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.

Building trust: How brands can answer common data security questions

This week, Faraday is thrilled to announce a series of product updates that mark a revolutionary moment in our own history, as well as the broader AI practice. These updates don’t just make our users’ lives easier, though that’s certainly a major part of it. They also bring us one step closer to the world that all of us at Faraday are invested in building: a world where brands can use AI in a manner that’s both widely accessible and intrinsically responsible.

What does accessible, responsible AI look like? How did we get here? And why does it matter? Read on to find answers—and join us in the revolution.

## Accessible AI for all

AI becomes accessible when _anyone_ can use it to make a prediction. You don’t need a million dollar marketing budget to get your foot in the door, you don’t need a data science degree to build a model, and you don’t need to write a line of code. Today, for the first time, Faraday users can cast all those barriers aside and make predictions about their customers’ behavior in just a few clicks.

This level of accessibility has been four years in the making, and while it seems like a no-brainer today, it was a risk when we committed to the new product roadmap. Our 2018 decision to invest in a sweeping, accessible AI product update was a gamble—and maybe a somewhat audacious decision at the time.

Back then, we were growing increasingly aware that brands wanted more control over their predictions. But pivoting from what was an intensely collaborative experience to a fully self-serve platform would require thousands of engineering hours, all for a technology that was still more of a buzzword than a common business solution. The software had to take a quantum leap forward.

Four years later, [AI is everywhere](https://hbr.org/2021/09/ai-adoption-skyrocketed-over-the-last-18-months) and Faraday’s quantum leap is here. We’re thrilled to be able to offer AI to the entire brand landscape, from retail to finance and beyond, paving the way for more and more companies to connect with their customers.

## Responsible AI for all

There’s a reason they say that with great power comes great responsibility, and AI is no exception.

Machine learning algorithms train on historical data, using patterns from the past to make predictions about the future. It should come as no surprise that, left unchecked, these algorithms will surface injustices that have been baked into decades of consumer data, reflecting common biases around race, gender, class, religion, and more. It’s completely possible to mitigate these biases and produce responsible predictions that avoid or even correct for past injustices, but it takes expert know-how.

For the first six years of our company’s life, we were tightly involved in every predictive model and we could be stewards of the crucial ethics involved. With the world moving toward AI for all, we had to make sure that it was truly AI for all and deeply embed responsible AI principles into everything we were doing.

As a result, Faraday has doubled down on responsible AI. We’ve always employed proven approaches for mitigating bias and stood by our ethical consumer data practices, but now we’re rolling out automatic bias detection and mitigation for our users as well. We’re serious about building a future where AI helps—not harms—our community, and we wouldn’t release a new wave of features without ensuring that our users are equipped to use them responsibly.

## The revolution is here

When Robbie, Seamus, and I started Faraday, AI was largely reserved for the Amazons and Walmarts of the world. The cost to develop, train, and supervise predictive models was exorbitant, and only the biggest corporations could afford that kind of investment. They’ve been reaping the benefits—and the profits—that come with predicting customer behavior ever since.

This summer, we’re ringing in an AI revolution at Faraday, one where any brand, not just the Fortune 100, can use practical, powerful AI without compromising on ethics. We’ll continue building on these updates for the rest of 2022 and beyond, and we hope you’ll join us for the ride.

This summer, we’re ringing in an AI revolution at Faraday, one where any brand, not just the Fortune 100, can use practical, powerful AI without compromising on ethics.

Faraday launches accessible, responsible, powerful AI for all

With summer in full swing, Faraday’s product team and developers have been working hard to create some incredibly exciting updates to the Faraday platform. The _why_ behind these changes is rooted in two of our core product pillars:

1. **AI for all**. The benefits of machine learning should not be reserved solely for the Fortune 500 or those with advanced degrees. The promise of easy AI, scalable cloud-based infrastructure, and a good UI should unlock the benefits of consumer behavior predictions for all brands.
2. **Making data science fun**. Using Faraday should be intuitive and easy. You should feel empowered to ideate, experiment, and deploy, all on your own.

