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

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 1500+ 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

[Sealed](https://sealed.com) is on a mission to bring home heating and cooling into the 21st century by designing, managing, and financing home weatherization and electrification projects. Better for you, better for the environment. They came to Faraday because they wanted to be able to score their leads in a real-time, dynamic, and reliable way, and they wanted to get to know their customers so that they could more effectively personalize their marketing campaigns.

We met with [Emily Stack](https://www.linkedin.com/in/emily-elizabeth-stack/), Marketing Analytics Manager, and [Fatima Rakla](https://www.linkedin.com/in/fatima-rakla/), Director of Lifecycle Marketing at Sealed to talk about the challenges they were experiencing while undergoing rapid growth, and how Faraday helped them navigate.

## Scoring leads in a scalable way

Before Faraday, Sealed built an in-house lead scoring system that scored on a one-time basis. They quickly discovered that they were growing so fast that their one-time system was not only unsustainable, but the scores grew out of date shortly after they were scored. They needed something dynamic to keep up with their growth. **“We wanted something that was smart enough to change as we were changing,”** Stack explained.

Faraday was a perfect fit for Sealed, with a [lead scoring solution](https://faraday.ai/solutions/lead_management) that provides not only real-time scoring, but also integrates easily with [HubSpot](https://faraday.ai/integrations/predictions-with-hubspot). Through dynamic, real-time scoring of their most likely-to-buy leads, Sealed is able to prioritize their leads more efficiently and determine which are worth the time & effort of their sales team to follow up with in order to drive the most revenue. They were even able to pre-qualify a new segment of leads that was previously not working, leading to a **5% increase in leads that marketing could deliver to sales**.

Furthermore, they’re using their new lead scoring solution to create lookalikes on Facebook, which have shown strong performance through Faraday’s ability to provide predictive audiences with a higher likelihood of conversion, helping to optimize their overall ad spend.

[The Faraday Identity Graph](https://faraday.ai/features/data) enabled Sealed to enrich their first-party behavioral data with hundreds of privacy-centric, people-first attributes, including hobbies, property data, shopping preferences, life events, and more. This enabled Sealed to truly understand their customers and use a people-first approach to their marketing, without having to rely on cookies or first-party data alone. Previously, Sealed was engaging with all of their leads equally—now, with their leads enriched and scored earlier in the funnel, the team is able to align the right content, at the right time, and in the right channel. They’re even deploying direct mail campaigns for their top-scoring leads.

## Personalization where it counts

!["faraday's personas really changed the game in terms of how we think about current markets, future markets, and how we should be messaging to our customers." - Fatima Rakla Director of Lifecycle Marketing at Sealed](/images/blog/cs-sealed-pull-quote.jpg)

While knowing which leads are worth engaging has been a huge unlock for Sealed, they soon discovered that using the Faraday Identity Graph to empower personalization has had a major positive impact on how they engage with their customers—both present and future. Faraday’s machine learning algorithms developed three unique personas for Sealed. Understanding their personas enabled Sealed’s internal teams to align on creating relevant, personalized customer journeys for each persona. Rakla and Stack’s teams were then able to develop talk tracks for their personas, and leveraged data on rep-to-persona performance to align reps with personas that they had a higher win-rate closing with.

These insights allowed Sealed to scale up their sales rep productivity by revealing that some reps had a much higher win rate with a specific persona. Those reps are now able to coach others on what works and what doesn’t with each persona. Not only that, but by being able to track persona membership over time, they came to realize that the persona they were best at converting wasn’t their most abundant, so they had to be smart about how they assigned reps.  **“We need to start understanding what this other persona really needs and what our value prop for them is,”** detailed Stack, outlining how these new insights led to internal conversations around how to build campaigns going forward.

Alongside the improvement in rep productivity, Sealed is now personalizing new customer onboarding journeys through a tailored email series. For example, through the demographic and socioeconomic data from the Faraday Identity Graph, Sealed found that their Cam persona is early forties, more middle income, and purchases smaller homes than their Rick persona, and even found that Cam is more likely to respond to text than email. They use this data to guide creative and outreach that resonates with Cam, and in the channel that Cam prefers to receive it in.

