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

## 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

At Faraday, we often hear from clients about their experiences working with our competitors. These conversations give us valuable insights into how our platform stands out in the market, and where we have room for improvement. Recently, one of our clients shared a specific experience that really resonated with us, and we wanted to take the opportunity to highlight it to our audience.

This client, who recently worked with [Think Unlimited](https://thinkunlimited.com/) before moving to Faraday, pointed out some key areas where we outshine our competition. As she put it:

*"The transparency of the data science behind the modeling \[at Faraday\] was refreshing. There were no secrets. It was our data, and it was given back to us in a digestible fashion. We could also easily access the experts behind the science to ask questions."*

We believe that transparency is crucial, and we’ve built our platform with the goal of making data science accessible and understandable for our users, so this was rewarding to hear.

This client also observed a significant difference in the quality of predictions between Faraday and Think Unlimited, noting that Think Unlimited's data science just didn’t seem as robust under the hood. This aligns with our commitment to providing high-quality, validated predictive models that you can trust.

But those observations are just the start. Let’s take a deeper dive into some of the key differentiators we've observed ourselves and how they contribute to Faraday’s unique value proposition.

## Faraday vs. Think Unlimited: Our core differentiators

### 1\. Real-time scoring with Faraday's Lookup API

One of Faraday's core strengths is our ability to score leads in real time. Thanks to our powerful [lookup API](https://faraday.ai/docs/features/lookup-api), users can get immediate feedback on lead quality, before bids are even placed. By receiving immediate feedback, businesses can optimize decisions in real-time, leading to better outcomes in high-pressure or fast-moving situations, such as the [ping post process](https://faraday.ai/blog/so-what-is-a-ping-post-anyway). Think Unlimited, on the other hand, lacks this level of real-time scoring capability, which can slow down the workflow, impact decision-making timelines, and in the case of the ping post process, result in lower quality leads being purchased. 

### 2\. A more robust data set for better predictive power

Our built-in Faraday Identity Graph is a rich third-party [data set](https://faraday.ai/features/faraday-identity-graph) that goes far beyond basic metrics by incorporating a wide range of insights that enhance the accuracy and depth of our predictive models. In addition to traditional data points, we include rich demographic, lifestyle, and behavioral data and this combination of static and dynamic data allows us to understand not just who a customer is, but how they engage with products, services, and brands. Using these third-party insights, Faraday’s models can create a dynamic, multi-dimensional picture of potential customers, leading to more accurate predictions of future actions, such as conversion or product interest.

Faraday’s robust data set also helps solve the [cold start problem](https://www.sciencedirect.com/topics/computer-science/cold-start-problem) by providing this rich data from day one, meaning that brands don’t have to wait for a build-up of their own first-party data before accurate scoring can commence. It also allows them to immediately target new customer segments or groups that they don’t have existing data for, facilitating expansions and allowing them to quickly roll out new campaigns and products.

The depth of our data enables Faraday to generate more accurate, quick, and actionable insights compared to our competitors, empowering businesses to make data-driven decisions with confidence. 

### 3\. Score explainability: know exactly why leads get their score

Faraday emphasizes clear [score explainability](https://faraday.ai/blog/faraday-score-explainability) with all of our models. This means that users can actually know exactly why each lead receives a particular score. This level of transparency is crucial for businesses that need to understand the "why" behind their results. Having this clear visibility into the model’s scoring also helps businesses gain confidence in their decisions and ensures they can optimize their approach based on a better understanding of the process.

That earlier client specifically highlighted this point as being one of the biggest advantages she appreciated with Faraday compared to Think Unlimited, where such transparency can be harder to come by.

### 4\. Flexible integration across use cases

Faraday isn’t just limited to lead scoring. Our platform is adaptable and can fill a range of other use cases, including [product recommendation](https://faraday.ai/docs/how-to/use-cases/customers/next-best-offer), [lead suppression](https://faraday.ai/templates/lead-suppression), and even [churn prediction](https://faraday.ai/templates/churn-scoring), [plus a bunch of other options](https://faraday.ai/templates). This versatility allows businesses to leverage Faraday’s insights across different departments and applications, and also sets them up for success when expanding their business or launching new types of campaigns they haven’t undertaken before. Think Unlimited’s capabilities are more narrowly focused, which can hinder adaptability and expandability.

### 5\. Access to experts

At Faraday, we take our data science seriously. But we also understand that predictive modeling is inherently complex and that just presenting where data comes from doesn't necessarily mean that the client will be able to understand it. 

Score explainability measures are a critical first step but sometimes, the best way to ensure a score is actually explainable is pretty straightforward: just have an expert explain it. That’s why we made the game-changing choice last year to forward deploy our entire data science team, making them directly accessible to our clients—our CTO even works with a range of client accounts to ensure that they’re getting the most possible value out of their investment. This level of open communication and transparency is something Think Unlimited lacks and is the ultimate failsafe to ensure users are able to understand their predictions, and the models behind them.

### 6\. Fully API-based for seamless integration

[Faraday is fully API-based](https://faraday.ai/docs/reference), meaning it can be integrated into any system programmatically, offering unmatched flexibility and customization. This structure not only simplifies integration but also provides the flexibility to adapt the models into any custom workflows, making it an ideal choice for companies looking for a solution tailored to their unique needs. Our API is also well-documented, which facilitates integration into clients’ existing tech stacks and ensures easier operation throughout the customer lifecycle. 