We're thrilled to announce that as of today, four major updates are live. In this blog, we'll walk you through how each one works—here's what we'll cover:

- **Build your own predictions in Outcomes**: Describe the business outcomes you want predictions for, and Faraday will build and maintain predictive models for that outcome, with models getting more accurate over time as they learn from your data.
- **Deploy your predictions with ease in Pipelines**: Choose your likely-to-buy outcomes, your personas, and more; pick a specific population of customers; and target a destination for your deployment, like your data warehouse, cloud bucket, CRM, ESP, etc.
- **Plug into Faraday with Connections**: Plugging in your data is now a simpler process with fewer steps required. Plus, Connections paves the way for a future update where we'll implement true bi-directionality for your data and predictions in the Faraday platform.
- **Create more powerful Cohorts**: Cohort building now features the ability to specify certain trait requirements from your data or the Faraday Identity Graph in order for a customer to enter a cohort, adding an extra layer of depth to your predictions.

Let’s dive in.

## Build your own predictions in Outcomes

Back in December, we released an update that enabled Faraday users to build their own predictive personas within a cohort of their choosing based on various attributes from the [Faraday Identity Graph](/features/data).

That update had been the first major in-platform step toward user autonomy, and, more importantly, toward making AI accessible for all. Continuing on that path, Faraday users can now access an additional self-serve interface, this one for defining propensity (likelihood) predictions: **Outcomes**.

Outcomes allows you to easily describe the business outcomes you’re trying to predict in just a few clicks. Likely to buy? Sure. Likely to churn? You bet. Likely to buy _again_? That, too. Behind the scenes, Faraday takes over from here, applying dozens of strategies to build a range of candidate models, then selecting the one that most accurately predicts your outcome. Over time, Faraday automatically builds and rebuilds progressively more accurate models as you accumulate more data.

Our new Outcomes interface puts the power of experimentation in your hands, allowing you to easily try out different outcomes and view how effective the model will be across your use cases—all without needing a data scientist at your shoulder.

## Deploy your predictions with ease in Pipelines

Once you’ve defined your own prediction objectives, make them actionable with **Pipelines**.

Pipelines completely streamlines the prediction deployment process in Faraday. Previously, you may have used some combination of Reach, Monitor, and Inform to deploy predictions on-demand, on a scheduled basis, and in real-time via API. These three products have been unified into Pipelines for a more accessible and intuitive user experience.

Jump into Pipelines and choose your:

- **Payload:** a combination of your desired propensity outcomes, your personas, and more.
- **Population:** the people you want the predictions on.
- **Targets:** where you want your predictions deployed, including your data warehouse, cloud bucket, or anywhere else in your stack (CRM, ESP, etc).

Faraday takes over from here, keeping your predictions constantly up to date by refreshing scores and persona assignments when new data comes in, ensuring you’re never left wrong-footed when delivering personalized experiences to your most likely-to-buy customers.

With Pipelines, for the first time ever, you’re able to both describe and deploy your own predictions completely autonomously—without a data science degree.

## Plug into Faraday with Connections

Starting today, you’ll notice the Sources and Destinations areas of the Faraday platform have been combined into a single **Connections** view.

With Connections, you’re able to plug Faraday right into your data warehouse or cloud bucket by entering your location-specific details. Once done, it’ll be ready for selection as a deployment target in Pipelines. Dropping the number of setting screens required to complete your end-to-end connection from two to one should save you some clicks, and get your first predictions deployed that much faster.

Keep an eye out for more news here, because Connections is a major building block for a future update that will enable every Connection to be bi-directional—your predictions, wherever you want them, whenever you want them.

## Create more powerful Cohorts

Previously, **Cohorts** in Faraday were defined by event streams, or an event that each person in a cohort has experienced. This included the type of event (such as an order), when the person experienced the event (all orders, last X days, etc), the frequency of the event (such as number of times an order was placed), and finally, the value of the order.

