To salespeople and marketers, Salesforce is a very well-known commodity. Not only is it a CRM, but it has a suite of convenient addons and features for practically every use case under the sun, from forecasting to predictive lead scoring. Since these are addons and features directly within Salesforce, and they’re easy to integrate into the Salesforce part of your workflows.

In this article, we’re going to walk through the drawbacks of Einstein lead scoring (especially for B2C companies), and how Faraday’s approach to [B2C lead scoring](/blog/predictive-lead-scoring-b2c) offers faster, more versatile, and more holistic predictive lead scores that make use of all your data, not just what you have in your Salesforce account.

## Einstein lead scoring doesn’t quite cut it for B2C companies

While the seemingly just-add-it-on implementation of Einstein lead scoring is without a doubt eye-catching, the drawbacks that come with simple addons are inevitable when compared to feature-complete products that are built to accomplish the same task. The shortcut is rarely worth the time and effort invested when the results are often lackluster. Einstein lead scoring falls short for a few different reasons:

- **The cold start problem**: Einstein can’t make predictions on net new leads. It requires time for data to be collected on the individual before a score can be assigned. This leaves you either sitting on your hands while the lead is looking around at your competitors.
- **Limited data**: Data is the foundation of any effective revenue-driving strategy. With Einstein, only your first-party data is able to be used. For leads that you already have data on, you’ll have a rough idea of how they’ve interacted with your brand, but you won’t know what kind of person they are, how they shop, or what makes them tick.
- **Labor-intensive**: If you have non-sales data in disparate systems, like product data in Amplitude or Mixpanel, you’ll need to spend a significant amount of time manually plugging in your key metrics & datapoints to map out how leads should be scored.

## What’s the best B2C lead scoring software for Salesforce?

Here's the thing: the best lead scoring software is purpose-built, like Faraday, not an add-on, like Einstein. If you take the shortcut, it’ll show signs of rust quickly, leaving you scrambling for an alternative and wondering why you ever bothered in the first place. The good news is that Faraday offers a suite of customer predictions for every stage of the customer journey - one of which is lead scoring.

### Solving the cold start problem

Like I mentioned earlier, Einstein lead scoring leaves your reps sitting on their hands as new leads come in. You have no data on them, so you don’t know how to engage with them–or if they’re worth engaging at all. This delay leaves your leads plenty of time to check out your competitors’ offers, and to bite at the [personalized direct mail](/blog/personalized-direct-mail-strategy) postcard they just got in the mail.

Using Faraday, you can solve the [cold start problem](https://blog.hive.co/solving-the-problem-of-cold-start-ecommerce-product-recommendations/#) by scoring leads with propensity models for their likelihood to convert as soon as they come in, enabling your reps to act with them immediately. Whether it’s to prioritize the lead because their predictive lead score is high, or to disregard because it’s low, your reps’ time–their most valuable asset–is being used effectively. Lead prioritization is clear from the minute the lead first interacts with your brand, made possible by unifying your data with Faraday’s.

### Built-in consumer data

Your first-party data alone isn’t enough to not only solve the cold start problem, but also give you clear indicators for how to engage leads you already know about. You’re unlikely to know what the leads’ shopping preferences are, what kind of home they have, or their annual household income. With Faraday’s hundreds of [built-in, cookieless consumer attributes](/features/data), including demographics, psychographics, financials, property information, and more, every interaction you have with your best-scoring leads can be tailored just right.

Using this data in conjunction with your own lead data, you can even [personalize your outreach](/blog/predictive-personalization-machine-learning) with copy & creative that’s resonant with each individual, leaving you with campaigns that are both highly targeted to your best leads, and highly personalized for maximum impact. Use predictive personas to enable talk track alignment so that all of your reps hit the same key notes on every lead where it’s relevant. Similarly, you might find certain reps more effective than others with certain personas, leaving clear coaching opportunities to bring others into the fold. goals, it’s worth the effort to retain them.

### Sits anywhere in your stack

While Einstein lead scoring involves pulling your disparate data into Salesforce from various pieces of software, Faraday sits [anywhere in your stack](/integrations) that you need it to. Your data’s easily ingested from any software you use, used to make accurate, impactful customer predictions, and deployed back to [Salesforce](/predict/pipelines/lead_scores_in_salesforce) just as easily.

## How to implement lead scoring in Salesforce with Faraday

With Faraday, you can implement predictive lead scoring in Salesforce in a day, including scoring brand new leads in real-time as they come in–something Einstein lead scoring can’t do–regardless of where your data is. Here’s how it works.

