At Faraday, we often hear from clients about their experiences working with our competitors. These conversations give us valuable insights into how our platform stands out in the market, and where we have room for improvement. Recently, one of our clients shared a specific experience that really resonated with us, and we wanted to take the opportunity to highlight it to our audience.

This client, who recently worked with [Think Unlimited](https://thinkunlimited.com/) before moving to Faraday, pointed out some key areas where we outshine our competition. As she put it:

_"The transparency of the data science behind the modeling \[at Faraday\] was refreshing. There were no secrets. It was our data, and it was given back to us in a digestible fashion. We could also easily access the experts behind the science to ask questions."_

We believe that transparency is crucial, and we’ve built our platform with the goal of making data science accessible and understandable for our users, so this was rewarding to hear.

This client also observed a significant difference in the quality of predictions between Faraday and Think Unlimited, noting that Think Unlimited's data science just didn’t seem as robust under the hood. This aligns with our commitment to providing high-quality, validated predictive models that you can trust.

But those observations are just the start. Let’s take a deeper dive into some of the key differentiators we've observed ourselves and how they contribute to Faraday’s unique value proposition.

## Faraday vs. Think Unlimited: Our core differentiators

### 1\. Real-time scoring with Faraday's Lookup API

One of Faraday's core strengths is our ability to score leads in real time. Thanks to our powerful [lookup API](https://faraday.ai/docs/features/lookup-api), users can get immediate feedback on lead quality, before bids are even placed. By receiving immediate feedback, businesses can optimize decisions in real-time, leading to better outcomes in high-pressure or fast-moving situations, such as the [ping post process](https://faraday.ai/blog/so-what-is-a-ping-post-anyway). Think Unlimited, on the other hand, lacks this level of real-time scoring capability, which can slow down the workflow, impact decision-making timelines, and in the case of the ping post process, result in lower quality leads being purchased.

### 2\. A more robust data set for better predictive power

Our built-in Faraday Identity Graph is a rich third-party [data set](https://faraday.ai/features/faraday-identity-graph) that goes far beyond basic metrics by incorporating a wide range of insights that enhance the accuracy and depth of our predictive models. In addition to traditional datapoints, we include rich demographic, lifestyle, and behavioral data, and this combination of static and dynamic data allows us to understand not just who a customer is, but how they engage with products, services, and brands. Using these third-party insights, Faraday’s models can create a dynamic, multi-dimensional picture of potential customers, leading to more accurate predictions of future actions, such as conversion or product interest.

Faraday’s robust data set also helps solve the [cold start problem](https://www.sciencedirect.com/topics/computer-science/cold-start-problem) by providing this rich data from day one, meaning that brands don’t have to wait for a build-up of their own first-party data before accurate scoring can commence. It also allows them to immediately target new customer segments or groups that they don’t have existing data for, facilitating expansions and allowing them to quickly roll out new campaigns and products.

The depth of our data enables Faraday to generate more accurate, quick, and actionable insights compared to our competitors, empowering businesses to make data-driven decisions with confidence.

### 3\. Score explainability: know exactly why leads get their score

Faraday emphasizes clear [score explainability](https://faraday.ai/blog/faraday-score-explainability) with all of our models. This means that users can actually know exactly why each lead receives a particular score. This level of transparency is crucial for businesses that need to understand the "why" behind their results. Having this clear visibility into the model’s scoring also helps businesses gain confidence in their decisions and ensures they can optimize their approach based on a better understanding of the process.

That earlier client specifically highlighted this point as being one of the biggest advantages she appreciated with Faraday compared to Think Unlimited, where such transparency can be harder to come by.

### 4\. Flexible integration across use cases

Faraday isn’t just limited to lead scoring. Our platform is adaptable and can fill a range of other use cases, including [product recommendation](https://faraday.ai/docs/how-to/use-cases/customers/next-best-offer), [lead suppression](https://faraday.ai/templates/lead-suppression), and even [churn prediction](https://faraday.ai/templates/churn-scoring), [plus a bunch of other options](https://faraday.ai/templates). This versatility allows businesses to leverage Faraday’s insights across different departments and applications, and also sets them up for success when expanding their business or launching new types of campaigns they haven’t undertaken before. Think Unlimited’s capabilities are more narrowly focused, which can hinder adaptability and expandability.

