[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 datapoints 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 datapoints. 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.

Leveling up your Klaviyo lists with Faraday personas

If you are reading this, you are probably talking to Faraday about a potential contract and have a healthy amount of skepticism as to whether we can actually do what we say we are going to do.

And here’s the thing: you’re right to be skeptical. With tight budgets and increasing pressure to show ROI, you can't afford to bet on unproven solutions or spend countless cycles figuring out if a tool is really going to live up to the promise. You need a way to separate legitimate predictive tools from the noise.

Every week, we get to talk to dozens of CMOs, CTOs, & CEOs across industries who are trying to discern the same thing: which tools are legitimate and which ones are not. As a leader of a high profile brand, you are inundated with promises of predictive insights and transformation, but you need more than just promises – you need guarantees.

That's where our holdout evaluation comes in. It's not a proprietary methodology we invented – it's an industry-standard approach used by data scientists to validate predictive models and the custom predictive datapoints built on them.

**What makes us different is simple: we run this validation before you sign any contract.** Because frankly, if we can't prove we'll deliver real results for your business, we don't want to waste your time.

Below we’ve outlined what we are doing “behind the scenes” and how you can evaluate if we will add the value you need. 

## What is a holdout evaluation?

A holdout evaluation is a method for validating predictive models by comparing their predictions to real-world outcomes. We **set aside a portion of recent conversion data**—data the model has never seen before—and use it as an independent benchmark, against which predictions can be compared.

This “holdout set” acts as a real-world proving ground. It is untouched until after the predictive model has been trained. 

Meanwhile, the rest of your historical data is combined with rich third-party insights from the [Faraday Identity Graph](https://faraday.ai/docs/abstractions/traits) ([which includes 1,500 attributes on 240M+ U.S. consumers and their households](https://faraday.ai/features/faraday-identity-graph)). We use this combined data to train the new predictive model.

Training a predictive model simply means finding patterns in historical conversions. In other words, what can we learn from your past customers to predict future outcomes? Once trained, the model generates predictions—scoring leads based on their likelihood to convert and only after these predictions are made do we compare them to the **real conversion rates** in the holdout set.

If the highest-scoring leads actually convert at higher rates than lower-scoring ones, the model is validated as accurate and reliable. 

Holdout evaluations ensure predictive models aren’t just theoretical—they work in the real world, using real data, to deliver real results.

## How holdout validation works at Faraday

We've streamlined this validation process to be transparent and straightforward:

1. ![customer conversion data](/images/blog/holdout_testing_diag/holdout_testing_diag-01.png)

   #### Prepare your data and set aside a holdout group

   We start with your recent [lead conversion data](https://faraday.ai/blog/first-party-vs-third-party-data-marketing). Before any processing occurs, we reserve a portion of your most recent leads as a "holdout set"—data that the model will never see during training. This ensures we have an independent benchmark for evaluating predictions.

2. ![merged with FIG](/images/blog/holdout_testing_diag/holdout_testing_diag-02.png)

   #### Combine data with Faraday Identity Graph

   With the holdout set reserved, we then combine remaining data with Faraday’s [built-in, rich  consumer data](https://faraday.ai/features/faraday-identity-graph) sourced from the Faraday Identity Graph. This expanded dataset provides a deeper view into key predictive signals, like property and demographic information, life events, buying habits, and more.

3. ![model is trained and predictions begin](/images/blog/holdout_testing_diag/holdout_testing_diag-03.png)

   #### Train the model and begin predictions

   Using this enriched dataset (excluding the holdout set), we train a predictive model to identify patterns in conversion behavior—learning from traits, behaviors, and other signals.

   Once the model is trained, it scores each lead based on its likelihood to convert, generating a ranked list of opportunities—from high-value leads to those less likely to convert.

4. ![model is validated](/images/blog/holdout_testing_diag/holdout_testing_diag-04.png)

   #### Compare predictions to reality to validate model

   Finally, we put the model to the test: we compare its predictions against the real outcomes in that untouched holdout set. If the highest-scoring leads don't convert at significantly higher rates, we go back to the drawing board.


## How to know if a holdout validation was successful

A holdout validation is considered successful when the following criteria are met:

1. **Clear correlation between predicted probability and actual conversion rates**  
   The model’s predicted likelihood to convert should directly align with the actual conversion rates. If the predictions are accurate, high-probability leads should convert at a noticeably higher rate than lower-probability ones.