### Upload your lead and customer data from Salesforce

To start, you plug in your data by mapping your data fields to Faraday properties. You’ll match customer & lead data like names, emails, and addresses to match their identities into Faraday.

[![Faraday dataset](/images/blog/einstein_lead_scoring/einstein-lead-scoring-dataset.png)](/images/blog/einstein_lead_scoring/einstein-lead-scoring-dataset.png)

Then, in cohorts, you’ll utilize both your data and Faraday’s built-in consumer data to group your data into interesting groups of people, which in this case are your [customers](/docs/cohorts/customers_from_salesforce) and [leads](/docs/cohorts/leads_from_salesforce).

[![Faraday leads cohort](/images/blog/einstein_lead_scoring/einstein-lead-scoring-cohort.png)](/images/blog/einstein_lead_scoring/einstein-lead-scoring-cohort.png)

### Predict personas to reveal rich insights

If you really want to make your brand’s engagement stand out to your leads, you can predict personas to reveal who your leads are as people and as consumers. Through trait breakdowns like age, gender, household income, hobbies, shopping styles, and more, you can turn your newest lead, Jane Doe, whom you know nothing about, into Jane Doe, married mother of 3, Amazon-centric shopper, and dog mom with a household income of \$200k.

Your talk tracks & creative couldn’t be easier & more resonant.

[![Faraday customer personas](/images/blog/einstein_lead_scoring/einstein-lead-scoring-personas.png)](/images/blog/einstein_lead_scoring/einstein-lead-scoring-personas.png)

### Build a predictive outcome to score leads in Salesforce

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. Here, you’re defining the propensity to convert, or the likelihood that a lead is going to convert, so you’ll choose your **leads** as your eligibility cohort.

[![Faraday lead conversion outcome](/images/blog/einstein_lead_scoring/einstein-lead-scoring-outcome.png)](/images/blog/einstein_lead_scoring/einstein-lead-scoring-outcome.png)

With your lead scoring prediction defined, create a pipeline to make it actionable by choosing your **likelihood to convert** outcome and (optionally) your **persona set** as your payload, and your **leads** cohort as your population to include.

[![Faraday lead scoring pipeline](/images/blog/einstein_lead_scoring/einstein-lead-scoring-pipeline.png)](/images/blog/einstein_lead_scoring/einstein-lead-scoring-pipeline.png)

Next, Faraday’s machine learning algorithms then get to work predicting how likely people in your leads cohort are to convert and become customers, then they’re assigned scores on an individual basis that you can filter & activate on.

### Deploy predictive lead scores back into your Salesforce instance

With your predictions made, deploy your predictive lead scores to your Salesforce account so that each lead is enriched with their predictive lead score–and optionally, their predictive persona–so that, at-a-glance, your reps know exactly how and when to engage for the highest impact in order to drive revenue through conversions.

[![Faraday lead scores in Salesforce](/images/blog/einstein_lead_scoring/einstein-lead-scoring-salesforce.png)](/images/blog/einstein_lead_scoring/einstein-lead-scoring-salesforce.png)

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

Salesforce’s Einstein lead scoring addon is more attractive on paper than in practice. Learn how an alternative enables you to incorporate more than just lead data, while also scoring net new leads.

An Einstein lead scoring alternative for B2C companies on Salesforce

## What is predictive personalization?

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

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

## Benefits of using predictive personalization for personalization at scale

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

### Enhanced insights through vivid consumer data

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

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

### Proactive engagement

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

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

### Personalization at scale

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

### Internal talk track alignment

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

## Implementing predictive personalization with Faraday

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

### Connect data and define customers

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

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

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

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

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

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

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

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

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

Predictive personalization: Using machine learning to personalize customer experiences

## What is customer churn?

Churn refers to the point at which a customer decides to cease using a product or service that your company offers. Churn can occur for a variety of reasons, but the bottom line is that a churned customer brings no revenue to your business. When it comes to churn, no business can ever completely eliminate it, but there are strategies to consider that can help your business with churn prevention and mitigation.

