Are you searching for effective ways to engage with your customers? Look no further than [direct mail](/blog/personalized-direct-mail-strategy)! In today's digital age, where consumers are bombarded with marketing emails, direct mail campaigns are emerging as a refreshing and impactful strategy.

Join us for an interactive workshop where we'll unveil the remarkable benefits of using direct mail in today's competitive landscape. Watch and learn as the team explores the resurgence of direct mail and its unique ability to shine in the digital era!

**From this workshop, you'll learn**:

- Why are so many modern brands using direct mail?
- How to optimize direct mail targeting and personalization with Faraday
- Top use cases for direct mail in B2C marketing

Join Faraday's Directors of Accounts Alex Noyes and Nick Haggerty, as well as Director of Marketing Perry McDermott, where they discuss modern direct mail targeting and personalization tactics you can use to get better results from your campaigns.

Direct mail marketing tactics you need to know about

## What is direct mail marketing?

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

## Benefits of personalized direct mail

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

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

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

## How can direct mail be personalized?

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

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

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

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

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

### Use insights to inform your personalization

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

![Poplar merge tag postcard](/images/blog/direct-mail-merge-tag-poplar.png)

_Image via [Poplar](https://heypoplar.com/articles/personalized-direct-mail)._

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

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

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

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

## Pillars of a personalized direct mail strategy

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

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

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

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

### Connect data and define customers

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

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

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

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

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

### Predict personas to reveal rich insights

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

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

### Predict propensity to buy to target best fits

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

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

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

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

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

### Deploy personalized direct mail predictions to your stack

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

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

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

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

Churn looks different for every business, but the ultimate goal remains the same: retaining customers. To succeed in this competitive landscape, it is crucial to have [real-time insights](/predict/category/churn_scores) that can identify customers at risk of churning and engage with them based on their specific needs and preferences.

But where should you start?

Join us in this interactive workshop where we will demonstrate how Faraday's AI capabilities can be leveraged to accurately predict customer churn. By staying ahead in your [churn prevention](/blog/churn-prevention-machine-learning) strategy, you can take proactive measures to retain valuable customers.

**From this workshop, you'll learn**:

- Why modern brands are investing heavily in customer retention
- Ways to reduce churn throughout your customer lifecycle
- Important considerations to effectively predict and prevent churn
- How to get churn predictions into your stack using Faraday
- Activation and engagement ideas to consider

Join Faraday's Directors of Accounts Alex Noyes and Nick Haggerty, as well as Director of Marketing Perry McDermott, where they dive into why brands are investing heavily in retention, and how to do it effectively.

Using churn predictions to improve retention rates

A smart [lead scoring strategy](/blog/predictive-lead-scoring-b2c) is key to ensure you're not wasting time on bad-fit leads that were never going to convert anyway. While it's possible to develop a smart lead scoring strategy manually, it's time-consuming and leaves room for human error. With a predictive lead scoring strategy built by both your data and Faraday's permissioned, opt-in consumer data on nearly every U.S. adult, all the time that would've previously been spent building and maintaining the strategy manually can instead be spent going after best-fit leads.

Join Strategic Account Manager, Nick Haggerty, and Director of Marketing, Perry McDermott, as they walk through how you can utilize Faraday's predictive lead scoring capabilities in your acquisition strategy.

**From this workshop, you'll learn**:

- Benefits of predictive lead scoring for B2C
- How data influences predictive lead scoring strategies
- How to set up lead scoring pipelines in Faraday
- Best practices for planning and implementation
- Activation tips and real-world examples

Join Strategic Account Manager, Nick Haggerty, and Director of Marketing, Perry McDermott, as they walk through how you can utilize Faraday's predictive lead scoring capabilities in your acquisition strategy.

Using predictive lead scores to improve conversion rates and reduce CAC

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

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

## Lift and precision before model calibration

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

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

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

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

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

## Lift and precision with model calibration

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

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

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

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

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

Marketing campaigns have never been so predictable.