2. **High-scoring leads convert at significantly higher rates**  
   The top-scoring leads should consistently perform better than those ranked lower. This demonstrates that the model is identifying truly valuable leads.

3. **Consistent performance across different customer segments**  
   The model should show reliable results across various segments—whether you’re targeting different demographics, regions, or other customer profiles. If the model works well across a broad set of data, it proves its reliability and scalability.

When a model meets these criteria, it’s ready to generate predictive datapoints—whether that means optimizing [lead buying](https://faraday.ai/docs/how-to/use-cases/leads), refining your [direct mail strategy](https://faraday.ai/templates/lead-suppression), or enhancing your email [personalization](https://faraday.ai/templates/personalization) approach.

## Questions you should ask us

We want you to push us on what matters most for your business. Here are key questions to evaluate if we're the right fit:

1. **How does this model's performance compare to the lift you are seeing with  similar businesses in our industry?**

   While we can’t share client specifics, we can provide insight into broader trends and expected performance benchmarks.

   → **Why this matters:** Understanding industry benchmarks helps you gauge whether predictive modeling is truly a competitive advantage for your business.

2. **How much historical data do we need for reliable predictions?**

   The answer varies by business model and conversion volume. We’ll help determine whether you have enough data—or how to supplement it if needed.

   → **Why this matters:** Predictive models work best when trained on strong data. Knowing the threshold for reliability ensures you're making data-driven decisions with confidence.

3. **What happens if the model doesn't perform as expected?**

   We have a structured iteration process to refine models when necessary, ensuring they reach meaningful predictive power.

   → **Why this matters:** No model is perfect on the first run. Understanding the process for continuous improvement ensures long-term success.

4. **How will this integrate with our existing marketing stack?**

   We’ll provide technical specs, discuss implementation timelines, and outline the resources needed to get up and running.

   → **Why this matters:** Seamless integration means faster time-to-value and less friction in your workflow.


## Why holdout evaluations work

The businesses who win this year will be those who successfully incorporate predictive modeling into their core workflows. It's no longer a nice-to-have – it's a need-to-have. But we know you can't afford to make decisions based on hunches or unproven tools. That's why we're eager to validate our value up front. 

At the end of the day, three things matter to us:

1. **Confidence before commitment:**  
   You see proof before you commit

2. **Higher marketing efficiency:** 
   Your marketing dollars go to the leads most likely to convert

3. **Stronger sales performance:** 
   Your sales team focuses on opportunities worth their time

If we can't demonstrate value, we'll be the first to tell you we're not the right fit. But if we can – and we usually do – we want you to know the exact savings or additional revenue you can expect to see over the course of the next year with Faraday. 

Interested in generating value you can trust? Reach out to learn more about what we can do for your business\!

Faraday uses holdout testing to validate predictive datapoints before you commit, ensuring they deliver real, measurable results.

Predictive datapoints generate tangible value, and we can prove it

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

## Challenges of personalization at scale

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

### Data, and a lot of it

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

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

### Finding the relevant data

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

### Personalization at scale

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

### Responsibly-sourced data

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

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

## Benefits of personalization at scale

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

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

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

### Consumers demand personalized content

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

### Building brand loyalty

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

## How to achieve personalization at scale

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

### Collect good first-party data

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

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

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

### Use AI for customer segmentation

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

## How Faraday supports personalization at scale

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

### Unified, responsible data

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

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

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

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

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

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

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

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

### Predicting the next best product for every individual

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

### Providing personalized discounts to foster customer loyalty

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

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

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

The modern marketer’s guide to personalization at scale

A [ping post](https://activeprospect.com/glossary/ping-post/) is a real time lead distribution method where lead sellers send basic lead details (the "**ping**") to potential buyers, who then evaluate the lead based on predefined criteria. If a buyer decides to bid on the lead, they receive the full details (the "**post**") and complete the purchase. This system allows for fast, competitive bidding, commonly used in industries like home services, financial services, and insurance.

While this method offers efficiency, it also introduces challenges. Many businesses end up paying for leads that don’t convert, leading to wasted budget and ineffective outreach efforts. The ability to filter and score leads before purchasing them is crucial to maintaining a high return on investment (ROI) and optimizing marketing spend.

## How we use Ping Posts at Faraday

The challenge with ping-post lead buying is that companies often spend money on leads that don’t convert. At Faraday, we integrate with ActiveProspect’s [LeadConduit](https://activeprospect.com/leadconduit/) to enhance this process with predictive scoring. By applying machine learning at the ping stage, brands can bid only on high-converting prospects, filtering out low-quality leads in real time. This optimization reduces wasted spend and improves overall campaign efficiency.

