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

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

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

Aggregated traits change that.

## What aggregated traits are

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

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

Aggregated traits exist to answer a simple question:

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

## Identity matching at Faraday

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

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

## How it works: the aggregation waterfall

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

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

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

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

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

A few important notes:

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

## Why aggregated traits matter

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

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

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

## How this might show up in your industry

### Aggregated traits for home services

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

### Aggregated traits for retail & e‑commerce

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

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

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

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

## Transparency and control

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

## To wrap things up

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

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

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

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

**See our complete guide:** [Predictive AI for financial services (debt, mortgage, lending)](/blog/debt-mortgage-lending-complete-context-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) uses custom predictive models built on 1,400+ rich, consented data points from the Faraday Identity Graph to remove low-intent leads from direct mail campaigns. The process follows a clear, 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 1,400 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 data points 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/ML models 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

**See our complete guide:** [Personalization tactics that actually drive conversions](/blog/personalization-tactics-that-drive-conversions).

---

[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 data points 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 data points.

[![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

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 those data points 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)

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

**See our complete guide:** [Powered by Faraday — embedding predictive AI in your platform](/blog/powered-by-faraday-complete-guide-embedded-predictive-ai).

---

Our [Powered by Faraday](https://faraday.ai/solutions/powered-by-faraday) partners work with Faraday to supercharge their features with customer predictions, ensuring their customers receive the best solutions on the market.

To accomplish this goal, they need a subaccount for each of their customers so that all data is siloed and secure. In this guide, we'll walk through the steps to create a subaccount.

## Finding your Faraday API key

The first thing you'll need in order to create a subaccount is your Faraday API key. You can find this in the Faraday Dashboard, under **Settings** in the left-hand nav, then **Account**. Click **copy** next to _production environment_.

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

## Creating a subaccount with the Faraday API

Next, head over to our [API reference docs](/docs/reference), and specifically the [createAccount](https://faraday.ai/docs/reference/createaccount) request.

Here, you'll want to do three things:

[![Arrows pointing to three steps to create a subaccount in Faraday's reference documentation](/images/blog/product/api-request.png)](/images/blog/product/api-request.png)

1. First, paste your API key into the **token** field.
2. Next, replace "Acme Wind Turbines" with the name you'd like to give your subaccount. We suggest something like "Your Company Name - Customer Company Name."
3. Last, click **send API request**.

With these three steps done, your new subaccount will be accessible via the account switcher in the upper left corner of the Dashboard.

[![Location of account switcher in Faraday](/images/blog/product/account-switcher.png)](/images/blog/product/account-switcher.png)

A short guide on creating a subaccount using Faraday's API.

How to create a subaccount using Faraday's API

At Faraday, we’re on a mission to help [businesses of all kinds](https://faraday.ai/stories) predict customer behavior. Whether you’re a [credit union](https://faraday.ai/stories/advia-credit-union) that wants to predict the next best offer for your current customers, a [construction brand](https://faraday.ai/stories/window-manufacturer) looking to increase lead conversion rates, a [sales platform](https://faraday.ai/stories/salesrabbit) set on enhancing your product with customer predictions to increase rep efficiency–or anything in between–we’ve got you covered.

While we’ve taken great leaps in the 10+ years since our founding to make this type of data science more accessible to non-data-scientists, we know that it can still be a struggle to interpret what exactly you should pay attention to within your outcomes and recommenders so that you can use your customer predictions with maximum effectiveness.

To this end, let’s take a quick look at an example of the output of an outcome and highlight the exact pieces of info you can focus on so that you can understand how to activate on your predictions. For recommenders, check out our [accompanying blog post](/blog/how-to-interpret-recommenders).

## Using the lift table

When you’re viewing an [outcome](https://faraday.ai/docs/abstractions/outcomes), the **lift table** is one of the analysis options available to you, and a great place to start.

Every outcome has an _average conversion rate_ — this is the portion of every eligible person that attained the outcome. Given a lead conversion outcome, for example, the average conversion rate is just the average lead conversion rate you’ve experienced to date.

In the lift table, you’ll find performance broken down by probability range into 10 groups, or _deciles_. The table sheds light on how likely people in each decile are to attain the outcome, including in comparison to that average attainment rate. The comparison of a given decile’s attainment rate to the average attainment rate is called **lift**, which you can see in the rightmost column. The higher the lift, the better the performance.

[![Faraday outcome's lift table with decile and lift highlighted](/images/blog/product/lift-table.png)](/images/blog/product/lift-table.png)

As you review the table, the columns you’ll want to keep an eye on are **decile** and **lift**. You can look at the lift on a scale around the value 1.0, which represents no change against your average conversion rate. When the lift moves positively away from 1.0, that means you’re in a decile that will do better than normal, while moving below 1.0 indicates the opposite.

