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

---

## What is predictive personalization?

Many brands are familiar with personalization, and the positive impact it can have on your marketing strategy. In recent years, advancements in machine learning and data activation have opened the door for more [authentic personalization at scale](/blog/unlocking-authentic-personalization-at-scale).

Predictive personalization is a term that involves using various machine learning predictions to create customized, personalized experiences for customers, leads, prospects, or any other interesting group of people. It enhances [personalization strategies across the entire customer journey](/blog/personalization-strategies-across-buyers-journey). Whether you’re focusing on–or looking to incorporate–product recommendations, lead scoring, churn prevention, repeat purchasers, or something else altogether, predictive personalization strengthens those strategies and provides clear indicators for how to engage so that every interaction your customers have with your brand is a delight.

## Benefits of using predictive personalization for personalization at scale

On its own, creating personalized content that’s relevant to each individual customer is a long, data-hungry process. Using machine learning for predictive personalization, however, simplifies the process and provides an array of benefits that enable you to achieve personalization at scale.

### Enhanced insights through vivid consumer data

Your first-party customer data includes key datapoints that describe your people your brand is interested in–your customers, leads, prospects, and more–through their interactions with your brand. This can help you identify the products & services that they’re interested in, so that you can create offers that are relevant to them.

Predictive personalization involves the unification of your first-party customer data with [rich, third-party, cookieless consumer data](/features/data)–after all, your customers exist outside of interactions with your brand. The end-result is a clear 360º view of your customers, leads, prospects, and any other group of people you’re interested in that gives you a firm understanding of who your customers are and what they’re like. [Insights](/blog/how-to-create-a-customer-insight-strategy) via demographics, psychographics, financials, hobbies, and more give you easy guidance for how to personalize your engagement in a highly resonant way.

### Proactive engagement

Regardless of the interaction in question, it’s always better, if given the option, to be proactive rather than reactive. Predictive personalization gives you the ability to personalize your content & creative before you’ve even interacted. You’ll find insights like how likely they are to churn and how much they’re likely to spend via their predicted LTV, so that you’re always one step ahead of the next action your customers are going to take.

[![Predictive personalization mock](/images/blog/predictive-personalization-mock.png)](/images/blog/predictive-personalization-mock.png)

### 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.

### Internal talk track alignment

While the benefits of predictive personalization on external-facing engagement are clear–[see how Sealed did it](/blog/sealed-lead-optimization)–there’s an additional benefit that’s equally as impactful: internal alignment of understanding who your customers are, and what makes them tick. Personas enable you to craft clear talk tracks on a persona-level basis so that, regardless of salesperson, prospects and leads will receive content that highlights the same resonant features. Predictive personalization can also help identify which of your salespeople are best at speaking to specific personas so that you can set priorities and internal coaching appropriately.

## Implementing predictive personalization with Faraday

Customer prediction platforms like Faraday allow you to implement predictive personalization with ease–without a data science degree, and without a single line of code. Let’s dive into Faraday to see just how easy it is.

### Connect data and define customers

First, you’ll map your customer data to the matching Faraday properties. Then, in cohorts, you’ll build a cohort for your [current customers](/docs/cohorts/customers_from_hosted_csv) by defining the event that makes them a customer, such as a purchase.

[![Predictive personalization customers cohort](/images/blog/predictive-personalization-cohort.png)](/images/blog/predictive-personalization-cohort.png)

### Build personas to reveal rich insights to enable predictive personalization

Predict personas with the help of Faraday’s rich, cookieless, opt-in consumer data on top of your own first-party customer data. Discover trait breakdowns like hobbies, shopping styles, gender, household income, and more to understand the nuances of how different portions of your customer base stand apart from each other. These breakdowns give you crystal-clear guidance on how to speak to your customers in a way that makes them feel understood.

[![Predictive personalization customer personas](/images/blog/predictive-personalization-personas.png)](/images/blog/predictive-personalization-personas.png)

### Use customer predictions to create proactive, relevant campaigns

Take predictive personalization to the next level by predicting likelihood to churn, repeat purchase, and more so that your content is not only personalized to match who your customers are, but that it’s also attuned to how they’ve interacted with–or _will_ interact with–your brand, and at any scale you need, wherever in your stack you need.

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

Explore the benefits of predictive personalization & the impact it can have on your marketing strategy through rich, effective customer insights.

Predictive personalization: Using machine learning to personalize customer experiences

**See our complete guide:** [How Faraday validates predictions — and how you can too](/blog/holdout-testing-with-faraday).

---

If you've ever used AI to make predictions, chances are you've asked yourself "How do I know these are . . . correct?" It's a fair question, and healthy skepticism is a critical component of any Responsible AI practice.

Luckily there are plenty of great options for confirming that the predictions you're getting are mostly right. First, a bit of background.

## Prediction basics: How do AI models make predictions?

To set the stage, let's review how prediction works. Throughout this post, I'll use the example of a company wanting to predict which of its leads are most likely to "convert" (become a customer). In Faraday you'd represent this prediction objective with an outcome like so:

![Lead conversion outcome configuration](/images/blog/testing_predictions/outcome.png)

### Finding patterns

Now we'll take you behind the scenes. To predict something, a system must comb through historical examples of that thing happening to find patterns.

Once you know the patterns, you can compare newly observed behavior to the crucial patterns that signal "true" - if they are similar, you predict "true," otherwise "false."

So, to follow our example, the first step is to pull together some historical lead data, including some leads that converted and some that didn't.

![Training data diagram](/images/blog/testing_predictions/training_data.png)

Here, the top row represents your leads over time at their first capture point. Some of them—the **<span style={{color: "green"}}>green</span>** dots—"convert" by becoming customers: we call these _examples_. Others—the **<span style={{color: "red"}}>red</span>** dots—go "stale" and represent failures: we call these _counterexamples_. It's too early to say what the few remaining gray dots will do, so we'll just ignore them.

One useful thing to note is that 6/13 of our leads converted, or about half the time. This will come in handy down the road.

Your prediction system will then use one of a variety of methods to look for interesting patterns. Is there something in common among the green leads? What about the red ones? How are they different? This identification of patterns (the model) is what lets the system make predictions about new leads.

### Using patterns to make predictions

So now, let's take those pesky remaining gray dots, as well as some new ones that might have popped up since we figured out our patterns. I've put a dark border around them:

![Data diagram including new leads](/images/blog/testing_predictions/new_leads.png)

Your prediction system will scrutinize each of these possibilities to see which patterns in your list it follows. If it follows a lot of the patterns associated with conversion, the system will predict that it will convert. If it follows a lot of the patterns associated with non-conversion, the system will predict non-conversion.