These features dramatically increase speed-to-deployment, with some clients seeing major cost savings or revenue growth [within their first month](https://faraday.ai/blog/optimized-direct-mail-outreach-in-one-month) after signing on. Think Unlimited, in contrast, lacks the API-based framework that makes Faraday so adaptable to a wide range of use cases and workflows.

## Quality predictions and transparency make the difference for our clients

At the end of the day, the quality of the predictions you rely on is what matters most. Our predictions are not only proven to be accurate and value driving, but they’re constantly monitored and revalidated to make sure we’re delivering as much value as possible to our clients. Additionally, Faraday’s data science, transparency, and flexibility make it the clear choice for businesses seeking high-quality predictive analytics that can scale and adapt to their needs, all while remaining understandable and accessible. Think Unlimited may have its place, but when it comes to powerful, explainable models and real-time scoring, Faraday leads the way.

Faraday outshines competitors like Think Unlimited with real-time scoring, a richer data set, transparent predictions, flexible integrations, expert support, and a fully API-based platform.

Faraday vs. Think Unlimited: Our core differentiators

[Personalization](/blog/predictive-personalization-machine-learning) is the holy grail of growth teams everywhere. At this point, everyone knows how important it is to speak to their customers and prospects like they’re real people, and not just orders and clickstreams. It’s often easier said than done, though, and brands with a significant amount of data and a large customer base may find themselves struggling to make heads or tails of how to organize it, or even know what data is worth focusing on.

## Challenges of personalization at scale

Doing personalization at scale right can provide incredible value to your brand, and has a major impact on how your customers see and interact with you. A daunting set of challenges is presented to most brands that try to tackle the issue themselves–let’s dive into a few of the most glaring challenges.

### Data, and a lot of it

To know more about–and how to speak to–your customers, you need to have data on them. Through the data you currently have, you’ll likely have some level of insight into the demographic, financial, and transactional background of your customer base as a whole. Though this data is strong, and gives a unique perspective due to it being exclusive to your brand, your customers exist outside of interacting with your brand. A healthy mix of [first-and-third-party data](/blog/first-party-vs-third-party-data-marketing) can provide additional layers of complexity and nuance to how you view your customers–and how you engage with them.

The problem is that you need a lot of strong data to personalize meaningfully, and the more data you collect, the harder it becomes to personalize at all.

### Finding the relevant data

Collecting the data is the first step to creating an impactful strategy for personalization at scale–but what do you do with it? Historically, data-savvy marketers have sifted through the terabytes of data they’ve collected to try and piece together what makes up their ideal customer, and what their key levers are. If your brand is thorough, this is a _ton_ of data to sift through, and the process of finding what’s relevant–on the whole as well as on the individual level–is colossal.

### Personalization at scale

By using machine learning for predictive personalization, the effort involved in scaling personalization across your entire customer base–past, present, and future–isn’t as herculean as it sounds. Machine learning algorithms can segment your customers into bespoke, cohesive personas that you can deploy [anywhere in your stack](/integrations), allowing you to create individualized, personalized campaigns for any given subgroup of people you’re interested in.

### Responsibly-sourced data

Privacy is front-and-center of any data-focused discussion in 2025–as it should be. It began with the (albeit slow-rolling) [death of third-party cookies](/blog/death-of-third-party-cookies-strategies), and will continue to be a primary focus of consumers moving into the future. Privacy matters, and largely, people are uncomfortable with things like cookies–previously critical to a third-party data strategy–tracking them creepily across the web to find out what they’re searching and where they’re visiting, even if the end-result is personalized content.

The data you collect and use should be [responsibly-sourced, opt-in, and permissioned](/features/data). It’s possible to provide a perfectly personalized experience while simultaneously being a privacy-forward brand, but it takes effort.

## Benefits of personalization at scale

With the barriers to true personalization at scale being so high, is the end-result worth it? With companies that personalize collecting [40% more revenue](https://www.mckinsey.com/capabilities/growth-marketing-and-sales/our-insights/the-value-of-getting-personalization-right-or-wrong-is-multiplying#0) than those that don’t, the answer is a resounding _yes_.

### The right message, at the right time, every time

It’s an adage as old as marketing itself, but it’s always going to be true when a single poorly-personalized, or otherwise irrelevant message could mean the loss of a customer–or potential customer–forever. With personalized content delivered to every individual you’re engaging with, at any scale of your campaign, you’ll always be delivering the right message, at the right time, every time.

### Consumers demand personalized content

With [61% of consumers](https://www.salesforce.com/resources/research-reports/state-of-the-connected-customer/) reporting that they feel that most companies treat them as a number rather than a person, it’s clear that personalized content is near and dear to the modern consumer’s heart. As a dog lover, you wouldn’t want to receive a postcard from a pet food brand focused exclusively on cats, would you? Your customers don’t either.

### Building brand loyalty

[78% of consumers](https://www.mckinsey.com/capabilities/growth-marketing-and-sales/our-insights/the-value-of-getting-personalization-right-or-wrong-is-multiplying#0) say that they’re more likely to buy again from brands that personalize their content. Loyal customers are also more likely to recommend brands they love to their friends & family, ensuring that, if you focus on keeping your customers happy, they’ll return the favor by shouting your brand’s name from the rooftops. Keeping a strong base of loyal customers is key to a good [churn prevention strategy](/blog/churn-prevention-machine-learning).

## How to achieve personalization at scale

It’s clear that the benefits of personalization outweigh the challenges, but how exactly can it be achieved? There are three key pillars to successful personalization at scale, and they all focus on your customer data.