We introduced the Segments console last year to allow users to define a group of people based solely on the Faraday Identity Graph (FIG)-based traits, like geography or income. While this was a great step for offering flexibility in terms of the types of population you could define in the Faraday platform, it was cumbersome having to navigate through both Cohorts and Segments to do so.

With today’s update, Segments and its functionality has been folded into Cohorts. Now, in one console, users can define their cohorts by selecting specific traits from either their customer data, or exclusively from FIG. Event streams are no longer required to build a cohort.

Prior to today, your "Recent bulk purchasers" cohort might’ve been simply based on the number of orders in the last 30 days. You can now refine this cohort further to say "Recent bulk purchasers in CA" or "Recent bulk purchasers with income >\$100k." On the flip side, you might also build a cohort without an event stream, such as "Dog owners in CA with an income >\$100k."

We hope that by allowing you to define your preferred population of people—whether that be based on an event stream like orders, FIG traits, or a combination of the two—gives you another building block upon which you can easily experiment with predictions through Outcomes, Personas, and Pipelines.

## What's next?

Coming up, we’ll be revamping data ingress to make it easier to map your data into Faraday. We see this as a critical step towards democratizing data science and empower you to create and maintain a powerful predictive layer atop your martech stack.

---

\*Explore our [product documentation](/docs/) or [talk to sales](/talk-to-sales) to learn more.

Introducing four major updates that empower users to build and deploy their own predictions in the Faraday platform—no data science degree required.

August 2022 product updates: Outcomes, Pipelines, Connections, Cohorts

Since the dawn of commerce, sellers have invested significant time and effort into figuring out what influences prospective buyers. Identifying your ideal customer—who they are, what they do, why they do it, and how they feel about various products and services—can help you craft powerful, people-first marketing campaigns that accelerate every sales cycle.

Building a customer insight strategy is the most efficient way to understand that ideal customer, evaluate your brand’s current situation, and adapt for the future. Here’s how you can get started.

## How to develop a customer insight strategy

To develop a customer insight strategy, you will need to focus on both qualitative and quantitative data. Gathering this information ensures you have a complete picture of who your ideal customer is, how they behave, and what their wants and needs are.

Identifying the key team players involved in the process and employing the right tools, techniques, and resources can take a lot of the guesswork out of building an insight strategy.

## Why customer insights are important for business growth

Competitive advantage begins with clear customer insights. Without a comprehensive customer insight strategy, brands are bound to miss out on crucial data that has the power to significantly impact their bottom line.

A customer insight strategy can unlock a wealth of information about your customers and what influences their purchases. Through a combination of consumer data, feedback, social listening, and website analytics, you can begin to draw conclusions about how your customers behave and why. This knowledge can help you improve the customer journey and create an experience that is truly memorable for those who purchase your product or service.

Having a strong foundational awareness of customer insights is the first step to developing a strategy of your own.

## Key components of a customer insight strategy

### Objectives

A clear understanding of why you want to gather customer insights can make developing a strategy easier. What is your reasoning behind gathering insights, and what do you hope to do with this data once you have it?

Maybe you’re hoping to increase revenue by a certain percentage next quarter or determine whether your current pricing strategy aligns with your competitors’. Maybe your ad campaigns aren’t converting like they used to and need to be refreshed. Or perhaps you’re exploring ways to optimize your current content strategy and attract a new audience. All these objectives can be achieved through a smart customer insight strategy.

### Research and data

A viable customer insight strategy calls for a combination of high-quality research and data. From customer interviews and social listening to surveys and focus groups, gathering data about your customers’ preferences can help influence your marketing strategy and content creation.

This emphasis on first-party data is increasingly important as the [end of third-party cookies](https://faraday.ai/blog/death-of-third-party-cookies-strategies) approaches, but you may also consider [responsibly sourced data](https://faraday.ai/features/data) from other vendors to add even more depth to your customer profiles.

Of course, high-quality data is of little use if there is no one to analyze it. A strong analytics team can help brands make sense of the noise and pull focus to the trends that matter most.

