[Interior Define](https://www.interiordefine.com/) provides well-crafted, customizable furniture and personalized guidance from interior design specialists — without breaking the bank. With increasing competition from other made-to-order furniture brands, Interior Define wanted to get in front of the right audiences and be top-of-mind for prospective customers.

To improve [customer engagement](https://faraday.ai/solutions/engagement_and_retention), Interior Define needed to gain deeper insight into who those customers are — beyond their past engagement. To support Interior Define’s omnichannel marketing strategy and continue to attract high-value customers, Faraday helped the brand intelligently distribute their SEM budget across key markets.

## Developing rich customer profiles

Understanding who your customers are is the first step in reaching hyper-targeted audiences with personalized, relevant ads. Interior Define wanted to know what distinguished their people who only ordered fabric swatches from those who fully converted and bought furniture. By enhancing their first-party data with rich third-party data, Interior Define was able to compare the two types of customers at a more detailed level. These deep insights can guide future nurture and ad campaigns targeting swatch customers, encouraging full conversion. Compared to the rest of the U.S. population, their customers had a few key lifestyle patterns worth paying attention to.

## Optimizing Google Ads campaigns with location-based predictions

[Looking to Google Ads](/integrations/predictions-with-google-ads) as a potential goldmine for new customers, Interior Define worked with Faraday to optimize their SEM campaigns. Faraday suggested bid adjustments in specific zip codes to help the brand prioritize their ad spend in geos where there was a high propensity of conversion.

Interior Define ran five campaigns on Google Ads, comparing the performance in zips that Faraday suggested bid adjustments for to the performance in zips where Faraday did not suggest bid adjustments. In the geos where Faraday suggested bid adjustments, they saw:

- +38% increase in conversion
- -21% decrease in CAC
- +95% increase in average conversion value
- +147% increase in overall ROAS

## Adopting the strategy to optimize Facebook Ads campaigns

After seeing the results on paid search, Interior Define implemented a similar location-based targeting strategy with Facebook Ads. They were using broad audiences to take advantage of the platform’s expansive reach and sophisticated algorithms, but it was time to squeeze more value out of their ad spend.

This time, instead of adjusting bids, Faraday pushed zip codes with a high propensity of conversion directly to Facebook Ads Manager. Interior Define was then able to target those zips while still using Facebook's broad audience capabilities.

It was another immediate win. By combining Facebook’s enormous reach with Faraday’s predictive geotargeting, Interior Define's cost-per-result dropped by 40% and they saw a 128% lift in ROAS.

## A truly customer-centric brand

In addition to offering beautifully crafted furniture and decor, Interior Define makes interior design guidance and execution readily accessible, providing more people than ever the opportunity to have professional assistance in curating their homes. The team puts the customer first, working both virtually and physically to ensure that each one has an enjoyable, personalized experience — whether they’re customizing a single couch or redecorating an entire room. If you’re thinking about redesigning your living space, we’d encourage you to [check them out](https://www.interiordefine.com/).

Learn how Interior Define used consumer predictions to optimize both Google Ads and Facebook Ads campaigns.

How Interior Define boosted ROAS 147% using consumer predictions

Every marketer knows that personalization is critical. Once considered a nice touch to spruce up campaigns, today it’s a full-on necessity for digital-first brands. Plus, the bar for personalization is getting higher and higher, to the point that consumers expect real-time, highly-curated interactions on every marketing channel.

The groundwork for personalized marketing begins with knowing your customer. Before you can so much as sketch out a campaign idea, you need to know who you’re marketing to. However, even this seemingly simple question is getting a lot more complicated thanks to the phase-out of the third-party cookie.

Nowadays marketers need to take stock of their cookieless identity resolution options and ensure they have the rich customer profiles they need to achieve true personalization.