## Why is churn prevention so important?

Studies have consistently shown that it costs more to acquire new customers than it does it to retain existing ones. On top of that, it’s often easier and more profitable to retain existing customers; you have a [60-70% chance of selling](https://blog.hubspot.com/service/statistics-on-customer-retention) to a current customer vs a 5-20% chance of making a new sale, and current customers spend [67% more](https://www.business.com/articles/returning-customers-spend-67-more-than-new-customers-keep-your-customers-coming-back-with-a-recurring-revenue-sales-model/) on average than new customers. Despite this, [44% of businesses](https://www.semrush.com/blog/customer-retention-stats/) focus on customer acquisition, while only 18% focus on customer retention. While you shouldn’t drop acquisition to solely focus on retention, a smart, effective strategy should include a healthy mix of acquisition and retention.

## What causes customer churn?

When a customer churns, it’s key to analyze your data to understand _why_ they churned so that you can take steps to ensure customers like them don’t churn in the future, or–if they’re not ideal customers, and therefore likely to churn anyway, avoid them entirely. Maybe one of your customers received poor customer service when they chatted in for support, or they received an email that looked like generic spam and included offers that weren’t relevant or resonant. Or, perhaps you’re acquiring too many discount shoppers - people who are only biting because you offered them steep discounts.

## Churn prevention strategies

Churn prevention strategies begin with having a strong foundation of customer data, and are solidified by being smart about how you use that data.

### Mitigate churn higher in the funnel

The other key benefit of defining and identifying your best-fit, high-LTV customers is that it can help you find more of them. Discount purchasers that only buy when you send them a 75% off coupon aren’t ideal customers, and are likely to churn once their promo window is up. With a solid understanding of who your ideal customer is, you can find more of them to target so that you’re less likely to need to worry about them churning in the future.

### Use data to identify causes of churn

Segmenting your customers into unique subgroups can have an impact on your overall strategy that’s twofold: first, you’ll find different attributes and traits that enable personalization across each individual subgroup, leading to frictionless engagement that includes personalized offers, copy, & creative, ensuring your customers feel understood.

Second, by analyzing the data that you have on your customers, you can discover patterns and similarities in those that churn, which can help you identify why they opted to make that critical decision. The same applies to the other side of the coin–you can segment out your ideal customers so that you can define what makes them ideal; maybe they’re big spenders, or simply consistent ones.

### Create personal and frictionless experiences

All it takes is one bad experience to turn a loyal customer into a churn. Ensuring that each experience your customers have with your brand is personal and frictionless goes a long way to keeping your customers on board. Your engagement, regardless of channel, should include content that’s relevant to who they are and what kind of shopper they are. This results in experiences that seamlessly deliver the exact information, products, and services that your customers are looking for, so that they never have the excuse they need to find an offer elsewhere.

### Invest in customer loyalty programs

[58% of customers](https://www.semrush.com/blog/customer-retention-stats/#loyalty-program-statistics) that are members of a brand’s loyalty program make a purchase at least once per month. Loyalty programs are an incredible way to keep your customers engaged with your brand on a regular basis. They’re also a two-way street: not only do they reward your customers for their loyalty through discounts, bundles, and other similar offers, but they reward your brand with customers that stay subscribed and keep coming back for more.

### Provide excellent customer service

Nobody likes having to contact customer service, but when they absolutely _have_ to, ensuring their experience is a delight is critical in order to keep their business. With excellent customer service, a customer can go from frustrated at something not working as expected–churn in the back of their mind–to relieved, satisfied, and still on board.

## How machine learning enhances churn prevention efforts

Manual churn prevention strategies are doable, but they’re time-consuming, resource-intensive, and data-hungry. Fortunately, churn management software utilizing machine learning can take the workload off of your teams’ shoulders, while providing you with actionable insights for a variety of use cases.

### Segmentation & insights

Traditional segmentation provides a limited level of insight into who your customers are due to the fact that your data is likely limited to just their interactions with your brand. In adopting machine learning through churn management software, your data is unified with [rich, third-party consumer data](https://faraday.ai/features/data) to reveal distinct subgroups within your customers, leads, and any other interesting group of people–these are your personas. These insights include demographics, psychographics, property, financials, hobbies, & more. Your customer–Jane Doe–who recently purchased a dog toy, becomes Jane Doe, dog owner, married, household income of over \$200k, has children in the household, and prefers to shop in-store rather than online. This level of insights serves you personalization guidance on a silver platter.

Machine learning also significantly simplifies the process of analyzing your segmented data. Instead of manually poring over every datapoint in an attempt to reveal similarities between churners–as well as those between ideal customers–machine learning accomplishes this for you in a fraction of the time, leaving you with a predictive model that’s easily interpretable and [actionable through predictive model reports](https://faraday.ai/blog/faraday-model-reports).