### 5\. Access to experts

At Faraday, we take our data science seriously. But we also understand that predictive modeling is inherently complex and that just presenting where data comes from doesn't necessarily mean that the client will be able to understand it.

Score explainability measures are a critical first step but sometimes, the best way to ensure a score is actually explainable is pretty straightforward: just have an expert explain it. That’s why we made the game-changing choice last year to forward deploy our entire data science team, making them directly accessible to our clients—our CTO even works with a range of client accounts to ensure that they’re getting the most possible value out of their investment. This level of open communication and transparency is something Think Unlimited lacks and is the ultimate failsafe to ensure users are able to understand their predictions, and the models behind them.

### 6\. Fully API-based for seamless integration

[Faraday is fully API-based](https://faraday.ai/docs/reference), meaning it can be integrated into any system programmatically, offering unmatched flexibility and customization. This structure not only simplifies integration but also provides the flexibility to adapt the models into any custom workflows, making it an ideal choice for companies looking for a solution tailored to their unique needs. Our API is also well-documented, which facilitates integration into clients’ existing tech stacks and ensures easier operation throughout the customer lifecycle.

These features dramatically increase speed-to-deployment, with some clients seeing major cost savings or revenue growth [within their first month](https://faraday.ai/blog/optimized-direct-mail-outreach-in-one-month) after signing on. Think Unlimited, in contrast, lacks the API-based framework that makes Faraday so adaptable to a wide range of use cases and workflows.

## Quality predictions and transparency make the difference for our clients

At the end of the day, the quality of the predictions you rely on is what matters most. Our predictions are not only proven to be accurate and value driving, but they’re constantly monitored and revalidated to make sure we’re delivering as much value as possible to our clients. Additionally, Faraday’s data science, transparency, and flexibility make it the clear choice for businesses seeking high-quality predictive analytics that can scale and adapt to their needs, all while remaining understandable and accessible. Think Unlimited may have its place, but when it comes to powerful, explainable models and real-time scoring, Faraday leads the way.

Faraday outshines competitors like Think Unlimited with real-time scoring, a richer data set, transparent predictions, flexible integrations, expert support, and a fully API-based platform.

Faraday vs. Think Unlimited: Our core differentiators

See our complete guide: [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 datapoint 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: [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 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

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

---

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

## Challenges of personalization at scale

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

### Data, and a lot of it

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

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

### Finding the relevant data

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

### Personalization at scale

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

### Responsibly-sourced data

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

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

## Benefits of personalization at scale

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

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

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

### Consumers demand personalized content

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

### Building brand loyalty

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

## How to achieve personalization at scale

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

### Collect good first-party data

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

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

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

### Use AI for customer segmentation

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

## How Faraday supports personalization at scale

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

### Unified, responsible data

Faraday unifies your first party customer data with detailed, opt-in, permissioned, cookieless consumer data, giving you the insights your brand needs while simultaneously being privacy-centric. Hundreds of 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 1400+ 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?

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

---

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

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

---

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 1400 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

**See our complete guide:** [Understanding why you need data (and what kind actually moves the needle)](/blog/what-data-do-you-need-for-marketing).

---

## 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?

The renewable energy sector is growing rapidly as consumers and businesses increasingly turn to sustainable solutions. While this growth presents a wealth of opportunities, it’s also been a bumpy ride, with market uncertainties and shifting dynamics creating challenges for providers. In this ever-changing landscape, success hinges on the ability to identify, prioritize, and place their bets on the right horse—or, in this case, the consumer segments with the most value-generating potential.
When a prominent solar energy provider faced this challenge in 2024, they realized that finding the best answer alone wasn’t necessary, or the best use of their specialized team’s energy. Instead they turned to Faraday.

## The problem: they were tired of guessing and wanted to make data-driven decisions they could feel confident in

This solar company faced a wide-ranging set of problems as they tried to grow their sales pipeline in 2024. Although they were determined to scale efficiently and outpace competitors, their existing targeting approach lacked precision, which led to wasted marketing spend in low-potential areas and with the wrong consumers. Additionally, they were overwhelmed by an abundance of raw data that made it difficult to derive actionable insights. Finally, rapid market growth had also attracted competitors, heightening the urgency for smarter acquisition strategies.

