Quantifying bias in predictive models is the first step towards building models with fair treatment of protected groups.

Faraday bias reporting: How we measure & report on bias

One of Faraday’s founding pillars is the use of [Responsible AI](/features/responsible-ai), which includes both preventing direct harmful bias and reporting on possible indirect bias. We’ve been building these features directly into the platform over the past several months, and today we’re going to walk through two new features in Faraday with **bias reporting** and **bias mitigation**.

## Bias reporting

To see the bias report on an outcome, head to [outcomes](https://app.faraday.ai/outcomes) and scroll down to the **bias** section to see the breakdown of bias discovered in the _data_, _power_, _predictions_, and _fairness_ categories, which we'll dive into below. These can also be retrieved via the API using [get outcome analysis](/developers/reference/getoutcomeanalysis).

[![Bias reporting on an outcome](/images/blog/faraday_bias_reporting/outcomes-bias-reporting.png)](/images/blog/faraday_bias_reporting/outcomes-bias-reporting.png)

Faraday uses four categories to report on bias for an outcome:

- **Data**: Measures selection bias in the underlying cohorts used in the outcome. In a Faraday outcome, two labels exist for the purpose of this blog post: _positive_, or the people from the attainment cohort that were previously also in the eligibility cohort, and _candidate_, or the people from the eligibility cohort.
- **Power**: Measures outcome performance on a subpopulation compared to baseline performance–for example, Faraday will compare how well the outcome performs on the subpopulation “Senior, Male” compared to everyone else.
- **Predictions**: Measures proportions of subpopulations being targeted compared to baseline proportions in order to see if the subpopulation is over or under-represented.
- **Fairness**: Measures overall fairness using a variety of common fairness metrics in the literature. For example, Faraday will look at whether or not the subpopulation "Senior Male" is privileged or underprivileged.

Each bias category uses groups of people called subpopulations, which are combinations of people defined in the [understanding bias reporting](/docs/product/outcomes#understanding-bias-reporting) section's **sensitive dimensions** dropdown table. Examples of these are "Teens with gender unknown" and "Senior Females." We're launching with two of these sensitive dimensions in **age** and **gender**, but will be rolling out more over time.

If you discover that one of your outcomes includes harmful bias, you can create a new outcome using the same cohorts, but enable bias mitigation options while creating it to compare results.

## Bias mitigation

When creating a new outcome, you can choose to mitigate bias by using the toggles in the **bias** section. Check out our [outcomes documentation](/docs/product/outcomes#mitigating-bias) for info on different bias mitigation strategies.

[![Bias mitigation in an outcome](/images/blog/faraday_bias_reporting/outcomes-bias-mitigation.png)](/images/blog/faraday_bias_reporting/outcomes-bias-mitigation.png)

<Callout type="warning" title="Taking action on bias">
Mitigating bias isn't something you should set-and-forget, meaning it's not recommended that you toggle both **age** and **gender** on for every single outcome you create. For example, if you're a womens' swimwear brand, your data will skew heavily toward women, and mitigating gender would negatively impact your lift.
</Callout>

Always keep the populations that you're making predictions on in mind when considering bias. In the above example, it's _totally_ fine to ignore the biased skew towards a certain gender or age group because they're your ideal customers.

We're thrilled to continue to drive Responsible AI forward by providing agency for our users to both discover and mitigate bias in their customer predictions. Stay tuned for a deep dive into the science behind how Faraday determines bias.

Learn how Faraday's bias management suite enables you to analyze and mitigate bias found in your customer predictions.

Introducing Faraday bias management

I've always loved Zapier. It really does feel like a superpower to set up a workflow with a zap: the gift that keeps on giving. This section has been on their homepage longer than anything else and I can see why — Zapier users all feel this way.

[![Testimonials from Zapier users](/images/blog/zapier/testimonials.png)](/images/blog/zapier/testimonials.png)

That's why I'm so excited and proud to share that you can now [incorporate Faraday predictions into your zaps](https://zapier.com/apps/faraday/integrations). With this new integration, you can score leads, assign customers to personas, make product recommendations to shoppers, forecast spend, and more, all within your automated Zapier workflows.

