If you're in marketing or sales, you've probably already heard of the [Telephone Consumer Protection Act (TCPA)](https://www.twilio.com/docs/glossary/what-is-telephone-consumer-protection-act-tcpa), which regulates how businesses can communicate with consumers through telemarketing calls and texts. You also probably knew that in 2023, they introduced a new [1:1 Consent rule](https://docs.fcc.gov/public/attachments/DOC-408396A1.pdf), which aimed to close the [lead generator loophole](https://www.orrick.com/en/Insights/2023/12/FCC-Closes-TCPA-Lead-Generator-Loophole-Requires-One-to-One-Consent?utm), tightening consent requirements and limiting how consumer phone numbers are distributed. This would have required businesses to solicit individual consent from each consumer they planned to contact by phone.


## So, what happened to the rule?

Although originally scheduled to take effect today (January 27, 2025), at the last minute, a postponement was announced, pushing the effective date to January 2026\. However, just hours after this delay, the [Eleventh Circuit Court vacated the rule entirely](https://natlawreview.com/article/eleventh-circuit-vacates-tcpa-one-one-consent-rule-immediately-after-fcc-postpones), eliminating its implementation altogether. So for now, the existing TCPA consent requirements remain unchanged.

**With the TCPA 1:1 Consent rule:**

For companies that rely on purchasing large lists of leads to grow, this rule would have required a significant shift in strategy. By curbing the "spray and pray" approach of casting a wide net with unqualified prospects, businesses would need to pivot toward precision targeting and engagement with smaller, more qualified audiences. While this might initially seem like a challenge, it offers a tremendous opportunity to refine marketing campaigns and focus resources on higher-value prospects.

The benefits of this shift would be multifold. Businesses would see an increase in ROI from more intentional and personalized campaigns, as every interaction would be designed to resonate with the recipient. Consumer trust would also improve, as individuals would have a clearer understanding of who is contacting them and why. The era of vague, broad-based consent would give way to meaningful, 1:1 engagement that prioritizes quality over quantity.

This approach would not only reduce waste but also empower companies to connect with the right consumers at the right time. While these changes might seem daunting, they would pave the way for more effective, trust-driven marketing—a strategy that businesses could turn into a competitive advantage with the right tools and expertise.

**Without the TCPA 1:1 Consent rule:**

We’re back to the world of massive, unqualified lists. Without the additional compliance requirements, businesses can continue to cast a wide net in their telemarketing efforts. However, this approach risks higher opt-out rates, lower engagement, and potential reputational harm if messages are perceived as spam. 

## You don’t need to wait for the government to tell you try smarter targeting, here’s how Faraday can help today

Whether the 1:1 Consent rule returns or not, targeting remains a critical factor in marketing success. At Faraday, we prioritize quality over quantity, helping businesses focus on the most qualified prospects instead of chasing unqualified leads. With our predictive tools, companies can:

- **Target the right consumers**: Faraday can analyze market trends and [customer data](https://faraday.ai/features/faraday-identity-graph) to identify the [consumer segments](https://faraday.ai/templates/customer-targeting) most likely to show interest in engagement. This ensures companies can focus their efforts on earning explicit consent from likely-to-convert individuals.  
- **Use the right tools**: With fewer leads available, focusing on the right prospects is more crucial than ever. For instance, instead of focusing on [lead rejection](https://faraday.ai/templates/lead-rejection), since the lowest common denominator of leads won’t be there already, use that budget instead on tools like [lead routing](https://faraday.ai/blog/how-to-use-ai-to-deliver-a-better-quality-pipeline) to make sure the new leads make it to the ideal sales rep and [adaptive discounting](https://faraday.ai/templates/adaptive-discounting) to pique their interest.  
- **Maximize value:** After you win your best-fit customers, leverage the various predictive tools available through Faraday like [churn scores](https://faraday.ai/blog/churn-prevention-machine-learning) and [product recommendation](https://faraday.ai/templates/next-best-offer) to ensure that you maximize their lifetime value.

Regulatory changes may come and go, but Faraday’s data-driven approach ensures that businesses stay ahead, delivering effective and efficient marketing strategies no matter the landscape.

## Need help navigating this change?

The world is full of unexpected outcomes, like the 1:1 Consent rule getting canceled hours before it went into effect\! At Faraday, we’re ready to help you adjust your strategy to target the highest-quality leads and stay ahead of industry trends regardless of how regulations change. Reach out today to ensure your lead generation stays ahead of the curve\!

The TCPA's new 1:1 rule would have required individualized consent to contact a lead by phone but was vacated at the last minute.

TCPA 1:1 Consent rule vacated, but smarter targeting with Faraday is already here

Restaurants love Toast because it focuses on _their_ problems:

<a href="https://pos.toasttab.com/blog/il-primo-pizzeria" target="_blank"><img src="/images/blog/new-imperative-for-vertical-saas-built-in-ai/toast-pizzeria.png" width="372px" /></a>

Toast is a VSaaS (vertical software-as-a-service) - software **purpose built for unique industry workflows**. This means faster time to value compared to generic "horizontal" solutions. For example, Toast has a dedicated "Pizza POS" system with [easy topping swaps!](https://pos.toasttab.com/restaurant-pos/pizza-pos) A pizzeria wouldn't want to reinvent that wheel - but they also wouldn't want to pick a POS system that doesn't support it.

