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.

AI to empower human intelligence, not just emulate it

What if you could embed AI-driven predictions into your platform—without the massive investment in data, engineering, and ongoing model maintenance?

Your customers want smarter insights: predictions on which leads will convert, which territories will perform best, and which prospects match their ideal profile. But building AI agents from scratch is a massive undertaking—one that requires specialized expertise, vast amounts of data, and months (or even years) of development. For many SaaS companies, the cost and complexity make it an unrealistic endeavor.

So what is to be done?

[![SalesRabbit partnership with Faraday landing page](/images/blog/pbf-blog/salesrabbit-story-header-image.png)](/images/blog/pbf-blog/salesrabbit-story-header-image.png)


## Introducing Powered by Faraday

This is precisely why we launched **Powered by Faraday**—a turnkey solution that allows SaaS companies to integrate AI-powered customer predictions into their platforms without having to build or maintain machine learning models themselves.

We’ve spent over a decade refining predictive models that help businesses understand and anticipate customer behavior and with **Powered by Faraday**, we bring these capabilities directly into the hands of product teams, enabling them to enhance their software with advanced AI-driven insights quickly and cost-effectively.

## What Powered by Faraday does

With **Powered by Faraday**, companies can:

* **Enrich their customer data** with 1,500+ built-in consumer attributes, adding valuable context to their existing datasets.  
* **Predict customer behavior** by using pre-built machine learning models that score leads, prioritize outreach, and identify high-conversion opportunities.  
* **Accelerate product development** by embedding plug-and-play AI models, cutting multi-year build times down to weeks or even days.  
* **Optimize costs and scalability** with transparent pricing and predictable expenses, avoiding the uncertainty of large-scale computational costs.

For SaaS companies looking to offer predictive capabilities without reinventing the wheel, **Powered by Faraday** is a game-changer.

## Faraday’s competitive edge

Many companies offer AI solutions, but **Powered by Faraday** stands out due to several key advantages:

* [**Seamless integration with a robust API**:](https://faraday.ai/docs/abstractions/traits) Powered by Faraday is designed for seamless implementation. Faraday’s well-documented, developer-friendly API makes embedding AI-driven insights into a product straightforward and efficient.   
* [**FIG (Faraday Identity Graph)**](https://faraday.ai/docs/abstractions/traits): A vast, privacy-compliant third-party dataset with 1,500+ attributes, enabling richer predictions than first-party data alone.  
* **Forward-deployed data science & professional services**: Hands-on support from data scientists who fine-tune models and ensure seamless deployment.  
* **Ethical, privacy-first AI**: A commitment to consumer privacy, avoiding personally identifiable information while delivering accurate predictions.  
* **Industry-specific models**: Pre-trained, vertical-specific machine learning models that accelerate time-to-value.  
* **Flexibility to design tailored solutions:** Faraday enables companies to integrate predictive capabilities without the constraints of rigid, one-size-fits-all AI solutions—offering the perfect balance between ease of adoption and customization.

By combining rich third-party data with hands-on AI expertise, Faraday helps SaaS companies deliver world-class predictive features faster and with greater accuracy than in-house or off-the-shelf solutions.

## Real-world use cases

Many companies have already leveraged **Powered by Faraday** to enhance their platforms. 

Here’s how two of them—[AccuLynx](https://faraday.ai/blog/acculynx-lead-intelligence-powered-by-faraday-api) and [SalesRabbit](https://faraday.ai/blog/salesrabbit)—are making AI-driven predictions a core part of their products:

### AccuLynx: Smarter lead prioritization for roofing contractors

[AccuLynx](https://acculynx.com/), a leading provider of business management software for roofing contractors, needed a way to help their customers prioritize sales efforts more effectively. They integrated [Faraday’s prediction API](https://faraday.ai/docs/reference) into their new [Lead Intelligence feature](https://acculynx.com/roofing-sales-lead-intelligence/), which automatically scores leads within AccuLynx’s platform, based on hundreds of demographic, financial, and property data points.

The result? Roofing contractors using AccuLynx no longer have to guess which leads to focus on. Instead, they get **AI-powered rankings** that help them close more deals efficiently—without needing in-house data science expertise.

As their VP of Product [Mark Rattin](https://www.linkedin.com/in/markrattin/) said, “Lead Intelligence is a game-changer for roofing companies,” … “AccuLynx customers no longer have to guess which leads to call first, or only rely on their experienced sales people to quickly close deals. Now they can improve their sales win rates by focusing their efforts on the best leads.”