![Diagram showing predictions](/images/blog/testing_predictions/prediction.png)

### Beware silly patterns

Easy, right? Too easy you might say. The reason to be skeptical here is that the patterns your prediction system found may be coincidences rather than reflect some underlying truth.

For example, your prediction system may say:

> Here's a pattern I found! Leads with one of these exact email addresses convert, all others don't: `amy@example.com, leon@example.com, ..., pat@example.com`

Sounds silly, but it happens all the time when the wrong kind of data is examined for patterns. The methods your prediction system uses aren't always clever enough to distinguish between goofy patterns and serious ones. Based on the data available, this pattern is actually incredibly powerful! In fact, it's 100% accurate.

Problem is, it will never work with any new leads, because they'll all have different email addresses and the system will therefore predict non-conversion for all of them.

## Three holdout testing methods to see if your predictions are accurate

The way to avoid this kind of silliness is to "test" your patterns to see if they're right. If they pass the test, you can trust them going forward.

Think about your tests in school: your teacher asked you questions and you provided answers. Your teacher knew the true answers. If your answers matched the teacher's true answers, you got an A.

The hardest part of all this is that somebody has to "know" the "true" answers. If only we had some lead data where we knew the right answer . . .

But of course we do! We know which leads actually converted and which didn't. We just can't use the _same_ data to both find patterns and put those patterns to the test.

There are a few great ways to split up your data for these two simultaneous purposes. They all rely on a "holdout"—setting aside a portion of the data to use exclusively for testing. This allows you to see whether the system "understands" enough to assess new data versus only being able to regurgitate answers for the questions it has already seen. Let's dive into these methods now.

### Method 1: random holdout

#### Setup

This is the most common approach, and it involves choosing a random subset of your historical data to set aside as a holdout. Only then do you use your prediction system to find patterns among the remaining data.

![Cross validation diagram](/images/blog/testing_predictions/random_holdout.png)

Here, we've used 50% of our data as a holdout. The system will find patterns exclusively among the remaining leads.

#### Testing

Now we're ready to "take the test." The leads in the holdout are the questions, and their true disposition (converted or not) is the answer key. But for the time being we pretend we don't have the answer key and we treat the holdout leads like new leads. What can the patterns we have tell us about these leads?

I'll illustrate those predictions with colored borders on the holdout leads:

![Prediction step of cross validation](/images/blog/testing_predictions/random_holdout-prediction.png)

Once we've used the patterns to make predictions about the holdout leads, we can dramatically reveal the answer key and see how accurate the patterns really are. Let's see how we did:

![Cross validation results](/images/blog/testing_predictions/cross_validation.png)

We got 4 right and 2 wrong for about 67% accuracy. Is that good or bad?

#### Evaluation

In real life you'll rarely get an A on this test. In some cases you'll predict conversion when the lead didn't actually convert (false positive). In other cases, you'll predict non-conversion when the lead did actually convert (false negative).

Recall that our actual lead conversion rate is about 50%. That means that a really dumb prediction system could always predict "convert" and get it right about 50% of the time, or flip a virtual coin and get the same result. By this measure, 67% is quite a bit better!

So assuming you got more right than wrong, it's an improvement. Before, your patterns were always right about your historical leads, but were useless for assessing new leads. Now, maybe your patterns aren't as perfect, but at least they're useful!

#### Conclusion

Finding the right balance between false positives and false negatives is part of the art of prediction and ultimately comes down to risk versus reward. It's out of scope for this post but I'll be writing one on that soon—subscribe below to get notified.

The upshot here is that the patterns the system found on the non-holdout leads appear to be effective in making predictions about the holdout leads. This is a very good sign that the patterns themselves are "real" and not based on silly coincidences.

The name for this approach is _cross-validation_. In practice, you actually perform this random holdout method multiple times to _really_ make sure your patterns are real. The Faraday platform automatically uses 3 passes like this every time we look for patterns.

### Method 2: recent holdout

#### Setup

Another approach is to use your most recent data as your holdout, a method called a _backtest_. As a refresher, here's what we started with:

![Training data diagram](/images/blog/testing_predictions/training_data.png)

To use this method, we temporarily "rewind" time to a previous day, like so:

![Rewound training data diagram](/images/blog/testing_predictions/recent_holdout.png)

From here, the process is much like above. Our prediction system attempts to find patterns looking backwards from our "pretend now," ignoring the lead data that has transpired since then, which becomes the holdout.

#### Testing

Then it uses those "old" patterns to examine recent leads. Again, predictions here are represented by the colored borders on the holdout leads:

![Backtest prediction step](/images/blog/testing_predictions/recent_holdout-prediction.png)

And finally we use our answer key to see how we did:

![Backtest results](/images/blog/testing_predictions/backtest.png)

#### Evaluation

Here again, we went 4 for 6, or 67%. By the same logic as above, not bad.

The big advantage of the recent holdout versus the random holdout in my mind is that it's easier to understand and, ultimately, trust.

The main disadvantage of this type of holdout is the system can only look for patterns within data spanning a shorter period of time, so if the important patterns take time to appear, then using a recent holdout won't perform as well as using a random holdout.

Faraday has this feature built in as an additional option if you want it, although it's very compute-intensive so it's not available on all accounts by default. If you don't see it and want to use it, just ask support.

### Method 3: secret holdout

You may hear other folks call this a blind holdout, although I hope to see the end of that ableist terminology soon.

The idea here is that you go to extreme lengths to hide your holdout from the prediction system—or even the company running the prediction system!—in order to boost your confidence even further.

Here, the principal doesn't even trust the teacher with the answer key! The diagram looks something like this:

![Secret holdout](/images/blog/testing_predictions/secret_holdout.png)

The user has pre-emptively removed a random portion of the data to serve as the holdout and shared the remainder with the prediction system, which has no idea what's in the holdout.

The system finds patterns using all the data it has. Then the user performs the test by presenting the holdout leads to the system _without their true dispositions_. The system makes its predictions as usual:

![Secret test prediction step](/images/blog/testing_predictions/secret_holdout-prediction.png)

The user then secretly compares these predictions to the "true" answer key to determine accuracy.

In practice the secret holdout is exactly the same as the random holdout, just a heck of a lot more complicated and time-consuming. Nevertheless, you could consider it if you don't trust your prediction system for one reason or another.