For the roofing industry, you _could_ customize Salesforce to track "Roof Area" - or you can use a [roofing CRM like Acculynx](https://acculynx.com/). There is a wonderful explosion of examples like this - companies that build exactly what their clients need, because they ONLY build for those clients:

- [bloomerang](https://bloomerang.co/) - for non-profits
- [mindbody](https://www.mindbodyonline.com/) - for wellness centers and spas
- [ALINE](https://alineops.com/) - for senior living
- [archy](https://www.archy.com/) - for dental offices
- and hundreds more.

With growth comes competition, and the question is: what's next for Vertical SaaS?

I would argue it is **incorporating AI features** that are specific to their industries. For VSaaS companies that haven't yet incorporated an AI feature, or are [looking to upgrade beyond first party data-powered features](/blog/why-first-party-data-and-rfm-is-not-enough), there are many options on the market. Here, we organize them into 3 levels - purpose-built, data warehouse-native, and low-level API. Each is useful for different use cases and organizations.

## Traditional focus on control points

Best-in-class VSaaS players have been focusing on [capturing "control points"](https://www.tidemarkcap.com/vskp-chapter/control-points-2-0). The control points are "back office" and "front office." Back office is the system of record, and VSaaS often starts by trying to become this - the "last system you turn off". Acculynx tracks roofing jobs and leads, while another Xero, an accounting VSaaS, [keeps the books](https://www.xero.com/us/security/data-protection/). From there, they can focus on integrating with or directly providing front-office technology - whatever the employee or customer interacts with. Every adult in the United States has probably held a [Toast POS](https://pos.toasttab.com/hardware/toast-go) in their hand.

## The new imperative - built-in AI features

What's next for VSaaS? How can you extend further into the value chain - or compete with an incumbent who has captured a control point? How can you [avoid ChatGPT-based extinction](https://www.tidemarkcap.com/post/ai-extinction-or-evolution)? One possibility is adding AI features specifically tuned for an industry, which only VSaaS can provide.

There are at least 2 reasons VSaaS is well positioned for AI.

- **Data**. As a system of record, a VSaaS might be the _only_ place a business can get the data it needs to make a critical decision or prediction.
- **Semantic layer**. Since VSaaS understands the semantics of its vertical - in other words, it knows what pizza toppings or roof sizes _mean_ - it can automatically build and use AI models.

As [Tidemark](https://www.tidemarkcap.com) puts it, **"flip your product from being reactive to proactive."** For example, Acculynx rates every roof lead 🟢 🟡 🔴 and [doubled conversion rates for clients who focused on green-light leads](https://www.prweb.com/releases/2023/4/prweb19276673.htm).

## Taking the first step

Assuming building AI into their product is critical path, VSaaS vendors must decide where to start. It can be helpful to think of the options as organized into levels from most out-of-the-box to lowest level. Different VSaaS companies will find different levels to be the most appropriate.

![](/images/blog/new-imperative-for-vertical-saas-built-in-ai/level-advantages.png)

### Level 2 - easiest - purpose built AI

The most accessible way for VSaaS to incorporate AI into its products is to adopt an existing lower-level component whose unit of prediction is the same - one that is purpose built for predicting against people, products, or search. For example, any system that deals with people can build predictions about their behavior by using Faraday's [business outcomes](https://faraday.ai/docs/product/outcomes). A system in charge of inventory can add intuitive AI-based search by incorporating Bloomreach's [product search](https://documentation.bloomreach.com/discovery/reference/product-search-category-api). Systems for managing creative assets can build next generation functionality by incorporating AI features like Cloudinary's [automated image editing and generative background replacement](https://cloudinary.com/products/cloudinary_ai).

### Level 1 - data warehouse native

Going a level down, VSaaS looking to develop its own AI capacity outside of what's already on the market in level 2 should evaluate the machine learning components [built into data warehouses](https://cloud.google.com/bigquery/docs/bqml-introduction). These are not just for one-off analyses; entire applications can be developed on top of them. This level sits at the sweet spot between specific units of prediction in level 2 and raw HTTP APIs in level 0. Generally controlled by SQL commands, they treat structured, unstructured, and even [file/object data](https://cloud.google.com/bigquery/docs/annotate-image) as tables and allow feature engineering, modeling, evaluation, and prediction as SQL queries. They are designed to process data at any scale, automatically shuffling data and managing memory without requiring manual partitioning or configuration by users. So, if you build a solution on top of them, you get scaling essentially built in.

Almost all data warehouses now allow you to do [generative AI](https://faraday.ai/blog/quickstart-use-bard-in-bigquery). This is much more attractive for batch use cases than creating error-prone HTTP connections to LLM APIs across many rows of data (you wouldn't use this for building chatbots that require low latency - see level 0). Don't forget about predictive use cases too - [time series modeling](https://towardsdatascience.com/time-series-forecasting-in-snowflake-using-sql-6ce7cedce862), for example, is available on all of the major data warehouses and allows sophisticated projections with built-in and custom holidays. [SQL-based classification and regression](https://cloud.google.com/bigquery/docs/reference/standard-sql/bigqueryml-syntax-create-boosted-tree) using XGBOOST and random forest are bread-and-butter use cases that fit perfectly with tabular input and output data; once you've done feature engineering with standard SQL functions, it's hard to imagine a simpler way. [Unsupervised clustering](https://docs.aws.amazon.com/redshift/latest/dg/tutorial_k-means_clustering.html) is also available.

