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

**In this video, you’ll learn:**

- How Faraday improves sentiment analysis accuracy with BigQuery LLM feature
- The benefits of generating personalized content directly within BigQuery
- How BigQuery’s LLM feature simplifies our workflows and data processes

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

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

How Faraday simplifies sentiment analysis and generates personalized content using BigQuery

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

Faraday is thrilled to highlight **LeadConduit**, the ActiveProspect integration for Faraday. Between LeadConduit helping you acquire, optimize, and distribute leads, and Faraday scoring your leads with likely-to-convert predictions, your leads have never been easier to manage. Read on for details on how ActiveProspect and LeadConduit can help scale your lead management efforts:

[ActiveProspect](https://activeprospect.com/) is a comprehensive SaaS platform that empowers companies across industries to scale quality lead acquisition from any source, protect against litigation risk by documenting proof of consent, and accelerate speed-to-lead with automated process flows. With almost 20 years of experience in the industry, ActiveProspect has developed a reputation as the industry-leading consent-based marketing platform.

ActiveProspect’s three main products –[TrustedForm](https://activeprospect.com/trustedform/), [LeadConduit](https://activeprospect.com/leadconduit/), and [LeadsBridge](https://leadsbridge.com/)– are designed to help businesses capitalize on marketing opportunities and maximize conversions.

- [TrustedForm](https://activeprospect.com/trustedform/#howitworks) is the de facto standard for documenting express written consent by providing independent certificates of authenticity used for legal compliance and campaign optimization.
- [LeadConduit](https://activeprospect.com/leadconduit/#howitworks) streamlines the leading buying process to help businesses efficiently acquire customers at scale. Custom lead flows automatically enhance and filter leads in real time to deliver the highest-quality prospects to your CRM or lead buyer.
- [LeadsBridge](https://leadsbridge.com/platform/) automates your customer acquisition workflows from major ad platforms with seamless integration to your revenue tech stack. Utilize over 380 integrations to sync your leads, target custom audiences, and conversion tracking to boost sales and productivity.

Overall, [ActiveProspect](https://activeprospect.com/get-started/) offers the only complete solution for consent-based marketing powered by patented technology. Its TrustedForm, LeadConduit, and LeadsBridge products are just a few examples of the company's commitment to delivering high-quality solutions that help businesses succeed in today's increasingly competitive marketplace.

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

Faraday is thrilled to highlight LeadConduit, the ActiveProspect integration for Faraday. Between LeadConduit helping you acquire, optimize, and distribute leads, and Faraday scoring your leads with likely-to-convert predictions, your leads have never been easier to manage.

Faraday integration spotlight: LeadConduit

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

Personalization is more important now than ever. The circumstances around COVID demanded that brands ramp up digital strategies in order to stay competitive, creating a considerable amount of noise in inboxes and ad spaces (though [direct mail is making a comeback](/blog/personalized-direct-mail-strategy)). Consumer inboxes, Instagram feeds, and the like are riddled with emails and ads–some personalized, some not–and each leaves a different impression. The result is that brands need to work harder & smarter to stand out from the competition.

However, achieving true [personalization at scale](/blog/personalization-at-scale-guide) is much easier said than done. Collecting [first-party](https://hightouch.com/blog/what-is-first-party-data) and behavioral event data allows for a comprehensive understanding of how customers engage with your brand. This foundational data must be joined across various data sources and can be further [enriched](/blog/first-party-vs-third-party-data-marketing) to construct a comprehensive and unified customer profile. Subsequently, artificial intelligence (AI) can be employed to create [customer predictions](/features/predictions), identifying ideal customers and the best engagement strategies for each. Finally, a [Reverse ETL](https://hightouch.com/blog/reverse-etl) platform activates customer profiles and AI models out of your data infrastructure and into the downstream tools that power your marketing and business operations.

## Use cases for AI-powered personalization

Personalization that’s driven by AI has applications across the entire customer journey. Let’s take a look at a few standouts.

### Customer retention through proactive engagement

It’s always better to be proactive rather than reactive, and engaging with your customers is no exception to this rule. While traditional engagement can be personalized after an individual has interacted with your brand through ads, purchases, form submissions, and so on, AI-powered personalization allows you to engage with resonant content from the very first interaction, leaving your customers feeling understood, and that you see them as more than just clickstreams. On top of that, adding [propensity to churn predictions](/predict/category/churn_scores) to the mix gives you an additional layer of proactivity.

