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

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

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

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

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

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

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

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

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

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

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

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

New additions to the team, board, and financing

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

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

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

## How to geotarget with Faraday

### Connect your data

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

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

### Create your place

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

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

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

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

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

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

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

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

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

### Create your cohort

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

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

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

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

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

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

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

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

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

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

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

How to create a geotargeted audience with Faraday’s API

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

---

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

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

## Finding your Faraday API key

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

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

## Creating a subaccount with the Faraday API

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

Here, you'll want to do three things:

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

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

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

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

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

How to create a subaccount using Faraday's API

**See our complete guide:** [Predictive AI for e-commerce and retail brands](/blog/retail-ecommerce-complete-context-guide).

---

[Burrow](https://burrow.com) is an innovative furniture brand founded in 2016\. From starting out with thoughtfully designed modular couches to recently expanding their offerings to shelving, coffee tables, and even rugs, they have continuously pushed to understand their customers and cater to their needs.

## The goal

Burrow wanted to personalize its customer journey to drive better conversion rates and more purchases as they expanded their product lines.

## The challenge

Like most companies, Burrow had a cold start problem: they knew a lot about their current customers, but very little about brand new customers or potential customers who had signed up for their newsletter.

## Faraday \+ Iterable

Enter Faraday \+ Iterable. With [Faraday’s Identity Graph](https://faraday.ai/features/faraday-identity-graph), the Burrow team suddenly had access to 1,400+ data points on almost every adult in the US they could combine with their contact data. And with Faraday’s AI tools, the team could identify the patterns and trends of their customers to build segments of leads who looked just like them.

All of these insights then [flow directly to Iterable](https://faraday.ai/docs/integrations/iterable), where their automated journeys enable customer engagement across multiple channels with minimal manual effort. By leveraging automation, informed by Faraday, Burrow delivers timely, relevant content that enhances the customer experience while saving time and resources.

## The results

By partnering with Faraday, Burrow was not only able to know what all of their leads looked like, but which looked like customers who had taken specific actions in the past—whether it was to purchase from a specific category, spend a certain dollar amount, or even prefer a specific color couch over another.

In this way, the Burrow team could engage leads who previously only had provided an email address and deliver highly tailored messaging that matched their consumer profile, with suggested products based on what they were most likely to care about.

All of this messaging was delivered with Iterable, which ensured it was delivered at the right time, and by the right channel for an optimal engagement experience.

With Faraday \+ Iterable, Burrow is building a [predictive customer experience](https://faraday.ai/blog/predictive-customer-experience-is-the-future) that makes it enjoyable for everyone to shop—whether you’re a repeat customer or simply signed up on the newsletter.

Burrow used data and machine learning to power personalization and drive higher conversion rates.

How Burrow uses customer predictions to drive intelligent growth

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

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

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

## Understanding AROOP

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

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

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

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

## Using the accuracy table

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

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

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

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

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

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

How to interpret recommenders 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 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

AI is everywhere. Businesses of all kinds are using it for every use case imaginable. From creating copy for their emails or website, to generating graphics for their blog, to [predicting customer behavior](https://faraday.ai/features/customer-behavior-predictions) and beyond. While using AI for the more innocuous cases like brainstorming ideas or generating art tends to leave you with the set-it-and-forget-it mentality, that’s not the case in the realm of predicting customer behavior. Here, [explainable AI](https://faraday.ai/blog/what-is-explainable-ai-why-is-it-important) is critical in helping non-data scientists understand what data your predictive models used to come to their conclusions, and whether or not you can trust them.

[![Graphic of dynamic prediction tenure scoring](/images/blog/transparent-reporting/transparent-reporting.png)](/images/blog/transparent-reporting/transparent-reporting.png)

For this reason, every model in Faraday comes with at-a-glance performance reporting so that you can quickly assess how strong your predictive model is. Beyond this, you can dig into things like [feature importances and directionality](https://faraday.ai/blog/faraday-model-reports) to see what individual features from the Faraday Identity Graph and [your own data](/blog/first-party-features) had the greatest impact in creating the predictive model, and even where specifically within those features they had such impact. Additionally, reporting on ROC and lift are included.

On top of this at-a-glance reporting, each model comes with a complete, in-depth technical report that digs into the nitty-gritty that data scientists love \- from analysis of training data to model calibration, cross-validation, and more. Further, Faraday strongly believes in [Responsible AI](https://faraday.ai/features/ai-safety-and-responsible-ai), which means that this technical report also includes [score explainability](https://faraday.ai/blog/faraday-score-explainability) and background on how Faraday came to the conclusion of its bias and fairness assessments.