### Level 0 - raw API

A very rich set of services, from Google's [Vertex AI](https://cloud.google.com/vertex-ai) to [OpenAI's Enterprise-friendly models](https://openai.com/enterprise), exist as HTTP APIs. This is the lowest common denominator and is accessible from all systems. It is also particularly appropriate for just-in-time calculations such as generative prompts used by chatbots or online fraud predictions using random forest models mounted alongside [feature stores](https://cloud.google.com/vertex-ai/docs/featurestore) for instant response. When planning an AI feature for a VSaaS, it might seem necessary to master an entirely new API. However, low-level systems are often better suited for specific real-time use cases, while asynchronous or batch workflows should first explore the potential of level 2 or level 1 solutions.

## Choosing a level

A few questions can suggest where to start:

- Is there already an out-of-the-box prediction infrastructure for your use case? Companies should always evaluate level 2 purpose-built solutions before committing to building low-level systems.
- What is the time between a prediction being created and its use? For example, if you're incorporating search, people expect results immediately; using an existing level 2 vendor may be the fastest approach. However, if no solution aligns with your industry needs, you may need to skip Level 1 and develop a [level 0 solution](https://cloud.google.com/vertex-ai/docs/predictions/get-predictions). Conversely, if you plan to rank a lot of records in asynchronous batch use cases, a level 1 solution is likely sufficient.
- Is there industry data that can extend a client's own data? For example, fraud detection vendors incorporate geographic fraud data in their scores. Faraday has a [national dataset of all US consumers](https://faraday.ai/features/data) that automatically enriches predictions made on its platform.
- What kind of scrutiny will your AI receive? For example, if you are making predictions about people, consider how you will implement [Responsible AI](https://www.brookings.edu/events/what-is-responsible-ai/). Some level 2 vendors [automatically build in reportable controls and metrics](https://faraday.ai/features/responsible-ai); some level 0 vendors [let you build your own](https://www.datarobot.com/blog/bias-mitigation-with-datarobot/).

## The imperative

If you were starting a VSaaS product from scratch, what would be your wedge? Some form of AI is a good guess. And if you are a VSaaS incumbent, how do you defend your position? You have an advantage in the data and semantic layer you already hold. A thoughtful approach to AI infrastructure options can boost your efforts and minimize your time to value.

Vertical SaaS (VSaaS) is software built exactly for a particular industry. The next generation of VSaaS is incorporating exactly the AI that people need.

A new imperative in vertical SaaS: built-in AI features

Companies are looking for the [fastest way to build AI into their software](/blog/new-imperative-for-vertical-saas-built-in-ai). Using techniques like RFM ([recency-frequency-monetary](<https://en.wikipedia.org/wiki/RFM_(market_research)>)), many are implementing AI features that learn from how people use websites, respond to emails, and pay for products. This process is called "first party data-based modeling". Companies like [Twilio](https://www.twilio.com/blog/first-party-data-marketing) and [Klaviyo](https://www.klaviyo.com/blog/predicting-churn-risk-our-new-model) have used this simple approach to rapidly add features to existing large products.

At the same time, increasing privacy regulation (did you know [Colorado](https://leg.colorado.gov/bills/sb21-190) and [Virginia](https://law.lis.virginia.gov/vacodefull/title59.1/chapter53/) have their own versions of [CCPA](https://oag.ca.gov/privacy/ccpa)?) and the [retirement of "third party cookies"](https://developer.chrome.com/en/docs/privacy-sandbox/third-party-cookie-phase-out/) have made companies [think "first party data only" is the only option left](https://digitalmarketinginstitute.com/blog/why-first-party-data-is-more-important-than-ever-and-how-to-use-it). It turns out this is not quite right - the very same transaction and customer records that make up first party data can be linked to [traditional, legal, consented offline data](https://www.acxiom.co.uk/blog/understand-your-online-and-offline-data-to-build-defined-data-strategy/) to create more powerful technology. Over time, companies that start with RFM [upgrade to using offline data too](https://www.postpilot.com/lookalike-audiences).

# Problem

Why is first party data not enough? **The total information contained in a user's activity may be insufficient to predict their future behavior** - the model may be weak. Over time, repeat customers _may_ build up enough data to be useful. But new users will always be a mystery. And once a first party model is built, it must be constantly monitored for new patterns in relatively noisy data. In other words, building AI features into software but limiting them to first party data makes them weaker, less widely applicable, and less stable.

## Activity vs affinity

First party activity data is traditionally thought of in terms of "recency" (time stamps), "frequency" (aggregation over time), and "monetary" (revenue impact). Product choice can be added to this (this person bought a pillow!) Does this contain sufficient data to predict their next purchase or how much they will spend over their lifetime? Often, it does not. It would be more predictive to know, in addition to RFM, their [lifestyle preferences and financial resources](https://faraday.ai/features/data). Activity data is a relatively incomplete view into a person's overall affinity for a product.

## Cold start problem

The first time a person shows up at your business, you don't know anything about them - so you can't make any first party-based decisions about them. This is called the "cold start problem" with first party data. Over time, you will know more about their purchase behavior, activity on your website, opens/responses to emails, etc., and RFM models will become more powerful (though still not as powerful as the same model with lifestyle and financial data mixed in). However, if a software company's clients need to make predictions at first contact, first party data will not help.