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

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

Faraday probability scores: Improving prediction accuracy through model calibration

## What is market sizing?

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

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

## Top down vs bottom up market sizing analysis

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

### Top down market sizing

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

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

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

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

**Pros**:

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

**Cons**:

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

### Bottom up market sizing

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

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

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

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

**Pros**:

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

**Cons**:

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

## Calculating market size with predictive analytics

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

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

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

### Traditional approach to bottom up market sizing

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

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

### Predictive approach to bottom up market sizing

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

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

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

## How to do bottom up market sizing with Faraday

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

### Connect data and define customers

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

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

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

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

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

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

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

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

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

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

### Deploy likely buyer scores to your analysis tools

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

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

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

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

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

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

B2C companies operate in highly competitive markets, where customer acquisition and retention are vital for sustainable growth. Nowadays, it can be extremely difficult for brands to acquire customers while maintaining healthy customer acquisition costs.

Successful B2C companies are investing heavily in modern data and machine learning solutions, like predictive lead scoring, to drive efficiencies and improve outcomes across their customer acquisition, engagement, and retention programs.

If you are just starting to learn about predictive lead scoring or evaluating solutions for your B2C business, this article is for you. Here’s what you’ll learn:

- What is predictive lead scoring?
- How B2C companies use predictive lead scoring
- Core components of predictive lead scoring for B2C
- Fundamentals of a good B2C lead scoring dataset
- How to implement predictive lead scoring using Faraday

## What is predictive lead scoring?

Lead scoring is a data-driven method of assessing and prioritizing leads based on their potential to convert into paying customers, and it’s a critical component of several B2C marketing and sales organizations. Simply put, leads with higher scores are considered more likely to convert, allowing sales and marketing teams to focus their attention on the right leads.

**Traditional B2C lead scoring** models often rely on a point-based system where humans determine the lead scoring criteria—typically a combination of basic demographic information and online behaviors such as pageviews, email engagement, or form submissions—and assign points to leads when they meet it.

While these point-based lead scoring models can help marketing and sales teams prioritize their efforts, they come with significant drawbacks. Lead scoring criteria are often arbitrarily decided, resulting in lead scores that may not accurately reflect a lead's true potential. Additionally, because they rely heavily on first-party engagement data, lead scores often end up being a lagging indicator of a lead’s conversion likelihood.

**Predictive lead scoring** leverages the power of machine learning to analyze historical lead data and build statistical models that can predict the likelihood of a lead becoming a customer. By analyzing much larger datasets, machine learning algorithms identify patterns and important features that contribute to a lead's likelihood of conversion.

![Image of Faraday model report features table](/images/blog/product/feature-importance.png)

_Note: the image above illustrates important features surfaced in [Faraday model reports](/blog/faraday-model-reports)._

This takes the guesswork out of choosing lead scoring criteria for traditional point-based models. With the right lead scoring dataset, these models can predict a lead’s likelihood to convert before they ever visit your website or open an email.

## How B2C companies use predictive lead scoring

Implementing the right predictive lead scoring system (one tailored for B2C lead scoring) unlocks powerful lead optimization and personalization options for marketing and sales teams.

- **Lead ranking and suppression:** A simple yet powerful use case for predictive lead scoring. Rank your leads, set a suppression threshold (e.g. bottom 70%), and only surface the likeliest-to-convert leads in your outreach tools. This enables sales and call center reps to prioritize their outreach and improve win rates across the board.

- **Lead routing and distribution:** A slightly more creative use case is to use predictive lead scores to tier your leads and distribute leads from the best tier to your best reps; a great way to maximize your win potential. One of the leading solar installers in the nation [improved call center conversions by 33% with this approach](/blog/solar-call-center-lead-conversion-boost).

- **Product recommendations:** If your predictive lead scoring system supports product-specific modeling, you can predict which products or services a lead is most likely to purchase. These product-specific lead scores enable marketing and sales teams to make informed product recommendations across relevant touchpoints.

- **Customized discounts/promotions:** Most predictive lead scoring models are built to predict the likelihood of a lead converting to a customer. Why not predict the likelihood of leads converting to high-LTV customers and offer custom discounts or incentives to the leads who are most likely to spend a lot? This is one of the most creative use cases we’ve seen.

## Core components of predictive lead scoring for B2C

Predictive lead scoring requires robust data (first-party and third-party), customizable and explainable models, and flexible integrations to bring lead data into the system and push predictive lead scores out to your marketing and sales tools.