Faraday supports secret holdout testing. When connecting your data, make sure to remove your holdout first, so Faraday never sees it. Configure your account as usual. Then upload your holdout—minus disposition—as a CSV dataset with a `holdout` trait, create a matching **Holdout** cohort, and use it as a the population in a new pipeline. Add the outcome you want to test to the payload, and deploy an identified CSV target. Now match Faraday's predictions back to your original holdout and analyze to evaluate accuracy.

## The real deal

Prediction systems finding silly patterns is a real problem—you could even say it's _the_ problem of machine learning. Luckily there are lots of ways to avoid these kinds of shenanigans, and good prediction systems employ them automatically.

Behind the scenes, Faraday runs random and/or recent holdout testing constantly to confirm it's finding legitimate patterns that are practically useful going forward.

I know all this (I helped build the system!), but I still treasure my ongoing skepticism, and you should too. Hopefully understanding how pattern validation works, and seeing how it operates behind the scenes, will help you develop a careful confidence in the predictions you build your workflows around.

Three holdout testing methods you can use to evaluate the accuracy of a model’s predictions.

How to set up holdout tests to evaluate prediction accuracy

## What is direct mail marketing?

Direct mail marketing is the act of a brand engaging with its customers and prospects through physical mail. It can come in various forms, from postcards, to letters, to branded water bottles, and everything in between. The most effective direct mail campaigns are highly personalized to resonate with a brand’s customers. Before email and digital engagement became the norm, direct mail was one of the best ways to reach your customers. While digital has since pushed direct mail marketing to the side, it’s had a resurgence in recent years because, in the age of digital media, personalized direct mail _stands out_–assuming you’ve done your due diligence and personalized it appropriately to match your customers’ wants and needs. Take [PostcardMania](https://www.postcardmania.com/blog/direct-mail-statistics-2023) for example, who has seen their revenue grow 17.56% each year post-pandemic in comparison to 4.67% pre-pandemic.

## Benefits of personalized direct mail

Personalized direct mail has a variety of benefits over digital to prove that it’s a great supplement to your engagement strategy. To start, the response rate of direct mail marketing is 9%, in [comparison](https://www.postgrid.com/direct-mail-statistics/) to the 1% of email, paid search, and social media. Additionally, it has a 29% ROI compared to 23% for paid search and 16% for online display. The benefits of personalized direct mail stem from two key features:

The first is that personalized direct mail can be tailored to a greater extent than its alternatives. Say that you know a group of your customers are avid runners that regularly buy running gear from your store. A digitally-focused brand might launch a campaign between major purchases for that group of people, where they want to offer them a free water bottle for being a loyal customer. An email offering the freebie will look a lot like other emails they receive, so it’ll likely be discarded, even if it’s genuine. With a brand savvy in personalized direct mail, the water bottle could be at their doorstep–delivered in a box personalized with running-focused creative–then in their hands as it becomes their daily drinker that they freely advertise for you.

The second is that, in the digital age, people are used to receiving all manner of marketing emails, to the point that it’s almost second nature to mark something as spam, or delete it. Personalized direct mail is more distinctive, and is much more likely to be seen by your customers. After all, 77% of people sort their physical mail immediately [according](https://www.pb.com/docs/US/pdf/Microsite/Nonprofit/ed_np_getyourmailopened_05MailMoment.pdf) to the USPS. With a chance that high for your mail to be seen, making use of personalized direct mail that resonates with your customers is the key to standing out.

## How can direct mail be personalized?

Personalized direct mail can be accomplished through several different means that utilize your first-party data, such as merge tags and creative variations. With machine learning and third-party consumer data on top of your first-party data, you can accomplish it more effectively, and at scale.

### Leverage data and machine learning for insights to guide personalization

To start, your customer data needs basic identifiers, like name and address, so that you know where to send your personalized direct mail. On top of that, you need to leverage all of your first-party engagement data, like products purchased, abandoned carts, subscriptions, and so on, to ensure any offers you make are relevant. You wouldn’t want to find a postcard with a discount in your mailbox for cat food when you don’t own a cat, would you?

Regardless of how strong your first-party data is, it provides limited insight into who your customers are beyond their interactions with your brand. With [rich, third-party consumer data](https://faraday.ai/features/data) that includes demographics, psychographics, lifestyle, financials, and more, you can take your segmentation to the next level, enabling you to customize your copy & creative so that it resonates.

In addition to leveraging both first-and-third-party data, using machine learning to identify distinct groups–personas–allows you to create personalized direct mail at scale, with each group can be targeted with its own resonant, relevant engagement.

### Use insights to inform your personalization

A uniquely powerful way to personalize direct mail is to make use of merge tags. With merge tags, you can personalize on an individualized basis so that any data you have that’s relevant to your customers can be utilized. This way, beneath the persona-level segmentation, your personalized direct mail is dynamic enough to include datapoints like, say, someone’s name, subscription level, or the city they live in. On top of that, if you have physical locations, merge tags can be used to point customers to your nearest storefront.

Personas also enable distinct creative variations that are relevant to each persona. For your Exercise Guru Elayne persona, you could include imagery of women running alongside a dog and while pushing a stroller, because the majority of the persona are dog owners and have children in the household.

Take your personalized direct mail even further by adding customized product offers and discount codes that are relevant to each persona. Maybe, utilizing your customer data, you know that a specific group of your customers have already purchased a pair of running shoes. You know they won’t need another pair for a while, so a promo for that will bounce off. An offer for a pair of running socks or shorts, on the other hand, is more likely to catch their attention.

![Postcard with personalized discount](/images/blog/direct-mail-discount.png)

_Image via [TheMailShark](https://www.themailshark.com/product-samples/postcards/)._

## Pillars of a personalized direct mail strategy

There are three pillars to a successful personalized direct mail strategy.

- **Insights**: Identify distinct customer segments and their related insights by generating [AI-powered personas](https://faraday.ai/predict/category/persona_assignments). Through these insights, you have clear indicators for how to engage your customers and leads in ways that are highly resonant with their lifestyles.
- **Customization**: Determine how you’ll customize direct mailers based on your insights, data, and promotions. The most impactful personalized direct mail campaigns will include a mix of products, imagery, discounts, and merge tags, ensuring you’re including the right content. Brands that offer personalized direct mail services, provide a range of these types of services.
- **Targeting**: The cost of direct mail can add up–especially if you’re not targeting the best fits. Predict for likelihood to [buy](https://faraday.ai/predict/category/likely_buyers), [convert](https://faraday.ai/predict/category/lead_scores), [repeat purchase](https://faraday.ai/predict/category/repeat_purchasers), [churn](https://faraday.ai/predict/category/churn_scores), or any other relevant datapoint so that you’re spending your time and money where they matter most.

