[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](https://faraday.ai/blog/how-they-did-it-bedding-brand) to embedding predictive lead scores in your [field sales app](https://faraday.ai/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](https://faraday.ai/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](https://faraday.ai/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](https://faraday.ai/docs/reference/createconnection) and [createDataset](https://faraday.ai/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](https://faraday.ai/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](https://faraday.ai/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](https://faraday.ai/docs/abstractions/cohorts) of people that you’ll target with that place. To do this, you’ll use the [createCohort](https://faraday.ai/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](https://faraday.ai/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](https://faraday.ai/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](https://faraday.ai/docs/how-to/predictions/likelihood-to-convert) as well as their customer persona via [persona set](https://faraday.ai/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.

How to: enhance your customer data with rich personas

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</strong></span>
          </li>
          <li>
            Kiwi
          </li>
          <li>
            ...
          </li>
        </ol>
      </td>
      <td><strong><span style="color:red">Orange</strong></span></td>
    </tr>
    <tr>
      <td>Jane</td>
      <td>
        <ol>
          <li>
            <strong><span style="color:green">Kiwi</strong></span>
          </li>
          <li>
            Apple
          </li>
          <li>
            Orange
          </li>
          <li>
            ...
          </li>
        </ol>
      </td>
      <td><strong><span style="color:green">Kiwi</strong></span></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

The modern marketer’s guide to personalization at scale

Unlocking authentic personalization at scale with Faraday

Unlocking growth with market opportunity analysis (MOA)

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

ChangelogSee all

Score aggregation

Market Opportunity Analysis (MOA) endpoints

Traits show page

Scope efficacy reporting module

Facebook special ad category overview

How to create a geotargeted audience with Faraday’s API

How to: enhance your customer data with rich personas

How to create a subaccount using Faraday's API

How to interpret outcomes in Faraday

How to interpret recommenders in Faraday