[![Faraday outcome report](/images/blog/churn-prevention-features.png)](/images/blog/churn-prevention-features.png)

_The above image displays a Faraday outcome report, detailing the features most important to the predictive model._

### Personalization

With rich, persona-level insights on every individual that your brand is interested in, all of your outreach and engagement can be personalized to a tee. This means that each engagement–from email to direct mail, and everything in between–provides resonant copy & creative that leaves your customers feeling understood and heard. Customer Jane Doe, a member of the Millennial May persona, can receive a direct mail postcard that includes creative that incorporates some of the traits mentioned above–dogs and children, for example–and copy that resonates with Millennials, capped off with directions to your nearest brick-and-mortar store to match their in-person shopping style.

### Churn prediction using machine learning

What if you could know whether or not a customer was going to churn, before they even knew they were considering the choice? Churn prediction using machine learning provides you with churn scores on an individual level, based on how likely the individual is to churn. This kind of insight gives you the ability to proactively reach out to customers that are likely to churn with deeply personalized outreach so that you can keep them on board. On top of that, churn predictions help you prioritize who to reach out to so that you’re not wasting time on bad fits.

### LTV prediction

To add another layer of depth and effectiveness to your churn prevention strategy, LTV prediction enables you to define cutoffs for how deep a discount someone needs to receive for retention to be profitable. If a customer has a churn score of 75–pretty likely to churn–but they have a predicted LTV that’s in line with your goals, it’s worth the effort to retain them.

## How to predict and prevent churn using Faraday

Churn management software can help you create an effective, [impactful strategy](/blog/churn-predictions-workshop) for finding & retaining your best customers. Let’s take a look at how easy it is to predict churn and deploy churn predictions to your stack with Faraday.

### Connect data and define customers

To kick things off, plug your customer data in by mapping your data fields to matching Faraday properties. Then, in cohorts, you’ll utilize both your customer data and Faraday’s built-in consumer data to build a cohort for your [current customers](/docs/how-to/cohorts/customers), and your [churned customers](/docs/how-to/cohorts/churned-customers).

[![Faraday churned customers cohort](/images/blog/churn-prevention-cohort.png)](/images/blog/churn-prevention-cohort.png)

### Build personas to reveal rich insights and improve your personalization efforts

Predict personas with the help of Faraday’s rich, cookieless, opt-in consumer data. Utilize trait breakdowns like age, hobbies, shopping styles, and more to understand the nuances of how different portions of your customer base stand apart from each other–every little piece of personalization in your engagement matters.

[![Faraday customer personas](/images/blog/churn-prevention-personas.png)](/images/blog/churn-prevention-personas.png)

### Build predictive churn outcomes to detect churn risks

Next, you’ll build your predictive outcome by defining your attainment cohort as your churned customers, and your eligibility cohort as your current customers. With these selections, you’re predicting how likely it is that any member of your customers cohort is going to enter your churned customers cohort by churning.

[![Faraday churn prediction outcome](/images/blog/churn-prevention-outcome.png)](/images/blog/churn-prevention-outcome.png)

Now that your predictive outcome is created, make your predictions actionable by creating a pipeline with your **likelihood to churn** outcome in the payload–alongside **personas** for easy personalization–and your **customers** as the population to include.

[![Faraday churn prevention pipeline](/images/blog/churn-prevention-pipeline.png)](/images/blog/churn-prevention-pipeline.png)

Faraday’s machine learning algorithms then predict how likely each of your current customers is to churn, while also revealing which people belong to what persona on an individual basis so that you know how to engage with them.

### Deploy churn predictions to your stack

With your pipeline created, deploy your churn scores–AI-generated personas attached–anywhere in your stack that you need them. Activate on your predictions by targeting your customers that are most likely to churn, and use their personas as guidance for how to engage with them in a way that’s highly resonant.

### Scale churn prevention by finding more of your best customers

The best churn prevention strategy is to acquire best-fit customers and ensure that they have a frictionless experience with your brand at every stage in the journey. Find more of your ideal customers by following the above guide for [building an outcome](/blog/churn-prevention-machine-learning#build-predictive-churn-outcomes-to-detect-churn-risks), but use your **customers** as the attainment cohort. Implementing both a retention strategy through churn scores and an acquisition strategy that focuses on only your best-fit customers can give you a serious leg up on your competitors.

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

Learn the benefits of focusing on churn prevention, churn prevention strategies, and the importance of predicting churn.

Churn prevention: How to reduce customer churn using machine learning

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

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.

Product