## Noisy data

Activity data is noisy. Many random factors can affect when a user opens a browser, comes to your site, stops, buys a product, or opens an email. First party-based models must be able to adapt quickly to the same changing environment. In some industries, like fraud detection, the ability to update models in real time to address ever-changing threat actors is a key feature. In other industries, the need or capacity to constantly refresh models based on noisy data it not necessarily positive. For example, a model that uses lifestyle or financial aspects to predict next best product is likely to exhibit more stable performance over time, especially if it incorporates (but does not rely solely on) first product purchased and time spent on site.

# Solution

What possible solutions are there? One is to use offline data - a supply chain that has existed for decades. Ever since sewing magazines realized their mailing lists offered valuable insight into people's hobbies, they have been asking their readers for permission to share their data with marketing partners. In terms of regulation, this data is consented - unlike web scraping or even some forms of first party data. Sewing is only one of the many niches with dedicated providers who can derive affinity data from purchases. And every major data aggregator - who has relationships with all the niche providers - has a vested interest in defending its continued use and teams dedicated to adapting to changing laws.

There are a few challenges to using offline data directly. The contracting process is lengthy and requires a very secure infrastructure to receive and store the data - not something all companies have (or even want). Also, there is a lot of data - hundreds of columns. It can take data science teams years just to understand what data is valuable to each market segment and build repeatable workflows to create models. Even if this is done, issues like proxy discrimination and bias propagation arise - and may disqualify the product's use in certain industries.

Are we back to square one - first party data only? Not necessarily. AI infrastructure companies like [Faraday](https://faraday.ai) that have offline data built-in are solving contracting, security, model discovery, and responsible AI concerns with standardized components. The most important benefit is time to value - software companies feeling the imperative to add AI can release it an order of magnitude faster.

## Contracting

Traditionally, buying offline data has been a multi-month contracting process with "what the market will bear" pricing. AI infrastructure companies like Faraday have standardized contracts and usage-based pricing, making it affordable to try new AI product initiatives without high upfront data costs.

## Security

Data security requirements for holding offline data are high. A data breach may release not only customers' order history but also more personal data. Having a SOC 2 that specifically audits for the protection of consumer data is a common requirement. AI infrastructure companies store offline data on behalf of other companies, letting them use it in batch and real time use cases without ever coming into possession of it.

## Legal and regulatory

Holding offline data always increases legal requirements. For example, if you hold more than first party transaction and activity data, responding to CCPA (and other) data access and data deletion requests becomes much more expensive. It also reclassifies a company as a data broker, forcing it to register in many states. AI infrastructure companies respond to such requests on behalf of users and are registered data brokers so that other companies don't have to be.

## Time to value

Data scientists need time to figure out what data matters, but data usually arrives in an unusable state. It must be cleaned and reformatted. Different techniques are then used to measure each data point's value to the business. Experimental models are built in Jupyter notebooks; finally, the whole process is handed over to a development team to make ready for production. AI infrastructure companies can accelerate this process, for example, by automatically matching first party transaction data to offline data and generating information gain metrics and models that can be backtested, field tested, and iterated on.

## Responsible AI

In addition to general public concern about malicious use of AI, specific industry regulators and trade groups have been adding requirements. Longstanding requirements - such as fair housing and fair lending - apply to AI just as much as traditional methods. Other requirements - such as model transparency and elimination of proxy discrimination - take on new forms when using automatically generated models. AI infrastructure companies can provide these as out-of-the-box reporting and mitigation tools instead of individual company data science or compliance teams implementing them on a one-off basis.

# Don't leave money on the table

First party data is not a magic bullet; offline data has a steep learning curve. AI infrastructure providers like Faraday, Twilio Segment, and Snowflake can help add AI to existing products faster and with less complexity, while also bypassing the limiting first party data-only stage.

1st party data-based AI modeling struggles with limitations like noisy data and the cold start problem. By integrating 3rd party data through providers like Faraday, companies can enhance predictions and accelerate AI deployment.

Why first party data (and RFM) is not enough

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

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

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

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

## What is predictive lead scoring?

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

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

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

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

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

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

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

## How B2C companies use predictive lead scoring

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

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

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

- **Product recommendations:** If your predictive lead scoring system supports product-specific modeling, [you can predict which products or services a lead is most likely to purchase](https://faraday.ai/templates/next-best-offer). These product-specific lead scores enable marketing and sales teams to make informed product recommendations across relevant touchpoints.

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

## Core components of predictive lead scoring for B2C

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

### Historical lead data

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

### Lead enrichment data

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

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

### Machine learning capabilities

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

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

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

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

### System integrations

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

## Fundamentals of a good B2C lead scoring dataset

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

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

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

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

## How to implement predictive lead scoring with Faraday

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

The Special Ad Categories setting on Facebook applies to ads that promote content related to sensitive areas such as:

- **Housing** — Ads about real estate, home rentals, or housing-related services.
- **Financial products and services** (expanded category as of October 2024) — Ads offering credit cards, loans, or other financial services such as insurance and investment advice.
- **Employment** — Ads about job opportunities or career services.
- **Social issues, elections, or politics** — Ads related to social causes, political campaigns, or elections.