Predictions are the key to improving customer engagement throughout the lifecycle—you can personalize outreach, customize offers, prioritize better opportunities—the list goes on.

## Working through an example Zap with Faraday predictions

Let's say we want to score new leads when they arrive in HubSpot, evaluating them for their likelihood to close and assigning them to a persona. This way, you'll be able to tailor your handling of these leads differently according to their predictions.

### Faraday setup

We'll assume you already have basic customer and lead data, along with their respective cohorts, set up in your Faraday account. If you're new to Faraday, check out the [docs](/docs/intro/welcome) and [Faraday Academy](/docs/academy) to get up to speed.

Next, you'll need to set up your two predictions if you don't have them already:

1. **Lead scoring outcome** — [docs](/docs/how-to/predictions/likelihood-to-convert)
2. **Customer personas** — [docs](/docs/how-to/predictions/customer-personas)

Finally, if you don't already have it, create a pipeline to get these predictions deployed:

- **Population** – everybody
- **Payload** – the two predictions above
- **Deployment** – Lookup API

That last bullet is important to get things working right on Zapier.

### Creating your zap with Zapier

I love Zapier's new LLM interface for creating zaps, and it works great here. Just head to your dashboard and type "When a new lead comes into HubSpot, score it using Faraday," like so:

[![Zapier prompt saying "When a new lead comes into HubSpot, score it using Faraday"](/images/blog/zapier/prompt.png)](/images/blog/zapier/prompt.png)

Zapier should automatically calculate your zap template for you—something like this:

[![Zapier template for "Score new leads in HubSpot with Faraday"](/images/blog/zapier/template.png)](/images/blog/zapier/template.png)

Hit that "Try it" button. This should load the template up in the zap editor, which will look something like this:

[![Zapier zap editor for "Score new leads in HubSpot with Faraday"](/images/blog/zapier/editor.png)](/images/blog/zapier/editor.png)

Click the HubSpot box to pull up settings for this trigger step. You'll have to choose your HubSpot account and then pick your lead capture form from the dropdown. It will look like this:

[![Zapier HubSpot settings for "Score new leads in HubSpot with Faraday"](/images/blog/zapier/hubspot.png)](/images/blog/zapier/hubspot.png)

Hit the test button, and it will find a recent lead from your form data. When you hit continue, you'll make your way into the Faraday action step settings.

Choose or add your Faraday account (you'll need your Faraday API key for this), then proceed to the Action section. In the "Select pipeline" dropdown, choose the pipeline you created earlier. If it's not on the list, make sure your Lookup API deployment was properly added to this pipeline in Faraday.

Then, use the form data from HubSpot to fill out as many of the identity fields as you can. In this case, we capture email in our lead form, so I'm using that (actually, the Zapier LLM had that preconfigured for me ✨):

[![Zapier Faraday settings for "Score new leads in HubSpot with Faraday"](/images/blog/zapier/faraday.png)](/images/blog/zapier/faraday.png)

After testing what you've got so far, it's time to store your predictions for this new lead somewhere. You could put them back in HubSpot with an "Update Contact" action, or whatever you'd like. Then publish your zap and you're all set!

**Ready to try Faraday with Zapier? [Create a free account](https://faraday.ai/signup) to get started today.**

Zapier is everybody's favorite workflow automation tool. Now, you can retreive predictions from Faraday in your Zaps.

Launch: Faraday predictions now in Zapier

AI is on everyone’s minds due to its myriad use cases and applications across industries. From chatbots getting people where they want to be faster, to providing [financial planning advice](https://quickbooks.intuit.com/au/blog/accountants-and-bookkeepers/accounting-ai/), to using ChatGPT to help out with a resume or piece of code, to making [customer predictions](/features/predictions) for likelihood to take an action and personalization–AI isn’t going anywhere anytime soon.

