rely on AI because it takes care of the busy work—handling repetitive tasks far more efficiently than we ever could—but more importantly, AI spots the connections we don’t. If 90% of people with Dutch gabled roofs buy BMWs, our models will detect that pattern, even if no human ever thought to check.

Faraday’s FIG does exactly this across 1,500+ attributes, helping businesses uncover the surprising, data-backed insights that fuel real growth.

## Breaking FIG down

Faraday’s Identity Graph (FIG) is what makes all of these insights possible. By synthesizing these unique attributes across hundreds of millions of U.S. adults, FIG gives businesses an unparalleled ability to understand and predict customer behavior.

But let’s be honest—1,500 is a lot of data points to consider. To help make sense of it all, we’ve broken FIG down in the following graphic to show the scale of the 1,500+ attributes and also breaking them out by category. A more detailed listing of all FIG attributes can be found and downloaded as CSV [here](https://faraday.ai/features/faraday-identity-graph).

![](/images/blog/fig-visual-diagram.png)

FIG doesn’t just provide more data—it provides better, structured, and predictive-ready data that businesses can actually use to identify new connections that they'd likely never uncover on their own.

If you’d like to learn more about this powerful resource, take a look at the other blogs in this series\!

## Closing thought

Sometimes, the smallest, most unexpected data points unlock the biggest opportunities. FIG delivers both the obvious and the hidden predictors—helping businesses make smarter, data-driven decisions.

Want to see how these insights can transform your business? [Let’s talk](https://magic-pr-5668-faraday-ai.vercel.app/talk-to-sales)\!

Faraday's Identity Graph (FIG) includes unexpected customer traits—like "roof type" or "interest in boating"—and detects patterns amongst them that humans might miss, helping businesses make smarter, data-driven decisions.

The unexpected data that powers smarter predictions

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](http://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.

## Faraday’s ongoing commitment to efficiency

This change also highlights a broader goal of Faraday. We believe AI tools should generate value but they should also automate processes so you can save time as well as money. That’s why we recently started describing our workflows as Agentic, and this is just another small step in an ongoing process (that we’ll always keep aiming to improve) to make our product as easy to use as possible.

This new CSV feature reflects our broader ideology: automation should streamline your work without compromising the depth of insight you need to drive business results. We're committed to continuously enhancing our platform to make your job easier.

## 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™

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

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 agents 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) (or AI agents) 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.

![][image4]

## 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 [1,500 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/support) 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.

How to create a Faraday account: A step-by-step guide

## How direct mail lead suppression works at Faraday

In the highly competitive financial services industry, direct mail remains a powerful tool for customer acquisition. However, without precise targeting, companies often waste significant resources sending mail to leads who are unlikely to convert. This inefficiency inflates marketing costs and dilutes campaign effectiveness.

Faraday’s predictive [lead suppression](https://faraday.ai/templates/lead-suppression/recharge) AI agent solves this challenge by leveraging data-driven insights to eliminate low-intent leads from direct mail campaigns. This process follows a structured approach:

### Step 1: Data ingestion & enrichment

Faraday begins by securely ingesting the client’s lead data. Using the  [Faraday Identity Graph](https://faraday.ai/docs/abstractions/traits) (FIG)—which includes over 1,500 attributes on 240 million U.S. consumers—our platform enriches this data with key demographic, behavioral, and property insights.

### Step 2: Predictive modeling & trend analysis

Our machine learning algorithms analyze historical conversion patterns to identify traits associated with high-intent buyers—specific to the brand. This predictive analysis allows us to assign a probability score to each lead, ranking them from most to least likely to convert.

### Step 3: Lead decile segmentation

Leads are grouped into ten deciles based on their likelihood of conversion. This segmentation provides a clear understanding of which leads offer the highest return on marketing investment and which are unlikely to engage.