### Collect good first-party data

Data is the most important feature of propensity to buy and customer predictions. Without a strong enough foundation, the predictive models that your data science team builds won’t have enough data to predict strongly off of, leaving you with questionable-at-best insights. You’ll want a strong mix of first-and-third-party data collected through tactics like web forms, surveys, and loyalty programs, where you can _really_ draw out what makes your customers unique.

### Optimize your data with first-party data enrichment

As good as your own customer data is, your customers have more going on in their lives than you can see through their interactions with your brand. You can buy data from companies like Acxiom or Epsilon, who have been collecting data for years and years, or you can leverage assets like publicly available census data to find more about who your customers really are.

### Use AI for customer segmentation

With your data collected and enriched, you need to segment your customers into unique subgroups, or personas. It can be manually done, but the process is incredibly labor-and-time-intensive, and is prone to human error. Using [AI for customer segmentation](/predict/category/persona_assignments) takes the load off of your team, leaving you ready to personalize campaigns at any scale, create internal, [persona-based talk track alignment](/blog/sealed-lead-optimization), and more.

## How Faraday supports personalization at scale

Faraday lets you easily identify distinct personas and predict customer behavior with machine learning, unlocking powerful insights to shape better personalization strategies at scale.

### Unified, responsible data

Faraday unifies your first party customer data with detailed, opt-in, permissioned, cookieless consumer data, giving you the insights your brand needs while simultaneously being privacy-centric. Hundreds of datapoints through demographics, psychographics, financials, property information, and more to be leveraged for your customer predictions.

Consumers have adapting needs, and [65% of consumers](https://www.salesforce.com/resources/research-reports/state-of-the-connected-customer/) expect brands to adapt to them. This is a laborious task manually, but with Faraday’s built-in data always staying up-to-date, your brand can adapt alongside your customers.

### Delivering personalized messaging and creative across multiple channels and touchpoints

Faraday takes your data–combined with Faraday’s–and uses AI to segment your customers into unique subgroups, or personas, based on hundreds of datapoints.

[![Faraday customer personas](/images/blog/personalization_at_scale_guide/personalization-at-scale-guide-personas.png)](/images/blog/personalization_at_scale_guide/personalization-at-scale-guide-personas.png)

With your customer personas predicted, easily kick off campaigns by deploying your entire persona set, or a single persona, to anywhere in your stack. Every individual your brand is interested in, revealed with clear-cut traits like _dog owner_, grouped with people that share similar interests, shopping behaviors, and more.

[![Faraday prediction deployment](/images/blog/personalization_at_scale_guide/personalization-at-scale-guide-deployment.png)](/images/blog/personalization_at_scale_guide/personalization-at-scale-guide-deployment.png)

Personalization at scale has never been easier, or more accessible.

### Predicting the next best product for every individual

Alongside your customer personas, you can optionally predict the propensity, or likelihood, that any action is taken by an individual. For example, take your Emerging Adult Eli customer persona and predict the likelihood that anyone in that persona will [repeat purchase](/predict/category/repeat_purchasers), and, further, predict the next best product for them based on similar shoppers.

### Providing personalized discounts to foster customer loyalty

Personalization extends beyond the copy and creative that’s in your engagement with a given customer–it can include providing strategic discounts to customers that have, for example, made a second or third purchase. This way, your customer predictions guide you toward the people that are most likely to repeat purchase so that you can then turn around and give them _just_ the right discount they need to keep coming back.

**Ready to achieve true personalization at scale? [Create a free account](https://faraday.ai/signup).**

Consumers are demanding personalized content at an increasing rate, but personalization at scale has traditionally been out of reach for many brands. Using a customer insights platform makes true personalization at scale more accessible than ever.

The modern marketer’s guide to personalization at scale

With 79% of consumers saying they’re only likely to engage with an offer if it has been personalized, personalization should be at the forefront of your team's marketing strategy year-in and year-out.

Faraday Strategic Account Managers Alexandra Noyes and Nick Haggerty recently gave a deep dive into how their clients are leveraging Faraday's bespoke, AI-generated personas to help them see eye-to-eye with their own customers. Every marketer knows that the right message, in front of the right customers, at the right time, is key, and Faraday enables you to plug these insights directly into your stack in just a few clicks. Hear from the experts as the duo explores personalization use cases that Faraday clients are currently employing, field questions from the audience, and more.

**Check out the recording below, where you'll learn how to**:

- Leverage consumer predictions to create personalized content
- Segment your audience with a focus on customer behavior
- Identify attributes that make leads more likely to convert

**Interested in learning more? [View the recording here](https://faraday.wistia.com/medias/0giacfkj0j 'Personalization Webinar').**

Join Faraday Strategic Account Managers Alexandra Noyes and Nick Haggerty as they dig into how clients are leveraging Faraday's bespoke, AI-generated personas.

Unlocking authentic personalization at scale with Faraday

Imagine you’re leading a growing retail business with 25 thriving locations. Now, it’s time to decide where to open the next five. Do you expand into an emerging market where demand is heating up, or double down on your presence in regions where your brand is already well known? Is there some other critical factor you haven’t considered? The stakes are high, and even seasoned executives can fall prey to personal biases.

Take, for example, a story we recently heard from a client about an experienced restaurant CEO who was set on opening a location in his beloved hometown—a move driven by emotion rather than data. Fortunately, his team knew to assess the market, which revealed the location wasn’t actually viable. While this upended their original plan, it ultimately saved them the effort and cost of opening a doomed restaurant. This story highlights how critical these decisions are for sustained growth and why relying on data-driven insights (from the beginning) is essential to avoid costly missteps and make confident, strategic choices.