## How to implement a personalized direct mail strategy with Faraday

With a customer prediction platform like Faraday, you can identify personas to gain rich insights and predict behaviors so that you can deliver resonant, personalized direct mail to likely-to-act audiences. All it takes is some historical data about your customers. Here’s how it works.

### Connect data and define customers

To start, you plug in your data by mapping your data fields to Faraday properties.

[![Direct mail dataset](/images/blog/direct-mail-dataset.png)](/images/blog/direct-mail-dataset.png)

Then, in cohorts, you’ll utilize both your customer data and Faraday’s rich, built-in consumer data to build the exact [cohort](https://faraday.ai/docs/cohorts/customers_from_hosted_csv)–or interesting group of people–you want. In Faraday, you’ll create a cohort for your customers so that you can find more people like them, and you’ll also build a cohort of people with mailable addresses, so that you’re also targeting unknown audiences. You'll do this by choosing the following traits when building a cohort:

- **Record quality code**: one of: mailable

[![Faraday direct mail cohort traits](/images/blog/direct_mail_guide/direct-mail-guide-cohort-traits.png)](/images/blog/direct_mail_guide/direct-mail-guide-cohort-traits.png)

### Predict personas to reveal rich insights

While you likely have customer data that indicates, in some ways, who your customers might be, predicting personas tells you who they really are, and what makes them tick. Dive into trait breakdowns like age, gender, household income, hobbies, and more–each one guiding you toward engaging with the right messaging.

[![Direct mail persona set](/images/blog/direct-mail-personas.png)](/images/blog/direct-mail-personas.png)

### Predict propensity to buy to target best fits

Next, you’ll build your predictive outcome by defining your attainment cohort as your customers, as that’s your prediction’s finish line–you want more people like your customers. In this example, we’re defining the propensity to buy, or the likelihood that anyone in the U.S. is going to become a customer by making a purchase, but you can predict for virtually any action that someone is likely to take, such as a conversion, repeat purchase, or churn.

[![Direct mail outcome](/images/blog/direct-mail-outcome.png)](/images/blog/direct-mail-outcome.png)

With your prediction defined, create a pipeline to make them actionable by choosing your **likely buyers outcome**, your **persona set**, and your **mailable cohort**.

[![Direct mail pipeline](/images/blog/direct-mail-pipeline.png)](/images/blog/direct-mail-pipeline.png)

Faraday’s machine learning algorithms then predict how likely people are throughout the U.S. to buy your product, while also revealing which people belong to what persona on an individual basis.

### Deploy personalized direct mail predictions to your stack

Likely buyer scores revealing your best fits across the country? Check. AI-generated personas providing clear guidance for personalization? Check. With these insights in-hand, deploy them anywhere in your stack you need them–for example, to a direct mail provider–then, design your copy & creative so that it caters to the personas you’re engaging with.

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

Learn the benefits of personalized direct mail marketing, how to personalize your direct mail campaigns, and how to use predictive analytics to maximize impact.

Personalized direct mail: How can direct mail be personalized at scale?

In the world of machine learning, we often encounter two important concepts: the raw propensity score and probability. Though they may sound similar, they represent distinct aspects of a machine learning model's predictions.

## Example of propensity scores vs predicted probabilities

Imagine you have a model that predicts whether it will rain tomorrow. The raw propensity score is like the model's initial hunch or gut feeling about the outcome. It's a numeric value that the model calculates based on various factors like historical weather data, humidity, temperature, and more. The [propensity](https://faraday.ai/blog/propensity-models-predict-customer-behavior) score indicates how likely the model thinks it will rain or not. However, the propensity score itself doesn't give us a clear understanding of how confident the model is in its prediction.

This is where [probability](/blog/faraday-probability-scores) comes into play. The predicted probability aims at providing an answer to the question “how likely is the prediction?” It represents the likelihood of a particular outcome occurring. For example, a score of 0.75 for rain means the model is fairly confident it will rain tomorrow. However, a probability of 0.75 indicates that there is a 75% chance that it will rain tomorrow.

In summary, the raw propensity score is the model's initial guess, while probability quantifies the model's confidence in that guess. Understanding this difference can help us interpret machine learning [predictions](/features/predictions) and make better-informed decisions based on the model's level of certainty.

## Understanding model calibration

The model calibration process refers to the alignment between a model's predicted probabilities and the actual likelihood of events occurring. A good calibration ensures that the predicted probabilities are close to the true theoretical probabilities. Not all machine learning models are perfectly calibrated after training. Some models might be overconfident, providing probabilities that are too extreme, while others might be underconfident, consistently underestimating or overestimating probabilities.

### Importance of model calibration

Calibration is crucial because it impacts decision-making. If a model is well-calibrated, we can trust its probability estimates and use them to make informed choices, such as whether to take an umbrella based on the probability of rain.

Calibrating a model involves post-processing its probability scores to bring them in line with the true probabilities. Various calibration techniques can be employed, such as Platt scaling or isotonic regression, which adjust the raw propensity scores to produce well-calibrated probabilities. These techniques are typically applied during model validation and fine-tuning to ensure that the model's predictions are more accurate and reliable.

The calibration process ensures that when the model predicts a probability, it reflects the true likelihood of the event occurring. For example, if a model predicts a 70% probability of rain, it should be accurate in approximately 70% of the cases where it makes that prediction.

In summary, model calibration in machine learning is all about making sure a model's predicted probabilities accurately reflect the real-world likelihood of events happening. A well-calibrated model is more reliable and can lead to more effective decision-making.

## Benefits of using predicted probabilities

Using predicted probabilities instead of raw propensity scores offers several significant benefits that enhance the interpretability, decision-making, and overall performance of the models:

### Interpretability

Predicted probabilities provide a more intuitive and understandable representation of a model's output. Raw propensity scores are often abstract values that lack clear context, while probabilities are easily interpretable as the likelihood of an event occurring. This makes it easier for stakeholders and end-users to comprehend the model's predictions and build trust in its results.

### Uncertainty quantification

Predicted probabilities convey the model's level of certainty about its predictions. Models can provide probability scores ranging from 0 to 1, indicating their confidence in the predicted outcomes. This uncertainty quantification is valuable in decision-making, as it allows users to consider the risk associated with a prediction and adjust their actions accordingly.