### Step 4: Establishing a suppression threshold

We work with clients to establish a **cut-off threshold**, ensuring that leads below a certain probability score are removed from direct mail outreach. This helps reduce wasted marketing spend while increasing the efficiency of each campaign.

### Step 5: Optimized mailing list delivery

Once the suppression model has been applied, we provide the client with a refined, high-value lead list, ensuring that marketing efforts are directed toward the most promising prospects.

## How lead suppression adds value to financial services clients

For financial services companies—especially those in debt consolidation, lending, and wealth management—**precision targeting** is critical. Predictive lead suppression delivers multiple key benefits:

### Reduced marketing waste

By removing low-intent leads from direct mail campaigns, financial institutions significantly cut costs associated with printing and postage, maximizing their marketing budget.

### Higher conversion rates

Focusing outreach on high-intent consumers improves conversion rates, leading to greater customer acquisition efficiency.

### Enhanced customer insights

Predictive modeling not only identifies which leads are least likely to convert but also provides valuable insights into the characteristics of high-intent buyers, allowing for more refined targeting in future campaigns.

### Increased return on investment (ROI)

By reallocating marketing spend to the most promising prospects, companies see a higher ROI on direct mail efforts, turning a traditionally broad-channel marketing approach into a high-precision acquisition strategy.

## Case study: Debt consolidation company saves over $100K per month

### Challenge: High direct mail costs with low returns

A leading [debt consolidation company](https://faraday.ai/stories/debt-consolidation-company) relied heavily on direct mail for customer acquisition but was struggling with inefficiencies. A large percentage of their outreach was going to low-intent consumers, inflating costs and reducing campaign effectiveness.

### Solution: Faraday’s predictive lead suppression

The company partnered with Faraday to implement a [custom lead suppression model](https://faraday.ai/templates/lead-suppression/recharge), which analyzed their historical customer data alongside external consumer attributes to pinpoint the least-likely-to-convert leads.

### Implementation: Multi-segment predictive modeling

While the initial suppression model provided immediate savings, Faraday’s data science team took optimization further by identifying four distinct customer segments within the company's audience. Instead of using a single predictive model, we developed four tailored models, each optimized for its respective segment. A rigorous validation process confirmed that this approach yielded significantly better results.

### Results: Immediate cost savings & higher revenue

By implementing Faraday’s predictive suppression approach, the company achieved:

* **600,000 fewer** pieces of wasted direct mail per month  
* **$100,000+ in monthly savings** on direct mail costs  
* **An additional $20,000 per month** in revenue due to higher targeting precision  
* **10x ROI** on their predictive modeling investment

This company didn’t just reduce waste—it transformed how they acquire customers. By leveraging AI-driven predictive suppression, they turned direct mail into a high-precision growth engine, ensuring that every marketing dollar was spent on reaching the right audience.

### In conclusion

For financial services firms, predictive lead suppression is a game-changer. By eliminating low-value leads and refocusing efforts on high-intent prospects, companies not only reduce costs but also increase customer acquisition efficiency and boost revenue. With Faraday’s AI-driven insights, financial institutions can turn their direct mail campaigns into powerful, data-driven marketing strategies that drive sustained growth.

Want to harness this predictive power for your brand? [Sign up today](https://login.faraday.ai/u/signup?state=hKFo2SBYd0tiZ1hRSmU4UlhCV052VFI4dzBzUWx5bU9pejM1a6Fur3VuaXZlcnNhbC1sb2dpbqN0aWTZIFhhbDZFLW9YSWxJd2NBWmNmYWFld0hXdUR5WGFJakNxo2NpZNkgMDF6SWFodVNaV203SmRVMFNtWmVNUHMzZ2ZHcklqWTI)!

Faraday’s predictive lead suppression leverages AI to remove low-intent leads from direct mail campaigns, helping financial services companies quickly maximize marketing efficiency and ROI by only targeting high-value prospects.

Direct mail lead suppression for financial services clients

You know how valuable predictive analytics could be to your business, but you haven’t signed up yet. So, what’s holding you back?

