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

**See our complete guide:** [Powered by Faraday — embedding predictive AI in your platform](/blog/powered-by-faraday-complete-guide-embedded-predictive-ai).

---

We are excited to announce that [AccuLynx](https://acculynx.com/), a leading provider of cloud-based business management software for roofing contractors, has launched a new Lead Intelligence feature that uses AI to predict homeowner buying behavior.

[Lead Intelligence](https://acculynx.com/roofing-sales-lead-intelligence/), powered by [Faraday's prediction API](https://faraday.ai/developers), taps into responsibly sourced consumer data and machine learning to enrich and score Acculynx customers’ leads every week, and provides users with up-to-date lead rankings that can help prioritize their sales efforts and close more business efficiently.

“Lead Intelligence is a game-changer for roofing companies,” said Mark Rattin, VP of Product at AccuLynx. “AccuLynx customers no longer have to guess which leads to call first, or only rely on their experienced sales people to quickly close deals. Now they can improve their sales win rates by focusing their efforts on the best leads.”

## Here’s how it works

Previously, Acculynx users could set a lead's priority level depending on what they thought of the lead. This was of course based only on a limited set of data that the user had in front of them and their intuition.

Lead Intelligence uses Faraday to analyze hundreds of data points on all of their customers’ leads, including demographic, financial, property data and more, which then provides a score that indicates the likelihood of a lead converting into a customer.

By leveraging Faraday's API, Acculynx was able to develop the Lead Intelligence feature in a matter of months, providing users with a game-changing tool included in all subscriptions and saving significant engineering and data science resources that would otherwise be required.

We at Faraday are proud to support Acculynx’s mission of providing roofing contractors with easy-to-use technology tools that help them work smarter and grow their business.

How AccuLynx uses Faraday’s prediction API to power a game-changing Lead Intelligence feature for roofing companies across the US.

Acculynx launches AI-powered lead intelligence with Faraday prediction API

If you have at least 2000 payments, Stripe data probably contains your buyer signal - the unique patterns that predict who will buy from you.

Last month, a friendly brand exported about 10,000 payment records from Stripe as a CSV. Then they uploaded them to Faraday and, within 24 hours, got their results:

![Buyer signal showing importance of different features](/images/blog/buyer-signal.png)

Let's focus on one feature - "Science/new technology." If a person is interested in this, they are much more likely to buy this brand's products:

![Likelihood to buy based on science interest](/images/blog/science-directionality.png)

Faraday is batteries-included - we know things like whether people like science thanks to our [built-in consumer data](https://faraday.ai/features/data).

Now, this brand can predict the likelihood of more than **240 million Americans** to buy their product. Faraday can take any list of people and sort it by likelihood to buy... or Faraday can generate the list in the first place, using a ["Deployment"](https://faraday.ai/docs/abstractions/deployments).

So... how did they get this? First, by doing a [Stripe Payment Report](https://support.stripe.com/questions/exporting-payment-reports). In particular, doing a [**Successful Payments**](https://dashboard.stripe.com/payments?status%5B%5D=successful) report.

![You want Succeeded payments](/images/blog/stripe-payment-export-succeeded.png)

Faraday only needs the following fields:

![PII, date, and amount fields](/images/blog/stripe-custom-columns.png)

(Later, you can set up an automated integration. But for now, since this is a relatively stable model based on inherent consumer characteristics, it should be stable for at least a few weeks.)

In Faraday, you map the fields:

![Address mapping](/images/blog/stripe-dataset-identity-mapping.png)

Then, [follow the standard process for importing transactions](https://faraday.ai/docs/cohorts/customers_from_hosted_csv#option-a-each-row-represents-a-transaction-event).

And finally, you make a [likely buyers model](/docs/abstractions/outcomes).

In total, if this takes longer than 15 minutes, [email the CTO](mailto:seamus@faraday.ai) because that's a critical bug!

Happy Signal Hunting!

-Seamus

PS. When this brand loaded our model into Facebook, we immediately started beating Facebook's internal model, even though we were still in learning phase...

![Beating Facebook](/images/blog/stripe-adset.png)

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

If you have at least 2000 payments, Stripe data probably contains your buyer signal - the unique patterns that predict who will buy from you.