Looking for cookieless options for enhanced customer data? Eager to add more sophisticated personalization strategies to your arsenal? Here's what this article covers:

- Customer insights and segmentation
- Personalized advertising
- Personalized product recommendations
- Personalized promotions

## Personalization starts with segmentation

Third-party cookies have always gone hand in hand with personalized marketing, since they’ve unlocked access to swaths of data that marketers can use for surgically precise segmentation. But [the end of the cookie](https://faraday.ai/blog/death-of-third-party-cookies-strategies) doesn’t have to sound the death knell for highly personalized ads. If anything, it’s ushering in a new era of more meaningful personalization strategies, where people see content that speaks to them as people, not a click to be retargeted.

### Adding consumer data to the mix

Segmentation is still key to achieving human-centric interactions. Without third-party cookies, you can certainly segment your first-party data, but frankly, it won’t offer enough nuance to craft personalized campaigns. Now more than ever, marketers need to prioritize [data enrichment from responsible sources](https://faraday.ai/features/responsible-ai). This cookieless consumer data enables brands to build out rich customer profiles based on a wealth of demographic, psychographic, and firmographic data.

Once you’ve enhanced your first-party data, you’ll have the full picture you need to segment your audience in a meaningful way. This might mean segmenting by income level, household size, or even hobbies and interests. The savviest brands go even further and leverage machine learning algorithms to parse through billions of data points, surface the most significant patterns in their audience, and cluster their customers into highly accurate persona groups.

All of this paves the way for true [personalization at scale](https://faraday.ai/predict/category/persona_assignments). By segmenting your audience into clusters or personas, you can craft highly personalized campaigns that speak to each unique segment and orchestrate more meaningful interactions across channels.

If that sounds like a plan, read on for the strategies you can use to start bringing those campaigns to life.

## Personalizing ad campaigns

Personalized advertising is a fantastic tool for connecting with good fits across the buyer’s journey. Since consumers are bombarded with ads all day long — social media, TV, radio, podcast, flyers, billboards — meaningfully personalized ads help cut through the noise and create an immediate connection.

### Using personas to create custom audiences

It’s one thing to segment your existing audience based on [rich consumer data](https://faraday.ai/features/data), but quite another to personalize ads for potential customers who aren’t yet known to you. Lookalike audience options from platforms like Facebook used to reliably expand brands’ reach to new pools of good fits, but with the advent of iOS 14 and Apple’s privacy updates, these lookalikes have lost a lot of signal and are less cost-effective by the day. You’ll need additional strategies up your sleeve to reach new audiences.

One such strategy involves seeding Facebook lookalikes with AI-driven personas, which gives Facebook a more defined seed audience to work with and enhances its ability to surface consumers that fit your personas. Think of it like giving Facebook’s algorithm a bit of a boost. Then, you can craft highly personalized ad campaigns for individuals you would never have reached otherwise. Brands doing this typically see increased conversion rates and lower CPA.

Another option is to skip Facebook’s native lookalikes and leverage AI to build your own custom lookalike audiences. In this strategy, algorithms can uncover great fits from an identity graph and match them to your brand’s personas, providing you with fresh audiences to upload to Facebook or any other ad platform — greatly expanding your audience beyond what these platforms would normally surface.

With these in place, and your personas already established, you can personalize your ad campaigns with confidence in spite of having never interacted with the audience before.

## Personalized product recommendations

Once someone has made themselves known to your brand— for instance, subscribing to your email list, creating a free online account, or signing up for a rewards program — personalized product recommendations are another invaluable strategy. You can leverage insights in your CRM to create a truly personalized experience on your website, in-store, via email marketing, and more.

Crucially, consumers have high expectations for these experiences. In such a competitive market, where shoppers can find dozens of similar products and services online with just a quick Google search, it’s the real-time, personalized interactions that help brands stand out from the crowd and earn their trust. Orchestrating this type of personalization requires sophisticated automation and algorithms to make recommendations instantaneously and at scale.

### Personalized recommendations based on journey stage

Truly personalized recommendations tend to come back to great life cycle marketing. Once you have an accurate picture of where current and potential customers fall on the buyer’s journey, you can map recommendations to each stage — such as promoting a product quiz or survey to your social followers in order to collect more first-party data, launching email campaigns that are tailored to your unique personas, or nudging customers about new releases.