### Internal talk track alignment

The benefits of AI-powered personalization aren’t exclusive to external outreach. Savvy brands are adopting it to create frictionless experiences for their customers by ensuring every sales rep is on the same page when it comes to [talking points](/blog/sealed-lead-optimization) for each buyer persona. Additionally, it’s likely that some reps on your team will be more successful with certain personas than others, which can be used to foster persona-specific coaching among reps.

## How Faraday and Hightouch enable hyper-personalization

If you want to build this robust personalization, good news: Faraday and Hightouch have partnered to help brands hyper-personalize every customer journey with ease. Let’s dive into the highlights of the partnership that brands hungry for true personalization can reap the benefits of.

### Unify customer data in the warehouse

Hightouch enables you to build a Composable Customer Data Platform ([CDP](https://hightouch.com/blog/composable-cdp)) directly on your existing data infrastructure. In practice, this means that you are able to store, unify and activate all of your customer data directly in/from our data warehouse rather than splitting data in different silos.

Traditional CDPs collect [clickstream](https://hightouch.com/blog/clickstream-data) and [event data](https://hightouch.com/blog/event-tracking), store it in their own data silo, perform [identity resolution](https://hightouch.com/blog/what-is-identity-resolution) to build [Customer 360](https://hightouch.com/blog/what-is-customer-360) records, and then sync data to marketing tools. The Composable CDP does all of these functions, but around your data warehouse so that it can unify all customer data, including clickstream events but also offline interactions, third-party enrichment data, data from operational servers, and more. This is an essential structure for personalization because it ensures that AI models and marketers alike have access to the full view of what each customer does while providing brands with the piece of mind that data remains within their own infrastructure.

You can use ETL tools like Fivetran to collect some of this data in the warehouse, and [Hightouch Events](https://hightouch.com/platform/event-collection) to collect digital events into it as well. Hightouch’s [Customer 360 Toolkit](https://hightouch.com/platform/customer-360) then enables you to unify customer records in the warehouse by performing identity resolution.

### Use AI for customer predictions & first-party data enrichment

With your first-party customer data consolidated in the warehouse, you can use Faraday to enrich it with [built-in, permissioned, and opt-in consumer data](/features/data), then predict the likelihood that they’ll take virtually any action.

Start by using [AI for customer segmentation](/blog/predictive-personalization-machine-learning), leaving you with rich insights into who your customers are to guide the personalization of copy & creative. Next, build customer loyalty with a strong [churn prevention strategy](/blog/churn-prevention-machine-learning) by predicting churn scores, make your sales reps as time-efficient as possible by implementing [predictive lead scoring](/blog/predictive-lead-scoring-b2c) to find which leads are worth pursuing, expand your brand’s reach with confidence using an [AI-powered market sizing analysis](/blog/bottom-up-market-sizing-predictive-analytics), and more.

### Turn predictions into personalization with Reverse ETL

Now that you’ve consolidated all of your customer data in the warehouse, enriched it, and used AI for predictions, it’s time to put that data to work. Reverse ETL syncs data from your warehouse to your everyday tools. You can do this and build different audiences without writing any code using Hightouch’s [Customer Studio](https://hightouch.com/platform/customer-studio). It’s easy to build a different audience based on their attributes and predictive models and trigger various actions like personalized email campaigns directly from your warehouse source of truth into your everyday marketing tools.

## How to embed personalized, predictive high-spender scores in your stack with Faraday and Hightouch

Let’s walk through how you can use AI to predict high-spender scores to find your most valuable customers, create bespoke personas to enable hyper-personalization, then embed them in your stack by leveraging Faraday and Hightouch.

### Connect your data and define your dataset

First, you need to sync your data warehouse with Faraday by creating a connection, after which you’ll define a dataset so Faraday knows who is who in your data. Once your data is defined, you can easily group it into unique segments that we call cohorts–like your customers and high-spending customers.

[![Faraday dataset](/images/blog/faraday_hightouch/faraday-hightouch-dataset.png)](/images/blog/faraday_hightouch/faraday-hightouch-dataset.png)

### Predict high spend scores to find your most valuable customers

With your data in Faraday, you’ll build an outcome to describe the predictions you want to make–high spender scores, in this case. You’ll plug in your _customers_ cohort and your _high spenders_ cohort so that Faraday will predict how likely people in the former will enter the latter by spending big.