Predictive scoring works by analyzing historical data, behavioral signals, and demographic datapoints to assess a lead’s likelihood of converting. Thanks to the built-in  [Faraday Identity Graph](https://app.faraday.ai/traits) ([which comes preloaded with 1500+ attributes on nearly every US consumer and their households](https://faraday.ai/features/faraday-identity-graph)), our models score each lead against rich third-party data before a bid is placed instead of relying on limited seller-provided information. This creates the opportunity for businesses to make data-driven decisions in real time, ensuring they focus only on high-quality opportunities.

For example, an insurance provider purchasing thousands of leads daily might bid on any lead that meets basic criteria, but many of those leads never convert—driving up acquisition costs. If each lead costs \$10 and converts at a rate of 1 in 5, their effective customer acquisition cost (CAC) would be \$50. But not all leads are equal. By rejecting the bottom 40%—the least likely to convert—they could improve their conversion rate significantly, lowering their CAC to just $30.

In this scenario, instead of wasting budget on low-quality leads, they could acquire more customers for the same spend. With predictive lead scoring, they could make these decisions in real time, ensuring they only invest in leads with real potential.

## Even without TCPA 1:1 consent rule, better targeting still matters

[While the expected TCPA 1:1 consent rule did not go into effect earlier this year](https://faraday.ai/blog/tcpa_1-1_vacated), that doesn’t mean businesses can ignore lead quality or continue to enact spray and pray targeting in 2025\. Just because you’re not being forced to change doesn’t mean you shouldn’t. Rising acquisition costs and increasing competition make it critical to invest in leads that are both high-quality and likely to convert. Faraday’s real-time predictive scoring ensures that brands maximize their ROI while staying ahead of the curve. By leveraging AI-driven insights, lead buyers can refine their targeting strategies and remain competitive in an evolving marketplace.

Additionally, compliance concerns aren’t going away. Even without the immediate pressure of TCPA amendments, consumer privacy laws and regulations continue to evolve, and businesses that proactively refine their lead acquisition strategies will be better prepared for future changes. More rigorous lead assessment doesn’t just improve efficiency—it also reduces the risk of compliance issues and enhances the overall customer experience by ensuring outreach efforts are more relevant and targeted.

## Looking ahead

The lead generation landscape will continue to evolve, and companies that prioritize precision over volume will have the advantage. Rather than buying as many leads as possible and hoping some might convert, businesses need to shift toward smarter, data-driven strategies that optimize every dollar spent.

Faraday’s predictive lead scoring is a powerful tool for staying ahead, allowing businesses to purchase leads with confidence and efficiency. Whether regulatory shifts happen or not, the need for better targeting and smarter decision-making isn’t going away.

Ping posts are a real time lead distribution method where basic lead details are sent to potential buyers, who then decide whether to bid before receiving the full lead information.

So what is a Ping Post anyway?

Using machine learning to identify customer personas in your data can help improve your understanding of your customer base, and with the right infrastructure, deliver personalized experiences at a greater scale.

In a nutshell, the process entails using an algorithm to segment your customer data into distinct groups, or personas. Faraday uses a time-tested method called k-means clustering.

## What is k-means clustering?

K-means clustering is an unsupervised clustering algorithm that was first introduced in 1957. An unsupervised algorithm does not require the data to be labeled in order to train the model. This is an important feature of the k-means algorithm because it allows for the discovery of subgroups within the data without any previous assumptions about possible groupings — in this case, personas.

## How does k-means clustering work?

When presented with a data set, the k-means algorithm first introduces random center points around which each datapoint will eventually be clustered (circles, Fig. 1). Then each datapoint is assigned to the group of the center to which they are closest.
This happens by calculating the distance from a datapoint to each cluster center using an equation known as the Euclidean distance measure. For two dimensions, the Euclidean distance is represented by the diagonal line connecting the point to each of the cluster centers. The point is then assigned to the cluster center with the shortest diagonal line (Fig. 2).

Once each point has been assigned to the randomly chosen cluster centers, the centers then "update," moving to the center of the group by recalculating the mean (average) distances to all the points in the group (Fig. 3).