With most modern ESPs and email marketing platforms, such as Iterable, Klaviyo, and MailChimp, you can also build powerful workflows to trigger personalized product recommendations based on specific actions. For a financial organization, this could look like an email with more information on home loan options after a lead schedules a consultation. For an apparel brand, it could mean triggering an alert that an item has come back in stock for every rewards member who'd added it to their virtual wish list.

By aligning recommendations with an individual’s journey stage and automating them to be delivered at the perfect moment, you create the real-time, personalized interactions today’s consumers crave — and you’ll see the results in your revenue.

### Personalized recommendations with predicted purchases

For marketing teams that have already dialed up product recommendations and want to offer even more valuable suggestions to potential customers, there’s also the option to take it one step further with predictive journey mapping. Rather than make an assumption about what a person is going to purchase, this entails using AI to actually predict what they’ll get.

These [predictions](https://faraday.ai/features/predictions) are far more accurate than any other product recommendation you could make, allowing your marketing efforts to become less like a sales pitch and more like the advice of a trusted friend. Just like automated email campaigns, these predictions can be triggered from a platform like Iterable to deliver the perfect recommendation to a given individual at the perfect time.

## Personal discounts and pomotions

Sometimes consumers need a little extra encouragement to get them to commit. For these individuals, a well-timed promotion or discount can make an enormous difference. The trick is to tailor the promotion to the consumer in order to protect your bottom line — after all, you don’t want to offer 15% off or a first month free to someone who’s likely to pay full price, but you definitely want that to be an option for valuable lead that’s on the fence.

### Using personalized discounts to build brand affinity

To be certain that you’re only offering discounts to the customers that matter most, customer lifetime value (LTV) predictions can eliminate an enormous amount of guesswork. LTV is difficult to determine on your own, but a wide variety of AI tools available to marketers make it a piece of cake.

With LTV predictions uploaded to Salesforce, HubSpot, or wherever you track your customer information, it becomes much clearer how to personalize discounts for distinct audiences. You may want to try rewarding your most valuable customers with a discount, as a small thanks for their brand loyalty, or you may want to test out special offers for customers with middling LTV to see if that extra push inspires them to spend even more.

### Using personalized discounts to retain high-value customers

Another proven discount strategy relies on [churn prediction](https://faraday.ai/predict/category/churn_scores). As with all things predictive, AI is the best tool to utilize here, with churn predictions at least five times more accurate than those that traditional methods produce. Once you understand exactly which customers are at the highest risk of falling away, you can intervene proactively, with a personalized touch, rather than reactively.

For instance, you can tailor the discount to the level of churn risk. High-value customers at great risk of churn might deserve a generous discount offer, whereas customers with lower risk can get a smaller one. And just like with product recommendations and LTV predictions, churn predictions can be delivered seamlessly to wherever your team needs them most, whether that’s triggering an email drip or refreshing the customer profile in your CRM — empowering you to provide a seamless customer experience.

## Level up your personalization strategies today

Whether you’re just getting started with personalization or are looking to truly master 1:1 communication at scale, you’ve got numerous opportunities available at every stage of the buyer’s journey. Consumers overwhelmingly prefer personalized offers, and an increasingly crowded digital space is making it harder and harder to gain an edge — so it’s more important than ever to double down on personalization before it’s too late.

Explore practical personalization strategies that leading brands use to create more enjoyable experiences across the customer lifecycle.

5 ways to personalize experiences across the customer lifecycle

Iterable interviews Faraday's CEO to unlock the secrets to automated personalization using consumer data and AI responsibly.

AI and the future of personalized marketing with Iterable & Faraday

Many retailers acquired entirely new audiences during the pandemic. With those new customers come new opportunities. Keeping them is the key to continued success. Faraday CEO Andy Rossmeissl discusses data-driven retention strategies with industry experts from Reebok, Lob, and Mattel at this eTail Virtual panel. [Check out the webinar now](https://faraday.wistia.com/medias/1ht8amrq2s)!

Reebok and Walmart-Mattel pair with Faraday and Lob to share how they use data to drive retention.

Creating a customer retention strategy with data at its core.