Don’t _just trust us_ (or any other AI that you use\!) \- our reports speak for themselves.

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

From at-a-glance performance reporting to feature importances with directionality to in-depth technical reporting, Faraday helps you understand the models we build.

Transparent predictive model reporting with Faraday

See our complete guide: [Predictive AI for financial services (debt, mortgage, lending)](/blog/debt-mortgage-lending-complete-context-guide)

[Direct mail remains a core acquisition strategy for many financial services providers](https://www.lob.com/blog/the-benefits-of-direct-mail-marketing-in-todays-economy), but it comes with a hefty price tag. Each mailer represents an investment, and when sent indiscriminately, costs can spiral with little return.

When this client first came to us at the end of 2024, like many in the industry, they relied primarily on instinct-based direct mail strategies, meaning a significant portion of their outreach was reaching consumers who were unlikely to convert. They knew they were overspending but lacked the tools to pinpoint which mailers were worth sending—and which could be cut without hurting revenue.

This client needed a solution that could optimize their outreach, reduce waste, and maintain strong enrollment numbers. Most of all, they wanted confidence in whatever decision they made—all without disrupting their existing workflow. So we provided them a custom predictive data point that did exactly that.

## Lead suppression with Faraday

And that’s where Faraday came in. As soon as we identified their challenges, we quickly devised a targeted strategy to help.

The answer? Faraday’s [lead suppression](https://faraday.ai/templates/lead-suppression/recharge) template.

To enact this strategy, we started by inputting the provider’s recent lead data into our system (maintaining rigorous data privacy standards like HIPAA, GDPR, and SOC-2 compliance throughout the process). This data was then enriched through our [identity resolution](https://faraday.ai/blog/identity-resolution) process with 3rd party data from the built-in Faraday Identity Graph (FIG), which [includes over 1,400 attributes on more than 240 million U.S. consumers and their households](https://faraday.ai/features/faraday-identity-graph), and features nuanced demographic and behavioral indicators that influence consumer actions—ranging from property ownership to life events and purchasing habits.

From there, new predictive models were created and trained on this historical enrollment data (now enriched with 3rd party insights), identifying patterns and trends that distinguished high-value leads from low-value ones. Then, using advanced machine learning techniques, the system assigned each lead a predictive score, ranking them into ten distinct deciles based on their likelihood to convert. With this scoring system in place, a [cut-off threshold](https://medium.com/swlh/determining-a-cut-off-or-threshold-when-working-with-a-binary-dependent-target-variable-7c2342cf2a7c) was established to determine the point at which the cost of printing and sending a mailer outweighed the expected return, ensuring that outreach efforts remained cost-effective.

Finally, Faraday delivered an optimized, high-fit mailing list back to the provider and armed with this refined list, they were able to move forward with confidence, ensuring that every mailer sent had a greater likelihood of driving enrollment. For a visual breakdown of this process, check out our recent blog on lead suppression, [here](https://faraday.ai/blog/holdout-testing-with-faraday).

So how did it go?

## The results (so far)

If you noted the timestamp on this article, you might be thinking to yourself, “Well, they reached out at the end of 2024 and this was published on Feb 21, 2025, so did they even get the models rolled out yet?”

Well, despite the short timeframe, they not only deployed the models and sent direct mail based on their predictions, but have also already seen a significant reduction in costs, all while maintaining strong engagement with high-quality leads.

Indeed, by suppressing low-likelihood recipients, they were already able to send over **80,000 fewer mailers**, producing **a net savings of nearly $40,000**, demonstrating the immediate financial benefit of a more targeted approach.

These early financial wins were certainly affirming, but it had only been a month. How did we know that they were actually the result of our model and not just an uptick consumer interest this year?

### Ongoing model validation

At Faraday, we take the [validation](https://faraday.ai/docs/reference/getscopeefficacy) of our models very seriously. We do this before predictions are deployed through our [holdout testing process](https://faraday.ai/blog/holdout-testing-with-faraday) and continue testing on an ongoing basis even after successful implementation. In this case even though we were already seeing positive results, we wanted to see data that confirmed (or challenged) our assumptions. So we conducted further analysis on our models after we started getting results back to confirm that our predictive scoring was actually aligning with real-world outcomes.

And we found what we were looking for: Leads with the lowest predictive scores showed below-average conversion rates, validating the suppression strategy. Meanwhile, leads in the highest-scoring segments consistently outperformed the average, reinforcing the value of prioritizing the right audience. And what’s even better is that this correlation is only expected to strengthen as more data flows through the model, further refining predictions and maximizing efficiency.