Sounds like a win to us\!

[![AccuLynx landing page](/images/blog/pbf-blog/acculynx-landing-page.png)](/images/blog/pbf-blog/acculynx-landing-page.png)

### SalesRabbit: AI-Driven insights for field sales teams

[SalesRabbit](https://salesrabbit.com/), a leader in field sales software, wanted to take their [DataGrid](https://salesrabbit.com/datagrid/) solution to the next level. While their platform already provided canvassers with property and homeowner data, they saw an opportunity to go further—predicting which homeowners were most likely to convert based on behavioral and demographic insights.

By integrating [Faraday’s agentic customer prediction models](https://faraday.ai/templates), SalesRabbit was able to:

* Guide sales reps toward high-potential neighborhoods and properties.  
* Optimize canvassing routes for better efficiency.  
* Improve app performance by caching predictive insights in advance.

By leveraging **Powered by Faraday**, SalesRabbit transformed DataGrid into DataGrid AI, offering their customers a predictive lead scoring tool in a fraction of the time it would’ve taken to build in-house.

[![SalesRabbit DataGrid AI landing page](/images/blog/pbf-blog/salesrabbit-datagridai.png)](/images/blog/pbf-blog/salesrabbit-datagridai.png)

## **Conclusion: AI-Powered predictions without the headache**

For SaaS companies looking to bring predictive insights to their customers, building AI models from scratch is often too expensive, time-consuming, and resource-intensive. Powered by Faraday removes those barriers, making it easy to integrate best-in-class consumer predictions into your product without the heavy lift.

Whether you want to help your customers prioritize leads, optimize sales territories, or enhance customer insights, Powered by Faraday lets you do it faster, more affordably, and at scale.

Want to see how it works? Check out the full stories from [AccuLynx](https://faraday.ai/blog/acculynx-lead-intelligence-powered-by-faraday-api) and [SalesRabbit](https://faraday.ai/blog/salesrabbit), or reach out to [Dave](https://www.linkedin.com/in/davidtoddsmall/) or the [Faraday](https://faraday.ai/talk-to-sales) team to explore how Powered by Faraday can enhance your platform.

AI is revolutionizing how SaaS companies can leverage predictive insights, and at Faraday, we’re making it easier than ever to integrate powerful, ethical AI-driven capabilities without the hefty overhead.

AI-Powered insights for SaaS companies, the fast way

I’m really happy to share some company updates with you, including new financing and additions to our leadership here at Faraday.

![Robin Spencer](/images/authors/robin.jpg)

First up is that [Robin Spencer](https://www.linkedin.com/in/robinbspencer/) has joined as COO. She arrives here from Clearbit, where she was COO for several years leading up to their acquisition by Hubspot in December. Robin had been working with us quietly behind the scenes for much of the year and has had a tremendous impact on our team and company strategy already. I couldn’t be more pleased that she’s now fully on board to steer our go-to-market teams and business operations through the next stage of scaling. I can already tell Robin is a game changer for us.

Here’s what she has to say about joining Faraday:

“It is special to return to Vermont and continue doing the work I love for a company built here. I'm grateful for the opportunity to work with a team that genuinely cares about our clients and each other. Faraday has a lot of potential, and I'm eager to help us realize it so we can support more companies.”

![Peter Levine](/images/authors/peter.jpg)

I’m also thrilled to announce that my friend [Peter Levine](https://a16z.com/author/peter-levine/) has joined our board as an independent board advisor. (While Peter is a partner at a16z, this position is in a personal capacity.) From his days as an operator to his role now at arguably the best tech venture firm in the world, Peter brings a massive amount of best practices knowledge on how great software companies grow and thrive.

I also asked Peter to share some thoughts on why he’s excited about Faraday:

“As soon as I met Andy and the team at Faraday I knew this company was something special. In a world where AI makes big promises but rarely delivers on them, Faraday has found a genuinely practical application that’s been helping thousands of brands for over a decade.”

Finally, we’ve closed some additional financing. Longtime Faraday ally Intercap, headed by board member Jason Chapnik, led the round as part of a coalition that included some of our earliest angels. Jason noted: “Faraday had been developing its technology for several years before the rest of the world caught on to AI. Now is the time for us to capitalize on our lead.”