After the cluster centers have updated to their new positions, each point is reassigned to a new group using the Euclidean distance measure again. Most of the points will remain in their original groups, but some points will be assigned to a new group (Fig. 4). So, then the centers must be updated again to be at the center of the new group. This pattern repeats until the centers no longer move.

[![K-means diagram](/images/blog/k-means_diagram.png)](/images/blog/k-means_diagram.png)

## Using k-means clustering for customer personas

All of the above is how k-means clustering worked back in 1957. Since then there have been upgrades allowing us to use something called categorical features. At Faraday, we create persona groups using a mix of these features.

As far as algorithms go, k-means is rather straightforward, but has one major downfall: you have to tell it how many groups to look for, and it will always succeed in finding that many groups. That means if you tell the algorithm to look for 500 groups, it will find them — even if there is no reason for there to be 500 distinct groups within the data.

If you're a marketer, you probably don't want to create 500 unique ads for every campaign. But should you make four? Or six? Or ten?

## How to find the optimal number of personas using k-means clustering

We target the optimal number of groups hidden within the data, rather than guessing and hoping we are right. We’ll share a few methods we use to find the optimal number of personas for our users.

### The elbow method: finding diminishing returns

The most common metric for measuring the success of k-means clustering is called the ratio of sum of squares (rss). At Faraday, we determine the optimal number of personas by comparing the rss for k-means models with two to fourteen personas. We employ the elbow method, which shows the cutoff after which we see diminishing returns for increasing the number of personas. For most clients, the optimal number of distinct personas is usually between four and seven.

**Going beyond the elbow method**

The elbow method is simple, but it has a downside: sometimes, there just isn't a clear single point that is optimal. Maybe three personas could be optimal, maybe six, and we have to intervene based on our client’s business needs to choose one or the other as the optimal number of personas.

Additionally, while reducing variability within each group is an extremely useful way to measure k-means clustering success, it doesn't tell the whole story. We believe that an important aspect of clustered groups is stability, which isn't considered with the elbow method selection process. Our clients expect that their persona groups won't shift every time they convert a lead. This is why we are currently researching a new method for determining the optimal number of personas, which focuses on the stability of the persona group.

### The bootstrap method: focusing on stability

Our first step to assess the stability of k-means clustering involves resampling the client’s data using the bootstrap method. Like k-means clustering, bootstrapping has been around for a while – it was first [introduced in 1979 by Bradley Efron](https://projecteuclid.org/journals/annals-of-statistics/volume-7/issue-1/Bootstrap-Methods-Another-Look-at-the-Jackknife/10.1214/aos/1176344552.full). The basic idea behind bootstrapping is to take data from the pool we have, with the ability to pick the same datapoint multiple times. This is a method known as sampling with replacement.

The bootstrap method provides us with a powerful tool to measure how much our desired clustering outcome could change with different data. We can get a good sense of how much the personas will change with the addition of new customers. Our goal is to minimize how much influence single customers, or small groups of customers, have on the resulting personas. We want personas that remain relatively unchanged by single customers. In other words, we want more stability.

## Pulling it all together

Once we have the results from bootstrapping, we use the ratio of sum of squares again to measure just how stable the personas are. What we’re looking for is a ratio close to 1, which would indicate that there is no variability within the personas.

The stability metric for determining the optimal number of personas shows promising results for the basic examples in this blog post. At Faraday, we’re actively working on applying this methodology to the more complex persona groupings our clients rely on. Stay tuned for more developments with respect to improving our personas methodology.

Learn how machine learning is used to identify distinct clusters—or personas—in your customer data.

How k-means clustering is used to identify customer personas

A leading **debt consolidation** company relied on direct mail as a key channel for acquiring new customers. However, a significant portion of their marketing budget was being spent on sending mail to leads who were unlikely to convert. This inefficiency not only increased costs but also diluted the impact of their outreach efforts, making it difficult to connect with high-intent buyers.

## Smarter targeting with Faraday's lead suppression

To address this challenge, the company partnered with Faraday to implement a [predictive lead suppression model](https://faraday.ai/templates/lead-suppression/recharge). This process began with Faraday ingressing their lead data to assess the key characteristics of their existing customer base. Then, through careful analysis of their historical conversion patterns—as well as the inclusion of custom predictive datapoints, built using [rich 3rd party data and attributes on 240M+ US consumers](https://faraday.ai/features/faraday-identity-graph) that comes preloaded through the [Faraday Identity Graph (FIG)](https://faraday.ai/docs/abstractions/traits)—we were able to predict which of their leads were most likely to convert.