[64% of businesses](https://www.forbes.com/advisor/business/ai-statistics) feel that various aspects of their organization will see increased productivity due to AI, and it’s no surprise when you think about the countless tasks that people have been doing for ages–segmentation, data analysis, and so on–and how much time & effort is saved by offloading them to AI. The use of AI for generating content in particular has scaled so fast that discussions abound on the impact that this AI revolution can and will have on human jobs, and brands like [NPR](https://www.npr.org/2023/06/07/1180768459/how-to-identify-ai-generated-deepfake-images) and [MIT](https://www.technologyreview.com/2022/12/19/1065596/how-to-spot-ai-generated-text/) are publishing guides for how to spot AI-generated content.

## The black box problem

The issue at the heart of this AI quandary is known as the [black box problem](https://umdearborn.edu/news/ais-mysterious-black-box-problem-explained). In short, the problem is that while what we’re seeing AI accomplish is incredible–and seemingly accurate in many ways, at first glance–by and large, we don’t understand how it reaches its particular conclusions, and what data it uses to reach them.

Faraday has long pursued making AI that uses [responsible data](/features/responsible-ai) to make responsible predictions accessible for all, and part of this pursuit has been tackling the black box problem. Starting today, every customer propensity prediction made using Faraday infrastructure can include _explainability metadata_.

## Explainable AI now in Faraday

[![Image of explainability checkbox in Faraday](/images/blog/product/explainability/explainability-checkbox.png)](/images/blog/product/explainability/explainability-checkbox.png)

Sounds good, but what does this mean?

Faraday has always provided detailed technical reports that analyze the impact of key data points on predictions at the overall level.

Now, each prediction, down to the individual level, has its score explained by the features in your data that had the highest impact on that individual’s prediction.

[![Image of a Faraday outcome feature importance for number of children and age](/images/blog/product/explainability/explainability-features.png)](/images/blog/product/explainability/explainability-features.png)

In the above example outcome, we see that, overall, the **approximate number of children** and **age** are the most predictive factors. And we can see what ranges lead to what average impact. For example, people with no kids have—all else equal—a slightly higher probability than those with 1–2 kids. This kind of aggregate reporting has always been available in Faraday.

[![Image of a Faraday score explainability output](/images/blog/product/explainability/explainability-output.png)](/images/blog/product/explainability/explainability-output.png)

Above, we see an example of CSV output from a pipeline. John is impacted by this outcome’s major factors, age and number of children, and leaves him with a low probability.

Jane, on the other hand, exhibits a more unusual combination of traits that give her a much higher probability for different reasons. For Jane, both her household income and millennial lifestyle saw her conversion probability higher than John because this business often sees conversions from people with those traits–even if they’re not the dominant traits.

## Explainable AI is Responsible AI

The central idea of Responsible AI is to understand our models—how they work, why they produce the results they do—enough to identify and correct their shortcomings. That’s why we have invested so heavily in, for example, bias detection and mitigation here at Faraday.

But even as we develop a strong understanding of and confidence in our models in the aggregate, we must not lose sight of the impact that prediction can have on individuals. We should demand nothing less from our AI models than that they explain themselves. Explainability is as core to Responsible AI as transparency and fairness.

A simple statement that explains a given prediction may not seem like much at first glance, but we feel otherwise. The _just trust us_ hand-wave that has been the motto engraved on the black box around AI for decades has been preventing our industry from moving forward with respect and responsibility for businesses and people alike–until now. Now you can see exactly what data helped AI reach the conclusion that it did, and feel confident that you’re making responsible predictions with responsible data.

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

Learn how every individual you make customer predictions on had their score calculated.

Removing the black box around customer predictions with Faraday score explainability

**See our complete guide:** [Churn prediction using machine learning — a complete guide](/blog/churn-prevention-machine-learning).

---

Brand loyalty has never been more impactful for B2C businesses than it is in 2023. Industry is awash with competition, leaving consumers with the ability to quickly jump from one brand to another after a bad experience. Whether that experience was with customer service, your website, or something else entirely, that churned customer is unlikely to come back–no matter what promos you drop into their inbox. When a 5% increased retention rate can result in up to a [95% increase in profits](https://www.invespcro.com/blog/customer-acquisition-retention/), it’s no wonder that brands are looking for every edge they can in order to keep their customers on board and delighted.