[![Faraday high spenders outcome](/images/blog/faraday_hightouch/faraday-hightouch-outcome.png)](/images/blog/faraday_hightouch/faraday-hightouch-outcome.png)

### Predict customer personas to guide personalization

In addition to your spend prediction, you can use AI to predict personas within your customer base so that you can engage with incredibly resonant targeting & messaging. Faraday combines your data with rich, built-in data, then uses machine learning algorithms to segment your customers into bespoke personas based on similar trait groupings. These personas provide useful insights into traits like household income, marital status, and shopping style that you can use to guide engagement. You can even take your personalization a step further by adding more traits for analysis, like health & exercise or dog ownership, to see who your customers really are.

[![Faraday customer personas](/images/blog/faraday_hightouch/faraday-hightouch-personas.png)](/images/blog/faraday_hightouch/faraday-hightouch-personas.png)

Now that your personas are segmented, use the resulting traits to create resonant copy & creative for your engagement. For example, you could include images of older women exercising in your emails to resonate with customers in the Glamorous Grandma Gloria persona.

### Create a pipeline to apply high spender scores & customer personas

With both your customer personas and your high spender outcome created, you’re ready to make them actionable by applying both personas and high spender scores to your customers.

[![Faraday pipeline creation](/images/blog/faraday_hightouch/faraday-hightouch-pipeline.png)](/images/blog/faraday_hightouch/faraday-hightouch-pipeline.png)

Now that you’ve applied your high-spender predictions and personas to your customers, create a deployment to sync them to your data warehouse.

[![Faraday pipeline deployment](/images/blog/faraday_hightouch/faraday-hightouch-deployment.png)](/images/blog/faraday_hightouch/faraday-hightouch-deployment.png)

### Create custom audiences in Hightouch Customer Studio based on Faraday predictions

Hightouch offers a visual audience builder that makes it easy to define custom segments based on your business criteria. For example, you could build an audience of customers who left an item in their cart but did not complete a purchase. You can similarly build audiences based on your predictive scores from Faraday, to turn those predictive scores ultimately into different user groups that you’ll take different action with.

[![Hightouch Customer Studio gif](/images/blog/faraday_hightouch/faraday-hightouch-customer-studio.gif)](/images/blog/faraday_hightouch/faraday-hightouch-customer-studio.gif)

From there, Hightouch uses reverse ETL to sync your customer predictions to your preferred activation platform, like HubSpot, Iterable, or Mailchimp, where you can create a personalized campaign to target your high-value customers. You can sync additional fields from your warehouse to these platforms, to fully utilize the personalization strategies that you’ve devised for these customers with Faraday. You can set up these syncs on a recurring schedule as needed, so your personalization campaigns can refresh as you run new predictions through Faraday.

**Ready to achieve true personalization at scale?** [Create a free Faraday account](https://faraday.ai/signup), and [book a demo with Hightouch](https://hightouch.com/demo?ref=/).

Faraday and Hightouch partner to help brands put AI to work, creating highly-tailored customer experiences.

Hyper-personalizing customer journeys with AI

**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

These days, marketers are looking for every advantage to help them stand out from the crowd. From [personalization](/blog/predictive-personalization-machine-learning) to utilizing AI tools like ChatGPT to lend a hand with copy, consumers are getting more picky about which brands they interact with, and it’s forcing brands to adapt. One such advantage makes use of predictive analytics and machine learning to enable brands to stay one step ahead of their customers and their competition: propensity to buy.

## What is propensity to buy?

It’s likely that either you or someone on your team has found themselves muttering “I wish I knew whether or not this lead is going to be a waste of time,” or “if only I knew this customer was about to churn” at some point recently. Predicting propensity to buy–or propensity, in short–is using a machine learning algorithm like [random decision forest](/blog/propensity-models-predict-customer-behavior) to predict how likely someone is to take an action. And it doesn’t just apply to new customer acquisition, or even just lead conversion. You can predict the propensity for virtually _any_ action, meaning that your sales and marketing tactics become proactive rather than reactive.

## Benefits of propensity to buy

Propensity to buy models can be applied to an incredible variety of use cases that provide real, actionable insights for the modern marketer. Let’s take a look at three.

### Market sizing

Expanding your brand’s reach into a new territory is inherently risky. Manual market sizing analysis can help mitigate the risk, and smaller campaigns can test the waters and see if there are any bites from your ideal customers, but then you’re spending both time–your most valuable resource–and money, on a strategy that may or may not pan out. Sure, brands have been doing that forever–but with propensity to buy models [removing the guesswork in a market sizing analysis](/blog/bottom-up-market-sizing-predictive-analytics), it can be done accurately and with confidence.