At Faraday, we understand the challenges businesses face when it comes to making strategic growth decisions. That’s why we offer **market opportunity analysis (MOA)**, a powerful tool that helps you confidently identify actionable opportunities that are backed up by data.

## What is market opportunity analysis?

MOA equips your team with a clear understanding of your current market reach and untapped potential. By analyzing your existing customer data and comparing their traits to the population at large, we can help you pinpoint exactly where the next big opportunities lie. Whether you're looking to expand geographically, target new customers, or optimize product offerings, our approach ensures your growth is backed by solid insights.

## How it works at Faraday

MOA provides your team with a data-driven roadmap to your most valuable and actionable growth opportunities, but how does it actually work? Here's how we approach it:

![](/images/blog/moa_analysis_diagram.png)

First, we conduct a detailed assessment of your current market penetration. By analyzing your existing customer base, we identify key traits such as purchasing behaviors, regional preferences, and lifetime value to create an ideal customer profile. Then, leveraging data from the [Faraday Identity Graph (FIG)](https://faraday.ai/features/faraday-identity-graph), we develop a tailored predictive model to evaluate the broader U.S. market and identify prospective customers who closely match your existing audience. This allows us to quantify the untapped potential within the customer segments most likely to drive growth.

Once we’ve identified the high-potential customer segments, we dig deeper into the specific opportunities within those groups. This involves determining which product or service offerings are best suited to each persona to maximize their lifetime value. We also take a close look at geographic trends, mapping out areas where your target customers are most concentrated from the regional level all the way down to zip code, metro area, or even postal route. This data empowers businesses to strategically allocate resources with significantly more confidence and precision than before.


## Unleashing the power of the Faraday Identity Graph (FIG)

A comprehensive MOA requires access to a detailed dataset of the broader population to serve as a benchmark for comparison. Fortunately, Faraday’s **Faraday Identity Graph (FIG)** provides exactly that, and much more. With rich data on nearly all American adults, the FIG offers a robust and comprehensive view of the population, significantly enhancing the reach and depth of our MOA.

The FIG goes beyond basic demographic information, offering nuanced insights into consumer behaviors, preferences, and buying patterns. By tapping into these granular details, businesses can understand their target market in a way that’s far more accurate and actionable than with competing data sources. 

## Ready to explore what's next?

MOA can greatly benefit businesses by identifying high-potential customer segments and emerging market trends. By understanding where the highest-value opportunities lie, companies can optimize their marketing strategies and resources, focusing on areas with the highest growth potential. This leads to more targeted and personalized campaigns, which can improve conversion rates, enhance customer engagement, and increase profitability. MOA additionally provides companies with the confidence to know that their strategies are backed by data.

Growth doesn't happen by chance. With Faraday's MOA, you can take the guesswork out of targeting strategy and focus on results. Contact us today to discover how we can help unlock your business's full potential.

MOA empowers businesses to make strategic growth decisions by uncovering untapped potential and high-value opportunities in their market.

Unlocking growth with market opportunity analysis (MOA)

B2C companies operate in highly competitive markets, where customer acquisition and retention are vital for sustainable growth. Nowadays, it can be extremely difficult for brands to acquire customers while maintaining healthy customer acquisition costs.

Successful B2C companies are investing heavily in modern data and machine learning solutions, like predictive lead scoring, to drive efficiencies and improve outcomes across their customer acquisition, engagement, and retention programs.

If you are just starting to learn about predictive lead scoring or evaluating solutions for your B2C business, this article is for you. Here’s what you’ll learn:

- What is predictive lead scoring?
- How B2C companies use predictive lead scoring
- Core components of predictive lead scoring for B2C
- Fundamentals of a good B2C lead scoring dataset
- How to implement predictive lead scoring using Faraday

## What is predictive lead scoring?

Lead scoring is a data-driven method of assessing and prioritizing leads based on their potential to convert into paying customers, and it’s a critical component of several B2C marketing and sales organizations. Simply put, leads with higher scores are considered more likely to convert, allowing sales and marketing teams to focus their attention on the right leads.

**Traditional B2C lead scoring** models often rely on a point-based system where humans determine the lead scoring criteria—typically a combination of basic demographic information and online behaviors such as pageviews, email engagement, or form submissions—and assign points to leads when they meet it.

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

**Predictive lead scoring** leverages the power of machine learning to analyze historical lead data and build statistical models that can predict the likelihood of a lead becoming a customer. By analyzing much larger datasets, machine learning algorithms identify patterns and important features that contribute to a lead's likelihood of conversion.

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

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

This takes the guesswork out of choosing lead scoring criteria for traditional point-based models. With the right lead scoring dataset, these models can predict a lead’s likelihood to convert before they ever visit your website or open an email.

## How B2C companies use predictive lead scoring

Implementing the right predictive lead scoring system (one tailored for B2C lead scoring) unlocks powerful lead optimization and personalization options for marketing and sales teams.

- **Lead ranking and suppression:** A simple yet powerful use case for predictive lead scoring. [Rank your leads, set a suppression threshold (e.g. bottom 70%), and only surface the likeliest-to-convert leads in your outreach tools](https://faraday.ai/templates/lead-suppression). This enables sales and call center reps to prioritize their outreach and improve win rates across the board.