These challenges translated into tangible pain points like lower conversion rates, wasted time, and inefficient marketing spend. Without a clear roadmap to identify and prioritize high-potential customers, their ability to compete effectively in the fast-evolving solar market was under threat.

## The solution: Faraday’s predictive analysis tools gave them data driven answer and confidence

Realizing these challenges could hinder their growth, the company decided to seek outside expertise and while these problems might seem insurmountable, they’re what we get up for.

Almost immediately after onboarding and hearing about their issues, we identified two significant ways we could support their expansion goals and help them to better understand their market: Persona Analysis and Market Opportunity Analysis.

### Persona Analysis

Faraday’s [Persona Analysis](/templates/thematic-personalization) is a process that helps companies uncover the traits and behaviors most closely associated with their ideal customers. By analyzing first-party data from the client as well as rich third-party data from our proprietary [Faraday Identity Graph](/features/faraday-identity-graph), we create detailed profiles of a company’s highest-value personas. These represent distinct socio-demographic groups that share common characteristics and behaviors, making it easier to design marketing strategies tailored to their specific needs.

For this solar provider, Persona Analysis revealed the unique traits that drive solar adoption. By understanding these factors, the company could refine its messaging, focus on the most relevant customer segments, and craft outreach strategies that resonated with their target audience.

But that on its own might not be enough. This tools allows us to know what _type_ of person to target, but not necessarily how many of them exist and are reachable

### Market Opportunity Analysis

That’s where [Market Opportunity Analysis (MOA)](https://faraday.ai/blog/unlocking-growth-with-market-opportunity-analysis) comes in. Building on these insights and persona profiles, Faraday conducted a comprehensive Market Opportunity Analysis, a process which combines the client’s first-party data with broader market trends and dynamics to quantify their existing market. To conduct this analysis, Faraday builds a complex and individualized predictive model which compares that quantified market to the population at large, represented through [Faraday’s Identity Graph](https://faraday.ai/features/faraday-identity-graph)—a robust and proprietary dataset containing detailed information on nearly every U.S. adult. This enables the identification of the totality of potential new customers who resemble the company’s top-performing personas available in the U.S. An example is pictured below.

![Example of our Market Opportunity Analysis](/images/blog/moa_map.png)

The analysis doesn’t stop there though. It then visually maps these high-potential customers by state, zip code, and even down to the individual level. These dynamic predictions enable clients to focus on specific regions and neighborhoods with the highest concentration of ideal customers. These insights are paired with additional predictive tools, such as lead routing and direct mail targeting, ensuring marketing efforts can be directed with precision and confidence.

Through this dual approach, the solar provider gained actionable insights about market potential and customer behaviors. These findings informed advertising campaigns, local outreach initiatives, and third-party logistics strategies, enabling the company to allocate resources strategically and maximize their impact.

## The results

The impact of Faraday’s Persona and Market Opportunity Analysis changed the company’s entire outlook. Where as before, they had marketed broadly based on vague demographic factors, they could now target their spend down to the top zipcodes, and even to the individuals most likely to convert, suggesting products that were more likely to sell and talking to their ideal consumer in a way that they were most likely to respond to.

A particularly telling example of the power of these tools came when the solar provider, who primarily relies on their installation trucks for marketing, discovered through Market Opportunity Analysis that some of the highest-potential zip codes were in rural areas—places where the trucks wouldn't typically reach. To ensure these high-fit prospects were engaged, they decided to run a targeted direct mail campaign in those regions, making sure they could be seen by the people who mattered most.

Faraday's tools didn’t just provide actionable data—they gave the solar provider the confidence to act without hesitation. In this case that meant switching up their marketing strategy significantly. By moving away from broad, guesswork-based targeting, they were able to focus their efforts and dollar specifically on high-potential customers and this makes all the difference.