For many businesses, and especially those in notably security-conscious industries like healthcare and finance, data security is the biggest concern. You want the insights that AI and predictive analytics can provide, but you need to know your customer data is safe. At Faraday, we understand that concern. That's why we’ve built our entire platform around safeguarding your sensitive data, from collection to analysis.

In this blog, we’ll walk you through the steps we take to ensure your data remains secure, private, and ethically handled, so you can trust us with the insights that power your business growth. But first, we want to talk about our Faraday Identity Graph and why we take our security so seriously.

### The Faraday Identity Graph, and why we take privacy so seriously

The [Faraday Identity Graph (FIG)](https://faraday.ai/docs/abstractions/traits) is at the core of everything we do. It’s a powerful tool that [allows us to generate accurate, actionable insights from U.S. consumer data](https://faraday.ai/blog/how-faraday-enriches-first-party-data-with-fig), helping brands understand their customers at a deeper level (we also sell this data with [data appends](https://faraday.ai/blog/data-appends-blog)). But with great power comes great responsibility. We recognize that the data we work with is highly sensitive, and we take every precaution to handle it with the utmost care.

FIG isn’t only concerned with unlocking insights; it’s also designed with privacy as a top priority. [From the very beginning, we’ve built it with robust security features to ensure that consumer data is kept safe and secure.](https://faraday.ai/security) Whether it’s protecting personal information or ensuring compliance with privacy laws, our mission is to uphold the trust you place in us by following the strictest data handling practices.

So let’s start at the top and take a look at some of the foundational security practices we follow to safeguard your data.

### SOC 2 compliance

[SOC 2 (System and Organization Controls 2\)](https://secureframe.com/hub/soc-2/what-is-soc-2) is a security framework designed to ensure that organizations protect customer data from unauthorized access and security incidents, and is often viewed as the gold standard for this purpose. It evaluates five key areas: security, availability, processing integrity, confidentiality, and privacy.

At Faraday our compliance with SOC 2 compliance standards means we undergo rigorous audits of our security practices every year, with an independent third party verifying that our data protection measures meet the highest industry standards.

Our security policy is built around important principles like the "principle of least privilege" (where users only have access to the data they absolutely need), prevention of supply chain attacks, and strict access controls. We also participate in  [SOC 2 Type II](https://secureframe.com/hub/soc-2/type-1-vs-type-2) audits, which not only check that security controls are in place but also assess their effectiveness over time. This ongoing process ensures that we continuously meet top-tier security protocols and maintain a secure environment for your data.

### Compliance with privacy regulations

To protect your consumer data, we comply with key privacy regulations, including [HIPAA](https://www.cdc.gov/phlp/php/resources/health-insurance-portability-and-accountability-act-of-1996-hipaa.html), [CCPA](https://oag.ca.gov/privacy/ccpa), and [GDPR](https://gdpr-info.eu/), to ensure your data is secure and handled responsibly.

For CCPA and other state privacy laws, we offer an easy opt-out system, allowing individuals to remove themselves from our datasets. What’s more, we extend this opt-out through a reciprocal process to our vendors, so when either Faraday or a vendor receives an opt-out request, the data is removed from both systems—ensuring full compliance across our ecosystem.

As for GDPR, since our Faraday Identity Graph focuses on U.S. data, we proactively filter out non-U.S. data at the "perimeter," ensuring we only process data we are authorized to use.

When handling Client data, Faraday operates as a Data Processor/Service Provider, meaning we will only use client data for the specific contracted purpose and will support our client’s own compliance with state data privacy laws. 

### Data vendor standards: permissioned and ethically distributed 

Ensuring responsible data handling goes beyond secure storage and processing—it also means sourcing data from reputable vendors. At Faraday, we take extra care in selecting the data providers we work with.