### Threshold selection

Predicted probabilities offer flexibility in choosing decision thresholds. Depending on the specific application and requirements, decision-makers can adjust the probability threshold for classifying positive and negative outcomes. This adaptability allows for a fine-tuning of the model's behavior to suit the desired trade-offs between false positives and false negatives. For example, in the case of a lead conversion, a profitability threshold can be established by taking into account cost of acquisition for each lead, expected profit from actual customers, and probability of conversion.

### Calibration and reliability

When models are well-calibrated, predicted probabilities accurately reflect the real-world likelihood of events. Calibrated probabilities provide more reliable estimates, leading to improved decision-making and better performance in critical applications.

### Ensemble and stacking

Predicted probabilities facilitate model ensembling and stacking techniques. By combining the probabilities from multiple models, ensemble methods can often yield better overall performance compared to individual models or raw propensity score-based approaches.

## Benefits and drawbacks of raw propensity scores

Using raw predictions from models instead of calibrated scores has its own set of pros and cons. Let's explore them.

### Pros of using raw propensity scores:

**Simplicity and Speed**: Raw predictions are the direct output of the model without any additional post-processing, making them easier and faster to obtain. In real-time applications or scenarios where efficiency is critical, using raw propensity scores can be advantageous.

**Suitable for Ranking**: Raw propensity scores can still be useful for ranking predictions. Even though they may not directly represent probabilities, they can help rank instances based on their likelihood of being a positive or negative outcome, which is often sufficient in certain applications.

### Cons of using raw predictions:

**Inconsistent Ranking**: Raw propensity scores might not consistently reflect the true likelihood of events occurring. Two instances with similar raw propensity scores might have significantly different actual probabilities, leading to inaccuracies in ranking and decision-making.

**Suboptimal Decision Thresholds**: Using raw predictions often means relying on fixed decision thresholds (e.g., 0.5 for binary classification), which might not be suitable for all applications. This can lead to suboptimal trade-offs between precision and recall.

**Misinterpretation of Scores**: When raw propensity scores are not calibrated, users might incorrectly interpret them as reliable probabilities, leading to misguided decisions and a lack of trust in the model's predictions.

In summary, while using raw predictions can be straightforward and computationally efficient, it comes with the trade-off of lacking calibrated probabilities and potential inconsistencies in ranking. In many real-world applications, calibrated scores are preferred as they provide interpretable probabilities, better decision thresholds, and improved trust in the model's predictions.

## When should you use propensity score percentiles vs. predicted probabilities?

In the field of machine learning, the choice between using percentiles and probabilities depends on the specific context and the nature of the problem being addressed.

### Using propensity score percentiles

**Outliers are important**: If you are concerned about extreme values or outliers in your data, percentiles can be more robust in representing the distribution. For instance, the 95th percentile captures the value below which 95% of the data lies, making it less sensitive to extreme values.

**Data skewness**: When dealing with heavily skewed datasets, especially those with long tails, percentiles can be more informative than probabilities. Percentiles provide a clearer understanding of the data's spread and central tendencies, even in the presence of skewed distributions.

**Interpretability**: Percentiles can be easier to interpret and explain to non-technical stakeholders. For example, stating that a value is at the 75th percentile means that it is higher than 75% of the other values in the dataset.

### Using predicted probabilities

**Well-calibrated predictions**: Probabilities are essential when you need calibrated predictions. If you want reliable estimates of the likelihood of certain events, probabilities are more suitable. Well-calibrated models can provide accurate and informative probabilities for decision-making.

**Decision thresholds**: In many applications, using probabilities allows you to set appropriate decision thresholds based on your specific needs. For instance, in medical testing, you might want to set a higher threshold to prioritize precision over recall, depending on the consequences of false positives and false negatives.

**Uncertainty quantification**: Probabilities are crucial for quantifying uncertainty around predictions. Confidence intervals and prediction intervals can be constructed using probabilities, giving a range of likely outcomes.

**In summary, use percentiles when you need robust representation of data distribution and are concerned about outliers or skewed data. On the other hand, use probabilities when you require calibrated predictions, decision thresholds, and uncertainty quantification, especially in the context of machine learning models.**

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

Propensity scores and predicted probabilities represent distinct aspects of a machine learning model's predictions. Understand the difference and when you should use them.

Propensity vs probability: Understanding the difference between raw scores and probabilities

Faraday’s data science team has made significant improvements to the accuracy and interpretability of customer predictions within the platform; we’re going to give you a peek under the hood at how Faraday predictions are made.

The data science team introduced a process called **model calibration**, which measures how well the model's predictions match the actual outcomes. This method of calibration results in **probability scores**; predictions that are even more accurate and easier to interpret. Let’s take a look at a model report to break down the changes.

## Lift and precision before model calibration

In the below **pre-calibration** graphic, note the large dip in the lift curve (red arrow). With this lift curve, the top deciles of propensity scores, or the people you’d generally be targeting with a marketing campaign based on these results, have lower lift in the population (green square), indicating that there’s room for improvement.

A lift curve with strong predictability should show a more gradual dip than you see below. Similarly, the positive attainment cohort _home and living_ (blue square)–or the outcome you want to predict for–is very close to 50%, which could also be improved through model calibration. This would result in pipeline deployments having, say, a Jane Doe with a 0.8 score that could be interpreted as _pretty likely_ to achieve the outcome.

[![Image of Faraday model report lift table pre-calibration](/images/blog/probability-pre-calibration.png)](/images/blog/probability-pre-calibration.png)

_The above graphic displays a predictive [model report](/blog/faraday-model-reports) pre-calibration. Model reports can be found in an outcome's analysis view_.

Because Faraday uses multiple predictive models to create an outcome, it makes more sense to compare scores across models rather than treat scores from different models equivalently.

## Lift and precision with model calibration

Now, with model calibration implemented in the below graphic, the lift curve’s dip (red arrow) is much less significant for the top deciles, resulting in an outcome that has a higher positive attainment (blue square) in _home and living_.

[![Image of Faraday model report lift table post-calibration](/images/blog/probability-post-calibration.png)](/images/blog/probability-post-calibration.png)

_The above graphic displays a predictive [model report](/blog/faraday-model-reports) post-calibration. Model reports can be found in an outcome's analysis view._

So what does this mean? With probability scores, the groups of your prediction’s population–the deciles–are ranked in decreasing lift values, resulting in higher lift in your top attainers, or the people most likely to achieve the outcome.

With this update, Faraday’s predictions give you probability scores that are directly correlated to the probability of an individual achieving your outcome. If Jane Doe in your population has a 0.8 probability score, there’s an 80% chance that she will achieve the outcome.

Marketing campaigns have never been so predictable.