Other founders will know that a startup is a community—and this remains the case even after almost 12 years. When longtime neighbors double down and new ones show up to help, really magical things can happen. 2025 is going to be a great year.

Robin Spencer joins as COO, Peter Levine joins the board, and we closed some additional funding.

New additions to the team, board, and financing

We're happy to announce a new partnership between Faraday and Simpli.fi, the leading Advertising Success Platform, which provides programmatic advertising solutions and workflow software to over 2,000 media teams, agencies, and brands.

Under this partnership, Simpli.fi will incorporate Faraday's [identity graph](/features/faraday-identity-graph) of more than 1,500 consumer attributes and individual-level [customer behavior predictions](/features/customer-behavior-predictions)into its sophisticated media buying program. Simpli.fi users can now create highly effective lookalike audiences, powered by Faraday, that enable marketers to target their best-fit customers with relevant engagement at scale.

"It's about combining the science and the art," Faraday CEO Andy Rossmeissl says while chatting with Simpli.fi's David McBee about lookalike audiences on [Simpli.fi TV](https://simpli.fi/simplifi-tv/andy-rossmeissl-simpli-fi-tv). Faraday's data science–combined with the artistic approach in which Simpli.fi makes these insights actionable–results in campaigns that show real ROI.

Clients interested in leveraging customer behavior predictions to create lookalike audiences of likely buyers and deploy them through Simpli.fi can contact their account manager to get started.

Faraday's identity graph and customer behavior predictions come together with Simpli.fi's media buying program to create lookalike audiences that enable marketers to target likely buyers with relevant engagement at scale.

Introducing a new partnership between Faraday and Simpli.fi

Faraday is thrilled to highlight **LeadConduit**, the ActiveProspect integration for Faraday. Between LeadConduit helping you acquire, optimize, and distribute leads, and Faraday scoring your leads with likely-to-convert predictions, your leads have never been easier to manage. Read on for details on how ActiveProspect and LeadConduit can help scale your lead management efforts:

[ActiveProspect](https://activeprospect.com/) is a comprehensive SaaS platform that empowers companies across industries to scale quality lead acquisition from any source, protect against litigation risk by documenting proof of consent, and accelerate speed-to-lead with automated process flows. With almost 20 years of experience in the industry, ActiveProspect has developed a reputation as the industry-leading consent-based marketing platform.

ActiveProspect’s three main products –[TrustedForm](https://activeprospect.com/trustedform/), [LeadConduit](https://activeprospect.com/leadconduit/), and [LeadsBridge](https://leadsbridge.com/)– are designed to help businesses capitalize on marketing opportunities and maximize conversions.

- [TrustedForm](https://activeprospect.com/trustedform/#howitworks) is the de facto standard for documenting express written consent by providing independent certificates of authenticity used for legal compliance and campaign optimization.
- [LeadConduit](https://activeprospect.com/leadconduit/#howitworks) streamlines the leading buying process to help businesses efficiently acquire customers at scale. Custom lead flows automatically enhance and filter leads in real time to deliver the highest-quality prospects to your CRM or lead buyer.
- [LeadsBridge](https://leadsbridge.com/platform/) automates your customer acquisition workflows from major ad platforms with seamless integration to your revenue tech stack. Utilize over 380 integrations to sync your leads, target custom audiences, and conversion tracking to boost sales and productivity.

Overall, [ActiveProspect](https://activeprospect.com/get-started/) offers the only complete solution for consent-based marketing powered by patented technology. Its TrustedForm, LeadConduit, and LeadsBridge products are just a few examples of the company's commitment to delivering high-quality solutions that help businesses succeed in today's increasingly competitive marketplace.

**Ready to try Faraday? [Create a free account](https://faraday.ai/signup).**

Faraday is thrilled to highlight LeadConduit, the ActiveProspect integration for Faraday. Between LeadConduit helping you acquire, optimize, and distribute leads, and Faraday scoring your leads with likely-to-convert predictions, your leads have never been easier to manage.

Faraday integration spotlight: LeadConduit

Personalization is more important now than ever. The circumstances around COVID demanded that brands ramp up digital strategies in order to stay competitive, creating a considerable amount of noise in inboxes and ad spaces (though [direct mail is making a comeback](/blog/personalized-direct-mail-strategy)). Consumer inboxes, Instagram feeds, and the like are riddled with emails and ads–some personalized, some not–and each leaves a different impression. The result is that brands need to work harder & smarter to stand out from the competition.