### Ad targeting

Similarly, propensity to buy models can be applied across the entire U.S. population, providing [likely buyer scores](/predict/category/likely_buyers) that let you easily filter out the bad fits. By eliminating the bad fits that were never going to buy in the first place, the extra spend that would’ve gone toward showing them ads that weren’t relevant can go toward ones that resonate perfectly with your ideal customers.

### Retention campaigns

The effectiveness of propensity to buy extends beyond acquisition campaigns–they’re equally as important and effective for retention as well, through [propensity to churn models](/blog/churn-prevention-machine-learning). The dream of knowing that a customer’s going to churn before they make that critical decision becomes a reality through actionable churn scores. The higher the score, the more likely they are to churn, giving your team crucial insight and warning to keep them on board through a discount, promo offer, or some other attractive deal.

## How do propensity models work?

Let’s take a look under the hood: how does it work? Propensity models use machine learning algorithms to pore over your customer data to find historical patterns in it. Customers placing orders, churning, [ordering again](/predict/category/repeat_purchasers), and more are compared and analyzed to compare each person to another for any similarities. Often utilizing algorithms like random decision forests or logistic regressions, propensity to buy models–while data-hungry, resource-intensive, and time-consuming–offer an attractive variety of impactful use cases.

## How to build propensity to buy models

You can build models to predict propensity to buy and use those scores directly in your stack to gain the benefits that come with knowing when a lead is [likely to convert](/predict/category/lead_scores), a customer churn, or some other use case. Let’s dig into some key aspects of building propensity to buy into your stack.

### A strong foundation of data

Data is the most important feature of propensity to buy and customer predictions. Without a strong enough foundation, the predictive models that your data science team builds won’t have enough data to predict strongly off of, leaving you with questionable-at-best insights. You’ll want a strong mix of [first-and-third-party data](/blog/first-party-vs-third-party-data-marketing), and that data should include demographics, psychographics, lifestyle, property, financial, and more so that your data is well-rounded.

### Data science to build predictive models

With your data locked in, you need data scientists to build predictive models. Regardless of which machine learning algorithms your team uses, they’ll need time to build the models and for the models to iterate–machine learning needs time to learn the data that it’s working with, and will grow smarter the longer the algorithms run. This often takes anywhere from 5 months to over a year.

### Systems integration to fit in your stack

The last major pillar of building propensity to buy models is finding a place for it in your stack. You can have propensity to buy scores sent directly to your database or warehouse, to your CRM, or any other activation platform. Depending on where you decide to implement, you’ll need to build an integration of some kind that enables your workflows to continue uninterrupted–a process that can take several weeks to complete.

## How Faraday can help implement propensity to buy in your stack

Faraday is purpose-built to cut the build-it-yourself timeline of building propensity to buy down from monthslong down to mere days. With Faraday, your brand has access to [built-in, cookieless, permissioned consumer data](/features/data) on nearly every U.S. adult, providing rich insight into your customers, and a data science team that has been at it for over a decade. Propensity scores for any action your brand is interested in can be embedded anywhere in your stack through hundreds of [pre-built integrations](/integrations) to place your predictions exactly where you need them.

### Predict propensity to buy to stay proactive

Make use of Faraday’s propensity models to predict–in just a few clicks–who’s (or where’s) most [likely to buy](/predict/category/likely_buyers), who’s likely to churn, what leads are most likely to convert, & more. Propensity to buy models with machine learning make your brand proactive so that you’re always delivering the right message at the right time.

[![Faraday propensity to buy outcome](/images/blog/propensity_to_buy_machine_learning/propensity-to-buy-machine-learning-outcome.png)](/images/blog/propensity_to_buy_machine_learning/propensity-to-buy-machine-learning-outcome.png)

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

Using propensity to buy models to target your most likely-to-buy customers is an attractive prospect, but it can be a long, resource-draining process. Read about how customer prediction platforms can drastically shorten your time-to-value.

Predicting a customer’s propensity to buy using machine learning

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

---

## Difference between first and third party data

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

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

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

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

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

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

### Customer insights

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

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

### Predicting customer behavior

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

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

## First party data collection techniques

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

### Web forms & surveys

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

### Loyalty programs

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

### Online engagement

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

## First party data enrichment

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

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

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

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

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

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

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

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

### Predict propensity to take virtually any action

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

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

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

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

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

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.

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