While these financial results would be impressive on their own, they also highlight how quickly Faraday’s models can be deployed and optimized. The contract was signed in late-December, and within weeks, the provider was already seeing meaningful returns. This quick turn around, combined with the ability to continuously refine and improve targeting over time, positions them for even greater efficiency and cost savings in the months and years ahead.

## Next steps

With direct mail suppression already demonstrating clear value, the provider is now looking to further optimize their customer outreach efforts. A key next step? [Call center list scoring](https://faraday.ai/stories/call-center).

Similar to how Faraday’s predictive models helped refine direct mail targeting, they can also help [prioritize leads](https://faraday.ai/templates/lead-prioritization) for call center campaigns. By focusing call center agents’ efforts on high-likelihood prospects, the provider can reduce wasted calls, improve conversion rates, and increase overall campaign efficiency.

We can’t wait to see how they use this technology to generate even more value for their business\!

## In conclusion

In just a short time, this financial services provider has revolutionized its direct mail strategy, reducing unnecessary costs while maintaining strong engagement with high-value leads. Faraday’s predictive modeling has enabled them to move beyond broad, instinct-driven targeting to a data-backed approach, ensuring each mailer sent delivers greater value. And with tens of thousands of dollars saved in only a few weeks, the provider has already realized the immediate financial benefits of a more targeted outreach strategy.

What’s more, looking forward, the provider is well-positioned to refine their targeting approach even further. By continuously updating and optimizing their predictive models, and exploring additional use cases like call center list scoring, they can maximize the efficiency of all customer acquisition channels.

For this company, this is just the beginning of a smarter way to decide who’s worth reaching—and when.

If you'd like to know how Faraday can produce similar results for your business, **sign up for free**!

In their first month with Faraday, this financial services provider used lead suppression to cut 80,000 low-value mailers and save nearly $40,000.

How this financial services provider optimized their direct mail outreach in just one month

**See our complete guide:** [What is the Faraday Identity Graph?](/blog/whats-in-faraday-identity-graph).

---

## What is identity resolution?

Identity resolution is the process of taking all of the files and databases of people data in your organization and figuring out "who is who" so that you can unify individual records into a single entity. For example, one database might have email, and another database might have street address; how do you connect them? Identity resolution is the answer.

Within identity resolution, there are two broad categories: **known identity** and **anonymous** (unknown) **identity**. Known identity is figuring out the email, phone number, and/or street address of someone. If you have known identity, you can find someone in the real world. Anonymous identity is defining some IP address, device ID, browser fingerprint, or cookie that represents a person without actually knowing who they are in the real world. In either case, you’re trying to get a join key or universal ID that lets you combine data from different sources across time.

How does identity resolution work? In a few different ways:

- You _explicitly_ collect known identifiers like email in web forms or addresses in orders/transactions.
- You _implicitly_ collect anonymous identifiers like IP addresses, cookies, as they browse.
- You stitch together known and anonymous identifiers when people click links in emails, log in, enter billing and shipping data, etc.
- You connect known identifiers with marketing data like age, gender, and household income using third parties like Faraday.
- You de-duplicate and "master" records: e.g. by saying that two different emails belong to the same person
- You upload unknown identifiers to ad networks and they resolve them into users on their platforms.

Finally, it's important to know the terms first- and third-party data. **First-party data** is data that you collect and own, and can include both known and anonymous identifiers. It also includes transaction amounts, order details, product details, etc. **Third-party data**, on the other hand, is data that is owned by others, but is available for specific uses. (You may also hear about “zero-party data,” which is a special kind of first-party data that users explicitly provide, such as by filling out surveys.)

When you unify first and third party data, you can get age, gender, household income, and other traits for a user so that you know both what they bought (first-party data) and who they are (third-party data). You’ll generally get the highest enrichment or match rate by starting with known identity, but it's possible to start with anonymous identity, too.

## Why does identity resolution matter?

If your business has more than just one useful database of information (which is highly likely), identity resolution is mandatory in order to properly understand not only who’s who within your data, but also how they’re engaging with your business. Doing so opens up a world of use cases that can be done efficiently because you’re able to group similar customers and leads together: [personalize their journeys](https://faraday.ai/templates/thematic-personalization), recommend first-and-[next best offer](https://faraday.ai/templates/next-best-offer), plan [retention campaigns](https://faraday.ai/templates/churn-scoring), and more.

For example, transaction data stored in back office systems can be combined with behavior observations in the marketing or product teams, with call logs from customer-facing teams, and so on. To illustrate this latter point, customer [churn prediction](https://faraday.ai/blog/churn-prevention-machine-learning) is often much stronger when it takes into account activity like messaging or calling.