### Audience

There is no one-size-fits-all approach to customer insights. Before you can begin gathering valuable data, you’ll need to take a close look at which segment of customers you want to explore. For example, analyzing your current customer base will require a different approach than analyzing potential customers. Knowing whose insight is valuable in achieving certain objectives will ensure you use your time and resources as effectively as possible.

Keep in mind: customer feedback doesn’t always have to be about you to be useful. It doesn’t have to be gathered exclusively from your own customers, either. Finding out what people think about your competitors, for example, can provide a glimpse into what drives customers within your industry to buy.

### Customer journey map

Data-driven insights have the power to affect every level of decision-making when creating and marketing a product. As you gather data about your target audience, it should help you build a better understanding of your brand’s customer journey. Leverage those insights to create or refine a map of customer interactions with your brand—that way, you can skip the guesswork when it comes to what messaging and offers your audience should get at every step of their journey.

For instance, if most customers hear about your brand through digital channels but make their final purchase in-store, your campaigns should facilitate that leap from online to offline interactions. On the other hand, if most customers interact with multiple digital touchpoints before buying online, you’ll want to use those insights to guide them from touchpoint to touchpoint.

### Customer personas

Once you’ve thoroughly collected and analyzed your customer data, and determined what the typical customer journey looks like, you can take your customer insight strategy one step further to develop customer personas.

Personas are a means of clustering your customer base into distinct groups that share certain similarities. One persona group may contain customers who mostly have young families, live in the suburbs, and like to shop at brick-and-mortar stores; another may be digitally-native young professionals or wealthy urbanites. By honing in on the key customer insights that define each persona, you can tailor your campaigns to their unique interests and [better personalize every customer interaction](https://faraday.ai/predict/category/persona_assignments).

### AI enhancements

Sifting through such an enormous amount of data and unifying insights across platforms can be a huge endeavor. For some brands, off-the-shelf AI tools are the most straightforward way to enrich and make sense of all their customer data. Some of the ways they achieve this with AI include:

- Enhancing customer data with an identity graph to create detailed customer profiles
- Scoring leads based on how likely they are to purchase a product
- Predicting the lifetime value of potential or current customers
- Developing powerful quantitative persona groups
- Building lookalike audiences of good fits for use on any marketing channel
- Identifying which customers are at highest risk of churning

## Conclusion

Just about every successful brand relies on customer insights to inform their decision-making. Once you’ve pulled together all the components of your customer insight strategy, you can review them holistically to determine which insights offer the most potential for your team to work with.

An effective customer insight strategy can take the pressure off your sales and marketing teams, improve overall customer satisfaction, drive customer retention, and increase profitability over time. There’s no question that it’s worth your team’s time to get it right.

At Faraday, we offer a number of customer insight tools that allow brands to enhance their first-party data with responsibly sourced, opt-in consumer data and develop powerful, personalized marketing campaigns.

Learn what goes into a successful customer insight strategy and how you can start gathering the data you need to truly understand your customers.

How to create a customer insight strategy

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

Third-party cookies and digital marketing have long gone hand in hand. For years, brands have relied on these small pieces of code to help them develop buyer personas and ad targeting strategies. But with data privacy growing increasingly important and consumers pushing back hard against data sharing, marketers must now navigate a world in which third-party cookies are quickly becoming obsolete.

The shift away from third-party cookies has its merits. Data privacy is a real issue for today’s consumers as concern over identity theft, viruses, and other cybersecurity threats put a growing number of internet-connected devices at risk.

A cookieless world can even be good for marketers too. It opens the door for brands to interact with their audience in a more human-centric manner, ultimately paving the way for better personalization. But the clock is ticking on third-party cookies and brands that haven't adapted already will need to pivot quickly in order to prepare.

## What are third-party cookies?

Third-party cookies are small bits of code that track internet users’ searches, website visits, and purchases, among other data. Whereas first-party cookies are dropped onto a website by its owner in order to monitor activity on their own asset, third-party cookies pass information back to an outside organization, like an advertiser.