### Historical lead data

Historical lead data provides the necessary foundation for building accurate predictive models. This first-party data includes information about past leads’ behaviors, attributes, and their outcomes (e.g., whether they converted into customers or not).

### Lead enrichment data

Lead enrichment data is crucial for predictive lead scoring because it enhances models' accuracy and depth of understanding about leads. For B2C lead scoring, this third-party consumer data typically includes demographic information, financial information, lifestyle information, and property information.

_Note: Given the changing data privacy landscape, it’s crucial that your predictive lead scoring system uses responsibly sourced consumer data. [Avoid data from third-party cookies and social scraping](/blog/death-of-third-party-cookies-strategies)._

### Machine learning capabilities

There are several ways to leverage machine learning for predictive lead scoring, all with varying degrees of complexity.

Most CRMs and marketing automation platforms now offer built-in predictive scoring capabilities. These solutions typically leverage blackbox (unexplainable) machine learning and rely heavily, if not entirely, on the first-party you have in the system. They’re easy to implement, but lack third-party consumer data which is crucial for B2C lead scoring.

If you have data scientists and engineers with enough bandwidth to spare, you can try taking a homegrown approach. However, in-house development costs can be substantial so unless you already have machine learning pipelines and are licensing consumer data, we recommend [using our API to score leads instead](/developers/quickstarts/score-leads).

Predictive analytics and customer prediction platforms like Faraday operate somewhere in the middle, providing users with better control of their models while streamlining laborious machine learning processes. With Faraday, users connect their lead and customer data and tell the system what to predict (e.g. lead conversion likelihood). The system handles the rest (data enrichment, model building, score deployment, etc.). We’ll show you [how to implement predictive lead scoring with Faraday](/blog/predictive-lead-scoring-b2c#how-to-implement-predictive-lead-scoring-with-faraday) later on.

### System integrations

Integrations are necessary for predictive lead scoring to impact revenue. Predictive lead scoring systems need to ingest new lead and customer data to improve models and deploy predictive lead scores into your marketing and sales systems. Ensure that your system can connect flexibly with your data sources and marketing and sales platforms.

## Fundamentals of a good B2C lead scoring dataset

For B2C companies, best-in-class predictive lead scoring requires a combination of historical data and third-party consumer data. Historical lead and customer data enables machine learning algorithms to distinguish successful outcomes (e.g. leads converting into paying customers) from unsuccessful outcomes. Consumer data lets the algorithm identify important “real life” characteristics about the leads that convert into customers versus those that don’t. For the best results, you will need the following data:

- **Identity data** (e.g. name, physical address, and phone number) is necessary to match your lead and customer records with the correct consumer profiles acquired from third-party data sources. This matching process is commonly referred to as identity resolution.
- **Transactional data** (e.g. lead acquisition date, purchase date, and order value) is necessary to define important cohorts like customers, leads, and stale leads. The machine learning algorithm will examine these cohorts to identify patterns that indicate the likelihood that an individual will move from the “leads” cohort to the “customers” cohort.
- **Engagement data** (e.g. pageviews, email subscriptions, and free trial signups) enables the algorithm to understand how certain behaviors contribute to a lead’s likelihood of conversion.
- **Consumer data** (e.g. demography, lifestyle, property, and financial information) enables the algorithm to understand how different human characteristics contribute to a lead’s likelihood of conversion. Here’s a small snapshot of Faraday’s [built-in consumer data](/features/data):

![Image of Faraday traits console](/images/blog/leads-traits-console.png)

Now that you’re familiar with predictive lead scoring and what’s required to generate accurate B2C lead scores, we’ll show you how to set up predictive lead scoring pipelines with Faraday.

## How to implement predictive lead scoring with Faraday

With Faraday, you can implement best-in-class predictive lead scoring within a day, without support from your dev team. To do this, you’ll simply upload your lead and customer data and use [predictive lead scoring recipes](/predict/category/lead_scores) to easily configure predictive models and deploy lead scores to your stack.

![Image of Faraday recipes wizard](/images/blog/leads-recipes.png)

You can upload your historical lead and customer data with a CSV file or create a connection to your database or data warehouse. Faraday automatically enriches your data with built-in consumer data profiles, adding hundreds of attributes to your lead and customer records.