How to determine buyer signal from Stripe payments

Purchasing jewelry is traditionally positioned around the moment. Whether it’s for romance or a special occasion, jewelry campaigns tend to focus on everything but the actual wearer. [gorjana](https://gorjana.com/) is the fine jewelry company that says otherwise by providing jewelry you’ll live in.

With this focus on the wearer, it comes as no surprise that gorjana wanted to know who their ideal customers were, so they teamed up with Faraday to validate their customer profile and reach more potential customers across digital channels and direct mail.

We recently chatted with [Elton Graham](https://www.linkedin.com/in/eltonxgraham/), Chief Marketing Officer at gorjana, to further explore their challenges and how they worked with Faraday to solve them.

## Gaining customer insights by clustering for personas

Previously, gorjana had a pretty good hunch about who their core customers were, but were looking to confirm and deepen their understanding of them with more data. In came Faraday, unlocking a wealth of data that they could leverage to not only confirm their profile, but validate their core customer segments with the people-first focus they were looking for.

To do this they leveraged the [Faraday Identity Graph](https://faraday.ai/features/data), enriching their first-party data with 1400+ responsibly sourced attributes on nearly every adult consumer in the U.S. With this combination of data, gorjana was able to build brand-specific customer personas using Faraday’s built-in machine learning capabilities. These quantitatively developed personas enabled the gorjana team to identify similarities and differences between their core customer segments to further understand how their retail-only, ecommerce-only, & omni-channel customers compared.

## Calculating total addressable market (TAM) with potential buyer predictions

[Faraday’s potential customer predictions](https://faraday.ai/features/predictions) are often used to create custom audiences for customer acquisition campaigns. The gorjana team took it a step further and leveraged those predictions to calculate their TAM and penetration rates in designated market areas across the U.S. This prediction-based TAM analysis is currently helping to inform go-to-market decisions and campaign strategies across the country.

## Reaching more potential customers across paid digital and direct mail

With a stronger understanding of their customers, the gorjana team leaned into their potential customer predictions to create custom audiences of high propensity to buy consumers for their digital ad campaigns. The team saw meaningful improvements in cost per acquisition and return on ad spend with these custom audiences from Faraday.

When asked what’s on the horizon for gorjana, their response was to expand their use of Faraday’s potential customer audiences to direct mail, and to build new predictive outcomes to identify likely gifters.

We love working with gorjana - they’re a people-first brand making fine jewelry for everyday wear. [Check them out](https://gorjana.com/) to find the perfect pieces for your jewelry stack.

At gorjana, Elton Graham’s team used Faraday to gain customer insights, validate core customer segments, and reach more potential customers with data-driven campaigns.

How gorjana fuels growth with responsible data and AI

AI matters when it makes something you already do work better. Faraday is a [Google Cloud partner](https://cloud.google.com/blog/products/ai-machine-learning/building-an-open-generative-ai-partner-ecosystem?ref=faraday-blog) that helps hundreds of US brands grow their revenue using AI. These brands did growth initiatives, lead scoring, LTV maximization, acquisition campaigns, personalization and churn reduction before Faraday—but with Faraday, they improve their margins, finding hidden signals that may cover every adult in the United States.

Built on [Google Cloud AI](https://cloud.google.com/products/ai?ref=faraday-blog), Faraday lets brands use the world's most advanced artificial intelligence (AI) and machine learning (ML) tools by choosing from tried-and-true recipes. Users sign into the self-service app at https://app.faraday.ai, pull key data from their existing data stacks, define their business objectives, and let Faraday - powered by Google Cloud’s [BigQuery ML](https://cloud.google.com/bigquery-ml/docs?ref=faraday-blog) - do feature engineering, model optimization, cross-evaluation, backtesting, bias detection, and scoring. What's more, Faraday is batteries-included, with built-in life event, demographic, financial, and property data on 240 million US adults. This consented, opt-in data is included by default in Faraday's models, without clients ever having to come into contact with privacy-sensitive data.

Seamus Abshere, Faraday CTO says that Faraday could only be built on Google Cloud:

"Google Cloud is 3-5 years ahead of the competition because its integration of AI with data warehouse is already mature. For years, our data scientists and developers have been working in the same system - BigQuery ML. Google Cloud's approach to AI breaks down traditional barriers between data analysis and software development. It makes our data scientists happy to directly develop infinite scale production systems," Abshere said.