Third-party cookies heavily influenced ad targeting for nearly three decades. Then, amidst growing concerns over data privacy, it all came crashing down.

In late 2019, Firefox announced that it would[ block third-party tracking by default](https://blog.mozilla.org/en/products/firefox/todays-firefox-blocks-third-party-tracking-cookies-and-cryptomining-by-default/). Apple followed suit in early 2020, though its [Safari browser has included cookie-blocking features since 2017](https://www.zdnet.com/article/apple-blocks-third-party-cookies-in-safari/). Additionally, Apple introduced [new privacy features in iOS 14.5](https://developer.apple.com/app-store/user-privacy-and-data-use/), allowing users to customize data privacy settings for individual apps.

Also in early 2020, Google announced it would be [phasing out cookies from its Chrome browser](https://blog.chromium.org/2020/01/building-more-private-web-path-towards.html)—a move that will[ take effect in 2023](https://www.bbc.com/news/technology-57611701). This shift away from third-party data accessibility has led marketers to contemplate the future of ad campaigns.

## Why does the death of third-party cookies matter?

With user data no longer as readily up for grabs, brands must adapt their approaches to ad targeting and predicting buyer patterns and behavior. Marketers will need to be more strategic with their data and explore new ways to segment audiences.

Unfortunately, not all marketers are prepared for the task. According to a survey by Epsilon, [54% of marketers](https://www.adweek.com/resource-library/marketers-still-arent-ready-for-a-world-without-cookies/) still don't feel fully prepared for the death of third-party cookies.

Brands that hesitate or refuse to think outside the box for future data collection will likely fall behind the crowd. Ensuring cost-effective advertising in a changing digital world means being vigilant about new technology and data sources. The key is to connect users across multiple, respectful touch points, such as opt-in data opportunities and contextual ads.

## Strategies to prepare

### Take stock of your first-party data

How effective have you been at collecting first-party data? Identifying methods to collect opt-in first-party data, such on your website and through email marketing, will help you respect your users’ privacy while still meeting marketing goals. If you’re currently using a content management system (CMS), the analytics dashboards should provide valuable insight into key metrics that can work to your advantage and help you identify where you may need to fill in some gaps.

### Get consent

Want access to solid user data? Just ask. By providing incentives, such as discounts or a loyalty program, you can invite users to share additional data with you and make it a win-win situation—they get a tangible benefit, you get opt-in data that’s fair game for marketing outreach. Just make sure you’re transparent about how you’ll use it. Building trust with your audience is essential to growing brand loyalty.

### Embrace AI

Predictive modeling uses artificial intelligence to better understand your audience and uncover prospective buyers without relying on cookie-based data. AI tools can work backward from your desired outcome to identify which consumers are likely to be your next best customers, based on a combination of your first-party data and responsibly sourced opt-in data.

### Implement contextual advertising

Contextual advertising is when ads are matched to the content on a given site page, as opposed to the individual site visitor. Because ads are related to the content a user is already consuming, many consider this type of targeting less intrusive than behavioral targeting. It offers promise for marketers who want to continue serving the most relevant ads possible while still respecting consumer privacy.

### Provide value

Third-party data made it easy for marketers to rest on their laurels. Now, brands must try even harder to remain relevant and earn their audience’s attention. There’s no better way to achieve this than to continue to invest in quality products, services, and content. At the end of the day, nothing is more important than providing value to your customers.

### Explore new strategies

Paying close attention to innovative tools and data sources can help marketers keep up with ever-evolving privacy challenges. Fortunately, a growing array of resources are helping marketers adapt their first-party data strategies and make ad targeting more effective. Remain open to exploring different options and you’ll likely find one that aligns with your goals.

## Let Faraday guide you through a cookieless future

The death of the third-party cookie isn’t all bad news for marketers. Taking the time to make more meaningful connections with your audience and investing in future-proof solutions will mean wiser ad spending in the long run.