Using this analysis, Faraday’s model identified the bottom 10%—the least-likely-to-convert leads—and suppressed them from the company’s direct mail campaigns. This approach immediately reduced wasted marketing spend and allowed the company to reallocate resources toward higher-value prospects.

## Or see Faraday's lead suppression process broken down visually

1. ![Faraday Identity Graph](/images/blog/lead-supp-panel-0.png)

   ### Don’t start from scratch

   Faraday comes preloaded with 1500 attributes on 240M+ US consumers and their households.

   This powerful dataset provides high-signal datapoints—including property data, demographics, life events, and buying habits—that can shape smarter decisions.

2. ![client data](/images/blog/lead-supp-panel-1.png)

   ### Your lead data is shared with Faraday

   Your lead data is collected and ingressed into Faraday securely.

   This guarantee is achieved through our robust data security policies, which include HIPAA, GDPR, and SOC-2 compliance.

3. ![model is trained and predictions begin](/images/blog/lead-supp-panel-2.png)

   ### We train predictive model by analyzing client data for patterns

   Faraday’s Identity Graph & AI/ML predictive modeling enables us to identify unique trends in your data set and specifically assess your leads’ likelihood to convert.

4. ![leads are sorted into deciles](/images/blog/lead-supp-panel-3.png)

   ### We identify how likely each lead is to convert

   We identify 10 deciles to be able to help you determine which leads are the best (most likely to convert after receiving mail) and the worst (those that will cost more in postage/print than they will generate in value, or simply won’t convert).

5. ![cut-off threshold is established](/images/blog/lead-supp-panel-4.png)

   ### We establish a cut-off threshold

   Faraday then helps you to calculate the “cut-off” threshold. This determines where the cost to print/send the mailer is greater than the likelihood to convert to revenue.

6. ![targeted client list](/images/blog/lead-supp-panel-5.png)

   ### We return the targeted list

   Once Faraday has removed the low-fit leads, you can use the new list to target your direct mail process and proceed with your next mail drop.

## Optimized results, supported by data scientists

Although this model was already delivering significant value, our data science team wasn’t satisfied.

That was because this company recognized four distinct customer segments within their broader population—each with unique characteristics. Although the one-size-fits-all approach had made sense initially to drive results, we hypothesized that a single model that handled all leads at once was likely not actually able to provide the highest level of precision possible. Instead, we considered creating four additional models which would be individually tailored to each unique customer segment.

While we were confident that this more granular approach would improve results, at Faraday, we prioritize empiricism over intuition so we decided to run rigorous validation tests. And indeed, after comparing conversion trends between the original model alone and all five models together, we were able to prove—rather than assume—the added value of the more granular approach. For the client, this meant even better precision in their targeting and more return on their direct mail spend.

While this process required a deeper investment of our data science team’s time, the outcome was clear: greater accuracy, better optimization, and ultimately, higher value. At Faraday, this isn’t just a one-off added service—it’s how we operate. Strategic data science investment is the only way to unlock insights at this level, and this project stands as a testament to that philosophy.

## The result

The impact of Faraday’s predictive lead suppression was immediate and substantial:

- **At least 600,000 fewer pieces of wasted direct mail** sent to low-intent leads monthly
- **Over $100,000 saved each month** in direct mail costs
- **An additional \$20,000 in monthly revenue, or $240,000 annually**, thanks to these targeted models’ better precision in predicting which customers would convert

With these enhancements, the company is now seeing a **10x return on investment (ROI)** from Faraday’s predictive modeling.

## Beyond cost savings: data-driven growth

By investing in Faraday’s predictive modeling and data science expertise, the company didn’t just cut costs—they redefined the way they acquire customers. Suppressing low-intent leads was just the beginning. By refining their approach with segment-specific models, they turned direct mail into a high-precision growth engine, ensuring that every dollar spent reached the right audience.

With this new strategy, the company isn’t just saving money—it’s driving measurable, sustained growth. Faraday’s predictive lead suppression has become a core part of their marketing playbook, proving that the right data science investment doesn’t just optimize campaigns—it transforms businesses.

Through predictive lead suppression, this company was able to cut at least 600,000 pieces of least-likely-to-convert direct mail each month.