Faraday works with customers like Boll & Branch, Advia Credit Union, Aspiration, Snap Finance, Tula Skincare, Ethos Life Insurance and ADT to grow revenue. Boll & Branch increased its DTC revenue using persona predictions, one of the options available in the Faraday app:

"Using Faraday, we saw a 30% lift in conversion with our personalized email campaigns," says Katia Unlu, Chief Digital Officer at Boll & Branch.

Advia Credit Union doubled auto loan conversions using Faraday propensity predictions, sending direct mail and email promotions to the best-fit members.

“Faraday’s platform gives organizations a framework for applying AI to improve critical programs like marketing or consumer campaigns,” said [Manvinder Singh, Managing Director, Partnerships](https://www.linkedin.com/in/manvinder/?ref=faraday-blog) at Google Cloud. “We’re delighted to support Faraday’s platform and growth on Google Cloud, and to make their platform more accessible to customers via the Google Cloud Marketplace.”

The Faraday platform was built on Google Cloud, and utilizes Google Cloud AI and ML services to deliver more capability and value to customers.

Faraday is now available in the Google Cloud Marketplace.

Faraday is a proud Google Cloud partner and uses Google Cloud AI to help US brands grow revenue with cutting-edge AI and ML tools.

Faraday gives clients access to Google Cloud AI using tried-and-true recipes

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

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

## Features of importance

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

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

## Directionality

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

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

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

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

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

Faraday model reports: Understanding feature importance and directionality

So many companies offer promises of health, beauty, and style, but few manage to deliver on all three. [Canopy](https://getcanopy.co) is the rare brand that truly does it all. They launched in 2020 with a line of reimagined humidifiers and diffusers designed to optimize your home and reinvigorate your wellness routine, and have been growing at breakneck speed ever since.

Growth has always been a top priority for the young DTC brand, which is why they teamed up with Faraday to accelerate their acquisition strategy even further. In particular, they knew that to hit their goals, they needed to better understand their customer base, efficiently scale their Facebook prospecting, and bolster sales during seasonal slow periods.

Within two months of using Faraday,Charlie Denby, Director of Growth at Canopy, saw improvements in CPA. Year over year, the results were even more promising: Faraday ended up contributing to a 251% reduction in Facebook CPA when comparing Canopy's Facebook audiences to their Faraday-enhanced audiences, a 172% increase in Facebook ROAS, and a 176% increase in new customer acquisition.

Take a look into how they did it, as we explore their intial challenges and how they leveraged Faraday to solve them.

## Developing rich customer profiles

The Canopy team always had a hunch about who their customers were, at least in terms of age and gender. But they needed a deeper level of customer insights in order to more efficiently target that audience at scale.

Canopy tapped into our consumer identity graph, which contains [opt-in consumer data](/features/data) on 240 million US consumers—all sourced without third-party cookies. By matching their data to ours, they unlocked a wealth of demographic, financial, and lifestyle insights about their customer base.

They also took advantage of Faraday’s machine learning algorithms to uncover four distinct customer personas that demystified the core audiences they wanted to target. These personas allowed them to definitively differentiate their skincare enthusiasts from, say, their wellness gurus; understand which customer fell into which category; and cater to each everyone’s unique needs.

“The impact of implementing Faraday can be felt very quickly across the organization,” Denby says. “It helped us really get a granular sense of who our customer is and where they are. It's also armed us with the ability to tailor messaging to the different segments within our existing customer base.”

## Scaling Facebook Ads around iOS 14

Like many digitally native brands, Facebook advertising is critical to Canopy’s acquisition strategy. However, that meant that the release of iOS 14, which drastically reduced Facebook’s advertising functionality, dealt them a major blow. Cost-per-acquisition immediately skyrocketed. “The prospecting looked like a different business,” Denby explains. “It just wasn’t working.”

Canopy started working with Faraday in the aftermath of the iOS update, hoping to use our AI-driven solutions to identify and reach their target audience in a more cost-effective manner. To achieve that, we built them a machine learning model to score every adult in the US based on their propensity (likelihood) of becoming a Canopy customer. Since our models are powered by our responsibly-sourced identity graph, they’re not impacted by the end of third-party cookies or the tracking limitations that Apple introduced in iOS 14.

