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.

How Advia Credit Union generates $2.7 million in new auto loans with Faraday’s consumer product recommendations

In the competitive landscape of direct-to-consumer (DTC) ecommerce, [personalization](/blog/personalization-at-scale-guide) is a key differentiator.

[Boll & Branch](https://www.bollandbranch.com/), a prominent DTC linens company, used Faraday to provide customer predictions that guide their marketing strategies.

## The problem

Boll & Branch was looking for a more accurate way to effectively segment their customer base so they could deliver more personalized and relevant content.

With a wide range of products and a diverse customer demographic, it was crucial to understand more about their customers, their preferred products, and channels.  They found that their initial segments had no correlation to specific products or marketing channels, limiting their ability to launch an effective personalization strategy.

## The solution

To address this challenge, Boll & Branch used Faraday's persona predictions to identify customer personas that represented distinct socio-economic clusters. Each persona had preferred products and marketing channels.

This approach differed from a traditional persona exercise in which a brand must either form personas from a qualitative process (e.g. surveys, focus groups, etc.) or leveraging pre-computed personas like PRIZM codes.

With [qualitatively derived personas](/blog/predictive-personalization-machine-learning), a brand can't easily assign new customers to a persona, unless a customer responds to survey questions. Given most customer surveys have single digit response rates, this limits a brand's ability to segment their customer base.

Pre-computed personas have the benefit of being universal, but are generic demographic clusters vs. specific patterns derived from a brand's data. This often results in persona segments with little variation in customer behavior.

Faraday's persona models leverage [rich data](/features/faraday-identity-graph) on nearly every U.S. adult–allowing for universal assignment–paired with an AI clustering algorithm that builds personas from a training set provided by a client's own customer data. The results are customer personas that can be applied to all known contacts in a brand's customer base, while also reflecting the socio-demographic nuances across this dataset.

### Data integration and persona development

Boll & Branch used the Faraday platform to identify three distinct personas based on a set of socio-demographic factors. They then were able to see that these segments had distinct purchasing behavior, preferences, and other relevant metrics.

These personas were then assigned to individual customers in Boll & Branch's [BigQuery](/integrations/predictions-with-bigquery) data warehouse, providing the metadata needed to support their personalization initiatives.

### Personas + attribution

Traditionally, Boll & Branch thought their demographic skewed older, and as a result they invested heavily in radio advertising. Pairing Faraday's personas with Boll & Branch's internal attribution system allowed them to see what kinds of customers were coming from which channels, what discount codes different personas were responding to, and what products they were purchasing.

An example of this was the marketing team's identification of their highest value persona, whom the team nicknamed "Jen": a wealthier, settled, homeowner, that preferred clean modern aesthetics.

This qualitative insight gave Boll & Branch the confidence to organize marketing towards the Jen persona - from branding, product design, prospecting, all the way down to engagement and retention.

As Daphne Chow, Senior Manager of Consumer Strategy & Analytics puts it, Faraday helped Boll & Branch "see what kind of consumer we are seeing as crucial to our success, and our whole organization has rallied around that goal."

For example, they could see that individuals in the Jen persona were heavy users of social media, and as a result began being more strategic with their channel mix.

Knowing Jen also allowed them to conduct focus groups and surveys with members of that cohort, where they found they were influenced by interior design personalities - guiding them to a successful partnership with the star designer, Nate Berkus.

### Personas + email campaigns

Because every contact in their system was assigned a persona, Boll & Branch could also use these assignments to power their email campaigns. The team designed personalized content for each persona: not only did they use creative that aligned to the socio-demographic makeup of each persona, they also were able to draw insights about what products each of their customer personas over-indexed on, allowing them to select highly relevant product recommendations for each persona.

This approach ensured that each customer received content that resonates with their unique preferences and needs.

## The results

The implementation of Faraday's personas into Boll & Branch's marketing strategy yielded impressive results. By delivering personalized experiences through targeted email campaigns, the company achieved a **30% lift in conversion rates**. This significant improvement underscored the value of utilizing advanced predictive analytics to drive personalized marketing efforts.

Boll & Branch's partnership with Faraday exemplifies the power of data-driven personalization in ecommerce. By identifying distinct customer personas and tailoring marketing messages accordingly, the company was able to enhance customer engagement and drive higher conversion rates.

Or, in the words of their Chief Digital Officer, Katia Unlu, "Faraday is in our DNA."

Boll & Branch used customer predictions to drive hyper-personalized customer experiences, resulting in a 30% lift in coversion rates.

How Boll & Branch achieved a 30% lift in conversion rates with customer predictions

A household name to many, Cabot Creamery provides high-quality dairy products to the New
England market. But despite their limited regional presence, they have amassed a loyal
following email subscribers — roughly 300,000 strong — from across the country.
To better reach these brand enthusiasts, Cabot worked with Faraday to learn more about
their subscribers, as well as gauge new market opportunities outside of New England.

To better reach their brand enthusiasts, Cabot worked with Faraday to learn more about their
email subscribers. Offline, Cabot is looking to expand their physical presence in grocery stores and other retailers beyond their current New England market. To identify the best cities for success, they leveraged Faraday’s market sizing capabilities.

## Gaining a deeper understanding of Cabot's most loyal fans

To grow their email subscription list and engage customers, Cabot captures emails from
online promotions, in-store sampling, and giveaways—but their real-world insight into these
subscribers was limited. Enhancing their first-party data with rich third-party attributes, Faraday gave Cabot a holistic picture of their email subscribers, going so far as to make comparisons between subscribers in different regions of the country to see what sets each geo apart.