Enter customer churn prediction software. Previously, brands ran manual calculations, comparisons, and segmentations to try and find common characteristics of churned customers. Now, thanks to advancements in AI and machine learning, customer churn prediction software allows savvy brands to embed churn scores directly into their stack. In this article, we’ll walk through three reasons you need churn predictions in your stack, key features to look for when seeking customer churn prediction software, and the best ones to choose from.

## 3 reasons why B2C brands need customer churn prediction software

**Acquisition is more expensive than retention**. According to [American Express](https://www.americanexpress.com/en-us/business/trends-and-insights/articles/retaining-customers-vs-acquiring-customers/), it’s 5-6x more expensive to acquire than it is to reactivate an old customer. A successful retention campaign could result in a major positive impact on revenue if your customers.

**It’s easier to sell to existing customers**. With [Semrush reporting](https://www.semrush.com/blog/customer-retention-stats/) that you have a 60-70% chance to sell to an existing customer compared to a 5-20% chance to sell to a new customer, campaigns that have a retention focus are much more likely to prove out than their purely-acquisition counterparts.

**Returning customers spend more than new customers**. Customers that are delighted with the products and services they receive from a brand are happy to come back for more–and [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/). Fostering customer retention through loyalty programs and other similar offerings can keep those customers coming back to spend big with far less effort and cost than peppering Instagram feeds with targeted acquisition ads.

## Churn prediction software features to look for

Modern martech platforms come with a variety of bells and whistles, with addons for seemingly every use case. For churn prediction software, there are a few key features that will give your brand the competitive edge by enabling high-impact retention campaigns.

### Built-in consumer data

All customer predictions are built upon a foundation of data. While your first-party data is critical to knowing how your customers interact with your brand, it’s not likely to be enough for you to know who your customers really are, what their shopping habits are, or what their lifestyles are like. [Built-in, opt-in, responsible consumer data](/features/data) enables you to make personalized customer predictions that actually resonate with the people you’re engaging with by revealing demographics, psychographics, financials, property information, and more.

### Seamless integration

There’s nothing worse than finding a promising piece of software, only to find that there’s no feasible way for you to fit it into your stack. The best churn prediction software sits [directly in your stack](/integrations) to enable customer predictions wherever and whenever you need them. Embedding churn predictions directly into your infrastructure and workflows means that you'll always have them on hand.

### Stays up-to-date

People change–lifestyles, shopping habits, and so on–and your customers are no exception. With [65% of consumers](https://www.salesforce.com/resources/research-reports/state-of-the-connected-customer/) reporting that they expect brands to adapt with their changing needs, it’s critical that your churn prediction software stays up-to-date.

### 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.

## Best customer churn prediction software for consumer brands

With AI becoming as popular as it is in the martech space, there are plenty of offerings on the market for savvy brands to pick up. Not all of them are created equal, though, and there are a few that stand apart from the crowd in their ability to scale effective customer retention strategies.

### 1. Faraday

[Faraday](https://faraday.ai) is a customer prediction platform that uses AI to segment and analyze customer data to find patterns that indicate how likely a customer is to churn.

[![Faraday churn recipe](/images/blog/churn_prediction_software/churn-prediction-software-recipe.png)](/images/blog/churn_prediction_software/churn-prediction-software-recipe.png)

Through an intuitive user interface, Faraday enables you to segment you data into subgroups based on event or trait data, like customers, then use rich, built-in consumer data to find what makes you customers unique through expandable, filterable trait analysis and time-lapse dashboards. From there, in just a few clicks, you’ll describe the predictive outcome you need–churn scores in this case, though [Faraday offers many others](/features/predictions)–and a bespoke predictive model is created and scored for what kind of results can be expected from using these churn predictions.