- **Lead routing and distribution:** A slightly more creative use case is to use predictive lead scores to tier your leads and distribute leads from the best tier to your best reps; a great way to maximize your win potential. One of the leading solar installers in the nation [improved call center conversions by 33% with this approach](/blog/solar-call-center-lead-conversion-boost).

- **Product recommendations:** If your predictive lead scoring system supports product-specific modeling, [you can predict which products or services a lead is most likely to purchase](https://faraday.ai/templates/next-best-offer). These product-specific lead scores enable marketing and sales teams to make informed product recommendations across relevant touchpoints.

- **Customized discounts/promotions:** Most predictive lead scoring models are built to predict the likelihood of a lead converting to a customer. Why not predict the likelihood of leads converting to high-LTV customers and [offer custom discounts or incentives to the leads who are most likely to spend a lot](https://faraday.ai/templates/adaptive-discounting)? This is one of the most creative use cases we’ve seen.

## Core components of predictive lead scoring for B2C

Predictive lead scoring requires robust data (first-party and third-party), customizable and explainable models, and flexible integrations to bring lead data into the system and push predictive lead scores out to your marketing and sales tools.

### Historical lead data

Historical lead data provides the necessary foundation for building accurate predictive models. This first-party data includes information about past leads’ behaviors, attributes, and their outcomes (e.g., whether they converted into customers or not).

### Lead enrichment data

Lead enrichment data is crucial for predictive lead scoring because it enhances models' accuracy and depth of understanding about leads. For B2C lead scoring, this third-party consumer data typically includes demographic information, financial information, lifestyle information, and property information.

_Note: Given the changing data privacy landscape, it’s crucial that your predictive lead scoring system uses responsibly sourced consumer data. [Avoid data from third-party cookies and social scraping](/blog/death-of-third-party-cookies-strategies)._

### Machine learning capabilities

There are several ways to leverage machine learning for predictive lead scoring, all with varying degrees of complexity.

Most CRMs and marketing automation platforms now offer built-in predictive scoring capabilities. These solutions typically leverage blackbox (unexplainable) machine learning and rely heavily, if not entirely, on the first-party you have in the system. They’re easy to implement, but lack third-party consumer data which is crucial for B2C lead scoring.

If you have data scientists and engineers with enough bandwidth to spare, you can try taking a homegrown approach. However, in-house development costs can be substantial so unless you already have machine learning pipelines and are licensing consumer data, we recommend [using our API to score leads instead](/developers/quickstarts/score-leads).

Predictive analytics and customer prediction platforms like Faraday operate somewhere in the middle, providing users with better control of their models while streamlining laborious machine learning processes. With Faraday, users connect their lead and customer data and tell the system what to predict (e.g. lead conversion likelihood). The system handles the rest (data enrichment, model building, score deployment, etc.). We’ll show you [how to implement predictive lead scoring with Faraday](/blog/predictive-lead-scoring-b2c#how-to-implement-predictive-lead-scoring-with-faraday) later on.

### System integrations

Integrations are necessary for predictive lead scoring to impact revenue. Predictive lead scoring systems need to ingest new lead and customer data to improve models and deploy predictive lead scores into your marketing and sales systems. Ensure that your system can connect flexibly with your data sources and marketing and sales platforms.

## Fundamentals of a good B2C lead scoring dataset

For B2C companies, best-in-class predictive lead scoring requires a combination of historical data and third-party consumer data. Historical lead and customer data enables machine learning algorithms to distinguish successful outcomes (e.g. leads converting into paying customers) from unsuccessful outcomes. Consumer data lets the algorithm identify important “real life” characteristics about the leads that convert into customers versus those that don’t. For the best results, you will need the following data:

- **Identity data** (e.g. name, physical address, and phone number) is necessary to match your lead and customer records with the correct consumer profiles acquired from third-party data sources. This matching process is commonly referred to as identity resolution.
- **Transactional data** (e.g. lead acquisition date, purchase date, and order value) is necessary to define important cohorts like customers, leads, and stale leads. The machine learning algorithm will examine these cohorts to identify patterns that indicate the likelihood that an individual will move from the “leads” cohort to the “customers” cohort.
- **Engagement data** (e.g. pageviews, email subscriptions, and free trial signups) enables the algorithm to understand how certain behaviors contribute to a lead’s likelihood of conversion.
- **Consumer data** (e.g. demography, lifestyle, property, and financial information) enables the algorithm to understand how different human characteristics contribute to a lead’s likelihood of conversion. Here’s a small snapshot of Faraday’s [built-in consumer data](/features/data):

![Image of Faraday traits console](/images/blog/leads-traits-console.png)

Now that you’re familiar with predictive lead scoring and what’s required to generate accurate B2C lead scores, we’ll show you how to set up predictive lead scoring pipelines with Faraday.

## How to implement predictive lead scoring with Faraday

With Faraday, you can implement best-in-class predictive lead scoring within a day, without support from your dev team. To do this, you’ll simply upload your lead and customer data and use [predictive lead scoring recipes](/predict/category/lead_scores) to easily configure predictive models and deploy lead scores to your stack.

![Image of Faraday recipes wizard](/images/blog/leads-recipes.png)

You can upload your historical lead and customer data with a CSV file or create a connection to your database or data warehouse. Faraday automatically enriches your data with built-in consumer data profiles, adding hundreds of attributes to your lead and customer records.

![Image of Faraday traits console](/images/blog/leads-persona-set.png)

Once your data is in the system, just choose a lead scoring recipe, add your leads and customers, then hit save.