Key insights included age, an interest in cooking and mail-order food, and — surprisingly —
pet ownership. Many of these common interests among email subscribers varied from place
to places across the country. With this in mind, Cabot can confidently personalize future
content to resonate with specific segments of their email subscribers.

## Identifying high-potential markets with location-based predictions

To engage new customers outside of New England, Cabot needed to find the best locations
for future expansion. Faraday analyzed the brand’s areas of interest, predicting which zip
codes in New York City and ten metro areas in the Southeast were potential goldmines.
These geospatial predictions will not only inform Cabot’s market expansion, but can guide
location-based marketing initiatives, like SEM and traditional media buys. Even the placement
of in-store sampling events can benefit from these predictions.

Learn how the beloved brand used consumer predictions to guide its market expansion strategy.

How Cabot Creamery uncovered high-potential markets for expansion

[Community First Credit Union](https://www.communityfirstfl.org/) (CFCU) started in 1935 in a Jacksonville high school basement and has since evolved into 19 branches and over 135,000 members. CFCU's success is rooted in its commitment to its members, offering tailored products to meet individual needs and utilizing innovative technologies to deliver an exceptional banking experience.

## The problem: finding the right members

A mere 19% of Americans have three or more products with their primary financial institution–that is, where their checking account lives–despite the average American holding [eight financial products](https://www2.deloitte.com/content/dam/Deloitte/us/Documents/financial-services/us-kickingitupanotch-092614.pdf).

In order to remain competitive & ensure long-term success, CFCU knew that they had to grow their "wallet share" with [existing members](/solutions/customer-engagement). More members adopting CFCU's comprehensive financial solutions deepens the financial relationship between the credit union and its members, fostering loyalty and long-term engagement. This diversification helps in spreading risk and maintaining a more stable financial base, while reducing the need for costly marketing and acquisition efforts.

The challenge they faced was how to accomplish this effectively. CFCU lacked the resources to compete directly with larger banks and fintech companies. That meant simply shoveling more ad spend into their [direct mail](/blog/personalized-direct-mail-strategy) program wasn't an option - CFCU needed a more efficient approach that could increase response rates from both new and existing members _without_ increasing spending.

## The obstacle: reaching the right members efficiently

Before Faraday, CFCU used traditional segmentation, like a member's current product mix, their income, and identifying members to whom they could extend pre-approved offers of credit. What they found is that this approach often failed to capture the nuanced preferences and [behaviors](/features/customer-behavior-predictions) of individual members. Because segments were built off broad categories, the generic messages tailored to these segments didn't resonate with recipients. [Personalized](/blog/personalization-at-scale-guide) and relevant content were essential to better find and engage loyal members.

CFCU understood that AI offered opportunities for more effective segmentation, on top of an unlock for personalizing a member's journey.  Their existing member data provided the historical examples of customers who had grown wallet share from one product to many. Their existing direct mail providers had exposed CFCU to the immense trove of additional third party data that could be leveraged to create distinct segments.

At the same time, they knew that were signficant costs associated with this data, the infrastructure needed to run AI models, and the in house talent needed to develop that level of modeling.

## Why Faraday? 

### Identity resolution & easy product recommendations

Partnering with Faraday turned out to be the unlock that CFCU needed to realize this vision of using AI to grow wallet share amongst their members.

They used the Faraday platform to connect their member data to Faraday's identity graph, giving them instant access to [hundreds of data points](/features/faraday-identity-graph) beyond those they had used to segment members in the past. Using Faraday's modeling engine, CFCU was able to build product recommendations that were personalized for each member and for all prospects in their charter territory.

With this segmentation in hand, CFCU used Faraday to easily build mailing lists of the individuals they most wanted to target in their direct mail campaigns.

## The results

### Acquisition campaign

CFCU used the Faraday platform to predict which consumers were most likely to become their next loyal members, defined as a member with a high account balance and strong net interest revenue. To test the model, CFCU ran a direct mail campaign with a 50/50 audience split (50% from high-scoring model predictions; 50% from traditional segmentation techniques).

The new members generated using Faraday's targeting saw **61% higher average account balances** and **24% higher net interest revenue** than the new members acquired using traditional segmentation techniques.

### Next product recommendation

Faraday also predicted which members were most likely to need a checking account, auto loan, first mortgage, and more. To test the model's predictions and get in front of members with the highest potential for engagement, CFCU ran a direct mail campaign with the same technique in a 50/50 audience split (50% based on Faraday's next product recommendation; 50% from traditional segmentation techniques).

The members contacted with offers recommended by Faraday saw **54% higher net interest revenue** and **88% lift in projected ROI**.

With Faraday, Community First Credit Union used customer predictions to drive highly effective acquisition and engagement campaigns with great results.

How Community First Credit Union unlocks 61% higher account balances using Faraday’s predictive AI

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

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

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

## What is a Facebook lookalike audience?

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

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

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

## How to create a Facebook lookalike audience

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

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

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

## Benefits of Facebook lookalike audiences

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

### Tracking

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

### ROI

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

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

### Customizations

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

### Enhancements

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

## Using Facebook lookalike audiences effectively

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

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

## Alternatives to Facebook lookalike audiences

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

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

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

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

## Level up your lookalike audiences today

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

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

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

How Facebook lookalike audiences can bolster your ad campaigns

[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.

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

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