Thirstie is a technology company that offers an end-to-end solution for the alcohol industry, connecting brands with consumers through an integrated e-commerce platform. Their services include regulatory compliance, delivery logistics, and data analytics to streamline the online sale and delivery of alcoholic beverages.

## Challenge: Pioneering direct channel consumer data for the alcohol industry

E-commerce has revolutionized the way alcohol brands reach their consumers, offering a direct sales channel that bypasses traditional distribution models.

This direct relationship unlocks the most valuable data asset for a brand, a list of end customers. Prior to the emergence of an e-commerce channel for alcohol, alcohol beverage brands' customer insights were limited to the data they could garner from their distributors and retail sales data.

Recognizing the opportunity within this order data lie, Thirstie set out to make consumer insights a central component of their service offering. Their goal was to go beyond mere transaction data, providing brands with deep, actionable insights into a given brands’ customer base.

## Obstacle: Going beyond transaction data with predictions

While Thirstie had ample transaction data, they lacked any additional data on these buyers. Thirstie’s ability to deliver customer insights meant they would need access to 3rd party consumer data and the analytical resources to make sense of it all.

Challenge one, was licensing and reconciling thousands of transactions with a large consumer database would be both expensive and complex, involving substantial investment in ETL and computing power.

Challenge two, was identifying meaningful patterns and trends consistently and at scale.

## Why Faraday: Plug and play persona analytics

Facing a long development cycle in a competitive market, Thirstie looked to Faraday as an infrastructure provider who could not only handle data enrichment and ETL, but also the modeling workload to derive insights from this combined dataset. With access to Faraday’s Identity Graph, Thirstie only needed an email address associated with an online transaction to tap into hundreds of data points on every customer in their database. The consumer data covered the full spectrum of demographic, financial and psychographic data, ensuring a complete view of each brand's customers could be built.

With this combination of transaction and consumer data, Thirstie used Faraday’s k-means clustering models to produce customer persona sets by alcohol beverage type.

Each time a transaction was observed, Thirstie used Faraday to assign each buyer to the appropriate persona, populating this information automatically in their data warehouse. Instead of building a bespoke solution, Thirstie could link Faraday’s consumer data with their warehouse, ensuring data is refreshed daily.

Thirstie displayed these personas in an analytics dashboard in their product, providing its clients with a rich understanding of the distinct socio-demographic segments within their customer base.

Thirstie also made this data available at the order level, so that their clients could leverage it for personalized engagement and advertising..

## Results: A Faraday powered core offering reduces Thirstie’s sales cycle by 30%

By partnering with Faraday, Thirstie was able to quickly bring deep insights and customer personas to its users, on scalable infrastructure. They did this without having to increase their expenditures on data science resources or the licensing of third party data assets.

Since partnering with Faraday in 2020, Thirstie has seen its client base more than quintuple, and the length of its sales cycle has dropped by 30%.

In May of 2024, Thirstie was acquired by Cocktail Courier.

Thirstie, an e-commerce platform for beverage brands, built a new analytics feature, powered by  Faraday, that drove adoption.

How e-commerce platform Thirstie reduced their sales cycle by 30% with an analytics feature powered by Faraday

Buying a new house while selling your current one is a colossal headache — and a massive financial burden. You’re fixing up your place for the sale and prepping to move, scouring new neighborhoods and going to showings, not to mention finagling your finances to make the transition work while your equity is still tied up in your current house.

[Orchard](https://orchard.com/) is a technology-driven real estate company that’s leading the charge for a simpler, stress-free way to buy and sell a home. With Orchard, you can unlock the equity in your current home to buy and move into your dream home before you sell. Plus, they offer a host of innovative tools to assist you every step of the way, from finding and closing on the new place to prepping, listing, and marketing your current one.

There’s no question that Orchard is an invaluable resource for home buyers and sellers. But they operate in just eleven specific markets, including Atlanta, Denver, and Houston, and that hyperlocal focus posed a unique set of challenges for Orchard’s marketing team in their early years.
We recently sat down with Graham Lesko, Senior Manager, Growth Marketing at Orchard, to chat about those challenges and how Orchard teamed up with Faraday to solve them.