## Why the Setting Exists

This feature ensures compliance with laws and regulations that prohibit discrimination based on factors like age, gender, ethnicity, and location. If your company’s ad falls under these categories, Facebook limits your targeting options to promote fairness and equal opportunity.

### Key impacts on ad targeting

- No targeting by age, gender, or ZIP code.
- Location targeting is restricted to a 15-mile radius.
- No Lookalike Audiences: You cannot use Lookalike Audiences for Special Ad Categories. Instead, Facebook offers “Special Ad Audiences” based on similar behaviors but without using protected characteristics.
- Custom Audiences are allowed, but with additional restrictions: you can still use custom audiences, but you must certify compliance with [Facebook’s advertising standard](https://transparency.meta.com/policies/ad-standards/).

## Facebook’s October 2024 special ad category announcement

In October 2024, Facebook announced a few significant changes to special ad categories:

1. A new special ad category “[Financial products and services](https://www.facebook.com/business/help/510724041294968)” was introduced, replacing the previous Credit ads category. This expanded the type of companies and ads that were subject to special ad category limitations.
2. Certification that any ad set based on a customer list complies with Facebook’s advertising standards.
3. [Restriction on usage of shared custom lists](https://www.facebook.com/business/help/1452187872132363) for custom audiences.
   1. Ad accounts within the same business portfolio that share customer list custom audiences should make sure that all people with permissions to manage ad campaigns have the same business email domain. If the domains don’t match, then those shared customer lists will be unavailable for ads about housing, employment, and financial products and services.
   2. Usage of shared audiences from different business portfolios will not be permitted for ads about housing, employment, and financial products and services.

### Use of Faraday in Compliance with the Special Ad Category

Since the emergence of the Special Ads category, Faraday has supported businesses whose ad sets are subject to its rules and restrictions. The primary impact of Facebook’s October 2024 announcement is to increase the type of businesses that will be subject to these rules and restrictions.

If you are a business, whose Facebook ad sets are labeled a special ad category, Faraday recommends the following steps to ensure compliance of any custom list generated through the Faraday platform:

1. **Segmented audiences** — If you are using Faraday cohorts to generate a custom list, you should avoid building cohorts that specifically segment on restricted traits like age and gender, restricted locations like zip code, or other [prohibited information.](https://www.facebook.com/business/help/361948878201809?id=188852726110565)
2. **Modeled audiences** — If you are using a Faraday outcome (aka predictive model) to generate a modeled audience, you should:
   - Use trait blocking when you [build your outcome](https://faraday.ai/docs/academy/dashboard/making-predictions) to ensure that all traits associated with protected classes (e.g. age, gender, ethnicity, religion marital status, etc.) are not considered when building a predictive model. This ensures that your models will not knowingly consider any data that would violate Facebook’s policy against discrimination.
     ![Screenshot of Faraday dashboard](/images/blog/facebook-special-categories-1.png)
   - Use Faraday’s built in bias mitigation to account for implicit bias. Simply blocking traits from model training, does not account for the fact that there is often a correlation between other Faraday traits and certain protected class traits. [Faraday’s bias mitigation,](https://faraday.ai/blog/faraday-bias-mitigation-machine-learning) allows a user to neutralize or even correct for historical bias within their lead and customer base.

![Screenshot of Faraday dashboard](/images/blog/facebook-special-categories-2.png)

Please see [this blog post](https://faraday.ai/blog/how-to-optimize-ads-for-compliance-in-finance), for a more in depth look at common Faraday supported ad strategies that comply with financial regulations.

How to use Faraday for ads when you're in a special category

Facebook special ad category overview

I’m really happy to share some company updates with you, including new financing and additions to our leadership here at Faraday.

![Robin Spencer](/images/authors/robin.jpg)

First up is that [Robin Spencer](https://www.linkedin.com/in/robinbspencer/) has joined as COO. She arrives here from Clearbit, where she was COO for several years leading up to their acquisition by Hubspot in December. Robin had been working with us quietly behind the scenes for much of the year and has had a tremendous impact on our team and company strategy already. I couldn’t be more pleased that she’s now fully on board to steer our go-to-market teams and business operations through the next stage of scaling. I can already tell Robin is a game changer for us.

Here’s what she has to say about joining Faraday:

“It is special to return to Vermont and continue doing the work I love for a company built here. I'm grateful for the opportunity to work with a team that genuinely cares about our clients and each other. Faraday has a lot of potential, and I'm eager to help us realize it so we can support more companies.”

![Peter Levine](/images/authors/peter.jpg)

I’m also thrilled to announce that my friend [Peter Levine](https://a16z.com/author/peter-levine/) has joined our board as an independent board advisor. (While Peter is a partner at a16z, this position is in a personal capacity.) From his days as an operator to his role now at arguably the best tech venture firm in the world, Peter brings a massive amount of best practices knowledge on how great software companies grow and thrive.

I also asked Peter to share some thoughts on why he’s excited about Faraday:

“As soon as I met Andy and the team at Faraday I knew this company was something special. In a world where AI makes big promises but rarely delivers on them, Faraday has found a genuinely practical application that’s been helping thousands of brands for over a decade.”

Finally, we’ve closed some additional financing. Longtime Faraday ally Intercap, headed by board member Jason Chapnik, led the round as part of a coalition that included some of our earliest angels. Jason noted: “Faraday had been developing its technology for several years before the rest of the world caught on to AI. Now is the time for us to capitalize on our lead.”