We only collaborate with vendors who explicitly affirm in their terms of service that their data is properly permissioned, legally obtained, and authorized for use and distribution. We are committed to maintaining the highest standards of ethical data usage, ensuring that every piece of data we use aligns with the proper permissions and legal requirements so you can confidently leverage accurate, responsible insights.

### Addressing bias in consumer data

As a data provider, Faraday acknowledges the reality that consumer data is inherently shaped by [historical and systemic biases](https://developers.google.com/machine-learning/crash-course/fairness/types-of-bias). We acknowledge these biases but take active steps to mitigate their impact on our models and predictions.

For example, mortgage lending and credit scoring have historically exhibited bias, with certain demographic groups, particularly minorities and women, facing higher rejection rates or higher interest rates due to systemic biases embedded in financial models. These models often relied on historical data that reflected past discriminatory lending practices rather than objective risk. 

To avoid perpetuating these systemic biases, Faraday takes a range of actions. For instance, we normalize data to reduce geographic disparities and offer bias detection and mitigation strategies to address disparities along sensitive dimensions such as age and gender. 

Responsible AI and ethical data practices are at the forefront of our methodology, ensuring that our models and insights promote fairness and inclusivity.

### Secure data handling practices

Faraday accepts data exclusively through secure channels—no emailed files or unprotected transfers. We also support hashed data ingestion and egress, allowing brands to share encrypted email addresses and phone numbers for added security. While hashing is not a foolproof privacy solution, it remains a valuable safeguard, and we ensure it is an option for those who prioritize additional data protection.

### Customer data is never shared between clients

One of the most common questions we receive is whether we share customer data across different client accounts. The answer is unequivocally no. Our terms and data processing agreement (DPA) explicitly state that client data is used solely for delivering services to that client, and it is never shared, pooled, or combined with other clients' data in any way. We believe that maintaining clear, stringent data boundaries is essential for protecting client trust and upholding our ethical standards.

### Conclusion: a commitment to ethical data handling and security

At Faraday, we don’t just meet industry standards—we set them. From rigorous security audits to responsible data sourcing and compliance with privacy regulations, we prioritize the safety, privacy, and ethical handling of your data at every step. Our commitment to transparency and secure practices ensures that your customer data is protected, and our proactive efforts to address biases help foster fairness in every insight we generate.

By choosing Faraday, you’re not just getting advanced predictive analytics—you’re gaining a trusted partner dedicated to upholding the highest standards of data security and privacy. We believe that responsible data use is the foundation of meaningful insights and lasting business success.

If you have any questions about our data security policies or compliance measures, don’t hesitate to reach out. We're here to help you navigate the complexities of data protection, so you can focus on what truly matters—growing your business with confidence.

Safe and responsible handling of data is a primary priority at Faraday, but what does that really mean? In this blog, we'll unpack some of the ways we ensure your data remains secure, private, and ethically managed throughout our platform.

How Faraday ensures secure and responsible customer data handling

*Note to reader: The following is the perspective of a lowly marketer who has followed these debates closely, but it’s worth noting that these discussions have passed the desks of far more knowledgeable people. So if you want to know what [Spinoza](https://homepages.uc.edu/~martinj/Spinoza_&_Hobbes/English/Spinoza%20-%20Complete%20Works%20$2002$.pdf) and [Descartes](https://yale.learningu.org/download/041e9642-df02-4eed-a895-70e472df2ca4/H2665_Descartes%27%20Meditations.pdf) really said, read their work\!*

Imagine a world where a computer can write a poem that ‘rivals’ Shakespeare, but struggles to understand the simple concept of a joke. This is the paradoxical reality we often find ourselves in when working with large language models (LLMs) like [OpenAI’s](https://openai.com/) [GPT-4](https://openai.com/index/gpt-4/). 