## Getting local marketing right is tricky with Facebook Ads

Like many DTC brands, Orchard relies heavily on Facebook for top-of-funnel marketing. But when they came to Faraday, they were struggling to run targeted campaigns. Since they’re a real estate company, they face extra limitations in Facebook right off the bat in order to comply with fair housing and lending laws.

On top of that, Facebook’s native lookalike capabilities couldn’t handle the granular geographic targeting Orchard needed. “We got so narrow that any of the audience tools that these platforms offer off the shelf, like lookalike audiences or interest audiences, really weren’t that valuable for us,” Lesko explains. “We were forced to go broad within those [regions].”

With broad audiences, Facebook’s algorithm managed to deliver a decent volume of leads, but lead quality was poor. Just a small percentage became mid- and lower-funnel opportunities.

**The problem was clear:** they didn’t have the kind of data they needed to power more sophisticated targeting efforts. Without that data, they couldn’t reliably reach their ideal leads.

Orchard looked into a number of solutions, including contracting with third-party data vendors and working with Orchard’s data science team to leverage that extra layer of data in Facebook.
Ultimately, they chose to work with Faraday, taking advantage of our [responsibly-sourced identity graph](https://faraday.ai/features/data) and proven [predictive models](https://faraday.ai/features/predictions) to build the audiences they needed at a fraction of the cost of building their own AI pipeline.

## Reaching the right home buyers and sellers

Faraday started by building a machine learning model to identify people most likely to become Orchard customers. Since all our models are powered by our own identity graph, which contains consumer profile data on every US adult, it surfaces people that Facebook’s algorithms might overlook. Orchard’s audience was then uploaded to Facebook via direct integration.

By building a custom audience from scratch, Orchard was able to target just the most promising home buyers and sellers. They could finally focus on the audience that was likely to deliver the most value, and the difference was immediately noticeable.

“We’re driving higher intent users who really are looking to move,” Lesko says, “and not just get a valuation on their house.”

How much higher is that intent? Well, Orchard has seen a 30% lower customer acquisition cost with Faraday audiences compared to their original broad audiences. When it comes to ad spend efficiency, that’s a no-brainer.

## Improving attribution around iOS 14

When we asked Lesko if last year’s iOS updates and Facebook’s resulting attribution issues have changed how he thinks about his Facebook strategy, he told us it’s going to continue to play a dominant role in his channel mix.

“They give us the option to be hyperlocal in a way that a lot of other channels don’t,” he says. Running a campaign in specific regions is not something they can do, for instance, on podcasts, which are another important channel for his team.

“I would love to be able to diversify more, but until we’re a more national brand we’re going to be reliant on Facebook for the long term. So having a company like Faraday… is really huge for us.”

## Buy and sell your home with Orchard

We love working with Orchard — and so do thousands of happy homeowners and first-time buyers around the country, who appreciate the flexibility Orchard provides in the buying process and the extra certainty on the selling side. Who wouldn’t want a smoother path to their dream home?

[Check them out](https://orchard.com/) for a free home valuation now — in addition to helping homeowners buy and sell simultaneously, they also have offerings for those looking just to buy or just to sell.

It’s easy to drive lead volume on Facebook, but high-quality leads are in shorter supply. See how Orchard lowered CAC by building high-intent audiences with no-code AI.

How Orchard reaches high-quality home buyers and sellers using predictions

[Saatchi Art](https://www.saatchiart.com/) brings a gallery experience into the art buyer’s home, transforming the way people find and purchase original artwork. But their innovation doesn’t stop there. As more like-minded brands emerge, they knew that being able to quickly activate on customer predictions would give them a competitive edge. To do this, they wanted to target better leads on Facebook for their acquisition campaigns, but to target the best candidates for conversion, they first needed to know more about their existing customers.

## Unlocking unique attributes with enriched data

The first step in giving Saatchi Art a better understanding of their customers was to enrich their first-party data with hundreds of telling attributes from the [Faraday Identity Graph](https://faraday.ai/features/data). Ranging from demographic data to buying habits to lifestyle information, FIG revealed details about the brand’s customers that otherwise may have gone undiscovered.