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

Once churn scores are applied to your customers, Faraday automatically updates and refreshes them based on any new data or changes to data used in the predictions, ensuring that churn scores are never out of date with your customers’ wants and needs.

On top of that, Faraday easily integrates with hundreds of popular tools like [HubSpot](/predict/pipelines/churn_scores_in_hubspot), [Iterable](/predict/pipelines/churn_scores_in_iterable), and [Salesforce](/predict/pipelines/churn_scores_in_salesforce)–not to mention your database or data warehouse–so that you can build customer predictions directly into your stack without changing your overall workflows. With predictive churn scores in your stack, you can confidently engage customers that are most likely to churn before they take that critical step.

### 2. Amplitude

[Amplitude](https://amplitude.com/) is a digital analytics platform that enables you to analyze how users engage with your products or platform. Through detailed analytics, Amplitude helps brands understand their customers and optimize their products to drive growth.

[![Amplitude overview](/images/blog/churn_prediction_software/churn-prediction-software-amplitude.png)](/images/blog/churn_prediction_software/churn-prediction-software-amplitude.png)

### 3. Churnkey

[Churnkey](https://churnkey.co/) is a customer success and product experience software that helps users drive retention through features like personalized cancel flows. Though not a full AI platform, Churnkey uses AI to filter through product feedback to help brands better understand their customers.

[![Churnkey overview](/images/blog/churn_prediction_software/churn-prediction-software-churnkey.png)](/images/blog/churn_prediction_software/churn-prediction-software-churnkey.png)

**Get customer churn predictions in your stack. [Create a free account](https://faraday.ai/signup).**

With consumers getting more and more picky, churn prevention is critical to a smart growth strategy. Learn why your brand should embed customer churn prediction software in your stack to stay competitive.

Customer churn prediction software: why you need it, features to look for, and the best ones

It’s no secret that [personalized direct mail](/blog/personalized-direct-mail-strategy) marketing is a great way to engage with your customers. Whether you’re interested in driving [repeat purchases](/predict/category/repeat_purchasers) from current customers, [finding new ones](/predict/category/likely_buyers), winning back old ones, or something else entirely, direct mail has proven to be an [increasingly effective strategy](https://www.postgrid.com/direct-mail-statistics/) to make your brand stand out in an era awash with the same old email marketing tactics. In this feature guide, we’ll walk through how to use Faraday to enable direct mail marketing campaigns that are highly personalized, and highly effective.

## How can Faraday be used for direct mail?

Faraday can be used for your direct mail campaigns by creating targeted cohorts, or groups of people, based on attributes from the Faraday Identity Graph, such as their ability to receive mail via, as well as their physical address. In this guide, we'll walk through how to **create a cohort** for direct mail, and how to use it when **deploying a pipeline**.

## Creating a cohort for mailable U.S. customers

To create a new cohort:

1. Navigate to cohorts via the menu bar on the left-hand side and click **+ New cohort** in the upper right.

2. Optionally, select an **event** for the cohort. In this specific direct mail use case, we're going to skip this step. For more info on this, check out our [cohorts documentation](/docs/abstractions/cohorts).

3. Next, click the **Add trait** button, which is the most important part in designating that this cohort of people are able to receive your direct mail.

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

4. In the **Add trait** popup, we're going to find & select the following trait:

- **Record quality code**: To ensure that the residence can receive mail at all. Choose the option to "select exact values from list" to select 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)

<Callout type="info" title="Restricting by geography">

Optionally, add a _geography_ trait such as Postal code/ZIP to target specific locations. When using a cohort with a geographic restriction, like "Direct mailers in Burlington, VT," in a pipeline, the deployment will only include people in that locations.

</Callout>

5. Enter a unique name for the cohort, like _Direct mailers_, and click **Create cohort**.
6. Once finished, click **Save changes**. The cohort will begin building and will display with a _calculating_ message.

## Creating a pipeline to predict likely buyers for direct mail

While our _Direct mailers_ cohort is building (if we select it in a pipeline, the pipeline will build once the cohort is ready), we're ready to create a pipeline and deploy it via CSV, which will let us plug our direct mail cohort right into our preferred mailer platform. For a more detailed look at creating a pipeline, check out the [pipelines documentation](/docs/abstractions/pipelines).