We’re all hearing a lot about LLMs—AI systems that generate text, answer questions, and even compose blogs (although we promise this one comes from a certified [real boy](https://giphy.com/gifs/uyG3UwkbgTaOevKe9C)). These models are rapidly reshaping workflows across industries, automating tasks that once required human effort—from customer support to legal document review to coding assistance. But as they become more embedded in our work and lives, it’s worth asking: Are they truly intelligent? And, more importantly, what do we even mean by ‘intelligence’ when it comes to machines?

To unpack this question, let’s compare two different popular approaches to AI: **conversational** and **predictive**. While both are powerful, they operate in very different ways. Let’s take a closer look at how Conversational AI like GPT-4 works and how it compares to Predictive AI like [Faraday](http://Faraday.ai).

### Conversational AI vs. Predictive AI

[Conversational AI](https://cloud.google.com/conversational-ai?hl=en), like GPT-4, excels at generating human-like text. Trained on vast amounts of data, it learns patterns in language by analyzing how words and phrases relate to each other across different contexts. At its core, it predicts the most statistically likely next word in a sequence—similar to the predictive text on your phone’s keyboard, but on a much grander scale. This allows it to mimic conversation convincingly, but it doesn’t necessarily understand what it’s saying. It can generate fluent responses, but it struggles with complex logic, [math](https://garymarcus.substack.com/p/math-is-hard-if-you-are-an-llm-and), or real-world reasoning, which requires understanding beyond mere pattern recognition.

Think of it like a high-speed version of Mad Libs. While it can generate coherent and even insightful responses, it doesn’t understand the meaning behind them—it’s simply identifying statistical patterns and filling in the blanks. On that basis, one could argue that its intelligence is primarily performative—more like an advanced pattern recognition game than genuine comprehension or thought. 

Of course, this raises a deeper question: What actually counts as thought? Philosophers, cognitive scientists, and AI researchers have debated this for decades, and we’re not here to settle it today. But for the sake of this discussion, let’s focus on the practical distinction between AI that emulates behavior and AI that augments decision-making.

[Predictive AI](https://faraday.ai/templates) (like [Faraday](http://faraday.ai)), on the other hand, isn’t built to simulate conversation. Instead, it’s designed to analyze complex patterns in structured data and forecast outcomes, helping humans to make smarter, faster decisions. While Conversational AI generates responses based on linguistic patterns, Predictive AI identifies meaningful relationships between behaviors, demographics, and other variables—surfacing insights that would be difficult or impossible to detect manually. In that sense, Conversational AI is designed to  *sound human*, whereas Predictive AI is designed to *enhance human decision-making* by providing precise, data-backed foresight.

Let’s unpack how predictive AI works with a parallel from science fiction.

### A mathematical model for predicting the future

In Isaac [Asimov’s 1940s Foundation series](https://en.wikipedia.org/wiki/Foundation), mathematician Hari Seldon develops a science called psychohistory—[a statistical method capable of predicting the future.](https://media.tenor.com/TrAsjYbL720AAAAM/chris-pratt-wow.gif) Psychohistory doesn’t predict individual actions but instead foresees large-scale societal trends with remarkable accuracy, enabling leaders to anticipate and alter the course of history.

If you’re already using Faraday, you probably see a connection here. Like psychohistory, predictive AI helps businesses affect the future by anticipating customer behaviors. By analyzing vast amounts of data, predictive AI can forecast outcomes such as which leads are most likely to convert or what offer will resonate with a particular customer. This ability empowers businesses to make precise, data-driven decisions that can directly influence future outcomes.

But unlike psychohistory, which only understood the broad movements of society, predictive AI is powerful enough to make accurate, actionable predictions at the individual level. And in this sense **our product actually beats a system designed to save the galaxy**  (sorry Hari)\!

### Turing, Descartes, and machines that learn

But we came here to talk about intelligence. 

And when discussing intelligent machines, our minds often turn to the famous, controversial, and (critically) often misunderstood [Turing Test](https://www.techtarget.com/searchenterpriseai/definition/Turing-test), a benchmark proposed by the great [Alan Turing](https://en.wikipedia.org/wiki/Alan_Turing) to measure whether a machine can exhibit behavior indistinguishable from human intelligence. 