![Image of Faraday lead scoring in HubSpot recipe](/images/blog/leads-lead-scoring-in-hubspot.png)

Faraday will automatically build predictive models and provide model performance insights to help you determine what kind of results you can expect.

![Image of Faraday outcome score](/images/blog/leads-model-report.png)

Once you’re satisfied with your predictive lead scoring models, add deployment destinations (e.g. HubSpot) to your pipeline and turn it on.

![Image of Faraday lead scoring pipeline and deployment](/images/blog/leads-pipeline.png)

Just like that, you have predictive lead scores flowing into your stack.

![Image of Faraday lead scores in HubSpot](/images/blog/leads-hubspot-lead-scoring.png)

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

Understand how predictive lead scoring works and what's required to build effective B2C lead scoring models using machine learning.

Predictive lead scoring: Best practices for B2C lead scoring using machine learning

The Special Ad Categories setting on Facebook applies to ads that promote content related to sensitive areas such as:

- **Housing** — Ads about real estate, home rentals, or housing-related services.
- **Financial products and services** (expanded category as of October 2024) — Ads offering credit cards, loans, or other financial services such as insurance and investment advice.
- **Employment** — Ads about job opportunities or career services.
- **Social issues, elections, or politics** — Ads related to social causes, political campaigns, or elections.

## Why the Setting Exists

This feature ensures compliance with laws and regulations that prohibit discrimination based on factors like age, gender, ethnicity, and location. If your company’s ad falls under these categories, Facebook limits your targeting options to promote fairness and equal opportunity.

### Key impacts on ad targeting

- No targeting by age, gender, or ZIP code.
- Location targeting is restricted to a 15-mile radius.
- No Lookalike Audiences: You cannot use Lookalike Audiences for Special Ad Categories. Instead, Facebook offers “Special Ad Audiences” based on similar behaviors but without using protected characteristics.
- Custom Audiences are allowed, but with additional restrictions: you can still use custom audiences, but you must certify compliance with [Facebook’s advertising standard](https://transparency.meta.com/policies/ad-standards/).

## Facebook’s October 2024 special ad category announcement

In October 2024, Facebook announced a few significant changes to special ad categories:

1. A new special ad category “[Financial products and services](https://www.facebook.com/business/help/510724041294968)” was introduced, replacing the previous Credit ads category. This expanded the type of companies and ads that were subject to special ad category limitations.
2. Certification that any ad set based on a customer list complies with Facebook’s advertising standards.
3. [Restriction on usage of shared custom lists](https://www.facebook.com/business/help/1452187872132363) for custom audiences.
   1. Ad accounts within the same business portfolio that share customer list custom audiences should make sure that all people with permissions to manage ad campaigns have the same business email domain. If the domains don’t match, then those shared customer lists will be unavailable for ads about housing, employment, and financial products and services.
   2. Usage of shared audiences from different business portfolios will not be permitted for ads about housing, employment, and financial products and services.

### Use of Faraday in Compliance with the Special Ad Category

Since the emergence of the Special Ads category, Faraday has supported businesses whose ad sets are subject to its rules and restrictions. The primary impact of Facebook’s October 2024 announcement is to increase the type of businesses that will be subject to these rules and restrictions.

If you are a business, whose Facebook ad sets are labeled a special ad category, Faraday recommends the following steps to ensure compliance of any custom list generated through the Faraday platform:

1. **Segmented audiences** — If you are using Faraday cohorts to generate a custom list, you should avoid building cohorts that specifically segment on restricted traits like age and gender, restricted locations like zip code, or other [prohibited information.](https://www.facebook.com/business/help/361948878201809?id=188852726110565)
2. **Modeled audiences** — If you are using a Faraday outcome (aka predictive model) to generate a modeled audience, you should:
   - Use trait blocking when you [build your outcome](https://faraday.ai/docs/academy/dashboard/making-predictions) to ensure that all traits associated with protected classes (e.g. age, gender, ethnicity, religion marital status, etc.) are not considered when building a predictive model. This ensures that your models will not knowingly consider any data that would violate Facebook’s policy against discrimination.
     ![Screenshot of Faraday dashboard](/images/blog/facebook-special-categories-1.png)
   - Use Faraday’s built in bias mitigation to account for implicit bias. Simply blocking traits from model training, does not account for the fact that there is often a correlation between other Faraday traits and certain protected class traits. [Faraday’s bias mitigation,](https://faraday.ai/blog/faraday-bias-mitigation-machine-learning) allows a user to neutralize or even correct for historical bias within their lead and customer base.

![Screenshot of Faraday dashboard](/images/blog/facebook-special-categories-2.png)

Please see [this blog post](https://faraday.ai/blog/how-to-optimize-ads-for-compliance-in-finance), for a more in depth look at common Faraday supported ad strategies that comply with financial regulations.

How to use Faraday for ads when you're in a special category

Facebook special ad category overview

Faraday’s [customer behavior predictions](https://faraday.ai/features/customer-behavior-predictions) have a wide variety of use cases. From enhancing your customer engagement through personalization via [bespoke personas](/stories/persona_analysis) to embedding predictive lead scores in your [field sales app](/blog/salesrabbit) to increase rep productivity and drive growth, there’s no shortage of applications.

One application that’s proving to be more and more popular with our clients is geotargeting audiences based on the locations of physical stores and branches to enable personalized campaigns with a focus on retail shoppers. What this means is that these clients are able to specifically find the most likely buyers within a certain geographic radius around a point of interest, then run campaigns to draw people to their outlets, branches, and other physical locations.