1. Navigate to [Pipelines](https://app.faraday.ai/pipelines), and click **+ New Pipeline** in the top right to create a new pipeline.

2. In **population to include**, we'll grab the cohort we just created, _Direct mailers_, as we're only interested in the group of people we've selected: people in the U.S. who are able to receive direct mail.

3. In **population to exclude**, we can optionally add our _Customers_ cohort if we’re only looking for net-new customers.

4. For our **payload**, we'll select:
   - Our **propensity** (likely to buy) outcome. This should be a standard likely buyers outcome, with your _Customers_ cohort as the attainment, and _Anyone in Faraday’s identity graph_ as the eligibility. For more info on creating that outcome, see the [outcomes documentation](/docs/abstractions/outcomes). We’ll filter this outcome a little further on to target the top 20% most likely to buy people in this payload so that we're saving resources by not sending our direct mailer to people who aren't likely to buy.

   - Optionally, we can apply our **customer personas** to the people in this pipeline so that we can tailor creative & copy to their wants & needs.

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

5. Enter a unique **name**, and click **Save pipeline**. The pipeline will begin building, as indicated by a loading bar. You'll receive an email when the pipeline has finished building.

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

6. Once the pipeline is complete, head to the **Deployment** section inside the pipeline, and select **+ Add** under **Hosted CSV**.

7. We're presented with the choice of _Hashed, Referenced, Identified_, and _Aggregated_ as options for our predictions. Since we're creating a direct mail pipeline, select _Identified_ so that identities are not hashed, and we can see physical address information, and so we can filter by residence below.
   - In **Individual identification options**, we'll select _One row per residence (address)_ so that we don’t send mail to more than one person in a household.

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

8. Select **human friendly** for column headers that are easily readable, and click next.

9. Now we’ll expand **filter**, then click **add payload filter** so that we can specify that we’re only interested in the top 20% of likely buyers, ensuring we’re not wasting time and money on bad fits.
   - In the payload filter, select **propensity to buy - outcome percentile** to sort by score percentile, or the distributions of propensity scores within the _Direct mailers_ cohort we targeted.
   - Change the operator to **greater than or equal to (>=)** and type **81** into the next field. Since the scoring is inclusive, this means that, in this deployment, we’ll filter it in a way that we only receive the scores for the top 20% of likely buyers.

[![Faraday direct mail pipeline deployment](/images/blog/direct_mail_guide/direct-mail-guide-deployment-filter.png)](/images/blog/direct_mail_guide/direct-mail-guide-deployment-filter.png)

<Callout type="info" title="Filtering deployments">

If you have a specific budget to stay under, expand the **limit** option to add a cap on the number of rows you receive. For example, maybe each direct mailer costs \$10, and you have a budget of \$50,000. You’d set the limit to the top 5,000 rows.

</Callout>

10. Last, click **finish** to complete the deployment, after which it will begin building. We can use the download button to download the CSV of a completed deployment for upload into our direct mail platform, which will include the top 20% of likely buyers that are able to receive mail in the country, (optionally) with their predictive personas included so that we can tailor copy & creative appropriately.

[![Faraday direct mail pipeline deployment download](/images/blog/direct_mail_guide/direct-mail-guide-deployment-download.png)](/images/blog/direct_mail_guide/direct-mail-guide-deployment-download.png)

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

How to use each feature in Faraday to create predictions that enable you to accurately target direct mail campaigns through propensity predictions.

How to target direct mail campaigns using Faraday

See our complete guide: [How predictive lead scoring works — and why it outperforms traditional methods](/blog/predictive-lead-scoring-b2c)

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 & data points 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

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

---

## 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 data points 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 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. 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 data points 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.

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, 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 data point 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–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?

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

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

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

## What is market opportunity analysis?

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

## How it works at Faraday

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

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

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

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

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

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

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

## Ready to explore what's next?

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

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

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

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