“So it *basically* tested if the machines were sentient right?”

Well, not exactly. Turing himself viewed the test as more of an “[imitation game](https://plato.stanford.edu/entries/turing-test/)” than a definitive way to assess whether a machine truly “thinks”. His focus was less on the idea of thought itself, and more on the appearance of it—the ability of a machine to convincingly mimic human behavior and language.

The goal of the Turing Test wasn’t to prove that a machine could reason or understand in the way humans do, but rather to test if it could generate responses so human-like that an observer might be fooled into believing it was also actually a person. Passing the test would mean a machine could emulate the behaviors and language associated with human reasoning, but not necessarily that it possesses any true understanding behind them.

This idea interacts interestingly with the philosophy of [René Descartes](https://plato.stanford.edu/entries/descartes/), who famously said “[I think, therefore I am](https://www.britannica.com/topic/cogito-ergo-sum)”. Descartes believed  true human intelligence was defined by the ability to reason and, importantly, to use language to express reason. For him, language wasn’t just a tool for communication—it was the ultimate marker of conscious thought. He argued that only beings capable of reason, expressed through language, could be considered truly conscious. In this context, Descartes viewed machines and (sadly) animals, as [Automata](https://academic.oup.com/book/32221/chapter-abstract/268391060?redirectedFrom=fulltext)—[sophisticated mechanical systems that operated according to predetermined laws](https://www.amazon.com/Top-Race-Robot-Dog-Programmable/dp/B08G6PT5DC?source=ps-sl-shoppingads-lpcontext&ref_=fplfs&psc=1&smid=A17FAJD38FG3OS&gQT=1), but are incapable of thought or reason. 

Even though Automata might perform tasks that appeared purposeful, Descartes believed their actions were purely mechanical, driven by physical processes rather than any understanding or intention. For Descartes, the difference between humans and these objects was clear: humans could reason and engage with the world in a way that reflected true thought.

*Fast forward* to the world of modern AI, and we now have machines that can produce remarkably human-like language, making it seem as though they are reasoning or thinking. But are they really doing so?

While LLMs can string together words and phrases in ways that sound intelligent, they don’t truly “understand” the meaning in the way humans do. In this sense, LLMs seem to align with Descartes’ view of machines as sophisticated [mimics](https://media.tenor.com/c4HtDmmYreUAAAAM/mimic-animation.gif)—producing outputs that seem intelligent but, in reality, are merely a performance of an extremely complicated script.

But this also presents a challenge to Descartes’ assertion that language is the definitive marker of reason, as they demonstrate the ability to produce seemingly intelligent language without underlying comprehension.

And to be fair, it is also of course worth noting that Descartes was writing in the [17th century](https://www.marxists.org/reference/archive/descartes/1639/meditations.htm). A time when machines were more likely to resemble windmills than the complex computers we know today. So his idea of “machines” was far more limited, even if his philosophical view of them as unthinking, mechanical objects persists in many ways today.

But Turing didn’t just talk about AI that could communicate, he also discussed those that could learn from experience. In a 1947 lecture, he stated, [“What we want is a machine that can learn from experience,” and that the “possibility of letting the machine alter its own instructions provides the mechanism for this.”](https://www.britannica.com/science/history-of-artificial-intelligence) Instead of just following pre-written rules, such a system would improve over time through experience and trial-and-error. This is where the concept of [heuristics](https://www.merriam-webster.com/dictionary/heuristic) in problem-solving comes into play. Heuristics are rules of thumb that allow people (or models) to quickly find solutions based on past experiences and probabilities of success. Rather than seeking the perfect solution, which could be time-consuming and resource-intensive, heuristics prioritize finding a good-enough solution more efficiently.