In this guide, we’ll walk through how to create a geotargeted audience with Faraday.

## How to geotarget with Faraday

### Connect your data

To start, you’ll want to make sure that you have a [connection](/docs/abstractions/connections) created to wherever your data is stored. Doing this allows your customer predictions to seamlessly deploy back to your stack. If you’ve already got your connection created, double check that your [dataset](/docs/abstractions/datasets) is ready as well, as that’s how you’ll describe your data to Faraday–who’s who and what pieces of data mean what.

Both of these resources can be created with Faraday's API via the [createConnection](/docs/reference/createconnection) and [createDataset](/docs/reference/createdataset) requests, respectively. While these aren't required in order to complete the steps in this guide, they're required for you to be able to deploy your predictions directly back to your stack, or to add additional event-specific parameters mentioned in the [cohort section](/blog/how-to-create-a-geotargeted-audience-api#create-your-cohort).

### Create your place

In Faraday, you can create a point of interest–a **place**–around which you can create radius-based cohorts, or groups of interesting people. To do this, you’ll need your Faraday API key, which can be found in the _account_ tab of your [settings page](https://app.faraday.ai/settings/account).

[![Location of API key in Faraday](/images/blog/product/api-key-location.png)](/images/blog/product/api-key-location.png)

Once you’ve copied your API key, head over to the [createPlace](/docs/reference/createplace) request in API reference docs. Here, you’ll paste your API key into the `Token` field, then update the `addresses` field to include the address of the point of interest.

[![Location of createPlace API request in Faraday docs](/images/blog/product/geotargeted-audience/createplace-request.png)](/images/blog/product/geotargeted-audience/createplace-request.png)

A place can include more than one address, so if you’d like to take advantage of a radius around _all_ of your locations, you can add them all to a single place request within the `address` parameter. Here’s what your request might look like for a single address, though:

```bash
{
  "addresses": [
 {
   "city": "Burlington",
   "house_number_and_street": "431 Pine Street",
   "postcode": "05401",
   "state": "VT"
 }
  ],
  "name": "My Storefront"
}
```

Once your address info has been entered–don’t forget to give the place a name, like “My Storefront” above–then click **send API request** to create the place.

In the response window, grab the **place ID**, which you’ll use to create your radius-based cohort around your new place.

[![Location of createPlace API response in Faraday docs](/images/blog/product/geotargeted-audience/createplace-response.png)](/images/blog/product/geotargeted-audience/createplace-response.png)

### Create your cohort

With the place you’d like to geotarget your predictions around ready, you’re now ready to create the [cohort](/docs/abstractions/cohorts) of people that you’ll target with that place. To do this, you’ll use the [createCohort](/docs/reference/createcohort) request.

[![Location of createCohort API request in Faraday docs](/images/blog/product/geotargeted-audience/createcohort-request.png)](/images/blog/product/geotargeted-audience/createcohort-request.png)

In your request, you can optionally add parameters for things like `min_count` / `max_count` or `min_value` / `max_value` if you've created a connection and a dataset, but we’ll focus on the `place_conditions` parameter as that’s where you’ll specify the place you just created.

You can customize the cohort with `recency`, `frequency`, and `value` parameters based on the event you use to define the cohort, but for this example we’ll keep it simple and create a cohort using the following parameters:

1. A **name** for the cohort.
2. A **distance** of 10,000 meters around the place created above. Note that this value _must be in meters_.
3. The **place ID** saved from the createPlace request.

```
{
  "name": "Leads within 10km of storefront",
  "place_conditions": [
	{
  	"distance": 10000,
  	"place_id": "b1ab0efb-f732-478f-8310-6882383c4107"
	}
  ]
}
```

You can also optionally include an [event stream](/docs/abstractions/events) using the `stream_name` parameter if you’d like this radius-based cohort to also filter people that have completed an event of some kind.

With the createCohort request ready, click **send API request**, after which you should see a **201 created** response for a success.

Voilà, you’ve got a radius-based cohort created based on all your leads within 10km of your storefront.

With it, you can add it to a [pipeline’s](/docs/abstractions/pipelines) _population to include_ in order to target this group of people with your predictions. Add both their likelihood to convert via [outcome](/docs/how-to/predictions/likelihood-to-convert) as well as their customer persona via [persona set](/docs/how-to/predictions/customer-personas) to maximize effectiveness for your next direct mailer.

A guide on creating a geotargeted audience using Faraday's API.

How to create a geotargeted audience with Faraday’s API

In this guide, we’re going to walk through the technical steps Boll & Branch, a prominent D2C linens brand, took to configure Faraday to make the [customer persona predictions](https://faraday.ai/templates/thematic-personalization) they needed, and how they activated.

## Integrating with their BigQuery warehouse

Boll & Branch has a wealth of customer data in their BigQuery warehouse, and Faraday’s seamless [integration](https://faraday.ai/integrations/predictions-with-bigquery) with BigQuery was the first step in connecting their data with Faraday. The only info they needed on their end to get this connection set up was the ID of their project in BigQuery and the dataset’s name.

[![Boll & Branch's connection configuration in Faraday](/images/blog/howto/boll-and-branch/boll-and-branch-connection.png)](/images/blog/howto/boll-and-branch/boll-and-branch-connection.png)

From there, they created a Faraday [dataset](https://faraday.ai/docs/abstractions/datasets) to describe their data: which rows in their data represent what pieces of customer data? Creating this dataset served the brand two purposes, both key cornerstones in powering their customer predictions:

1. It enabled Faraday to identify people in their data through an identity set.
2. It pointed Faraday at the [events](https://faraday.ai/docs/abstractions/events) occurring in their data, which is how they’ll make their predictions–more on that later.