With [propensity predictions](/features/predictions) at their disposal, Canopy was able to focus on finding creative ways to reach likely customers on Facebook. They ended up finding the most success when they combined Faraday’s propensity scores with Facebook’s native targeting capabilities. Faraday generated a list of high-propensity zip codes—places with the highest concentration of likely customers—and Canopy layered that zip-code-level targeting onto Facebook’s broad audiences. That way, campaigns were laser-focused on areas around the country that were predicted to be most valuable to them.

Right now, most marketing teams are scrambling to find alternatives to Facebook in the wake of iOS 14. But for Denby, it’s the opposite. He’s stoked to continue scaling. “So many brands have challenges there,” he points out, “We do not, and we do not because of Faraday.”

## Mitigating seasonal downturns using consumer predictions

Another challenge that Canopy faced was the extreme seasonality of their sales cycle. Demand for humidifiers naturally spikes in colder, drier months and plummets in warmer ones, meaning that Canopy has to get extra creative in the summer in order to offset slower sales.

One of the keys to Canopy’s success is their willingness to experiment and iterate until they find a strategy that sticks. This is the perfect mindset to bring to an AI solution, with its near-limitless opportunities. Over the summer, Canopy started testing more campaigns for Little Dreams, their line of baby products.

Although Canopy’s customer base has traditionally been adults with a passion for skincare and wellness, Faraday’s AI-generated personas helped them better understand a small subset of customers purchasing humidifiers for their newborns. With those insights in hand, Canopy leaned into this previously untapped audience, trying out even more of [Faraday’s audience augmentation solutions for Facebook](/predict/pipelines/likely_buyers_in_facebook).

One method involved using the Faraday Identity Graph to build audiences of new parents, uncovering thousands of potential customers that Canopy had never been able to reach before. Another used a variety of AI-driven audiences, such as Canopy’s “mom” persona, to seed Facebook lookalikes and uncover even more potential customers with the specific attributes that Canopy wanted to target.

Through these efforts, Canopy was able to tap into a lucrative new audience that they’d never fully explored. By more effectively targeting new parents on Facebook, in addition to leveraging their other high-propensity audiences, they’ve completely dodged the lull in sales that they’d come to expect in the summer. According to Denby, “Our prospecting now, in the month of July, is better than last October.”

## What's next for Canopy?

While continued growth on Facebook remains a priority, Canopy is preparing to bring AI-driven predictions into other channels as well. Their next test will involve predicting customer churn with Faraday and looking to re-engage those customers via email.

We love working with Canopy—they’re an endlessly creative brand making countless homes healthier and happier. [Check them out](https://getcanopy.co) to get your hands on their latest expert-approved, environmentally friendly devices!

Learn how Canopy improved Facebook prospecting after iOS 14, plus overcome seasonal slow periods in sales, with Faraday's predictive capabilities.

How Canopy scales acquisition and mitigates seasonal downturn

While data security has always been an important part of doing business, in recent years it’s become an increasingly urgent consideration. Public scandals, like those of [Facebook and Cambridge Analytica](https://www.wired.com/story/cambridge-analytica-facebook-privacy-awakening/), and growing government regulations, to the tune of GDPR and CCPA, have consumers on high alert about how their data is being used.

If you find yourself among the many brands looking for ways to build trust with your audience, you’ll need to consider not only how you manage data security at your organization, but also how you communicate with your clients. Transparency is an important part of security—clients and collaborators alike should be able to quickly and easily understand what you’re doing to protect their data. The more effectively you achieve that, the more trust you’ll be able to build.

Read on to explore the three most common questions our customers ask us (and might ask you!) about data security, and how we answer them. Feel free to use these as a blueprint to guide your own practices, though the particulars will vary from industry to industry.

## “How is your company audited for data security?”

_Our answer: Check out our [SOC 2 audit](/security)!_

If you offer a cloud-based product or service, and want potential customers and collaborators to see you as a trusted partner, a clean SOC 2 audit should be your gold standard. SOC 2 audits are exhaustive reviews performed by certified public accountants to ensure that SaaS companies meet strict security requirements and provide a safe operating environment for managing sensitive data. In order to retain a clean SOC 2 audit, companies must renew their audit annually.