From there, they generated a customer insights report that broke out the key data that make their customers unique. This included, but was not limited to, home market value, education, and high-ticket home decor spending. These could later be activated to further [personalize customer engagement initiatives](https://faraday.ai/solutions/engagement_and_retention).

## Lead generation on Facebook

For acquisition, Saatchi Art relies heavily on Facebook. However, the shallow insights and increasing costs that come with the platform’s Lookalike audiences made it difficult to optimize their ad spend. Within six weeks of working with Faraday, Saatchi Art had a validated [lead generation model](https://faraday.ai/solutions/lead_management) that seamlessly delivered custom audiences to Facebook. Including valuable leads that Facebook Lookalikes historically excluded, Saatchi Art’s lead generation campaigns performed better and attracted their ideal customers. Spending their Facebook budget on the leads that showed the most promise for conversion lowered Saatchi Art’s cost-per-lead by 51% and cost-per-acquisition by 60%.

Faraday's predictive advertising models helped Saatchi Art score new, unique customers across the country and optimize their Facebook ad spend.

How Saatchi art implemented data-driven predictions to lead generation campaigns

With brand trust at an [all-time low](https://www.havasgroup.com/press_release/havas-meaningful-brands-report-2021-finds-we-are-entering-the-age-of-cynicism/), it’s a good time to ask: is your organization doing the right thing with consumer data? 50% of consumers say they’d pay more for a brand that takes a stand on important issues. With privacy at the heart of a global reassessment of social media, big tech, and surveillance, organizations at the vanguard of this issue will win in 2022.

The good news is that playing fair isn’t all that complicated. “When in doubt, follow your heart,” says Andy Rossmeissl, CEO of Faraday, the leading customer prediction platform. “Our motto from the start has been ‘don’t be creepy,’ and it’s remarkable how far that can take you.”

Looking for specifics? Here are the top 3 rules for using customer data ethically:

## Rule 1: Use identifiers people can recite from memory.

This rule sounds simple, but it’s surprisingly powerful. “Ask your mom if she knows any of her tracking cookies by heart,” suggests Rossmeissl. “Ask yourself what your mobile device ID is.” The digital surveillance panopticon relies on these [long, unique strings](https://www.quora.com/What-does-the-code-for-a-tracking-cookie-look-like) of gibberish assigned to virtually every eyeball in the world.

Instead, ask yourself what identifiers you have that you can actually recite. “I have an address on my mailbox for all the world to see, and you probably do too,” says Rossmeissl. “I put my number in the phone book, and my email address is all over the Internet.”

Relying on real-world identifiers, or “known identities,” has the additional benefit of steering brands away from collecting and using anonymous behavior, such as data collected from unidentified web and mobile visitors. “Until somebody has identified themselves to you," explains Rossmeissl, "tracking their behavior is, frankly, creepy.”

## Rule 2: Use data that comes from commercial behavior

“I call it the ‘laying in bed rule,’” Rossmeissl shares. “When I’m laying in bed and I read a news article on my tablet or like something on Instagram, that’s private. Watching me do that is creepy.”
The surveillance of private behavior has rapidly normalized, much to the dismay of both privacy experts and, increasingly, consumers alike. A 2019 study found that nearly two thirds of Americans believe they can’t go through daily life without their data being collected.

And yet marketers have long known the power of data-driven growth practices. How can brands [use consumer data while still respecting privacy](https://faraday.ai/features/responsible-ai)? Rossmeissl shares:

> We thought long and hard about this when we started Faraday in 2012. We drew on anthropology, architecture, history, and other disciplines for our research, but the result was fairly simple.

> It turns out that commerce is one of the main reasons humans started to live in communities. Markets are some of the earliest forms of non-residential architecture we know of. No matter how you look at it, commerce and the marketplace have been with us longer than virtually any other social structure.

This provides a clue: humans are quite used to buying and selling, and they’re used to doing it in public. We have evolved an innate ability to operate in the marketplace, and an accompanying understanding that this behavior is public.