To illustrate, look at the top decile — this contains the 10% of all people who scored highest. Faraday expects that out of everybody who receives a score this high, about 95% of them (the **precision**) will attain the outcome. This is way higher than normal for this outcome — in fact, about 90% higher (1.896x lift).

## Choosing a cutoff

If you only pursue this top decile, you’ll have a great success rate, but you’d be leaving a lot of other potential attainers behind. When predicting customer behavior like this, an important job for a human is to decide where the cutoff point (“threshold”) should be.

In general, you can use this scale to determine which people in your pipeline’s eligible population you should focus more on, and which you should focus less on. In the above table, you can see a positive lift all the way to the top 50% of scorers.

When you create a [deployment](https://faraday.ai/docs/abstractions/deployments) for your predictions, you can match this by applying a filter on the outcome’s probability percentile at “\> 50,” ensuring you’re focusing on places where you’re likely to see positive lift.

Advanced users can leverage probability values, along with profitability, to determine a cutoff based on expected value (EV).

A short guide on how to interpret your predictive outcomes in Faraday.

How to interpret outcomes in Faraday

At Faraday, we’re on a mission to help [businesses of all kinds](https://faraday.ai/stories) predict customer behavior. Whether you’re a [credit union](https://faraday.ai/stories/advia-credit-union) that wants to predict the next best offer for your current customers, a [construction brand](https://faraday.ai/stories/window-manufacturer) looking to increase lead conversion rates, a [sales platform](https://faraday.ai/stories/salesrabbit) set on enhancing your product with customer predictions to increase rep efficiency–or anything in between–we’ve got you covered.

While we’ve taken great leaps in the 10+ years since our founding to make this type of data science more accessible to non-data-scientists, we know that it can still be a struggle to interpret what exactly you should pay attention to within your outcomes and recommenders so that you can use your customer predictions with maximum effectiveness.

To this end, let’s take a quick look at an example of the output of a recommender and highlight the exact pieces of info you can focus on so that you can understand how to activate on your predictions. For outcomes, check out our [accompanying blog post](/blog/how-to-interpret-outcomes).

## Understanding AROOP

A [recommender](https://faraday.ai/docs/abstractions/recommenders)’s _metrics_ tab will show you the average rank of observed property (AROOP), which represents the average ranking of the true next event during training. In other words, the average number of recommendations it took to recommend the correct actual behavior.

What’s an observed property? Many brands use a Faraday recommender to identify the so-called “next best offer.” In this scenario, the event would be “transaction” and the property would be “product.” A recommender’s job is to predict which _product_ property will occur in the person’s next _transaction_.

Let's look at an example for a fruit seller. In the below table, John's actual behavior landed in one of the "top 2" recommendations from Faraday, while Jane's was in the top 1! The overall AROOP for the recommender would be the average of these ranks: **(<span style={{color: "red"}}>2</span> + <span style={{color: "green"}}>1</span>) / 2 = 1.5**.

<table>
  <thead>
    <tr>
      <th>Name</th>
      <th>Recommendations</th>
      <th>Observed property</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>John</td>
      <td>
        <ol>
          <li>
            Apple
          </li>
          <li>
            <strong><span style={{color: "red"}}>Orange</span></strong>
          </li>
          <li>
            Kiwi
          </li>
          <li>
            ...
          </li>
        </ol>
      </td>
      <td><strong><span style={{color: "red"}}>Orange</span></strong></td>
    </tr>
    <tr>
      <td>Jane</td>
      <td>
        <ol>
          <li>
            <strong><span style={{color: "green"}}>Kiwi</span></strong>
          </li>
          <li>
            Apple
          </li>
          <li>
            Orange
          </li>
          <li>
            ...
          </li>
        </ol>
      </td>
      <td><strong><span style={{color: "green"}}>Kiwi</span></strong></td>
    </tr>
  </tbody>
</table>

## Using the accuracy table

Looking at a recommender’s **accuracy table**, accuracy refers to how often the observed property is contained in the “top X” recommendations.

For example, if the accuracy for a threshold of 3 is 0.56, then this Faraday recommender correctly predicts the observed property to be in the top 3 recommendations 56% of the time.

[![Faraday recommender's accuracy table](/images/blog/product/accuracy-table.png)](/images/blog/product/accuracy-table.png)

Depending on your use case and the medium you're using recommendations in, you can use accuracy up to the appropriate threshold that makes sense for that medium. Choosing the threshold is the equivalent of finding the balance between the accuracy of the recommendation and overwhelming information. If you recommend _everything_ to the customer, you're guaranteed that their next action will be one of your recommendations, but that's not very helpful!

If you want to take a deeper look at how each trait (feature) impacted your predictive models, check out our blog on understanding [model reports](https://faraday.ai/blog/faraday-model-reports).

A short guide on how to interpret your predictive recommenders in Faraday.

Product