How this debt consolidation company optimized direct mail with lead suppression to save over $100K each month

Faraday has been pioneering the use of [Google Cloud's](https://cloud.google.com/gcp?utm_source=google&utm_medium=cpc&utm_campaign=na-US-all-en-dr-bkws-all-all-trial-e-dr-1710134&utm_content=text-ad-none-any-DEV_c-CRE_665735450627-ADGP_Hybrid+%7C+BKWS+-+EXA+%7C+Txt-Core-Google+Cloud-KWID_43700081237254438-kwd-6458750523&utm_term=KW_google%20cloud-ST_google+cloud&gad_source=1&gclid=CjwKCAiA2JG9BhAuEiwAH_zf3ipLZUgcmqUgvAG4mSazGgzYHHuc-Sxw_1kXaAED5OkUnKhoJ5VNGxoCrRIQAvD_BwE&gclsrc=aw.ds) BigQuery and with its recent addition of LLMs, we’ve unlocked even more powerful use-cases. In this video, Seamus dives into how LLMs are improving sentiment analysis, content generation, and streamlining our data processes—all within [BigQuery](https://cloud.google.com/bigquery?utm_source=google&utm_medium=cpc&utm_campaign=na-US-all-en-dr-bkws-all-all-trial-e-dr-1710134&utm_content=text-ad-none-any-DEV_c-CRE_724346015211-ADGP_Hybrid+%7C+BKWS+-+MIX+%7C+Txt-Data+Analytics-BigQuery-KWID_43700081227767773-kwd-47616965283&utm_term=KW_bigquery-ST_bigquery&gad_source=1&gclid=CjwKCAiA2JG9BhAuEiwAH_zf3rR-UNnTVF20r8oRT-f2T4EOwmKb8fymYRf1cOrh2cj3ZH1M7JjJThoCkDYQAvD_BwE&gclsrc=aw.ds), without needing complex infrastructure.

**In this video, you’ll learn:**
- How Faraday improves sentiment analysis accuracy with BigQuery LLM feature
- The benefits of generating personalized content directly within BigQuery
- How BigQuery’s LLM feature simplifies our workflows and data processes

<iframe width="560" height="315" src="https://www.youtube.com/embed/luRR3cyMqrM?si=IIbOHJLK5nwU6jaO" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" referrerpolicy="strict-origin-when-cross-origin" allowfullscreen></iframe>

Join Faraday’s CTO Seamus Abshere as he shares how we’re using Google Cloud’s BigQuery to transform our workflows with the new Large Language Model (LLM) capabilities.

How Faraday simplifies sentiment analysis and generates personalized content using BigQuery

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

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

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

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

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

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

Unlocking authentic personalization at scale with Faraday

## Difference between first and third party data

Nowadays, the most successful brands are data-driven organizations. For a long time, brands harnessed both first and third party data to strengthen their understanding of their customers, improve decision-making, create more personalized experiences, and more. Let’s zoom out: what’s the difference between first-party data and third-party data?

**First party data** is data that your brand collects on your customers and leads. Page visits, form submissions, purchases, and email engagement are all examples of first party data, and each one provides unique insights and context into how your customers and leads interact with your brand. Depending on how and where your brand collects data, first party data can come in a variety of forms: engagement, demographic, transactional, financial, lifestyle, and more.

**Third party data** is data that’s been collected by a third party, or not your brand. It comes in the form of demographics such as age or gender, psychographics such as hobbies and interests, and financial data such as household income. Third party data providers like Acxiom allow brands to purchase the data for use in marketing.

Until recently, third-party cookies and social scraping were common sources for data enrichment meaning that browsers would creepily follow consumers across the internet to track their interests, behaviors, and other personal information that was never willingly shared for commercial use. It enabled personalization at the expense of consumer privacy, but responsible brands have been preparing for the [death of third-party cookies](/blog/death-of-third-party-cookies-strategies).

## Benefits of first and third party data for consumer marketing

All types of data have their own individual merits when it comes to consumer marketing, and in order to create a cohesive, impactful [customer insight strategy](/blog/how-to-create-a-customer-insight-strategy), you need more than just your first party engagement data, otherwise you run the risk of sending the wrong message at the wrong time. A good combination of first and third party data plays a key role in two pillars of a strong marketing strategy: customer insights and predictions.

### Customer insights

A strong foundation of data enables marketers to segment customers and leads into distinct subgroups so that they can be targeted with relevant engagement, and with both first and third party data fueling your customer analytics tools, you’ll find that your customers and leads are more than just purchases and clickstreams–they’re real people, with real interactions outside of your brand.