“What we put in our cart at the grocery store is public,” says Rossmeissl. “What we buy walking around a farmer's market is public. And when we buy online, there's an extension of this understanding. Engaging in commerce is a collaborative act — there's a buyer and a seller — and also a performative one.”

The upshot is that we instinctually treat data arising from commercial transactions as fair game. Luckily, this kind of data is not only the most ethical, but also some of the most powerful available for predicting subsequent consumer behavior. Faraday includes over 300 data points like this on virtually every US adult, but you can also procure your own from reputable licensing groups like Epsilon.

## Rule 3: Let people easily opt out

“Imagine being in a store and being told you can’t leave,” says Rossmeissl. “That goes from creepy to nightmarish very quickly.”

The right to opt out is at the heart of CCPA, the landmark privacy legislation. And while the law only protects California residents, brands should meet its requirements across the board. Luckily, that’s quite simple: provide an easy to use opt-out link, and pass along opt-outs both up- and down-stream to your partners.

Beyond the fact that [honoring opt-outs is actually good for business](https://sendgrid.com/blog/honoring-opt-outs-is-good-business/), it’s the right thing to do.

Is your organization doing doing the right things with consumer data? Here are 3 common-sense rules to follow.

3 common-sense rules for ethical data use 

Launched in 2017, this insurtech company had been using Facebook Lookalikes to build brand awareness and find new customers. Managing everything from will creation to life insurance, this company faces heavy competition with traditional insurance providers, so their acquisition strategy has to be strong.

## Understanding customers for Facebook targeting

Faraday provided the insurtech company with the capability to understand their existing customers and build out the initial Facebook audiences around those insights that would fuel predictive audiences down the road. Optimizing their [acquisition campaign](https://faraday.ai/solutions/customer_acquisition) performance on Facebook would allow them to generate qualified leads efficiently and grow their revenue.

## Customer insights inform lead generation and personalization

Understanding their customers was the first step in knowing who to target in the future, on Facebook or otherwise. Syncing their customer data with Faraday allowed them to gain a holistic understanding of who their customers are in real life. These insights would allow them to know more about the kind of leads they were looking for, as well as inform future personalization efforts. Compared to the rest of the U.S. population, their customers had a few key lifestyle patterns worth paying attention to.

With Faraday's consumer predictions, this insurtech company was able to target leads most likely to convert on Facebook and lower CPL.

How an insurtech company engaged more qualified leads with consumer predictions

The [New York State Energy Research and Development Authority](https://www.nyserda.ny.gov/)(NYSERDA) has worked hard over the years to improve energy efficiency across the state, implementing both residential and commercial initiatives. In 2018, Faraday was chosen by NYSERDA to assist their efforts with a clean heating and cooling community campaign, with the hope that Faraday’s audience targeting capabilities would encourage more New York homeowners to adopt clean energy technology.

NYSERDA wanted to empower New York state’s HVAC installers, distributors, and manufacturers of clean heating and cooling technology with the marketing capabilities needed to deliver direct mail campaigns to households most likely to apply for and install new heat pumps in their homes. Leveraging the Faraday platform would both boost energy efficiency across the state and reduce HVAC businesses’ [customer acquisition](https://faraday.ai/solutions/customer_acquisition) costs.

## Expanding reach with predictive audiences

In the past, HVAC businesses have leveraged traditional segmentation techniques focused on limited data, such as demographics and location of residence. But this method often does not account for variables such as homeownership, property data, or financials. To find new leads that were likely to be eligible for clean energy heat pumps, NYSERDA contractors used the Faraday platform to predict which homeowners in the state would be eligible for and likely to install a new clean energy heat pump. With Faraday’s resulting predictive audience, the contractors reach over 1 million new households with direct mail campaigns promoting energy efficient HVAC systems.

## Audience analysis generated by machine learning models

Comparing audiences generated by Faraday with machine learning models to traditional segmentation methods, households included in Faraday’s audiences were more likely to be approved for a new heat pump and complete an installation. The analysis showed audiences that were 2.39x more likely to be approved for installation and 4.12x more likely to complete an installation.

Using Faraday's predictive targeting models, NYSERDA was able to target over 1 million new households in their direct mail campaign.

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