The existence of SOC 2 as a process reassures clients, but it also benefits the company. As Seamus Abshere, Faraday co-founder and Chief Technology Officer, puts it, “SOC 2 audits provide a chain of trust going from the client all the way to Faraday and to our vendors (like cloud hosting providers). At every level, auditors are involved to ensure that policies and procedures are followed.”

## “How is my data transferred and stored?”

_Our answer: All data is stored with trusted cloud vendors (Amazon, Google Cloud), encrypted in transit and at rest, and access is controlled using multifactor authentication._

Simply put, encryption is a no-brainer. When customers share data with brands, they’re trusting them to protect it from bad actors, and encryption is the standard practice for doing so. “At this point, using unencrypted data is like a surgeon not washing their hands before surgery,” asserts Abshere. “If you are not encrypting everything, it's just malpractice.”

It's standard practice for clients and vendors to ask about encryption, but at this point, the answer should always be "yes."

## “What data do you actually use, and how?”

_Our answer: We live by the *[*“If we don’t need it, we don’t collect, store, or transmit it”*](https://faraday.ai/blog/using-ethical-data/)* mentality._

It’s essential for brands to understand what data they have access to in order for them to put the proper safeguards in place. Here at Faraday, where we handle the data of hundreds of leading brands, we’re extremely clear with clients about what we use—and what we don’t.

**“We don’t want your account numbers, social security numbers, health records — any of that,” Abshere says.  “All we need are known identifiers (like billing information) and transaction information. We do not require or accept account numbers, so we don't affect our client's PCI DSS scope.”** In our case, we leverage this personally identifiable information (PII) to match consumers with our identity graph and train machine learning algorithms to make predictions about a brand’s current or potential customers.

Another important step is to prevent any co-mingling of your clients’ data, particularly if your clients are competitors. At Faraday, we understand this is a serious concern, and we’re careful to prevent it. “Data is logically separated, and everyone’s predictions are based only on their own data plus Faraday's identity graph,” explains Abshere. "Mixing client data would be a huge risk."

## Security is always a priority

No matter your role at a given company, you’re always a steward of your customers’ data. Various teams, from security to engineering to marketing, may operate on very different datasets and roadmaps, but it’s crucial that everyone contributes to a trusted relationship with clients and collaborators, working together to efficiently and transparently implement best practices.

Transparency is an important part of your security practices—clients and collaborators alike should be able to quickly and easily understand what you’re doing to protect their data.

Building trust: How brands can answer common data security questions

Since 2009, [Momentum Solar](https://momentumsolar.com/) has been one of the fastest growing solar installers in the country, helping thousands of homeowners make the transition to solar.

## The problem: optimizing the lead scoring process

Momentum Solar relies on their state-of-the-art call center as their main conversion channel, reaching out to leads acquired from aggregators to set up on-site assessments for solar installations.

But Momentum didn't become one of the nation's largest installers by being satisfied with _good enough_.

A solar installation is a significant investment, and it can be easy to get cold feet. When a sales rep needs to dedicate an afternoon to an on-site visit that can be canceled at any time, the cost to acquire a new customer can get expensive quickly. To help offset those costs, the Momentum team was looking for areas of optimization with the goal of reducing acquisition costs. One area they looked at for improvement was a lift in their lead conversion rate. They believed that [better lead prioritization](/solutions/lead-management) that allowed their call center to focus on the best leads was key to driving this lift.

## The obstacle: an incomplete picture of a solar customer

Traditionally, Momentum had leveraged a rules-based approach to lead scoring that focused on a few data points, such as lead source, lead demographics, home ownership status, and location of residence, to determine lead priority. The challenge with a rules-based approach is these data points are often subjectively chosen, and don't account for the full spectrum of both intuitive and unintuitive data points that are key indicators of whether or not a lead will convert.

Additionally, Momentum had a large collection of leads that had not initially converted. A major issue with the rules-based approach to lead scoring is that it's static, even though people aren't. With Momentum's existing approach, a lead's score–and in turn, priority–would never change.