The end of third-party cookies is coming. Here's what you can do to stay connected with the right audiences.

6 strategies to prepare for the death of third-party cookies

[Advia Credit Union](https://www.adviacu.org/)'s mission is to provide financial advantages to its members through solutions that address life's evolving needs, including mortgage refinancing, debt consolidation, and auto loans.

## The problem: engaging customers with the right message

The average American holds eight financial products, yet only [19% of Americans](https://www2.deloitte.com/content/dam/Deloitte/us/Documents/financial-services/us-kickingitupanotch-092614.pdf) have three or more products with their primary financial institution. For many credit unions, including Advia, growing their "wallet share" with [existing members](/solutions/customer-engagement) is key to their long term stability and success. The challenge is how to do this effectively. There is brand risk in simply bombarding members with unsuitable offers that could result in members ignoring all communications from the credit union. Worse, it could erode trust, create frustration, and sow overall member dissatisfaction.

For Advia, they needed a way to understand the personalized financial journeys for each of their members so that they could surface the [next best product recommendation](/features/customer-behavior-predictions) for each member. Such an approach would ensure optimal use of their marketing budget, minimize member dissatisfaction, and help grow wallet share with their members.

## The obstacle: using data and remaining compliant

Advia understood that their member data likely held the key for developing next best product recommendations. Unfortunately, their lead data scientist had recently left the organization, and Advia was looking at a lengthy ramp period to hire and onboard someone new.

Additionally, Advia needed to navigate a complex regulatory landscape. Advertising lending products comes with stringent compliance requirements to ensure fair lending practices and protect consumer privacy. The Equal Credit Opportunity Act (ECOA), for example, prohibits discrimination in credit transactions based on race, color, religion, national origin, sex, marital status, age, or receipt of public assistance. This means any data used for segmentation in advertising must not incorporate these protected classes, either intentionally or unintentionally, to avoid violations.

## Why Faraday?

To address these challenges, Advia partnered with Faraday. Advia used the Faraday platform to determine which of their primary retail financial products should be surfaced to each member. They initially tested this by looking at which members should receive information on their auto loan product.

Advia linked its historical member data with Faraday's identity graph, including information on member adoption of Advia's auto loan. With this training data, Advia created a set of predictive models that scored each member's propensity for an auto loan. They also leveraged Faraday's [bias identification and mitigation features](/blog/faraday-bias-management-announcement) to address historical bias in their training set, aiding their fair lending compliance efforts.

With Faraday's product recommendations in hand, Advia could segment their audience more intelligently. They identified the top percentile of members most likely to complete an auto loan application and targeted them through both personalized email and direct mail campaigns. This targeted approach ensured that the most relevant offers reached the right members, enhancing engagement and increasing the likelihood of loan applications.

## The results

The success of Advia's auto loan campaign was measured by the percentage of recipients that completed auto loan applications. Before using Faraday, Advia's benchmark for success was a 1.19% application rate.

**30-Day results**: After just 30 days of running the campaign that used Faraday's recommendation, Advia achieved a 2.41% application rate. This significant improvement resulted in **$1.1 million in new auto loans**.

**90-Day results**: The application rate continued to grow over the next 60 days achieving a 5.18% application rate after 90 days. This increase in engagement led to a total of **$2.7 million in new auto loans**, far surpassing the initial benchmark.

Advia Credit Union's use of Faraday's platform demonstrates the power of personalization and predictive analytics. Faraday gave Advia the ability to target the right members with relevant offers, leading to higher engagement and significantly improved loan application rates.

This approach not only boosted Advia's campaign performance, but also boosted Advia's regulatory compliance efforts, while safeguarding member trust. Advia's use of the Faraday platform exemplifies how financial institutions can harness data to drive better outcomes, provide superior service to their members, and use [AI responsibly](/features/ai-safety-and-responsible-ai).

The team at Advia Credit Union used customer predictions to deliver the right product to the right people in a direct mail campaign while using AI responsibly.

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