![Image of Faraday traits console](/images/blog/leads-persona-set.png)

Once your data is in the system, just choose a lead scoring recipe, add your leads and customers, then hit save.

![Image of Faraday lead scoring in HubSpot recipe](/images/blog/leads-lead-scoring-in-hubspot.png)

Faraday will automatically build predictive models and provide model performance insights to help you determine what kind of results you can expect.

![Image of Faraday outcome score](/images/blog/leads-model-report.png)

Once you’re satisfied with your predictive lead scoring models, add deployment destinations (e.g. HubSpot) to your pipeline and turn it on.

![Image of Faraday lead scoring pipeline and deployment](/images/blog/leads-pipeline.png)

Just like that, you have predictive lead scores flowing into your stack.

![Image of Faraday lead scores in HubSpot](/images/blog/leads-hubspot-lead-scoring.png)

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

Understand how predictive lead scoring works and what's required to build effective B2C lead scoring models using machine learning.

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

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

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

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

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

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

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

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

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

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

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

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

## Build a predictive outcome to identify likely buyers

Last, you follow the recipes for likely buyer predictions in [Google Ads](https://app.faraday.ai/recipes/likely_buyers_in_google_ads) and [Facebook](https://app.faraday.ai/recipes/likely_buyers_in_facebook_custom_audiences) to create your likely buyers outcome, selecting your _customers_ as your attainment cohort because that’s your end-goal–you want people to become customers and enter your _customers_ cohort.

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

## Exclude protected classes with trait blocking

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

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

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

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

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

## Push high-scoring locations to your ad platform

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

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

And your Facebook campaign:

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

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

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

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

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

Next up for them is adding [direct mail](/integrations/predictions-with-poplar) into the mix, but the possibilities are endless.

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

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

How to optimize ad targeting and comply with financial advertising regulations

Predictions are complex, and consumer predictions for marketing aren’t an exception. In many cases, you might find yourself given the green or red light to go after a lead, prevent a churn, or approach a customer in another way that’s specific to a business goal your org has. Having access to that knowledge is great, but it’s a black box–simply knowing whether or not a lead will bite isn’t enough if you want to truly understand your customers and the actions they take.

In Faraday, every predictive model you create for your business is scored based on how well you can expect the model to perform, and includes a full technical report describing how the model was built. While the full technical report includes a swathe of info for data science-minded users, for the sake of this guide we’re going to take a look at how **features of importance** and **directionality** impact a sample **lead conversion** model, which you can find in every outcome and recommender.

## Features of importance

![Image of features of importance in a Faraday outcome](/images/blog/product/feature-importance.png)

In each outcome and recommender, you’ll find a **data** section describing the top features of importance for that model broken down into tenures, or how long an individual existed within the eligibility cohort before entering the attainment cohort. The features that had the most impact in creating the model are listed in the chart, sorted by the level of impact. In the above image, you can see that features such as high ticket home decor quintile, activity, total online dollars quintile, and household income had a major impact on the model. This means that when thinking about who you’re targeting with these predictions, you can tailor creative and messaging appropriately–but that still feels a little broad. Let’s take a look at directionality to see exactly how these features of importance break down.

## Directionality

![Image of directionality in model report](/images/blog/product/feature-directionality.png)

When you take a deeper look at a feature of importance in feature directionality, you’ll find it broken down into ranges if the feature is numeric, or selections if not. In the above example, green indicates positive, and red negative. For **household income** (HHI), it’s clear that consumers with an HHI of over 75k rank higher on average (green), whereas all HHI ranges lower than 75k rank lower (red). For **shopping styles**, _luxury offline_ and _prefers online_ shoppers rank higher, while _retail therapy_ shoppers rank lower.

That’s cool and all, but what’s the takeaway?

In short, these breakdowns tell you that in this lead conversion model, the people that are most likely to convert are largely making more than 75k/yr, and prefer to shop online unless it's for luxury items. Using this info, your marketing to them can confidently personalize toward the positive features so that your content is relevant.

Learn how to analyze your predictive models to deeper understand your customer base.

Faraday model reports: Understanding feature importance and directionality

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

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