## Why Faraday?

### Fresh data + models

To optimize their lead conversion, Momentum used the Faraday platform to add a [predictive layer](/features/customer-behavior-predictions) to their lead prioritization system.

The value of predictive models is the quantitative, rather than qualitative, approach to lead scoring. Machine learning algorithms run millions of simulations to find patterns in large datasets, meaning they transcend subjective biases.

Instead of scoring a lead based on a few key points, Momentum used the Faraday platform to build a [lead scoring model](/blog/why-you-need-predictive-lead-scoring-today) that ranked leads based on their probability of becoming a customer. The model trained on Momentum's existing customer base and the [hundreds of first and third-party data points](/features/faraday-identity-graph) that Faraday associated with Momentum's customers.

Furthermore, the lead scores were updated as new customers' examples were added and lead behavior was observed. This ensured that Momentum's prioritization methodology was never static.

## The results

### Appointments set 33% faster

To test the efficacy of this new approach to prioritization Momentum split the scored leads into three groups: red, yellow, and green, with red being low probability leads and green being high probability leads. The goal was to see a _higher_ rate of lead-to-scheduled appointments rates for the yellow and green groups (comprising the top four deciles  of leads) and _lower_ conversion in the red group (bottom six deciles).

Each of these groups received the same call cadence and performance was measured against a baseline performance of 180 connected calls per scheduled appointment.

As expected, the green and yellow groups converted at increased rates compared to the baseline vs the red group, which converted at a lower rate. Specifically, the green group saw a **+191% lift in conversion rate** over baseline, while the yellow group saw a **+51% lift in conversion rate** over baseline. Equally as important, the red group converted 4% _lower_ than the baseline, further confirming where the call center should spend its time.

Ultimately, the call center needed **33% fewer outbound calls** to hit their scheduled appointment goal.

Momentum Solar used Faraday to add a predictive layer to their call center's lead scoring process, resulting in the call center needing 33% fewer outbound calls to achieve their appointment goal.

How Momentum Solar cut calls-per-appointment by 33% with predictive lead scoring

It should come as no surprise that digital marketing is ever-changing. In times like these, when social media reigns supreme and trends are rapidly evolving, it makes sense that good marketers constantly seek ways to evolve in terms of tools, techniques, and strategy. Although digital marketing trends rarely remain stagnant, there is one particular tool that continues to be popular across industries: the lookalike audience.

Whether you’re brand new to marketing or have been in the game for years, there are plenty of options for you to explore around lookalikes. New marketers may be wondering what a lookalike audience is and how it can help them reach a previously untapped audience; seasoned pros might feel like they’ve exhausted their options and need a new way to move the needle with their lookalikes.

Luckily, today’s ad platforms have made it easier than ever to mimic audiences and target them efficiently and effectively. And there are even more solutions, like predictive audiences built with enhanced first-party data and AI, that can take campaigns to a whole new level. Let's dive into the world of Facebook lookalikes in particular, since Facebook remains one of the most commonly used platforms in the digital advertising space.

## What is a Facebook lookalike audience?

We'll start with the basics—if this is old news to you, feel free to scroll down for more sophisticated tactics beyond traditional lookalikes.

[Lookalike audiences](https://www.facebook.com/business/help/164749007013531?id=401668390442328) allow Facebook marketers to target users that have similar interests to those of their current customer base. If you’ve had significant success advertising to a core group of current customers on Facebook’s ad platform, you may now be eager to find more users that share demographics or behaviors with those found in this core group. This is where lookalike audiences come in.

There are a variety of components you can use to build a lookalike audience, from similar demographics—such as age, location, and interests—to certain psychographics that influence buyer attitude. Lookalikes based on these criteria can prove extremely valuable when it comes to an ad campaign’s success because you already have a good sense of what's most likely to appeal to these potential new customers.

## How to create a Facebook lookalike audience

Creating a lookalike audience is a fairly simple process so long as you have access to the right tools. A Facebook ads account and administrator permissions, for example, are necessary to get started. You will also need to determine whether you need to install a Facebook pixel on your website. The pixel is a simple way to identify audiences that are interested enough in your product or service to visit your site.