Other founders will know that a startup is a community—and this remains the case even after almost 12 years. When longtime neighbors double down and new ones show up to help, really magical things can happen. 2025 is going to be a great year.

Robin Spencer joins as COO, Peter Levine joins the board, and we closed some additional funding.

New additions to the team, board, and financing

Faraday’s [customer behavior predictions](https://faraday.ai/features/customer-behavior-predictions) have a wide variety of use cases. From enhancing your customer engagement through personalization via [bespoke personas](https://faraday.ai/blog/how-they-did-it-bedding-brand) to embedding predictive lead scores in your [field sales app](https://faraday.ai/blog/salesrabbit) to increase rep productivity and drive growth, there’s no shortage of applications.

One application that’s proving to be more and more popular with our clients is geotargeting audiences based on the locations of physical stores and branches to enable personalized campaigns with a focus on retail shoppers. What this means is that these clients are able to specifically find the most likely buyers within a certain geographic radius around a point of interest, then run campaigns to draw people to their outlets, branches, and other physical locations.

In this guide, we’ll walk through how to create a geotargeted audience with Faraday.

## How to geotarget with Faraday

### Connect your data

To start, you’ll want to make sure that you have a [connection](https://faraday.ai/docs/abstractions/connections) created to wherever your data is stored. Doing this allows your customer predictions to seamlessly deploy back to your stack. If you’ve already got your connection created, double check that your [dataset](https://faraday.ai/docs/abstractions/datasets) is ready as well, as that’s how you’ll describe your data to Faraday–who’s who and what pieces of data mean what.

Both of these resources can be created with Faraday's API via the [createConnection](https://faraday.ai/docs/reference/createconnection) and [createDataset](https://faraday.ai/docs/reference/createdataset) requests, respectively. While these aren't required in order to complete the steps in this guide, they're required for you to be able to deploy your predictions directly back to your stack, or to add additional event-specific parameters mentioned in the [cohort section](https://faraday.ai/blog/how-to-create-a-geotargeted-audience-api#create-your-cohort).

### Create your place

In Faraday, you can create a point of interest–a **place**–around which you can create radius-based cohorts, or groups of interesting people. To do this, you’ll need your Faraday API key, which can be found in the _account_ tab of your [settings page](https://app.faraday.ai/settings/account).

[![Location of API key in Faraday](/images/blog/product/api-key-location.png)](/images/blog/product/api-key-location.png)

Once you’ve copied your API key, head over to the [createPlace](https://faraday.ai/docs/reference/createplace) request in API reference docs. Here, you’ll paste your API key into the `Token` field, then update the `addresses` field to include the address of the point of interest.

[![Location of createPlace API request in Faraday docs](/images/blog/product/geotargeted-audience/createplace-request.png)](/images/blog/product/geotargeted-audience/createplace-request.png)

A place can include more than one address, so if you’d like to take advantage of a radius around _all_ of your locations, you can add them all to a single place request within the `address` parameter. Here’s what your request might look like for a single address, though:

```bash
{
  "addresses": [
 {
   "city": "Burlington",
   "house_number_and_street": "431 Pine Street",
   "postcode": "05401",
   "state": "VT"
 }
  ],
  "name": "My Storefront"
}
```

Once your address info has been entered–don’t forget to give the place a name, like “My Storefront” above–then click **send API request** to create the place.

In the response window, grab the **place ID**, which you’ll use to create your radius-based cohort around your new place.

[![Location of createPlace API response in Faraday docs](/images/blog/product/geotargeted-audience/createplace-response.png)](/images/blog/product/geotargeted-audience/createplace-response.png)

### Create your cohort

With the place you’d like to geotarget your predictions around ready, you’re now ready to create the [cohort](https://faraday.ai/docs/abstractions/cohorts) of people that you’ll target with that place. To do this, you’ll use the [createCohort](https://faraday.ai/docs/reference/createcohort) request.

[![Location of createCohort API request in Faraday docs](/images/blog/product/geotargeted-audience/createcohort-request.png)](/images/blog/product/geotargeted-audience/createcohort-request.png)

In your request, you can optionally add parameters for things like `min_count` / `max_count` or `min_value` / `max_value` if you've created a connection and a dataset, but we’ll focus on the `place_conditions` parameter as that’s where you’ll specify the place you just created.

You can customize the cohort with `recency`, `frequency`, and `value` parameters based on the event you use to define the cohort, but for this example we’ll keep it simple and create a cohort using the following parameters:

1. A **name** for the cohort.
2. A **distance** of 10,000 meters around the place created above. Note that this value _must be in meters_.
3. The **place ID** saved from the createPlace request.

```
{
  "name": "Leads within 10km of storefront",
  "place_conditions": [
	{
  	"distance": 10000,
  	"place_id": "b1ab0efb-f732-478f-8310-6882383c4107"
	}
  ]
}
```

You can also optionally include an [event stream](https://faraday.ai/docs/abstractions/events) using the `stream_name` parameter if you’d like this radius-based cohort to also filter people that have completed an event of some kind.

With the createCohort request ready, click **send API request**, after which you should see a **201 created** response for a success.

Voilà, you’ve got a radius-based cohort created based on all your leads within 10km of your storefront.