However, achieving true [personalization at scale](/blog/personalization-at-scale-guide) is much easier said than done. Collecting [first-party](https://hightouch.com/blog/what-is-first-party-data) and behavioral event data allows for a comprehensive understanding of how customers engage with your brand. This foundational data must be joined across various data sources and can be further [enriched](/blog/first-party-vs-third-party-data-marketing) to construct a comprehensive and unified customer profile. Subsequently, artificial intelligence (AI) can be employed to create [customer predictions](/features/predictions), identifying ideal customers and the best engagement strategies for each. Finally, a [Reverse ETL](https://hightouch.com/blog/reverse-etl) platform activates customer profiles and AI models out of your data infrastructure and into the downstream tools that power your marketing and business operations.

## Use cases for AI-powered personalization

Personalization that’s driven by AI has applications across the entire customer journey. Let’s take a look at a few standouts.

### Customer retention through proactive engagement

It’s always better to be proactive rather than reactive, and engaging with your customers is no exception to this rule. While traditional engagement can be personalized after an individual has interacted with your brand through ads, purchases, form submissions, and so on, AI-powered personalization allows you to engage with resonant content from the very first interaction, leaving your customers feeling understood, and that you see them as more than just clickstreams. On top of that, adding [propensity to churn predictions](/predict/category/churn_scores) to the mix gives you an additional layer of proactivity.

### Internal talk track alignment

The benefits of AI-powered personalization aren’t exclusive to external outreach. Savvy brands are adopting it to create frictionless experiences for their customers by ensuring every sales rep is on the same page when it comes to [talking points](/blog/sealed-lead-optimization) for each buyer persona. Additionally, it’s likely that some reps on your team will be more successful with certain personas than others, which can be used to foster persona-specific coaching among reps.

## How Faraday and Hightouch enable hyper-personalization

If you want to build this robust personalization, good news: Faraday and Hightouch have partnered to help brands hyper-personalize every customer journey with ease. Let’s dive into the highlights of the partnership that brands hungry for true personalization can reap the benefits of.

### Unify customer data in the warehouse

Hightouch enables you to build a Composable Customer Data Platform ([CDP](https://hightouch.com/blog/composable-cdp)) directly on your existing data infrastructure. In practice, this means that you are able to store, unify and activate all of your customer data directly in/from our data warehouse rather than splitting data in different silos.

Traditional CDPs collect [clickstream](https://hightouch.com/blog/clickstream-data) and [event data](https://hightouch.com/blog/event-tracking), store it in their own data silo, perform [identity resolution](https://hightouch.com/blog/what-is-identity-resolution) to build [Customer 360](https://hightouch.com/blog/what-is-customer-360) records, and then sync data to marketing tools. The Composable CDP does all of these functions, but around your data warehouse so that it can unify all customer data, including clickstream events but also offline interactions, third-party enrichment data, data from operational servers, and more. This is an essential structure for personalization because it ensures that AI models and marketers alike have access to the full view of what each customer does while providing brands with the piece of mind that data remains within their own infrastructure.

You can use ETL tools like Fivetran to collect some of this data in the warehouse, and [Hightouch Events](https://hightouch.com/platform/event-collection) to collect digital events into it as well. Hightouch’s [Customer 360 Toolkit](https://hightouch.com/platform/customer-360) then enables you to unify customer records in the warehouse by performing identity resolution.

### Use AI for customer predictions & first-party data enrichment

With your first-party customer data consolidated in the warehouse, you can use Faraday to enrich it with [built-in, permissioned, and opt-in consumer data](/features/data), then predict the likelihood that they’ll take virtually any action.

Start by using [AI for customer segmentation](/blog/predictive-personalization-machine-learning), leaving you with rich insights into who your customers are to guide the personalization of copy & creative. Next, build customer loyalty with a strong [churn prevention strategy](/blog/churn-prevention-machine-learning) by predicting churn scores, make your sales reps as time-efficient as possible by implementing [predictive lead scoring](/blog/predictive-lead-scoring-b2c) to find which leads are worth pursuing, expand your brand’s reach with confidence using an [AI-powered market sizing analysis](/blog/bottom-up-market-sizing-predictive-analytics), and more.