By unifying first and third party data, your customer insights upgrade from “person that abandoned their cart” to “John Doe, married father of two, golf enthusiast, cat owner, and brick-and-mortar shopper.” And the best part is that this can be done at a scale that applies to entire segments of your customer base. The patterns are there in your data, you just need to find them.

### Predicting customer behavior

The value of pure customer insights through data is clear, but you can take your strategy a step further by using that data to [predict virtually any customer behavior](/blog/propensity-models-predict-customer-behavior) your brand is interested in. Likelihood to churn, convert, make a purchase, purchase again–whatever strategy you want to reinforce or employ, customer predictions using propensity models can get you there quickly and accurately.

Propensity models pore over your customer and lead data to find historical patterns, and then use those patterns to make the customer predictions you need. Plus, by using both first and third party data, your predictive models become even more accurate because they have more data to work with. Being reactive just doesn’t cut it anymore, and by predicting customer behavior, you can be proactive to ensure that lead converts, or that customer decides to stay on board.

## First party data collection techniques

Perhaps the most important benefit of first party data is that your brand is in complete control of how you collect it. Some strategies are more effective than others, but it’s important to implement a variety of strategies to ensure healthy engagement with both customers and leads.

### Web forms & surveys

Web forms can be a great way to capture basic information like name, email, mailing address, and possibly some financial or lifestyle information as well. In a similar vein, albeit more resource intensive, surveys are a fantastic way to poll your customers and leads to learn more about what they’re looking for, what they like about your brand (or even what they don’t!). Consider offering a small incentive, such as a free coffee, for any who fill out a survey.

### Loyalty programs

One of the best ways to engage with and retain your current customers is to implement a loyalty program. Loyalty programs enable brands to consistently engage with their customers through features like allowing customers to rack up points per purchase that can be redeemed for discounts or freebies. On top of the rewards you can offer your customers, it gives you another avenue through which you can engage with them–like sending automated, personalized emails when they reach a certain point or purchase threshold.

### Online engagement

Users will engage with your website in different ways, and the more data you collect, the more you’ll understand what it is that your visitors are looking for. Digital ads on a platform like Instagram can help you find the platform that fits your ideal customers. Visitor behavior on your site, such as scroll depth or visits on a certain page can similarly help you find what pages resonate most with them, which you can then use to guide engagement copy & creative.

## First party data enrichment

Modern brands are adopting a first party data enrichment strategy through third party data providers to gain a true understanding of who their customers are so that every engagement is a delight. It’s possible to both respect your customers’ privacy and use third party data to personalize engagement–you just need to be smart about it. Faraday helps brands do just that.

With cookies in the rear-view window, it’s more important than ever to ensure you’re being [responsible](/features/responsible-ai) and privacy-centric with any data you use. Avoid data that involves shady practices like social scraping, and look for [opt-in, permissioned data](/features/data)–it’s the way of the future, and not utilizing it is leaving your brand at a disadvantage.

So what are your options for first party data enrichment? The obvious answer is licensing directly from third-party data vendors like Acxiom or Epsilon. Something to note, however, is that you’ll need to sift through all that data to find what’s most relevant to your brand at any given time–your ideal customer profile will likely change over time, after all. On the other hand, many brands are adopting customer prediction platforms like Faraday so that they can access all the third party data they need, with machine learning algorithms that tell them what’s most important, saving time & resources that are better put elsewhere, like launching campaigns.

## How to leverage first and third party data with Faraday

Faraday enables your brand to generate actionable insights and customer predictions using both first and third party data–or first party data enrichment. No matter what your stack looks like, Faraday has [hundreds of integrations](/integrations) to ensure your predictions are where you need them, when you need them, and without a single line of code. On the flip side, if your brand is developer-minded, Faraday offers a [robust developer API](/docs/reference) so that you can implement predictions your own way.

### Predict personas to make personalization a breeze through rich insights

Like I mentioned earlier, first party data enrichment completely transforms what you know about your customers, and allows you to visualize clear indicators for who’s represented in any segment of people your brand is interested in. Turn that newly-discovered John Doe, married father of two, golf enthusiast, cat owner, and brick-and-mortar shopper–part of the Middle-aged Married Mike persona–into internal talk track alignment, guided personalization on any platform, and more.