With it, you can add it to a [pipeline’s](https://faraday.ai/docs/abstractions/pipelines) _population to include_ in order to target this group of people with your predictions. Add both their likelihood to convert via [outcome](https://faraday.ai/docs/how-to/predictions/likelihood-to-convert) as well as their customer persona via [persona set](https://faraday.ai/docs/how-to/predictions/customer-personas) to maximize effectiveness for your next direct mailer.

A guide on creating a geotargeted audience using Faraday's API.

How to create a geotargeted audience with Faraday’s API

In this guide, we’re going to walk through the technical steps Boll & Branch, a prominent D2C linens brand, took to configure Faraday to make the [customer persona predictions](https://faraday.ai/templates/thematic-personalization) they needed, and how they activated.

## Integrating with their BigQuery warehouse

Boll & Branch has a wealth of customer data in their BigQuery warehouse, and Faraday’s seamless [integration](https://faraday.ai/integrations/predictions-with-bigquery) with BigQuery was the first step in connecting their data with Faraday. The only info they needed on their end to get this connection set up was the ID of their project in BigQuery and the dataset’s name.

[![Boll & Branch's connection configuration in Faraday](/images/blog/howto/boll-and-branch/boll-and-branch-connection.png)](/images/blog/howto/boll-and-branch/boll-and-branch-connection.png)

From there, they created a Faraday [dataset](https://faraday.ai/docs/abstractions/datasets) to describe their data: which rows in their data represent what pieces of customer data? Creating this dataset served the brand two purposes, both key cornerstones in powering their customer predictions:

1. It enabled Faraday to identify people in their data through an identity set.
2. It pointed Faraday at the [events](https://faraday.ai/docs/abstractions/events) occurring in their data, which is how they’ll make their predictions–more on that later.

[![Boll & Branch's dataset configuration in Faraday](/images/blog/howto/boll-and-branch/boll-and-branch-dataset.png)](/images/blog/howto/boll-and-branch/boll-and-branch-dataset.png)

In their identity set, the team at Boll & Branch matched the people in their data–their customers in this particular use case–into the [Faraday Identity Graph](https://faraday.ai/features/faraday-identity-graph) by telling Faraday which rows in their data represented personally identifiable information (PII): name, email, physical address, and so on.

They then defined two events:

- “Customers,” which looked at the date of someone’s first order to define them as a customer.
- “Orders with product category,” which pointed toward an event property that specified which category in their catalog the product purchased is in. They planned to use this to see which category their customers were purchasing from so that they could offer recommendations.

## Defining their customers

With their data connected, the next step was to create [cohorts](https://faraday.ai/docs/abstractions/cohorts) of interesting people, or people that they wanted to make predictions on. As their team was initially focused on identifying who their current customers really are as people and consumers, their [_Customers_](https://faraday.ai/docs/how-to/cohorts/customers) cohort was where they began.

The brand defines their customers as people that have made a purchase, and so they chose the “customers” event that they created earlier on in their dataset to define their _Customers_ cohort.

[![Boll & Branch's cohort configuration in Faraday](/images/blog/howto/boll-and-branch/boll-and-branch-cohort.png)](/images/blog/howto/boll-and-branch/boll-and-branch-cohort.png)

Creating this cohort grouped everyone in the brand's data that had made a purchase together, and any time a new customer made a purchase, they’d automatically join this _Customers_ cohort thanks to the connection to BigQuery they made.

## Predicting customer personas

Now that their pieces were in place, Boll & Branch's team was ready to make their first prediction: [personas](https://faraday.ai/docs/abstractions/persona_sets). They navigated into [persona sets](https://app.faraday.ai/personas) and created a new persona set using the _Customers_ cohort they just created.

[![Boll & Branch's persona configuration in Faraday](/images/blog/howto/boll-and-branch/boll-and-branch-personas.png)](/images/blog/howto/boll-and-branch/boll-and-branch-personas.png)

In taking a look at the freshly-clustered persona set for their customers, they were able to quickly identify that their demographic didn’t skew quite as old as they thought it did. Additionally, they found that their highest-value and largest persona at 55% of their persona set, nicknamed “Jen,” were heavy social media users through Faraday’s relevant traits like `Active on social media` and `Instagram user`. This insight gave them clear direction as to where they should focus marketing efforts.

## Deploying their predictions with a pipeline

Analyzing their persona set within Faraday’s Dashboard gave their team a high-level overview of their customer makeup, and they were soon ready to deploy their predictions back into their BigQuery warehouse for activation. To do that, they needed a [pipeline](https://faraday.ai/docs/abstractions/pipelines).

In their pipeline, they added their _Customers_ cohort to the **population to include**, as they wanted to make predictions on their customers. Next, they grabbed their _Customer personas_ persona set for their **payload** so that they could apply their persona predictions to all of their customers.

[![Boll & Branch's pipeline configuration in Faraday](/images/blog/howto/boll-and-branch/boll-and-branch-pipeline.png)](/images/blog/howto/boll-and-branch/boll-and-branch-pipeline.png)

While the pipeline was building in the background and persona predictions were being applied to their customers, the team moved on to deploying their predictions back to their stack with a [deployment](https://faraday.ai/docs/abstractions/deployments).

They knew they wanted to sync their predictions back into their BigQuery warehouse, so they created a referenced deployment, which was an easy way to sync data back into their stack via a reference key–a way for them to specify which column in their data Faraday should use to match their predictions to the appropriate person.