**[Create a free Faraday account](https://faraday.ai/signup) today.**

How model calibration helps Faraday improve customer prediction accuracy through probability scores.

Faraday probability scores: Improving prediction accuracy through model calibration

## What is market sizing?

Market sizing is a method of calculating a company’s revenue potential in a geographic market, which can provide crucial insights for competitive brands to be aware of when looking to expand, otherwise resources are likely to be wasted.

Market sizing is not a new concept, but more recent innovations in data collection and predictive analytics solutions have opened the door for more accurate market size calculations, enabling brands to make better go-to-market decisions and expand with more confidence and direction than before.

## Top down vs bottom up market sizing analysis

There are two methods of market sizing analysis: **top down**, and **bottom up**. In this article, we’ll look at both, and then explore how using predictive analytics for bottom up market sizing can provide you more accurate estimates while drastically expediting the analysis process, and give you actionable insight into where you’re most likely to have success penetrating.

### Top down market sizing

When using top down market sizing to calculate your market potential, you start at the highest level of the market that you’re in, where you find the largest possible size of the market you could acquire, then multiply it by the percent of the market that your product fits in.

For example, say you’re a brand that sells humidifiers, and you’re planning to launch a new humidifier that’s perfect for maintaining moisture levels in a newborn’s nursery. With the top down approach, your research shows you that the US market for humidifiers is \$700 million, and 65% of humidifiers sold annually are single-room, like your new product.

> **\$700m X 65% = \$455m**

Since, for the most part, the numbers that are relevant to your company and industry are likely already published elsewhere, this approach involves doing some research to find the populations and market values you need rather than calculating them yourself.

**Pros**:

- **It’s fast**. The research involved and the calculation are both straightforward and quick, so there’s not much time investment in a top-level market size estimate.
- **Accessible data**. Regardless of the outlet you want to source your data from, people have been publishing industry reports and analyses for years, so the data you need can easily be found via some time on Google.
- **Helpful at-a-glance**. Since top down market sizing looks at broad trends and market-wide data, it’s useful to be able to know, at a glance, if the space you’re looking to go-to-market in is worth the investment you’d have to make.

**Cons**:

- **It’s very broad**. As mentioned above, it’s useful to run a top down market size analysis for an initial glance at the market, but it’s unlikely to be specific enough for your company’s needs.

### Bottom up market sizing

Bottom up market sizing starts on the opposite end in that it begins with your niche. Using the same humidifier example, you’ll likely want to find people with children in the household to target since your new humidifier is for nurseries.

Through extensive research, data collection, and surveys, you find that there are 4 million people in this target group. To calculate the bottom up market size, you’d then multiply the number of people by the annual revenue you’d expect from them for the product–your customers are unlikely to buy more than one humidifier in a year, and its cost is \$100 per unit, so we’ll use \$100.

> **4 million people X \$100 = \$400m**

So what’s the verdict? Top down or bottom up market sizing? Since it uses data that’s relevant to your company, bottom up market sizing provides a more accurate estimate of your potential market–if your company has the bandwidth to put in the time. Fortunately, predictive analytics can help cut down that long timeline dramatically while providing you with geographies where your most likely buyers live.

**Pros**:

- **Backed by your data**. Since bottom up market sizing uses your own data & analytics through your customers, product, and cost data, results are much more tailored to your company, and they’re more realistic to boot.
- **As niche as you want it**. Rather than relying on industry-wide assumptions, this strategy begins and ends with your company’s niche.

**Cons**:

- **Time-intensive**. Performing the data collection required to accurately run a bottom up market sizing analysis is a long process in that, whether you’re conducting interviews or surveys, you’ll need to wait for results to come in.
- **Resource-intensive**. Assigning teams to create surveys, conduct interviews, act on their results, and so on can occupy a significant portion of their time.

## Calculating market size with predictive analytics

In the end, bottom up market sizing wins out as a more effective approach to calculate market potential. The downsides, of course, are the longer timeline and additional resources required to do it effectively.

This is where predictive analytics comes in–leveraging machine learning to predict individuals’ propensity to buy your products or services. With machine learning, your time-to-value in performing a bottom up market sizing analysis is dramatically reduced because it eliminates the need to design surveys and conduct interviews, conduct research, and wait for results.

Better yet, rather than spending extra time using your analysis to plan where you _think_ your campaigns will have the most impact, likely buyer scores show you _exactly_ where your high-intent buyers are at any geographic zoom that suits your needs.

### Traditional approach to bottom up market sizing

As we reviewed earlier, the typical traditional approach involves multiplying the number of people in the segment by the annual cost of your humidifier, which is simple enough math once you’ve done the required research to find the number of people in the segment.

At the end of the day, there’s still a lot of guesswork involved. In your research, you uncovered the raw number of potential buyers in your market, but the only direction you might have as for where to direct your campaign are surveys, which are unlikely to have reliable penetration for guidance, and assumptions–maybe the 5 driest states in the U.S. are the best bets to market your humidifier?

### Predictive approach to bottom up market sizing

With the predictive approach, you’ll take the segment you created above, then use AI to predict the propensity to buy for everyone in the segment based on real historical data. Machine learning algorithms surface patterns in the data to calculate just how likely each individual in this segment is to buy the product in likely buyer scores, and their forecasted spend over their lifetime as a customer of yours.

These likely buyer scores can be filtered by county, state, city, or metro, so that you have crystal clear indicators where your high-intent buyers are. You can even use your own points-of-interest, like brick and mortar locations, to perform radius-based likely buyer targeting for pin-point accuracy.

With Faraday, the predictive approach can be done in just a few clicks, saving you a significant amount of time and resources, providing more accurate results, and helping align your strategy through clear indicators for the locations you’ll have the highest likelihood of penetration in.

## How to do bottom up market sizing with Faraday

Using Faraday, the predictive approach for bottom up market sizing can be embedded wherever in your stack you need it in less than a day. No developers, no data scientists, and no long research timeline required.

### Connect data and define customers

To start, you plug in your data by mapping your data fields to Faraday properties.

![Image of Faraday dataset](/images/blog/market-sizing-dataset.png)

Then, in cohorts, you’ll utilize both your customer data and Faraday’s built-in consumer data to build the [exact cohort](https://faraday.ai/docs/cohorts/customers_from_hosted_csv)–or interesting group of people–you want. To continue with the nursery humidifier example, you’ll define a cohort for your customers, and a cohort using the trait for _children in household_, sourced from Faraday’s [rich, cookieless, opt-in consumer data](https://faraday.ai/features/data), to target with your propensity to buy predictions.