[![Faraday customer personas](/images/blog/first_party_vs_third_party/first-party-vs-third-party-personas.png)](/images/blog/first_party_vs_third_party/first-party-vs-third-party-personas.png)

### Predict propensity to take virtually any action

With your personalization in-hand, use propensity models to predict who’s (or where’s) most [likely to buy](/predict/category/likely_buyers) your product, who’s worth [retaining](/predict/category/churn_scores), how to [prioritize your leads](/predict/category/lead_scores), & more. Customer predictions with machine learning turn your reactive marketing proactive, so that you’re always delivering the right message at the right time, and personalized to boot.

[![Faraday propensity to buy outcome](/images/blog/first_party_vs_third_party/first-party-vs-third-party-outcome.png)](/images/blog/first_party_vs_third_party/first-party-vs-third-party-outcome.png)

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

An effective marketing strategy is data-driven, and to create one, you need a strong mix of first and third party data. Learn the difference between first and third party data in marketing, and how smart brands are using first party data enrichment to drive growth.

First party vs third party data: What's the difference?

[Propensity predictions](https://faraday.ai/features/predictions) indicate the likelihood of an individual to perform a defined action (buy, buy again, churn, etc.), which enables businesses to grow and retain revenue by engaging with the right customers, leads, or audiences at the right time. These predictions are the foundation for Faraday’s custom predictive datapoints—scores tailored to your business objectives, trained on your historical outcomes, and built using advanced propensity models. To get accurate propensity predictions, you need a good propensity model. Here’s how Faraday approaches propensity modeling.

## Selecting the right propensity model

Our prediction system classifies against outcomes using an array of predictive algorithms, including individual decision trees, random decision forests, logistic regressions, and neural networks. At run time, we are attempting, in each case, to figure out which algorithm works best. Usually it’s random decision forests.

## How random decision forests work

Random decision forests are made up of individual decision trees, which are classifier algorithms that look like flow charts, showing the choices made to reach a certain outcome.

Predictions aren’t based on a single tree, though; we use dozens of trees to improve the accuracy of the algorithm, hence the random decision forest. We look at the universe of trees, their splits and branches, and choose the ones that are the most promising when it comes to creating a path towards the desired outcome. This path leads from seemingly arbitrary data to a classification: the “yes” or “no” to the outcome. In other words, we're defining someone as a “good potential customer.” (In the image above, the “yes” is “mostly converted” and the “no” is the “mostly did not convert.”)

While the classification seems binary, the reality is that often there's not a clear “yes” or “no.” We are dealing with the likelihood that someone performs an action, and that is never definitively 100% or 0%. During the prediction process, we employ propensity scores, which are a way of saying, “This person is more likely to be a good potential customer than not.” Every individual in the group we’re looking at is scored, and those in the top percentiles are recommended to the client as the best fits for the outcome they want to realize.

_Now, if there are so many predictive algorithms we can use, why do we choose random decision forests?_

## Benefits of predicting propensity using random decision forests

### Building explainable models

Well, for one, they’re the most explainable; with so many decision trees at play, we have a strong sense of the different importances at each node split in a tree. These importances show us which attributes contribute most toward the success of an outcome, or the most toward failure.

### Handling missing data

The random decision forest technique also handles missing data very well, especially compared to a logistic regression or neural network, which both tend to require values to make predictions. In a decision tree, if there is a missing value, it moves onto the next node because there are other decision paths in the case of missing data. When you scale that to a whole random decision forest, the prediction becomes very accurate and isn’t perturbed by missing data.

### Identifying collinearity

A third reason we use random decision forests is collinearity. Random decision forests may see that some data are correlated — say, mortgage value and income. A lot of the attributes the algorithm deals with are linearly related but don’t necessarily directly drive a certain customer behavior.

A random decision forest algorithm finds areas of greatest information gain, meaning that it may realize there is interdependence between attributes, but it will choose the feature that gives the algorithm the greatest leverage over the prediction. This is particularly helpful for us, as we deal with tremendous amounts of customer data, and data on humans is generally noisy and often subverts expected distributions and assumptions.

## How propensity predictions help improve marketing efforts

Propensity predictions are especially helpful in improving campaign targeting efforts across a number of marketing channels. By identifying individuals ranked in the top percentile of the analyzed group—those that have the highest likelihood of performing the desired action—businesses typically see improvements in efficiency metrics (cost per acquisition, return on ad spend, etc.). Here are some popular business use cases for propensity predictions:

- Reaching likely buyers
- Driving repeat purchases
- Scoring and prioritizing leads

Learn how propensity models help predict the likelihood of individuals to exhibit a defined behavior.

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