To set up a lookalike audience on Facebook, follow these steps:

1. Head to your Facebook Ads account and click on **Audiences**
2. Find the **Create Audience** dropdown menu and select **Lookalike Audience**
3. Select a seed list of users, such as a Custom Audience built from 1,000 of your best customers
4. Select the country or countries you’d like to target
5. Choose the size of your audience
6. Select **Create Audience**

## Benefits of Facebook lookalike audiences

There are several benefits associated with implementing lookalike audiences in your digital marketing campaigns, which is why this remains such a popular method for reaching new audiences on Facebook and other platforms as well.

### Tracking

Optimized campaigns call for analytics and tracking. Lookalike audiences on Facebook make this process quite easy. With lookalike audiences, you can monitor progress and cost, evaluate the road to conversion, and adjust your campaign as needed.

### ROI

One of the most frustrating parts about advertising is not really knowing whether your investment will pay off. Sending advertisements to the wrong audience costs time, money, and frustration. Creating lookalike audiences is one way to increase the likelihood that real prospects will see your ad, all without wasting resources on users who may have very little interest in your product or service.

Keep in mind that lookalike audiences are particularly useful if you are already working with a strong customer base, since you can upload a list of customers that you know are valuable in order for Facebook to surface more people like them.

### Customizations

Lookalike audiences allow for impressive customizations that make targeting audiences more efficient than ever. This is because customizations allow you to adjust everything from goals to budget and even the percent of the population that you target with your lookalike. On that latter point, our recommendation is to keep the percentage low. This allows Facebook’s algorithm to hone in on a narrow group of people that most closely resemble your seed audience, which can [produce higher cost-person-conversion and clickthrough rates](https://adespresso.com/blog/adespresso-experiment-facebook-lookalike-audience/).

### Enhancements

To take it a step further, you might even consider using a lookalike audience builder to gain access to additional customizations beyond that of the Facebook platform. At Faraday, those customizations include enhancing your first-party data with [responsibly sourced consumer data](https://faraday.ai/features/data) to help you more effectively understand and segment your audience, as well as building custom lookalike audiences that can be used beyond Facebook.

## Using Facebook lookalike audiences effectively

Relevant, effective ads cost less than those that don’t connect with your audience, but you don’t want to spend time and resources on creating lookalikes just because you can. Incorporating lookalike audiences into your ad campaign must be strategic to elevate your product or service.

To that effect, you'll still need to hone your copy, creative, and CTA to determine what resonates most with your audience. Since lookalike audiences are generally used to expand your brand reach and identify potential new customers, they work best for top-of-funnel campaigns. Consider A/B testing your ads to better understand this new audience and keep moving them through the buyer's journey.

## Alternatives to Facebook lookalike audiences

There’s no denying that lookalike audiences are a powerful tool for brands. However, there are a few key caveats marketers should be aware of, and additional solutions that may work even better depending on your circumstances.

First, it’s important to keep in mind that Facebook is able to build powerful lookalikes because it identifies patterns in user data that it collects from third-party cookies. With cookies due to be phased out over the next few years, Facebook’s lookalike capabilities are likely to grow far more limited.

Additionally, Facebook’s lookalike audiences work only on the Facebook platform, making it difficult for marketers to craft strategic campaigns that target an audience across channels. Given that the average buyer’s journey contains [multiple touch points](https://www.thinkwithgoogle.com/intl/en-cee/consumer-insights/consumer-journey/study-reveals-complexity-modern-consumer-paths-purchase-and-how-brands-can-make-inroads/), that’s a significant hurdle to overcome in an increasingly noisy digital world where marketers are competing for limited ad slots.

Luckily, there are other options for teams that need a more sophisticated means of orchestrating the buyer’s journey. Predictive audiences are a particularly promising method, leveraging responsibly sourced consumer data and machine learning algorithms to craft highly accurate lookalikes that can be used across channels.

## Level up your lookalike audiences today

Whether you’re just starting to experiment with lookalike audiences or are ready to expand into predictive solutions, nothing beats diving in and testing out your audience options.

For Facebook’s lookalike audiences, you may want to test different segments as seed audiences for your lookalikes to determine which list works best.

Whether you’re brand new to marketing or have been in the game for years, there are plenty of options for you to explore around lookalikes.

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