## Expanding with intent

As a fast-growing company, Sealed needs to be smart about expansion. Instead of manually collecting public record data from dozens of sources, they’re making use of FIG’s wealth of public record data and manipulating it with the Explore map to visualize what locations have the best mix of traits, such as household income and average credit score. In doing this, they have a clear indication of what locations are the most promising and can guide their expansion campaigns accordingly.

## What's next for Sealed?

Sealed is gearing up to kick off rapid expansion into new markets through a diverse channel mix–both paid digital and offline–with the help of their lead generation acquisition model and market location insights.

Using Faraday’s dynamic lead scores, Sealed was able to pre-qualify a new segment of leads that was previously not working, leading to a 5% increase in leads that marketing can deliver to sales.

How Sealed scaled lead scoring and personalization with Faraday

During 2020, many brands accelerated their e-commerce plans as Covid-19 kept consumers at home and storefronts closed. For one furniture and home decor retailer, transforming their digital strategy was imperative to stay competitive as consumers spent more on home renovation and decor. Ramping up their digital strategy, quickly reaching new valuable customers was a must.

With no time to build out a data science team, the retailer worked with Faraday to quickly gain deeper insight into their customers and develop in-depth [personas](https://faraday.ai/predict/category/persona_assignments).

But to continue to drive their online revenue, the brand would need to target new audiences with the same potential to be great customers.

## Identifying customer preferences with personas

After enriching the retailer’s customer data with rich [third-party data](https://faraday.ai/features/data), machine learning models were used to generate unique customer personas.

We found that most of their customers preferred shopping offline for furniture and home decor — which was no surprise, as this brand had traditionally operated in an offline retail setting.

But moving their business to an e-commerce platform, could they effectively reach those same customer types on Facebook, and would other potential customers want to engage with the brand online?

The brand’s customer personas were recognizable archetypes like these. As they transformed their digital strategy, it was crucial they knew how to resonate with their target audiences.

## Seeding persona-based audiences on Facebook

Of the six personas Faraday identified for the retailer, three were leveraged to seed audiences on Facebook. In less than a month, the persona-based audiences were exceeding the brand’s
baseline performance.

Not only was the traditional brand able to quickly pivot to a new digital strategy, but they got all the benefits of marketing-focused data scientists at a fraction of the cost.

Spending their Facebook budget on the leads that showed the most promise for conversion lowered their customer acquisition costs astronomically.

Faraday enabled this national brand to reach more buyers by combining consumer predictions with Facebook's lookalike audience tool.

How a home decor brand transformed traditional retail with a data-driven digital strategy

Seeing a boom in home improvement projects over the course of 2020, one leading window and building materials manufacturer wanted to take advantage of this opportunity to get in front of new leads.

Working with Faraday to [optimize their acquisition efforts](https://faraday.ai/solutions/customer_acquisition), this brand is setting the bar for how traditional businesses should engage the modern consumer.

## Maximize reach on Facebook

Mitigating overlap between various audience segments on Facebook can be a real challenge for brands looking to get in front of new people.

Leveraging the Faraday platform and custom audiences, this brand could easily maximize their reach beyond who Facebook would usually offer as new leads.

## Custom audiences expand omnichannel approach

After testing a variety of marketing strategies, the brand had a clear understanding of their target customer acquisition costs.

While their Facebook Lookalike audiences were working, their reach was limited, and both the audiences themselves and their corresponding performance weren’t translating to other digital channels.

To solve this, the brand built lookalike audiences in the Faraday platform and pushed them to Facebook. They found their new audiences brought them a trove of customers Facebook had been ignoring — all while staying under their ideal marketing costs.

What’s more, these same audiences can easily be pushed to the brand’s other marketing channels, ensuring a true omnichannel strategy. Faraday’s custom audiences and Facebook Lookalikes only overlapped 4%.

See how this building materials company used Faraday to target promising new audiences on Facebook that they hadn't been able to reach before.

How this building materials manufacturer expanded its reach with custom Facebook audiences

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.

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