Taking this a step further–when generative AI is available–identity resolution can help take unstructured data from call center transcriptions and combine it with structured data like transaction histories. This allows you to use sentiment analysis or entity extraction to create data attributes (i.e. predictive features) that can be combined with other first and third party features in predictive models, such as [lead scoring](https://faraday.ai/solutions/lead-management).

## Why identity resolution is difficult to implement

Identity resolution is important, but it's hard to implement. To start, the datasets involved need to have PII that can be compared. For example, maybe you have transactions with billing addresses included in one dataset, and views/clicks by email address in another. To resolve the identities in these two datasets, you’d need to purchase third-party email hashes connected to postal addresses so that both have billing address and email data, _then_ you’d need to ensure the data is standardized so that they can be compared.

The process is further complicated when you consider that people aren’t static–they move addresses, they have kids, their habits change–life goes on. You'll need to rent or purchase historical address data to make your graph complete. Your customers and leads change, and your data should be able to change with them.

## What does the identity resolution market look like?

There are a wide variety of identity resolution products on the market. Some are pre-integrated into the systems that use them, such as traditional "packaged" CDPs, purpose-built person-level AI infrastructure like Faraday, and others. These identity resolution systems primarily serve the purpose of the software they're integrated with, and offer the fastest time-to-value. Software in the master data management space like Reltio is dedicated to resolving and de-duping records for use by downstream systems.

There are also data warehouse-native identity resolution packages, such as Hightouch Identity Resolution or AWS Entity Resolution that run directly inside of a customer's cloud account or data warehouse. These provide standard functions for resolving identity that can be applied to any business, though they won't be immediately usable out of the box. TransUnion and other traditional data aggregators offer identity resolution packages on the Snowflake Marketplace that serve to connect first-party data to third-party identifiers and data. Finally, consultants like Deloitte will often build custom identity resolution projects on a per-client basis, if that client has previously purchased third-party data.

Due to competitive pressure from all of the data warehouse-native and standalone identity resolution providers, some software with built-in identity resolution like Faraday now allow data warehouse-native identity resolution too, which can be a good option that combines the low time-to-value with a flexible, proven base for other software and processes to be developed on top of.

## How Faraday’s identity resolution works

Faraday’s advanced identity resolution is built in, which means you don’t need to separately license any data or go through the laborious process of manually unifying it with your own to create distinct profiles of your customers and leads. All parts are included: from data and data unification that’s continuously up-to-date and responsibly sourced, to identity resolution and AI-powered customer predictions. Whether you’re interested in prioritizing your best leads and rejecting leads that won’t end up converting, seeing which customers are going to churn or finding their next best offer, Faraday has you covered from end to end.

To start, Faraday securely ingests your data through a [connection](https://faraday.ai/docs/abstractions/connections) to wherever your data is stored (or via CSV file), after which you describe your data to Faraday via [datasets](https://faraday.ai/docs/abstractions/datasets). In your datasets, you’ll tell Faraday what personally identifying information (PII) fields to look for (column mapping), as well as what [events](https://faraday.ai/docs/abstractions/events) take place in your data, which you’ll use to define your customer prediction objectives. Everything Faraday does is [fully API-enabled](https://faraday.ai/docs/reference), too.

With your data defined, we search the Faraday Identity Graph (FIG) for matching PII through fields like `email`, `first_name`, and `address`. Once a match is found, all data that you have on this individual is unified with and enriched by their data in FIG. From demographics, to financials, property data, and more, 1,400+ robust traits supplement your data on how they interact with your business with how they exist _outside_ of those interactions.

[![Graphic of Faraday identity resolution](/images/blog/identity-resolution/identity-resolution.png)](/images/blog/identity-resolution/identity-resolution.png)

The result is a unified, enriched customer profile that’s embedded directly into your applications and workflows–with customer predictions on top–for all of your customers and leads, ensuring that your teams have all of the information they need to engage effectively.

At the end of the day, identity resolution is critical to unlocking truly data-driven marketing and AI/ML projects. With it, all of your data comes together from its disparate sources to form unique identities for each individual. These identifiers cover their engagement with your business through visits, email addresses, transactions, etc, their marketing highlights like age and income, and any other traits that you have. With identity resolution, you can effectively predict which leads are most likely to convert and which you shouldn’t waste time and money on, which customers are most likely to churn, their buyer persona, what product or service to recommend next, and more.

Faraday handles the ML struggle so that you can focus on your downstream applications and workflows.

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

Faraday unifies your data with rich third-party data and applies advanced identity resolution techniques to reveal panoramic views of your customers and leads.