[![Boll & Branch's deployment configuration in Faraday](/images/blog/howto/boll-and-branch/boll-and-branch-deployment.png)](/images/blog/howto/boll-and-branch/boll-and-branch-deployment.png)

With their reference key set as _email_, every customer had their persona applied to them, and their data was then synced back into BigQuery based on the email column.

Now that everything was in place on the Faraday end for their persona predictions, they turned off [preview mode](https://faraday.ai/docs/abstractions/pipelines#preview-mode) so that their pipeline would constantly stay up-to-date as new customers entered their _Customers_ cohort.

## Making practical use of their predictions

In BigQuery, Boll & Branch’s analysts were able to manipulate their Faraday deployment to discover which of their customer personas over-indexed on what products by tallying products-per-persona, what colors of products they preferred, and more. This was key for them, as it enabled them to recommend personalized [next best offers](https://faraday.ai/templates/next-best-offer). Once they were satisfied with their findings, they pushed their predictions out to [Iterable](https://iterable.com) \- their goal was to launch an email campaign with their newfound personalization insights.

Drawing on the analysis they performed on each persona in Faraday’s Dashboard and the post-analysis they performed for product recommendations, they created a different [Iterable List](https://support.iterable.com/hc/en-us/articles/204780549-Lists-Overview) for each persona, then applied copy & creative based on the traits they discovered in Faraday’s Dashboard.

The result of applying these persona predictions to their email efforts was a **30% lift in conversion rate**.

Part of our "How they did it" series, this entry highlights exactly how Boll & Branch predicted their customer personas in Faraday, then deployed their predictions to Iterable for email activation.

How to: enhance your customer data with rich personas

Our [Powered by Faraday](https://faraday.ai/solutions/powered-by-faraday) partners work with Faraday to supercharge their features with customer predictions, ensuring their customers receive the best solutions on the market.

To accomplish this goal, they need a subaccount for each of their customers so that all data is siloed and secure. In this guide, we'll walk through the steps to create a subaccount.

## Finding your Faraday API key

The first thing you'll need in order to create a subaccount is your Faraday API key. You can find this in the Faraday Dashboard, under **Settings** in the left-hand nav, then **Account**. Click **copy** next to _production environment_.

[![Location of API key in Faraday's Dashboard](/images/blog/product/api-key-location.png)](/images/blog/product/api-key-location.png)

## Creating a subaccount with the Faraday API

Next, head over to our [API reference docs](/docs/reference), and specifically the [createAccount](https://faraday.ai/docs/reference/createaccount) request.

Here, you'll want to do three things:

[![Arrows pointing to three steps to create a subaccount in Faraday's reference documentation](/images/blog/product/api-request.png)](/images/blog/product/api-request.png)

1. First, paste your API key into the **token** field.
2. Next, replace "Acme Wind Turbines" with the name you'd like to give your subaccount. We suggest something like "Your Company Name - Customer Company Name."
3. Last, click **send API request**.

With these three steps done, your new subaccount will be accessible via the account switcher in the upper left corner of the Dashboard.

[![Location of account switcher in Faraday](/images/blog/product/account-switcher.png)](/images/blog/product/account-switcher.png)

A short guide on creating a subaccount using Faraday's API.

How to create a subaccount using Faraday's API

[Burrow](https://burrow.com) is an innovative furniture brand founded in 2016\. From starting out with thoughtfully designed modular couches to recently expanding their offerings to shelving, coffee tables, and even rugs, they have continuously pushed to understand their customers and cater to their needs.

## The goal

Burrow wanted to personalize its customer journey to drive better conversion rates and more purchases as they expanded their product lines.

## The challenge

Like most companies, Burrow had a cold start problem: they knew a lot about their current customers, but very little about brand new customers or potential customers who had signed up for their newsletter.

## Faraday \+ Iterable

Enter Faraday \+ Iterable. With [Faraday’s Identity Graph](https://faraday.ai/features/faraday-identity-graph), the Burrow team suddenly had access to 1,500+ data points on almost every adult in the US they could combine with their contact data. And with Faraday’s AI tools, the team could identify the patterns and trends of their customers to build segments of leads who looked just like them.

All of these insights then [flow directly to Iterable](http://faraday.ai/docs/integrations/iterable), where their automated journeys enable customer engagement across multiple channels with minimal manual effort. By leveraging automation, informed by Faraday, Burrow delivers timely, relevant content that enhances the customer experience while saving time and resources.

## The results

By partnering with Faraday, Burrow was not only able to know what all of their leads looked like, but which looked like customers who had taken specific actions in the past—whether it was to purchase from a specific category, spend a certain dollar amount, or even prefer a specific color couch over another.

In this way, the Burrow team could engage leads who previously only had provided an email address and deliver highly tailored messaging that matched their consumer profile, with suggested products based on what they were most likely to care about.

All of this messaging was delivered with Iterable, which ensured it was delivered at the right time, and by the right channel for an optimal engagement experience.

With Faraday \+ Iterable, Burrow is building a [predictive customer experience](https://faraday.ai/blog/predictive-customer-experience-is-the-future) that makes it enjoyable for everyone to shop—whether you’re a repeat customer or simply signed up on the newsletter.

Burrow used data and machine learning to power personalization and drive higher conversion rates.

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