### Turn predictions into personalization with Reverse ETL

Now that you’ve consolidated all of your customer data in the warehouse, enriched it, and used AI for predictions, it’s time to put that data to work. Reverse ETL syncs data from your warehouse to your everyday tools. You can do this and build different audiences without writing any code using Hightouch’s [Customer Studio](https://hightouch.com/platform/customer-studio). It’s easy to build a different audience based on their attributes and predictive models and trigger various actions like personalized email campaigns directly from your warehouse source of truth into your everyday marketing tools.

## How to embed personalized, predictive high-spender scores in your stack with Faraday and Hightouch

Let’s walk through how you can use AI to predict high-spender scores to find your most valuable customers, create bespoke personas to enable hyper-personalization, then embed them in your stack by leveraging Faraday and Hightouch.

### Connect your data and define your dataset

First, you need to sync your data warehouse with Faraday by creating a connection, after which you’ll define a dataset so Faraday knows who is who in your data. Once your data is defined, you can easily group it into unique segments that we call cohorts–like your customers and high-spending customers.

[![Faraday dataset](/images/blog/faraday_hightouch/faraday-hightouch-dataset.png)](/images/blog/faraday_hightouch/faraday-hightouch-dataset.png)

### Predict high spend scores to find your most valuable customers

With your data in Faraday, you’ll build an outcome to describe the predictions you want to make–high spender scores, in this case. You’ll plug in your _customers_ cohort and your _high spenders_ cohort so that Faraday will predict how likely people in the former will enter the latter by spending big.

[![Faraday high spenders outcome](/images/blog/faraday_hightouch/faraday-hightouch-outcome.png)](/images/blog/faraday_hightouch/faraday-hightouch-outcome.png)

### Predict customer personas to guide personalization

In addition to your spend prediction, you can use AI to predict personas within your customer base so that you can engage with incredibly resonant targeting & messaging. Faraday combines your data with rich, built-in data, then uses machine learning algorithms to segment your customers into bespoke personas based on similar trait groupings. These personas provide useful insights into traits like household income, marital status, and shopping style that you can use to guide engagement. You can even take your personalization a step further by adding more traits for analysis, like health & exercise or dog ownership, to see who your customers really are.

[![Faraday customer personas](/images/blog/faraday_hightouch/faraday-hightouch-personas.png)](/images/blog/faraday_hightouch/faraday-hightouch-personas.png)

Now that your personas are segmented, use the resulting traits to create resonant copy & creative for your engagement. For example, you could include images of older women exercising in your emails to resonate with customers in the Glamorous Grandma Gloria persona.

### Create a pipeline to apply high spender scores & customer personas

With both your customer personas and your high spender outcome created, you’re ready to make them actionable by applying both personas and high spender scores to your customers.

[![Faraday pipeline creation](/images/blog/faraday_hightouch/faraday-hightouch-pipeline.png)](/images/blog/faraday_hightouch/faraday-hightouch-pipeline.png)

Now that you’ve applied your high-spender predictions and personas to your customers, create a deployment to sync them to your data warehouse.

[![Faraday pipeline deployment](/images/blog/faraday_hightouch/faraday-hightouch-deployment.png)](/images/blog/faraday_hightouch/faraday-hightouch-deployment.png)

### Create custom audiences in Hightouch Customer Studio based on Faraday predictions

Hightouch offers a visual audience builder that makes it easy to define custom segments based on your business criteria. For example, you could build an audience of customers who left an item in their cart but did not complete a purchase. You can similarly build audiences based on your predictive scores from Faraday, to turn those predictive scores ultimately into different user groups that you’ll take different action with.

[![Hightouch Customer Studio gif](/images/blog/faraday_hightouch/faraday-hightouch-customer-studio.gif)](/images/blog/faraday_hightouch/faraday-hightouch-customer-studio.gif)

From there, Hightouch uses reverse ETL to sync your customer predictions to your preferred activation platform, like HubSpot, Iterable, or Mailchimp, where you can create a personalized campaign to target your high-value customers. You can sync additional fields from your warehouse to these platforms, to fully utilize the personalization strategies that you’ve devised for these customers with Faraday. You can set up these syncs on a recurring schedule as needed, so your personalization campaigns can refresh as you run new predictions through Faraday.

**Ready to achieve true personalization at scale?** [Create a free Faraday account](https://faraday.ai/signup), and [book a demo with Hightouch](https://hightouch.com/demo?ref=/).

Faraday and Hightouch partner to help brands put AI to work, creating highly-tailored customer experiences.