![Image of Faraday cohort](/images/blog/market-sizing-cohort.png)

### Predict propensity to buy to hone in on likely buyers

With your cohorts defined, build your predictive outcome by choosing your attainment, or your customers. Here, you’re defining your propensity to buy, or the likelihood that anyone in the U.S. is going to become a customer by making a purchase.

![Image of Faraday outcome](/images/blog/market-sizing-outcome.png)

Now that your predictions are defined, create a pipeline to make them actionable by choosing your **likely buyers outcome** and your **children in household** cohort.

![Image of Faraday pipeline](/images/blog/market-sizing-pipeline.png)

Machine learning algorithms then get to work predicting how likely people across the country with children in the household are to buy your new nursery humidifier. You can even further your bottom up market sizing analysis by generating customer personas and their predicted lifetime value with your brand.

### Deploy likely buyer scores to your analysis tools

Finally, deploy your likely buyer predictions back to your stack, via CSV file for versatility, or right back into your database or data warehouse for analysis in your BI tools. When deploying, filter to whatever aggregation level makes the most sense for your strategy: county, state, city, or metro. In this example, perhaps choosing state will reveal that the driest states in the country have the highest propensity to buy individuals.

![Image of Faraday pipeline](/images/blog/market-sizing-deployment.png)

With the predictive approach to bottom up market sizing, you have access to likely buyer scores anywhere in your stack in just a few clicks, providing insight into what geographic locations your company has the most high-intent buyers in so that your market size is more than just a rough estimate–it’s actionable and data-backed.

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

What is bottom up market sizing? What are its benefits? How can B2C brands accurately analyze markets without investing vast resources?

Bottom up market sizing: how to calculate market size using predictive analytics

**See our complete guide:** [Predictive AI for insurance brands](/blog/insurance-complete-context-guide).

---

If you’re a marketer in the finance industry, you know you have some heavy regulations to work with on [Google](https://support.google.com/adspolicy/answer/9917652?hl=en), [Facebook](https://www.facebook.com/business/help/2408667629202904), and other platforms, where consumer protections are put in place to help prevent discrimination. These regulations can make it difficult to reach your ideal customers when targeting new audiences, which likely means that you’re spending more than you need to on inefficient acquisition.

Complying with financial advertising regulations doesn’t mean you need to throw in the towel and accept worse performance. You can respect your customers’ privacy and be smart with your marketing strategy at the same time–you just need to think out of the box. That’s where built-in [third-party consumer data](https://faraday.ai/features/data) and [responsible AI](https://faraday.ai/features/responsible-ai) come in.

The data you have on your customers might be good, but purchases, adoptions, and clickstreams aren’t robust enough to give you a true leg up on your competitors, nor is it enough to ensure you’re not unknowingly discriminating against protected classes. Third-party data, when unified with your first-party customer data, can give you a 360º view of your customers, leads, and virtually any other group of people your business cares about.

With this breadth and depth of data in your pocket, you can then use AI to optimize your ad spend by surfacing your most likely-to-buy customers while remaining compliant by:

- Excluding protected classes from your predictions to ensure no unintended bias
- Targeting at the right aggregation level for your industry (counties, rather than zip codes, for example)
- Removing both human bias and error from the equation by using AI
- Monitoring and minimizing bias in predictive models through bias reporting

Faraday enables you to do all of these things–without a data science degree or programming knowledge–and in under 15 minutes. Here’s how.

## Connect your customer data to combine it with third-party attributes

To start, you plug in last quarter’s adoption data in by [mapping the data in Datasets](https://faraday.ai/docs/cohorts/customers_from_hosted_csv#option-a-each-row-represents-a-transaction-event).

![Image of identity set definition](/images/blog/finance-identity-set-definition.png)

Next, you use this new data to define a cohort–a group of people you’re interested in–of these new adopters.

![Image of cohort definition](/images/blog/finance-cohort-definition-booked-date.png)

## Build a predictive outcome to identify likely buyers

Now it’s time to predict which leads are most likely to convert. To do that, you’ll define an outcome using your _customers_ as your attainment cohort because that’s your end-goal–you want people to become customers and enter your _customers_ cohort.

Faraday makes this easy with step-by-step guides for [connecting to Google Ads](https://faraday.ai/docs/how-to/connections/google-ads) and setting up a [likelihood-to-convert prediction](https://faraday.ai/docs/how-to/predictions/likelihood-to-convert). Once your outcome is defined, Faraday will generate scores that tell you which individuals or locations are most likely to become customers.

![Image of new outcome screen](/images/blog/finance-new-outcome.png)

## Exclude protected classes with trait blocking

When describing your outcome, you choose _trait blocking_ to remove protected class traits. This ensures you’re not only complying with financial fair lending regulations, but are also using AI responsibly.

![Image of trait blocking checkbox](/images/blog/finance-trait-blocking.png)

## Choose the right aggregation level for your likely buyer predictions

Once your predictions are ready, set your aggregation level based on how high you’d like to zoom out of a location–such as by _county_ to remain compliant with Google’s restrictions on zip code targeting.

![Image of pipeline aggregation level](/images/blog/finance-pipeline-aggregation-level.png)

## Push high-scoring locations to your ad platform

With your highest-scoring locations in-hand, you plug them into your Google Ads campaign:

![Image of Google Ads campaign location upload](/images/blog/finance-google-ads-campaign-locations.png)

And your Facebook campaign:

![Image of Facebook campaign location upload](/images/blog/finance-facebook-campaign-locations.png)

That’s it! Your best-bet locations are plugged into Google and Facebook, and fewer than 15 minutes were spent making your predictions.

## Real results from a customer in the financial services space

A Faraday client in finance recently followed these exact steps to kick off a new acquisition campaign for an underperforming insurance plan, and they’re expecting a **3.2x lift in conversion rate** in the top 10% of individuals alone.

![Image of Faraday lift table](/images/blog/finance-lift-table.png)

Next up for them is adding direct mail into the mix, but the possibilities are endless.

[Sign up](https://faraday.ai/signup) free today.

In finance, remaining compliant to fair lending regulations doesn’t mean you need to accept worse acquisition performance. Learn how to use AI and third-party data to optimize ad spend by surfacing  your most likely-to-buy customers in under 15 minutes, while ensuring you're compliant to any relevant regulations.

How to optimize ad targeting and comply with financial advertising regulations

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

---

We are excited to announce that [AccuLynx](https://acculynx.com/), a leading provider of cloud-based business management software for roofing contractors, has launched a new Lead Intelligence feature that uses AI to predict homeowner buying behavior.