Predictive AI operates on this principle. When these models analyze new data, they adjust their internal calculations, improving their accuracy based on what has worked in the past. Unlike LLMs, which don’t fundamentally change after their initial training unless retrained on new datasets, predictive AI systems evolve dynamically. They continuously optimize their decision-making to deliver better, faster outcomes as they learn from new data and experiences.

### The future of AI: beyond emulation

In this author's opinion the future of AI should aim to augment, not just replicate, human behavior. 

Predictive AI, which excels in analyzing data and uncovering hidden patterns, serves as a prime example of this. By turning complex data into actionable insights, these models empower businesses to make more informed decisions. For instance, in marketing, predictive models can identify high-conversion customers and suggest tailored strategies, tasks that would be impossible for humans to undertake or scale efficiently on their own. Its continuous learning and evolution further refine its predictions, unlocking new levels of efficiency and impact over time.

But while the focus of this article thus far has been primarily on how LLMs focus on mimicking human behavior, that’s not the only thing they can do. For instance, LLMs help us interact with and quickly manage complicated systems, as is the case with AI agents. 

Consider a scenario where a patient needs to schedule a complex series of medical appointments. An AI agent, integrated with the hospital's scheduling system, can understand the patient's needs, check doctor availability, coordinate with insurance requirements, and send reminders, all through a simple conversational interface. This not only streamlines the process for the patient but also frees up hospital staff to focus on critical patient care. 

These tools can automate processes and save time by performing repetitive tasks or helping us interact more efficiently. They don't replace us or try to copy us; they help us do more and do it better. And they free up human effort for higher-value work, enabling creativity and decision-making. 

In this sense, both predictive AI and LLMs, despite their differing focuses, can amplify human abilities when used in the right contexts, showing that the key to successful AI implementation is using the right tool for the job.

### And really, what even is intelligence?

At this point in the article you might be asking, “Wasn’t this whole thing supposed to be about whether these AIs were intelligent?” 

The concept of intelligence, though, isn’t as simple as a binary choice between “smart” and “dumb”. As another enlightenment philosopher, [Baruch Spinoza](https://www.britannica.com/biography/Benedict-de-Spinoza) proposed with his philosophy of [monism](https://ndpr.nd.edu/reviews/spinozas-argument-for-substance-monism-why-there-is-only-one-thing/), consciousness and reason are not exclusive to humans. They are defined through an object's interactions with its surroundings, which exist on a spectrum throughout nature, from the simplest forms of interaction (those of a rock) to the complex reasoning of humans. 

And indeed, both predictive AI and LLMs, though different in their design and function, are part of this spectrum. Each possesses a distinct form of intelligence that, when used thoughtfully, can augment human decision-making, creativity, and problem-solving.

Ultimately, both tools—predictive AI and LLMs—are intelligent in their own right, but each excels in different areas. By approaching AI with the mindset of enhancing human abilities rather than simply emulating human behavior, we unlock its true potential. Predictive AI excels at providing insights and optimizing decision-making, while LLMs offer a powerful tool for generating human-like text, facilitating communication, and automating tasks (among a range of other applications). By understanding their strengths and applying them wisely, we can harness AI to not only complement human abilities but to push the boundaries of what is possible, opening up new opportunities for innovation, efficiency, and growth.

### Conclusion

Both LLMs and predictive models are intelligent, but in different ways. It’s about using them thoughtfully—recognizing the unique strengths each brings to the table.

In the end, the true value of AI isn’t in replicating human thought, but in extending our own potential. Predictive AI can enhance decision-making and strategic thinking, while LLMs offer powerful ways to facilitate communication and automate tasks. When used in harmony, these technologies can open up new opportunities for innovation, efficiency, and growth, creating a future where humans and AI work together to solve complex problems and drive meaningful change.

At the end of the day, it’s not much different from working with any other tool; drills are great for drilling, but don’t ask one to write an essay (that draft was terrible).

What makes a machine intelligent, and how can we harness its capabilities to improve our lives? This essay explores various approaches to that question, from Descartes to Turing.

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