## Conclusion

Faraday's persona and market opportunity analysis provided clarity that allowed the company to make informed decisions on where to invest their resources, cutting out low-value areas and optimizing their target audience. With the right data guiding them, they gained the confidence to expand in competitive markets and scale efficiently. This success story underscores the value of data-driven insights in driving market expansion and delivering long-term success.

What's next for them? We're going to predict (with _high confidence_) more sales\!

Using Faraday's predictive tools, this company transformed their marketing strategy by targeting only the highest-potential customers.

How a leading solar provider improved customer targeting with personas and market opportunity analysis

Imagine you’re leading a growing retail business with 25 thriving locations. Now, it’s time to decide where to open the next five. Do you expand into an emerging market where demand is heating up, or double down on your presence in regions where your brand is already well known? Is there some other critical factor you haven’t considered? The stakes are high, and even seasoned executives can fall prey to personal biases.

Take, for example, a story we recently heard from a client about an experienced restaurant CEO who was set on opening a location in his beloved hometown—a move driven by emotion rather than data. Fortunately, his team knew to assess the market, which revealed the location wasn’t actually viable. While this upended their original plan, it ultimately saved them the effort and cost of opening a doomed restaurant. This story highlights how critical these decisions are for sustained growth and why relying on data-driven insights (from the beginning) is essential to avoid costly missteps and make confident, strategic choices.

At Faraday, we understand the challenges businesses face when it comes to making strategic growth decisions. That’s why we offer **market opportunity analysis (MOA)**, a powerful tool that helps you confidently identify actionable opportunities that are backed up by data.

## What is market opportunity analysis?

MOA equips your team with a clear understanding of your current market reach and untapped potential. By analyzing your existing customer data and comparing those datapoints to the population at large, we can help you pinpoint exactly where the next big opportunities lie. Whether you're looking to expand geographically, target new customers, or optimize product offerings, our approach ensures your growth is backed by solid insights.

## How it works at Faraday

MOA provides your team with a data-driven roadmap to your most valuable and actionable growth opportunities, but how does it actually work? Here's how we approach it:

![](/images/blog/moa_analysis_diagram.png)

First, we conduct a detailed assessment of your current market penetration. By analyzing your existing customer base, we identify key traits such as purchasing behaviors, regional preferences, and lifetime value to create an ideal customer profile. Then, leveraging data from the [Faraday Identity Graph (FIG)](https://faraday.ai/features/faraday-identity-graph), we develop a tailored predictive model to evaluate the broader U.S. market and identify prospective customers who closely match your existing audience. This allows us to quantify the untapped potential within the customer segments most likely to drive growth.

Once we’ve identified the high-potential customer segments, we dig deeper into the specific opportunities within those groups. This involves determining which product or service offerings are best suited to each persona to maximize their lifetime value. We also take a close look at geographic trends, mapping out areas where your target customers are most concentrated from the regional level all the way down to zip code, metro area, or even postal route. This data empowers businesses to strategically allocate resources with significantly more confidence and precision than before.

## Unleashing the power of the Faraday Identity Graph (FIG)

A comprehensive MOA requires access to a detailed dataset of the broader population to serve as a benchmark for comparison. Fortunately, Faraday’s **Faraday Identity Graph (FIG)** provides exactly that, and much more. With rich data on nearly all American adults, the FIG offers a robust and comprehensive view of the population, significantly enhancing the reach and depth of our MOA.

The FIG goes beyond basic demographic information, offering nuanced insights into consumer behaviors, preferences, and buying patterns. By tapping into these granular details, businesses can understand their target market in a way that’s far more accurate and actionable than with competing data sources.

## Ready to explore what's next?

MOA can greatly benefit businesses by identifying high-potential customer segments and emerging market trends. By understanding where the highest-value opportunities lie, companies can optimize their marketing strategies and resources, focusing on areas with the highest growth potential. This leads to more targeted and personalized campaigns, which can improve conversion rates, enhance customer engagement, and increase profitability. MOA additionally provides companies with the confidence to know that their strategies are backed by data.

Growth doesn't happen by chance. With Faraday's MOA, you can take the guesswork out of targeting strategy and focus on results. Contact us today to discover how we can help unlock your business's full potential.

MOA empowers businesses to make strategic growth decisions by uncovering untapped potential and high-value opportunities in their market.

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