[Lead Intelligence](https://acculynx.com/roofing-sales-lead-intelligence/), powered by [Faraday's prediction API](https://faraday.ai/developers), taps into responsibly sourced consumer data and machine learning to enrich and score Acculynx customers’ leads every week, and provides users with up-to-date lead rankings that can help prioritize their sales efforts and close more business efficiently.

“Lead Intelligence is a game-changer for roofing companies,” said Mark Rattin, VP of Product at AccuLynx. “AccuLynx customers no longer have to guess which leads to call first, or only rely on their experienced sales people to quickly close deals. Now they can improve their sales win rates by focusing their efforts on the best leads.”

## Here’s how it works

Previously, Acculynx users could set a lead's priority level depending on what they thought of the lead. This was of course based only on a limited set of data that the user had in front of them and their intuition.

Lead Intelligence uses Faraday to analyze hundreds of data points on all of their customers’ leads, including demographic, financial, property data and more, which then provides a score that indicates the likelihood of a lead converting into a customer.

By leveraging Faraday's API, Acculynx was able to develop the Lead Intelligence feature in a matter of months, providing users with a game-changing tool included in all subscriptions and saving significant engineering and data science resources that would otherwise be required.

We at Faraday are proud to support Acculynx’s mission of providing roofing contractors with easy-to-use technology tools that help them work smarter and grow their business.

How AccuLynx uses Faraday’s prediction API to power a game-changing Lead Intelligence feature for roofing companies across the US.

Acculynx launches AI-powered lead intelligence with Faraday prediction API

If you have at least 2000 payments, Stripe data probably contains your buyer signal - the unique patterns that predict who will buy from you.

Last month, a friendly brand exported about 10,000 payment records from Stripe as a CSV. Then they uploaded them to Faraday and, within 24 hours, got their results:

![Buyer signal showing importance of different features](/images/blog/buyer-signal.png)

Let's focus on one feature - "Science/new technology." If a person is interested in this, they are much more likely to buy this brand's products:

![Likelihood to buy based on science interest](/images/blog/science-directionality.png)

Faraday is batteries-included - we know things like whether people like science thanks to our [built-in consumer data](https://faraday.ai/features/data).

Now, this brand can predict the likelihood of more than **240 million Americans** to buy their product. Faraday can take any list of people and sort it by likelihood to buy... or Faraday can generate the list in the first place, using a ["Deployment"](https://faraday.ai/docs/abstractions/deployments).

So... how did they get this? First, by doing a [Stripe Payment Report](https://support.stripe.com/questions/exporting-payment-reports). In particular, doing a [**Successful Payments**](https://dashboard.stripe.com/payments?status%5B%5D=successful) report.

![You want Succeeded payments](/images/blog/stripe-payment-export-succeeded.png)

Faraday only needs the following fields:

![PII, date, and amount fields](/images/blog/stripe-custom-columns.png)

(Later, you can set up an automated integration. But for now, since this is a relatively stable model based on inherent consumer characteristics, it should be stable for at least a few weeks.)

In Faraday, you map the fields:

![Address mapping](/images/blog/stripe-dataset-identity-mapping.png)

Then, [follow the standard process for importing transactions](https://faraday.ai/docs/cohorts/customers_from_hosted_csv#option-a-each-row-represents-a-transaction-event).

And finally, you make a [likely buyers model](/docs/abstractions/outcomes).

In total, if this takes longer than 15 minutes, [email the CTO](mailto:seamus@faraday.ai) because that's a critical bug!

Happy Signal Hunting!

-Seamus

PS. When this brand loaded our model into Facebook, we immediately started beating Facebook's internal model, even though we were still in learning phase...

![Beating Facebook](/images/blog/stripe-adset.png)

[Sign up](https://faraday.ai/signup) free today.

If you have at least 2000 payments, Stripe data probably contains your buyer signal - the unique patterns that predict who will buy from you.

How to determine buyer signal from Stripe payments

Purchasing jewelry is traditionally positioned around the moment. Whether it’s for romance or a special occasion, jewelry campaigns tend to focus on everything but the actual wearer. [gorjana](https://gorjana.com/) is the fine jewelry company that says otherwise by providing jewelry you’ll live in.

With this focus on the wearer, it comes as no surprise that gorjana wanted to know who their ideal customers were, so they teamed up with Faraday to validate their customer profile and reach more potential customers across digital channels and direct mail.

We recently chatted with [Elton Graham](https://www.linkedin.com/in/eltonxgraham/), Chief Marketing Officer at gorjana, to further explore their challenges and how they worked with Faraday to solve them.

## Gaining customer insights by clustering for personas

Previously, gorjana had a pretty good hunch about who their core customers were, but were looking to confirm and deepen their understanding of them with more data. In came Faraday, unlocking a wealth of data that they could leverage to not only confirm their profile, but validate their core customer segments with the people-first focus they were looking for.

To do this they leveraged the [Faraday Identity Graph](https://faraday.ai/features/data), enriching their first-party data with 1400+ responsibly sourced attributes on nearly every adult consumer in the U.S. With this combination of data, gorjana was able to build brand-specific customer personas using Faraday’s built-in machine learning capabilities. These quantitatively developed personas enabled the gorjana team to identify similarities and differences between their core customer segments to further understand how their retail-only, ecommerce-only, & omni-channel customers compared.

## Calculating total addressable market (TAM) with potential buyer predictions

[Faraday’s potential customer predictions](https://faraday.ai/features/predictions) are often used to create custom audiences for customer acquisition campaigns. The gorjana team took it a step further and leveraged those predictions to calculate their TAM and penetration rates in designated market areas across the U.S. This prediction-based TAM analysis is currently helping to inform go-to-market decisions and campaign strategies across the country.

## Reaching more potential customers across paid digital and direct mail

With a stronger understanding of their customers, the gorjana team leaned into their potential customer predictions to create custom audiences of high propensity to buy consumers for their digital ad campaigns. The team saw meaningful improvements in cost per acquisition and return on ad spend with these custom audiences from Faraday.

When asked what’s on the horizon for gorjana, their response was to expand their use of Faraday’s potential customer audiences to direct mail, and to build new predictive outcomes to identify likely gifters.

We love working with gorjana - they’re a people-first brand making fine jewelry for everyday wear. [Check them out](https://gorjana.com/) to find the perfect pieces for your jewelry stack.

At gorjana, Elton Graham’s team used Faraday to gain customer insights, validate core customer segments, and reach more potential customers with data-driven campaigns.

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