[![Boll & Branch's dataset configuration in Faraday](/images/blog/howto/boll-and-branch/boll-and-branch-dataset.png)](/images/blog/howto/boll-and-branch/boll-and-branch-dataset.png)

In their identity set, the team at Boll & Branch matched the people in their data–their customers in this particular use case–into the [Faraday Identity Graph](https://faraday.ai/features/faraday-identity-graph) by telling Faraday which rows in their data represented personally identifiable information (PII): name, email, physical address, and so on.

They then defined two events:

- “Customers,” which looked at the date of someone’s first order to define them as a customer.
- “Orders with product category,” which pointed toward an event property that specified which category in their catalog the product purchased is in. They planned to use this to see which category their customers were purchasing from so that they could offer recommendations.

## Defining their customers

With their data connected, the next step was to create [cohorts](https://faraday.ai/docs/abstractions/cohorts) of interesting people, or people that they wanted to make predictions on. As their team was initially focused on identifying who their current customers really are as people and consumers, their [_Customers_](https://faraday.ai/docs/how-to/cohorts/customers) cohort was where they began.

The brand defines their customers as people that have made a purchase, and so they chose the “customers” event that they created earlier on in their dataset to define their _Customers_ cohort.

[![Boll & Branch's cohort configuration in Faraday](/images/blog/howto/boll-and-branch/boll-and-branch-cohort.png)](/images/blog/howto/boll-and-branch/boll-and-branch-cohort.png)

Creating this cohort grouped everyone in the brand's data that had made a purchase together, and any time a new customer made a purchase, they’d automatically join this _Customers_ cohort thanks to the connection to BigQuery they made.

## Predicting customer personas

Now that their pieces were in place, Boll & Branch's team was ready to make their first prediction: [personas](https://faraday.ai/docs/abstractions/persona_sets). They navigated into [persona sets](https://app.faraday.ai/personas) and created a new persona set using the _Customers_ cohort they just created.

[![Boll & Branch's persona configuration in Faraday](/images/blog/howto/boll-and-branch/boll-and-branch-personas.png)](/images/blog/howto/boll-and-branch/boll-and-branch-personas.png)

In taking a look at the freshly-clustered persona set for their customers, they were able to quickly identify that their demographic didn’t skew quite as old as they thought it did. Additionally, they found that their highest-value and largest persona at 55% of their persona set, nicknamed “Jen,” were heavy social media users through Faraday’s relevant traits like `Active on social media` and `Instagram user`. This insight gave them clear direction as to where they should focus marketing efforts.

## Deploying their predictions with a pipeline

Analyzing their persona set within Faraday’s Dashboard gave their team a high-level overview of their customer makeup, and they were soon ready to deploy their predictions back into their BigQuery warehouse for activation. To do that, they needed a [pipeline](https://faraday.ai/docs/abstractions/pipelines).

In their pipeline, they added their _Customers_ cohort to the **population to include**, as they wanted to make predictions on their customers. Next, they grabbed their _Customer personas_ persona set for their **payload** so that they could apply their persona predictions to all of their customers.

[![Boll & Branch's pipeline configuration in Faraday](/images/blog/howto/boll-and-branch/boll-and-branch-pipeline.png)](/images/blog/howto/boll-and-branch/boll-and-branch-pipeline.png)

While the pipeline was building in the background and persona predictions were being applied to their customers, the team moved on to deploying their predictions back to their stack with a [deployment](https://faraday.ai/docs/abstractions/deployments).

They knew they wanted to sync their predictions back into their BigQuery warehouse, so they created a referenced deployment, which was an easy way to sync data back into their stack via a reference key–a way for them to specify which column in their data Faraday should use to match their predictions to the appropriate person.

[![Boll & Branch's deployment configuration in Faraday](/images/blog/howto/boll-and-branch/boll-and-branch-deployment.png)](/images/blog/howto/boll-and-branch/boll-and-branch-deployment.png)

With their reference key set as _email_, every customer had their persona applied to them, and their data was then synced back into BigQuery based on the email column.

Now that everything was in place on the Faraday end for their persona predictions, they turned off [preview mode](https://faraday.ai/docs/abstractions/pipelines#preview-mode) so that their pipeline would constantly stay up-to-date as new customers entered their _Customers_ cohort.

## Making practical use of their predictions

In BigQuery, Boll & Branch’s analysts were able to manipulate their Faraday deployment to discover which of their customer personas over-indexed on what products by tallying products-per-persona, what colors of products they preferred, and more. This was key for them, as it enabled them to recommend personalized [next best offers](https://faraday.ai/templates/next-best-offer). Once they were satisfied with their findings, they pushed their predictions out to [Iterable](https://iterable.com) \- their goal was to launch an email campaign with their newfound personalization insights.

Drawing on the analysis they performed on each persona in Faraday’s Dashboard and the post-analysis they performed for product recommendations, they created a different [Iterable List](https://support.iterable.com/hc/en-us/articles/204780549-Lists-Overview) for each persona, then applied copy & creative based on the traits they discovered in Faraday’s Dashboard.

The result of applying these persona predictions to their email efforts was a **30% lift in conversion rate**.

Part of our "How they did it" series, this entry highlights exactly how Boll & Branch predicted their customer personas in Faraday, then deployed their predictions to Iterable for email activation.