How Faraday unifies your data with the Faraday Identity Graph for advanced identity resolution

See our complete guide: [Customer personas explained — how Faraday builds them and what they unlock](/blog/what-are-ai-customer-personas)

[Klaviyo](https://www.klaviyo.com/) made a big splash last year, bringing entry-[level predictive scoring](https://faraday.ai/blog/why-you-need-predictive-lead-scoring-today) to their massive audience. And we love it—the industry is catching up to what we’ve been doing for years. With AI-powered tools like [predictive analytics](https://faraday.ai/templates/lead-prioritization), [personalized product recommendations](https://faraday.ai/templates/next-best-offer), and advanced [sentiment analysis](https://faraday.ai/blog/seamus-bigquery-interview) for their emails lists, Klaviyo is giving marketers powerful capabilities to personalize messaging, automate campaigns, and forecast outcomes. But if you’re looking to take it to the next level, there’s a critical missing piece: [deep consumer data points powered](https://faraday.ai/features/faraday-identity-graph) by [third-party data](https://faraday.ai/docs/abstractions/traits).

## Klaviyo’s predictive tools, and their limitations

[Klaviyo’s AI tools](https://www.klaviyo.com/features/ai) offer a solid foundation for email marketers, helping to predict engagement and automate messaging. But recent changes—including increasing costs and [stricter policies around active contacts and list sizes](https://help.klaviyo.com/hc/en-us/articles/24263920096027)—are making efficiency more important than ever.

Though Klaviyo’s AI is powerful, their analysis is also stymied by its lack of inclusion of third-party data. This type of rich demographic and behavioral data inherently improve the depth, nuance, and critically, speed-to-deployment of predictive data points. It also radically improves the accuracy and value that they can be expected to produce.

Only with this type of rich third-party data can marketers expect to achieve the highest levels of speed and precision with their predictive marketing, and that’s where Faraday’s built-in integrations comes in.

## Optimizing Klaviyo lists with Faraday personas

Integrating Faraday’s [personas](https://faraday.ai/docs/abstractions/persona_sets) and [customer segmentation](https://faraday.ai/docs/how-to/connections/segment) capabilities into your existing Klaviyo stack takes your email lists to the next level, allowing you to gain deep insights that Klaviyo alone can’t provide.

With Faraday, you can segment your email lists not just by basic engagement metrics but by [demographics, life events, property details, and other behavioral signals](https://faraday.ai/features/faraday-identity-graph). This level of insight empowers you to tailor content, messaging, and campaigns that are perfectly suited to your audience. Whether it’s understanding the impact of a new home purchase or identifying consumers experiencing major life events, Faraday’s data-driven approach helps you reach the right people at the right time with the most relevant messaging.

By adding these third-party insights to your Klaviyo platform, you unlock smarter segmentation, faster insights, and a deeper understanding of your customers—making your email marketing more efficient, relevant, and impactful.

### The inclusion of this data improves your Klaviyo lists by:

- **Building smarter segments** based on real-world demographics, life stages, and property data.
- **Refining automated journeys** to align with consumers’ actual behaviors and preferences.
- **Improving conversion rates** by sending the right message to the right people at the right time.

## The cold start problem—and how to overcome it

An additional benefit of integrating third-party data into your Klaviyo stack is the ability to avoid the [cold start problem](https://www.sciencedirect.com/topics/computer-science/cold-start-problem), a classic challenge faced by many marketing tools, including Klaviyo’s out of the box predictive scoring. While these tools are able to generate generally accurate predictions on long time customers using only first-party data, they quickly run into issues when targeting leads or recently acquired customers with very little observed behavior.

In these cases, the model struggles to quickly generate meaningful insights, as it lacks the foundation of prior data to build from. This delay in data accumulation can significantly limit the effectiveness of campaigns until enough data has been collected.

Faraday's inclusion of our third-party data eliminates this barrier by enabling you to build highly targeted customer segments right from the start. Instead of waiting months for data to accumulate, Faraday’s built-in data provides immediate insights, empowering you to craft campaigns optimized for engagement and conversion from day one. Without waiting for slow data accumulation, you can launch campaigns quickly with confidence.

## So who will actually convert? Let’s find out.

Your Klaviyo lists might still be growing, but are they optimized for conversion? Are you maximizing their value-generating potential? With Faraday, you can build a free persona model and see for yourself. Think of all the ways you could use this—better segmentation, more precise messaging, and automated journeys that actually resonate.

Klaviyo is a great start for any email marketing, but Klaviyo \+ Faraday together can take your email marketing to the next level\!

Enhance your Klaviyo lists with Faraday’s third-party data and personas to unlock deeper insights, smarter segmentation, and higher conversion rates—solving the cold start problem and optimizing your email marketing from day one.

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